International Journal of Computer Assisted Radiology and Surgery2010Journal Article, cited 17 times
Website
LIDC-IDRI
Algorithm Development
LUNG
PURPOSE: Knowledge of the exact shape of a lesion, or ground truth (GT), is necessary for the development of diagnostic tools by means of algorithm validation, measurement metric analysis, accurate size estimation. Four methods that estimate GTs from multiple readers' documentations by considering the spatial location of voxels were compared: thresholded Probability-Map at 0.50 (TPM(0.50)) and at 0.75 (TPM(0.75)), simultaneous truth and performance level estimation (STAPLE) and truth estimate from self distances (TESD). METHODS: A subset of the publicly available Lung Image Database Consortium archive was used, selecting pulmonary nodules documented by all four radiologists. The pair-wise similarities between the estimated GTs were analyzed by computing the respective Jaccard coefficients. Then, with respect to the readers' marking volumes, the estimated volumes were ranked and the sign test of the differences between them was performed. RESULTS: (a) the rank variations among the four methods and the volume differences between STAPLE and TESD are not statistically significant, (b) TPM(0.50) estimates are statistically larger (c) TPM(0.75) estimates are statistically smaller (d) there is some spatial disagreement in the estimates as the one-sided 90% confidence intervals between TPM(0.75) and TPM(0.50), TPM(0.75) and STAPLE, TPM(0.75) and TESD, TPM(0.50) and STAPLE, TPM(0.50) and TESD, STAPLE and TESD, respectively, show: [0.67, 1.00], [0.67, 1.00], [0.77, 1.00], [0.93, 1.00], [0.85, 1.00], [0.85, 1.00]. CONCLUSIONS: The method used to estimate the GT is important: the differences highlighted that STAPLE and TESD, notwithstanding a few weaknesses, appear to be equally viable as a GT estimator, while the increased availability of computing power is decreasing the appeal afforded to TPMs. Ultimately, the choice of which GT estimation method, between the two, should be preferred depends on the specific characteristics of the marked data that is used with respect to the two elements that differentiate the method approaches: relative reliabilities of the readers and the reliability of the region boundaries.
A resource for the assessment of lung nodule size estimation methods: database of thoracic CT scans of an anthropomorphic phantom
Gavrielides, Marios A
Kinnard, Lisa M
Myers, Kyle J
Peregoy, Jennifer
Pritchard, William F
Zeng, Rongping
Esparza, Juan
Karanian, John
Petrick, Nicholas
Optics express2010Journal Article, cited 50 times
Website
FDA-Phantom
LUNG
A number of interrelated factors can affect the precision and accuracy of lung nodule size estimation. To quantify the effect of these factors, we have been conducting phantom CT studies using an anthropomorphic thoracic phantom containing a vasculature insert to which synthetic nodules were inserted or attached. Ten repeat scans were acquired on different multi-detector scanners, using several sets of acquisition and reconstruction protocols and various nodule characteristics (size, shape, density, location). This study design enables both bias and variance analysis for the nodule size estimation task. The resulting database is in the process of becoming publicly available as a resource to facilitate the assessment of lung nodule size estimation methodologies and to enable comparisons between different methods regarding measurement error. This resource complements public databases of clinical data and will contribute towards the development of procedures that will maximize the utility of CT imaging for lung cancer screening and tumor therapy evaluation.
Accuracy of emphysema quantification performed with reduced numbers of CT sections
Pilgram, Thomas K
Quirk, James D
Bierhals, Andrew J
Yusen, Roger D
Lefrak, Stephen S
Cooper, Joel D
Gierada, David S
American Journal of Roentgenology2010Journal Article, cited 8 times
Website
NLST
lung
LDCT
An Image Processing Tool for Efficient Feature Extraction in Computer-Aided Detection Systems
In this paper, we present an image processing tool that supports efficient image feature extraction and pre-processing developed in the context of a computer-aided detection (CAD) system for lung cancer nodule detection from CT images. We outline the main functionalities of the tool, which implements a number of novel methods for handling image pre-processing and feature extraction tasks. In particular, we describe an efficient way to compute the run-length feature, a photometric feature describing the texture of an image.
Automatic fissure detection in CT images based on the genetic algorithm
Lung cancer is one of the most frequently occurring cancer and has a very low five-year survival rate. Computer-aided diagnosis (CAD) helps reducing the burden of radiologists and improving the accuracy of abnormality detection during CT image interpretations. Owing to rapid development of the scanner technology, the volume of medical imaging data is becoming huger and huger. Automated segmentations of the target organ region are always required by the CAD systems. Although the analysis of lung fissures provides important information for treatment, it is still a challenge to extract fissures automatically based on the CT values because the appearance of lung fissures is very fuzzy and indefinite. Since the oblique fissures can be visualized more easily among other fissures on the chest CT images, they are used to check the exact localization of the lesions. In this paper, we propose a fully automatic fissure detection method based on the genetic algorithm to identify the oblique fissures. The accurate rates of identifying the oblique fissures in the right lung and the left lung are 97% and 86%, respectively when the method was tested on 87 slices.
The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans
Armato III, Samuel G
McLennan, Geoffrey
Bidaut, Luc
McNitt-Gray, Michael F
Meyer, Charles R
Reeves, Anthony P
Zhao, Binsheng
Aberle, Denise R
Henschke, Claudia I
Hoffman, Eric A
Kazerooni, E. A.
MacMahon, H.
Van Beeke, E. J.
Yankelevitz, D.
Biancardi, A. M.
Bland, P. H.
Brown, M. S.
Engelmann, R. M.
Laderach, G. E.
Max, D.
Pais, R. C.
Qing, D. P.
Roberts, R. Y.
Smith, A. R.
Starkey, A.
Batrah, P.
Caligiuri, P.
Farooqi, A.
Gladish, G. W.
Jude, C. M.
Munden, R. F.
Petkovska, I.
Quint, L. E.
Schwartz, L. H.
Sundaram, B.
Dodd, L. E.
Fenimore, C.
Gur, D.
Petrick, N.
Freymann, J.
Kirby, J.
Hughes, B.
Casteele, A. V.
Gupte, S.
Sallamm, M.
Heath, M. D.
Kuhn, M. H.
Dharaiya, E.
Burns, R.
Fryd, D. S.
Salganicoff, M.
Anand, V.
Shreter, U.
Vastagh, S.
Croft, B. Y.
Medical Physics2011Journal Article, cited 546 times
Website
LIDC-IDRI
Computer Aided Diagnosis (CADx)
LUNG
PURPOSE: The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process. METHODS: Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. RESULTS: The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings. CONCLUSIONS: The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.
Equating quantitative emphysema measurements on different CT image reconstructions
Bartel, Seth T
Bierhals, Andrew J
Pilgram, Thomas K
Hong, Cheng
Schechtman, Kenneth B
Conradi, Susan H
Gierada, David S
Medical Physics2011Journal Article, cited 15 times
Website
National Lung Screening Trial (NLST)
LUNG
LDCT
PURPOSE: To mathematically model the relationship between CT measurements of emphysema obtained from images reconstructed using different section thicknesses and kernels and to evaluate the accuracy of the models for converting measurements to those of a reference reconstruction. METHODS: CT raw data from the lung cancer screening examinations of 138 heavy smokers were reconstructed at 15 different combinations of section thickness and kernel. An emphysema index was quantified as the percentage of the lung with attenuation below -950 HU (EI950). Linear, quadratic, and power functions were used to model the relationship between EI950 values obtained with a reference 1 mm, medium smooth kernel reconstruction and values from each of the other 14 reconstructions. Preferred models were selected using the corrected Akaike information criterion (AICc), coefficients of determination (R2), and residuals (conversion errors), and cross-validated by a jackknife approach using the leave-one-out method. RESULTS: The preferred models were power functions, with model R2 values ranging from 0.949 to 0.998. The errors in converting EI950 measurements from other reconstructions to the 1 mm, medium smooth kernel reconstruction in leave-one-out testing were less than 3.0 index percentage points for all reconstructions, and less than 1.0 index percentage point for five reconstructions. Conversion errors were related in part to image noise, emphysema distribution, and attenuation histogram parameters. Conversion inaccuracy related to increased kernel sharpness tended to be reduced by increased section thickness. CONCLUSIONS: Image reconstruction-related differences in quantitative emphysema measurements were successfully modeled using power functions.
Automated segmentation refinement of small lung nodules in CT scans by local shape analysis
Diciotti, Stefano
Lombardo, Simone
Falchini, Massimo
Picozzi, Giulia
Mascalchi, Mario
IEEE Trans Biomed Eng2011Journal Article, cited 68 times
Website
Radiomics
Segmentation
Computer Aided Diagnosis (CADx)
LUNG
LIDC-IDRI
One of the most important problems in the segmentation of lung nodules in CT imaging arises from possible attachments occurring between nodules and other lung structures, such as vessels or pleura. In this report, we address the problem of vessels attachments by proposing an automated correction method applied to an initial rough segmentation of the lung nodule. The method is based on a local shape analysis of the initial segmentation making use of 3-D geodesic distance map representations. The correction method has the advantage that it locally refines the nodule segmentation along recognized vessel attachments only, without modifying the nodule boundary elsewhere. The method was tested using a simple initial rough segmentation, obtained by a fixed image thresholding. The validation of the complete segmentation algorithm was carried out on small lung nodules, identified in the ITALUNG screening trial and on small nodules of the lung image database consortium (LIDC) dataset. In fully automated mode, 217/256 (84.8%) lung nodules of ITALUNG and 139/157 (88.5%) individual marks of lung nodules of LIDC were correctly outlined and an excellent reproducibility was also observed. By using an additional interactive mode, based on a controlled manual interaction, 233/256 (91.0%) lung nodules of ITALUNG and 144/157 (91.7%) individual marks of lung nodules of LIDC were overall correctly segmented. The proposed correction method could also be usefully applied to any existent nodule segmentation algorithm for improving the segmentation quality of juxta-vascular nodules.
Quantitative CT assessment of emphysema and airways in relation to lung cancer risk
Gierada, David S
Guniganti, Preethi
Newman, Blake J
Dransfield, Mark T
Kvale, Paul A
Lynch, David A
Pilgram, Thomas K
RadiologyRadiology2011Journal Article, cited 41 times
Website
NLST
The national lung screening trial: overview and study design
National
Lung
Screening
Trial
Research
Team
RadiologyRadiology2011Journal Article, cited 760 times
Website
NLST
lung
LDCT
An Automated Method for Locating Phantom Nodules in Anthropomorphic Thoracic Phantom CT Studies
Evaluation of reader variability in the interpretation of follow-up CT scans at lung cancer screening
Singh, Satinder
Pinsky, Paul
Fineberg, Naomi S
Gierada, David S
Garg, Kavita
Sun, Yanhui
Nath, P Hrudaya
RadiologyRadiology2011Journal Article, cited 47 times
Website
NLST
lung
LDCT
Cancer Screening
Reduced lung-cancer mortality with low-dose computed tomographic screening
The National Lung Screening Trial Research Team
Aberle, D. R.
Adams, A. M.
Berg, C. D.
Black, W. C.
Clapp, J. D.
Fagerstrom, R. M.
Gareen, I. F.
Gatsonis, C.
Marcus, P. M.
Sicks, J. D.
New England Journal of Medicine2011Journal Article, cited 4992 times
Website
NLST
LUNG
LDCT
BACKGROUND
The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.
METHODS
From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009.
RESULTS
The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).
CONCLUSIONS
Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number, NCT00047385.)
Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme
Zinn, Pascal O
Majadan, Bhanu
Sathyan, Pratheesh
Singh, Sanjay K
Majumder, Sadhan
Jolesz, Ferenc A
Colen, Rivka R
PLoS One2011Journal Article, cited 192 times
Website
Radiogenomics
Glioblastoma Multiforme (GBM)
Magnetic Resonance Imaging (MRI)
Computer Aided Detection (CADe)
BACKGROUND: Despite recent discoveries of new molecular targets and pathways, the search for an effective therapy for Glioblastoma Multiforme (GBM) continues. A newly emerged field, radiogenomics, links gene expression profiles with MRI phenotypes. MRI-FLAIR is a noninvasive diagnostic modality and was previously found to correlate with cellular invasion in GBM. Thus, our radiogenomic screen has the potential to reveal novel molecular determinants of invasion. Here, we present the first comprehensive radiogenomic analysis using quantitative MRI volumetrics and large-scale gene- and microRNA expression profiling in GBM. METHODS: Based on The Cancer Genome Atlas (TCGA), discovery and validation sets with gene, microRNA, and quantitative MR-imaging data were created. Top concordant genes and microRNAs correlated with high FLAIR volumes from both sets were further characterized by Kaplan Meier survival statistics, microRNA-gene correlation analyses, and GBM molecular subtype-specific distribution. RESULTS: The top upregulated gene in both the discovery (4 fold) and validation (11 fold) sets was PERIOSTIN (POSTN). The top downregulated microRNA in both sets was miR-219, which is predicted to bind to POSTN. Kaplan Meier analysis demonstrated that above median expression of POSTN resulted in significantly decreased survival and shorter time to disease progression (P<0.001). High POSTN and low miR-219 expression were significantly associated with the mesenchymal GBM subtype (P<0.0001). CONCLUSION: Here, we propose a novel diagnostic method to screen for molecular cancer subtypes and genomic correlates of cellular invasion. Our findings also have potential therapeutic significance since successful molecular inhibition of invasion will improve therapy and patient survival in GBM.
Collaborative projects
Armato, S
McNitt-Gray, M
Meyer, C
Reeves, A
Clarke, L
Int J CARS2012Journal Article, cited 307 times
Website
LIDC-IDRI
Removing Mixture Noise from Medical Images Using Block Matching Filtering and Low-Rank Matrix Completion
Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data--methods and preliminary results
Gevaert, Olivier
Xu, Jiajing
Hoang, Chuong D
Leung, Ann N
Xu, Yue
Quon, Andrew
Rubin, Daniel L
Napel, Sandy
Plevritis, Sylvia K
RadiologyRadiology2012Journal Article, cited 187 times
Website
Radiogenomics
LUNG
PET/CT
Non Small Cell Lung Cancer (NSCLC)
Metagenomics/ methods
Microarray Analysis
PURPOSE: To identify prognostic imaging biomarkers in non-small cell lung cancer (NSCLC) by means of a radiogenomics strategy that integrates gene expression and medical images in patients for whom survival outcomes are not available by leveraging survival data in public gene expression data sets. MATERIALS AND METHODS: A radiogenomics strategy for associating image features with clusters of coexpressed genes (metagenes) was defined. First, a radiogenomics correlation map is created for a pairwise association between image features and metagenes. Next, predictive models of metagenes are built in terms of image features by using sparse linear regression. Similarly, predictive models of image features are built in terms of metagenes. Finally, the prognostic significance of the predicted image features are evaluated in a public gene expression data set with survival outcomes. This radiogenomics strategy was applied to a cohort of 26 patients with NSCLC for whom gene expression and 180 image features from computed tomography (CT) and positron emission tomography (PET)/CT were available. RESULTS: There were 243 statistically significant pairwise correlations between image features and metagenes of NSCLC. Metagenes were predicted in terms of image features with an accuracy of 59%-83%. One hundred fourteen of 180 CT image features and the PET standardized uptake value were predicted in terms of metagenes with an accuracy of 65%-86%. When the predicted image features were mapped to a public gene expression data set with survival outcomes, tumor size, edge shape, and sharpness ranked highest for prognostic significance. CONCLUSION: This radiogenomics strategy for identifying imaging biomarkers may enable a more rapid evaluation of novel imaging modalities, thereby accelerating their translation to personalized medicine.
The Study on Data Hiding in Medical Images
Huang, Li-Chin
Tseng, Lin-Yu
Hwang, Min-Shiang
International Journal of Network Security2012Journal Article, cited 25 times
Website
Algorithm Development
Image analysis
Reversible data hiding plays an important role in medical image systems. Many hospitals have already applied the electronic medical information in healthcare systems. Reversible data hiding is one of the feasible methodologies to protect the individual privacy and confidential information. With application in several high quality medical devices, the detection rate of diseases and treating are improved at the early stage. Its demands havebeen rising for recognizing complicated anatomical structures in high quality images. However, most data hiding methods are still applied in 8-bit depth medical images with 255 intensity levels. This paper summarizes the existing reversible data hiding algorithms and introduces basic knowledge in medical image.
Correlation of perfusion parameters with genes related to angiogenesis regulation in glioblastoma: a feasibility study
Jain, R
Poisson, L
Narang, J
Scarpace, L
Rosenblum, ML
Rempel, S
Mikkelsen, T
American Journal of Neuroradiology2012Journal Article, cited 39 times
Website
Glioblastoma Multiforme (GBM)
BRAIN
TCGA
Radiomics
Radiogenomics
PET/CT
BACKGROUND AND PURPOSE: Integration of imaging and genomic data is critical for a better understanding of gliomas, particularly considering the increasing focus on the use of imaging biomarkers for patient survival and treatment response. The purpose of this study was to correlate CBV and PS measured by using PCT with the genes regulating angiogenesis in GBM. MATERIALS AND METHODS: Eighteen patients with WHO grade IV gliomas underwent pretreatment PCT and measurement of CBV and PS values from enhancing tumor. Tumor specimens were analyzed by TCGA by using Human Gene Expression Microarrays and were interrogated for correlation between CBV and PS estimates across the genome. We used the GO biologic process pathways for angiogenesis regulation to select genes of interest. RESULTS: We observed expression levels for 92 angiogenesis-associated genes (332 probes), 19 of which had significant correlation with PS and 9 of which had significant correlation with CBV (P < .05). Proangiogenic genes such as TNFRSF1A (PS = 0.53, P = .024), HIF1A (PS = 0.62, P = .0065), KDR (CBV = 0.60, P = .0084; PS = 0.59, P = .0097), TIE1 (CBV = 0.54, P = .022; PS = 0.49, P = .039), and TIE2/TEK (CBV = 0.58, P = .012) showed a significant positive correlation; whereas antiangiogenic genes such as VASH2 (PS = -0.72, P = .00011) showed a significant inverse correlation. CONCLUSIONS: Our findings are provocative, with some of the proangiogenic genes showing a positive correlation and some of the antiangiogenic genes showing an inverse correlation with tumor perfusion parameters, suggesting a molecular basis for these imaging biomarkers; however, this should be confirmed in a larger patient population.
Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening CT
Jeon, Kyung Nyeo
Goo, Jin Mo
Lee, Chang Hyun
Lee, Youkyung
Choo, Ji Yung
Lee, Nyoung Keun
Shim, Mi-Suk
Lee, In Sun
Kim, Kwang Gi
Gierada, David S
Investigative radiology2012Journal Article, cited 51 times
Website
NLST
lung
LDCT
Informatics in Radiology: An Open-Source and Open-Access Cancer Biomedical Informatics Grid Annotation and Image Markup Template Builder
Mongkolwat, Pattanasak
Channin, David S
Kleper, Vladimir
Rubin, Daniel L
Radiographics2012Journal Article, cited 15 times
Website
Interoperability
Annotation
metadata
In a routine clinical environment or clinical trial, a case report form or structured reporting template can be used to quickly generate uniform and consistent reports. Annotation and image markup (AIM), a project supported by the National Cancer Institute's cancer biomedical informatics grid, can be used to collect information for a case report form or structured reporting template. AIM is designed to store, in a single information source, (a) the description of pixel data with use of markups or graphical drawings placed on the image, (b) calculation results (which may or may not be directly related to the markups), and (c) supplemental information. To facilitate the creation of AIM annotations with data entry templates, an AIM template schema and an open-source template creation application were developed to assist clinicians, image researchers, and designers of clinical trials to quickly create a set of data collection items, thereby ultimately making image information more readily accessible.
Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration
Localization of target vertebrae is an essential step in minimally invasive spine surgery, with conventional methods relying on "level counting" - i.e., manual counting of vertebrae under fluoroscopy starting from readily identifiable anatomy (e.g., the sacrum). The approach requires an undesirable level of radiation, time, and is prone to counting errors due to the similar appearance of vertebrae in projection images; wrong-level surgery occurs in 1 of every ~3000 cases. This paper proposes a method to automatically localize target vertebrae in x-ray projections using 3D-2D registration between preoperative CT (in which vertebrae are preoperatively labeled) and intraoperative fluoroscopy. The registration uses an intensity-based approach with a gradient-based similarity metric and the CMA-ES algorithm for optimization. Digitally reconstructed radiographs (DRRs) and a robust similarity metric are computed on GPU to accelerate the process. Evaluation in clinical CT data included 5,000 PA and LAT projections randomly perturbed to simulate human variability in setup of mobile intraoperative C-arm. The method demonstrated 100% success for PA view (projection error: 0.42mm) and 99.8% success for LAT view (projection error: 0.37mm). Initial implementation on GPU provided automatic target localization within about 3 sec, with further improvement underway via multi-GPU. The ability to automatically label vertebrae in fluoroscopy promises to streamline surgical workflow, improve patient safety, and reduce wrong-site surgeries, especially in large patients for whom manual methods are time consuming and error prone.
Malignant nodule detection on lung CT scan images with kernel RX-algorithm
Roozgard, A.
Cheng, S.
Hong, Liu
2012Conference Proceedings, cited 24 times
Website
LIDC-IDRI
Algorithm Development
Computer Aided Detection (CADe)
In this paper, we present a nonlinear anomaly detector called kernel RX-algorithm and apply it to CT images for malignant nodule detection. Malignant nodule detection is very similar to anomaly detection in military imaging applications where the RX-algorithm has been successfully applied. We modified the original RX-algorithm so that it can be applied to anomaly detection in CT images. Moreover, using kernel trick, we mapped the data to a high dimensional space to obtain a kernelized RX-algorithm that outperforms the original RX-algorithm. The preliminary results of applying the kernel RX-algorithm on annotated public access databases suggests that the proposed method may provide a means for early detection of the malignant nodules.
External clinical validation of prone and supine CT colonography registration
Systems for Computer-Aided Detection (CAD), specifically for lung nodule detection received increasing attention in recent years. This is in tandem with the observation that patients who are diagnosed with early stage lung cancer and who undergo curative resection have a much better prognosis. In this paper, we analyze the performance of a novel feature-deselective neuroevolution method called FD-NEAT to retain relevant features derived from CT images and evolve neural networks that perform well for combined feature selection and classification. Network performance is analyzed based on radiologists' ratings of various lung nodule characteristics defined in the LIDC database. The analysis shows that the FD-NEAT classifier relates well with the radiologists' perception in almost all the defined nodule characteristics, and shows that FD-NEAT evolves networks that are less complex than the fixed-topology ANN in terms of number of connections.
Medical image thresholding using WQPSO and maximum entropy
Image thresholding is an important method of image segmentation to find the objects of interest. Maximum entropy is an image thresholding method that exploits entropy of the distribution in gray level of the image. The performance of this method can be improved by using swarm intelligence techniques such as Particle Swarm Optimization (PSO) and Quantum PSO (QPSO). QPSO has attracted the research community due to its simplicity, easy implementation and fast convergence. The convergence of QPSO is faster than PSO and global convergence is guaranteed. In this paper, we propose a new combination of mean updated QPSO referred to as weighted QPSO with maximum entropy to find optimal threshold for magnetic resonance images (MRI). The performance of this method outperforms other existing methods in literature in terms of convergence speed and accuracy.
Classificação Multirrótulo na Anotação Automática de Nódulo Pulmonar Solitário
Villani, Leonardo
Prati, Ronaldo Cristiano
2012Conference Proceedings, cited 0 times
A novel volume-age-KPS (VAK) glioblastoma classification identifies a prognostic cognate microRNA-gene signature
Zinn, Pascal O
Sathyan, Pratheesh
Mahajan, Bhanu
Bruyere, John
Hegi, Monika
Majumder, Sadhan
Colen, Rivka R
PLoS One2012Journal Article, cited 63 times
Website
Radiomics
Radiogenomics
Computer Aided Diagnosis (CADx)
Classification
TCGA-GBM
REMBRANDT
BACKGROUND: Several studies have established Glioblastoma Multiforme (GBM) prognostic and predictive models based on age and Karnofsky Performance Status (KPS), while very few studies evaluated the prognostic and predictive significance of preoperative MR-imaging. However, to date, there is no simple preoperative GBM classification that also correlates with a highly prognostic genomic signature. Thus, we present for the first time a biologically relevant, and clinically applicable tumor Volume, patient Age, and KPS (VAK) GBM classification that can easily and non-invasively be determined upon patient admission. METHODS: We quantitatively analyzed the volumes of 78 GBM patient MRIs present in The Cancer Imaging Archive (TCIA) corresponding to patients in The Cancer Genome Atlas (TCGA) with VAK annotation. The variables were then combined using a simple 3-point scoring system to form the VAK classification. A validation set (N = 64) from both the TCGA and Rembrandt databases was used to confirm the classification. Transcription factor and genomic correlations were performed using the gene pattern suite and Ingenuity Pathway Analysis. RESULTS: VAK-A and VAK-B classes showed significant median survival differences in discovery (P = 0.007) and validation sets (P = 0.008). VAK-A is significantly associated with P53 activation, while VAK-B shows significant P53 inhibition. Furthermore, a molecular gene signature comprised of a total of 25 genes and microRNAs was significantly associated with the classes and predicted survival in an independent validation set (P = 0.001). A favorable MGMT promoter methylation status resulted in a 10.5 months additional survival benefit for VAK-A compared to VAK-B patients. CONCLUSIONS: The non-invasively determined VAK classification with its implication of VAK-specific molecular regulatory networks, can serve as a very robust initial prognostic tool, clinical trial selection criteria, and important step toward the refinement of genomics-based personalized therapy for GBM patients.
Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases
Aggarwal, Preeti
Vig, Renu
Sardana, HK
Journal of Computers2013Journal Article, cited 5 times
Website
LIDC-IDRI
Classification
Computer Aided Detection (CADe)
LUNG
This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval (CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis.
Adaptive Multi-Column Deep Neural Networks with Application to Robust Image Denoising
Agostinelli, Forest
Anderson, Michael R
Lee, Honglak
2013Conference Proceedings, cited 118 times
Website
Head-Neck Cetuximab
Algorithm Development
Image denoising
Machine Learning
Deep Learning
Stacked sparse denoising auto-encoders (SSDAs) have recently been shown to be successful at removing noise from corrupted images. However, like most denoising techniques, the SSDA is not robust to variation in noise types beyond what it has seen during training. We present the multi-column stacked sparse denoising autoencoder, a novel technique of combining multiple SSDAs into a multi-column SSDA (MC-SSDA) by combining the outputs of each SSDA. We eliminate the need to determine the type of noise, let alone its statistics, at test time. We show that good denoising performance can be achieved with a single system on a variety of different noise types, including ones not seen in the training set. Additionally, we experimentally demonstrate the efficacy of MC-SSDA denoising by achieving MNIST digit error rates on denoised images at close to that of the uncorrupted images.
CT Colonography: External Clinical Validation of an Algorithm for Computer-assisted Prone and Supine Registration
Boone, Darren J
Halligan, Steve
Roth, Holger R
Hampshire, Tom E
Helbren, Emma
Slabaugh, Greg G
McQuillan, Justine
McClelland, Jamie R
Hu, Mingxing
Punwani, Shonit
RadiologyRadiology2013Journal Article, cited 5 times
Website
CT COLONOGRAPHY
Image registration
Computer Assisted Detection (CAD)
PURPOSE: To perform external validation of a computer-assisted registration algorithm for prone and supine computed tomographic (CT) colonography and to compare the results with those of an existing centerline method. MATERIALS AND METHODS: All contributing centers had institutional review board approval; participants provided informed consent. A validation sample of CT colonographic examinations of 51 patients with 68 polyps (6-55 mm) was selected from a publicly available, HIPAA compliant, anonymized archive. No patients were excluded because of poor preparation or inadequate distension. Corresponding prone and supine polyp coordinates were recorded, and endoluminal surfaces were registered automatically by using a computer algorithm. Two observers independently scored three-dimensional endoluminal polyp registration success. Results were compared with those obtained by using the normalized distance along the colonic centerline (NDACC) method. Pairwise Wilcoxon signed rank tests were used to compare gross registration error and McNemar tests were used to compare polyp conspicuity. RESULTS: Registration was possible in all 51 patients, and 136 paired polyp coordinates were generated (68 polyps) to test the algorithm. Overall mean three-dimensional polyp registration error (mean +/- standard deviation, 19.9 mm +/- 20.4) was significantly less than that for the NDACC method (mean, 27.4 mm +/- 15.1; P = .001). Accuracy was unaffected by colonic segment (P = .76) or luminal collapse (P = .066). During endoluminal review by two observers (272 matching tasks, 68 polyps, prone to supine and supine to prone coordinates), 223 (82%) polyp matches were visible (120 degrees field of view) compared with just 129 (47%) when the NDACC method was used (P < .001). By using multiplanar visualization, 48 (70%) polyps were visible after scrolling +/- 15 mm in any multiplanar axis compared with 16 (24%) for NDACC (P < .001). CONCLUSION: Computer-assisted registration is more accurate than the NDACC method for mapping the endoluminal surface and matching the location of polyps in corresponding prone and supine CT colonographic acquisitions.
Constructing 3D-Printable CAD Models of Prostates from MR Images
This paper describes the development of a procedure to generate patient-specific, three-dimensional (3D) solid models of prostates (and related anatomy) from magnetic resonance (MR) images. The 3D models are rendered in STL file format which can be physically printed or visualized on a holographic display system. An example is presented in which a 3D model is printed following this procedure.
Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers
Results of initial low-dose computed tomographic screening for lung cancer
Church, T. R.
Black, W. C.
Aberle, D. R.
Berg, C. D.
Clingan, K. L.
Duan, F.
Fagerstrom, R. M.
Gareen, I. F.
Gierada, D. S.
Jones, G. C.
Mahon, I.
Marcus, P. M.
Sicks, J. D.
Jain, A.
Baum, S.
N Engl J MedThe New England journal of medicine2013Journal Article, cited 529 times
Website
NLST
lung
LDCT
BACKGROUND: Lung cancer is the largest contributor to mortality from cancer. The National Lung Screening Trial (NLST) showed that screening with low-dose helical computed tomography (CT) rather than with chest radiography reduced mortality from lung cancer. We describe the screening, diagnosis, and limited treatment results from the initial round of screening in the NLST to inform and improve lung-cancer-screening programs. METHODS: At 33 U.S. centers, from August 2002 through April 2004, we enrolled asymptomatic participants, 55 to 74 years of age, with a history of at least 30 pack-years of smoking. The participants were randomly assigned to undergo annual screening, with the use of either low-dose CT or chest radiography, for 3 years. Nodules or other suspicious findings were classified as positive results. This article reports findings from the initial screening examination. RESULTS: A total of 53,439 eligible participants were randomly assigned to a study group (26,715 to low-dose CT and 26,724 to chest radiography); 26,309 participants (98.5%) and 26,035 (97.4%), respectively, underwent screening. A total of 7191 participants (27.3%) in the low-dose CT group and 2387 (9.2%) in the radiography group had a positive screening result; in the respective groups, 6369 participants (90.4%) and 2176 (92.7%) had at least one follow-up diagnostic procedure, including imaging in 5717 (81.1%) and 2010 (85.6%) and surgery in 297 (4.2%) and 121 (5.2%). Lung cancer was diagnosed in 292 participants (1.1%) in the low-dose CT group versus 190 (0.7%) in the radiography group (stage 1 in 158 vs. 70 participants and stage IIB to IV in 120 vs. 112). Sensitivity and specificity were 93.8% and 73.4% for low-dose CT and 73.5% and 91.3% for chest radiography, respectively. CONCLUSIONS: The NLST initial screening results are consistent with the existing literature on screening by means of low-dose CT and chest radiography, suggesting that a reduction in mortality from lung cancer is achievable at U.S. screening centers that have staff experienced in chest CT. (Funded by the National Cancer Institute; NLST ClinicalTrials.gov number, NCT00047385.).
Measurement of spiculation index in 3D for solitary pulmonary nodules in volumetric lung CT images
Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume
Gavrielides, Marios A
Zeng, Rongping
Myers, Kyle J
Sahiner, Berkman
Petrick, Nicholas
Academic Radiology2013Journal Article, cited 23 times
Website
Phantom FDA
RATIONALE AND OBJECTIVES: The aim of this study was to quantify the effect of overlapping reconstruction on the precision and accuracy of lung nodule volume estimates in a phantom computed tomographic (CT) study. MATERIALS AND METHODS: An anthropomorphic phantom was used with a vasculature insert on which synthetic lung nodules were attached. Repeated scans of the phantom were acquired using a 64-slice CT scanner. Overlapping and contiguous reconstructions were performed for a range of CT imaging parameters (exposure, slice thickness, pitch, reconstruction kernel) and a range of nodule characteristics (size, density). Nodule volume was estimated with a previously developed matched-filter algorithm. RESULTS: Absolute percentage bias across all nodule sizes (n = 2880) was significantly lower when overlapping reconstruction was used, with an absolute percentage bias of 6.6% (95% confidence interval [CI], 6.4-6.9), compared to 13.2% (95% CI, 12.7-13.8) for contiguous reconstruction. Overlapping reconstruction also showed a precision benefit, with a lower standard percentage error of 7.1% (95% CI, 6.9-7.2) compared with 15.3% (95% CI, 14.9-15.7) for contiguous reconstructions across all nodules. Both effects were more pronounced for the smaller, subcentimeter nodules. CONCLUSIONS: These results support the use of overlapping reconstruction to improve the quantitative assessment of nodule size with CT imaging.
Cancer Digital Slide Archive: an informatics resource to support integrated in silico analysis of TCGA pathology data
Gutman, David A
Cobb, Jake
Somanna, Dhananjaya
Park, Yuna
Wang, Fusheng
Kurc, Tahsin
Saltz, Joel H
Brat, Daniel J
Cooper, Lee AD
Kong, Jun
Journal of the American Medical Informatics Association2013Journal Article, cited 70 times
Website
TCGA-GBM
TCGA-BRCA
Digital pathology
Data integration
BACKGROUND: The integration and visualization of multimodal datasets is a common challenge in biomedical informatics. Several recent studies of The Cancer Genome Atlas (TCGA) data have illustrated important relationships between morphology observed in whole-slide images, outcome, and genetic events. The pairing of genomics and rich clinical descriptions with whole-slide imaging provided by TCGA presents a unique opportunity to perform these correlative studies. However, better tools are needed to integrate the vast and disparate data types. OBJECTIVE: To build an integrated web-based platform supporting whole-slide pathology image visualization and data integration. MATERIALS AND METHODS: All images and genomic data were directly obtained from the TCGA and National Cancer Institute (NCI) websites. RESULTS: The Cancer Digital Slide Archive (CDSA) produced is accessible to the public (http://cancer.digitalslidearchive.net) and currently hosts more than 20,000 whole-slide images from 22 cancer types. DISCUSSION: The capabilities of CDSA are demonstrated using TCGA datasets to integrate pathology imaging with associated clinical, genomic and MRI measurements in glioblastomas and can be extended to other tumor types. CDSA also allows URL-based sharing of whole-slide images, and has preliminary support for directly sharing regions of interest and other annotations. Images can also be selected on the basis of other metadata, such as mutational profile, patient age, and other relevant characteristics. CONCLUSIONS: With the increasing availability of whole-slide scanners, analysis of digitized pathology images will become increasingly important in linking morphologic observations with genomic and clinical endpoints.
MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set
Gutman, David A
Cooper, Lee A D
Hwang, Scott N
Holder, Chad A
Gao, Jingjing
Aurora, Tarun D
Dunn, William D Jr
Scarpace, Lisa
Mikkelsen, Tom
Jain, Rajan
Wintermark, Max
Jilwan, Manal
Raghavan, Prashant
Huang, Erich
Clifford, Robert J
Mongkolwat, Pattanasak
Kleper, Vladimir
Freymann, John
Kirby, Justin
Zinn, Pascal O
Moreno, Carlos S
Jaffe, Carl
Colen, Rivka
Rubin, Daniel L
Saltz, Joel
Flanders, Adam
Brat, Daniel J
RadiologyRadiology2013Journal Article, cited 217 times
Website
TCGA-GBM
Radiomics
Radiogenomics
Glioblastoma Multiforme (GBM)
BRAIN
PURPOSE: To conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival. MATERIALS AND METHODS: Because all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff alpha statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test. RESULTS: Interrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01). CONCLUSION: This analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.
Vector quantization-based automatic detection of pulmonary nodules in thoracic CT images
Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel CADe system for lung nodule detection based on a vector quantization (VQ) approach. Compared to existing CADe systems, the extraction of lungs from the chest CT image is fully automatic, and the detection and segmentation of initial nodule candidates (INCs) within the lung volume is fast and accurate due to the self-adaptive nature of VQ algorithm. False positives in the detected INCs are reduced by rule-based pruning in combination with a feature-based support vector machine classifier. We validate the proposed approach on 60 CT scans from a publicly available database. Preliminary results show that our CADe system is effective to detect nodules with a sensitivity of 90.53 % at a specificity level of 86.00%.
Multiparametric MRI of prostate cancer: An update on state‐of‐the‐art techniques and their performance in detecting and localizing prostate cancer
Hegde, John V
Mulkern, Robert V
Panych, Lawrence P
Fennessy, Fiona M
Fedorov, Andriy
Maier, Stephan E
Tempany, Clare
Journal of Magnetic Resonance Imaging2013Journal Article, cited 164 times
Website
This paper describes the design of a patient-specific, radiotherapy quality assurance target that can be used to verify a treatment plan by measurement of actual dosage. Staring from a patient's (segmented) MR images, a physical model containing insertable cartridges for holding dosimeters is printed in 3D. Dosimeters can be located at specific locations of interest (e.g., tumor, nerve bundles, urethra). The model (dosimeter insert) can be placed into a pelvis 'shell' and subject to a specified treatment plan. A design for the dosimeter insert can be efficiently fabricated using rapid prototyping techniques.
A reversible data hiding method by histogram shifting in high quality medical images
Huang, Li-Chin
Tseng, Lin-Yu
Hwang, Min-Shiang
Journal of Systems and Software2013Journal Article, cited 60 times
Website
Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers
Jain, Rajan
Poisson, Laila
Narang, Jayant
Gutman, David
Scarpace, Lisa
Hwang, Scott N
Holder, Chad
Wintermark, Max
Colen, Rivka R
Kirby, Justin
Freymann, John
Brat, Daniel J
Jaffe, Carl
Mikkelsen, Tom
RadiologyRadiology2013Journal Article, cited 99 times
Website
Radiomics
Glioblastoma Multiforme (GBM)
Magnetic Resonance Imaging (MRI)
molecular subtype
PURPOSE: To correlate tumor blood volume, measured by using dynamic susceptibility contrast material-enhanced T2*-weighted magnetic resonance (MR) perfusion studies, with patient survival and determine its association with molecular subclasses of glioblastoma (GBM). MATERIALS AND METHODS: This HIPAA-compliant retrospective study was approved by institutional review board. Fifty patients underwent dynamic susceptibility contrast-enhanced T2*-weighted MR perfusion studies and had gene expression data available from the Cancer Genome Atlas. Relative cerebral blood volume (rCBV) (maximum rCBV [rCBV(max)] and mean rCBV [rCBV(mean)]) of the contrast-enhanced lesion as well as rCBV of the nonenhanced lesion (rCBV(NEL)) were measured. Patients were subclassified according to the Verhaak and Phillips classification schemas, which are based on similarity to defined genomic expression signature. We correlated rCBV measures with the molecular subclasses as well as with patient overall survival by using Cox regression analysis. RESULTS: No statistically significant differences were noted for rCBV(max), rCBV(mean) of contrast-enhanced lesion or rCBV(NEL) between the four Verhaak classes or the three Phillips classes. However, increased rCBV measures are associated with poor overall survival in GBM. The rCBV(max) (P = .0131) is the strongest predictor of overall survival regardless of potential confounders or molecular classification. Interestingly, including the Verhaak molecular GBM classification in the survival model clarifies the association of rCBV(mean) with patient overall survival (hazard ratio: 1.46, P = .0212) compared with rCBV(mean) alone (hazard ratio: 1.25, P = .1918). Phillips subclasses are not predictive of overall survival nor do they affect the predictive ability of rCBV measures on overall survival. CONCLUSION: The rCBV(max) measurements could be used to predict patient overall survival independent of the molecular subclasses of GBM; however, Verhaak classifiers provided additional information, suggesting that molecular markers could be used in combination with hemodynamic imaging biomarkers in the future.
Radiogenomic correlation for prognosis in patients with glioblastoma multiformae
Medical imaging is fundamental to modern healthcare, and its widespread use has resulted in the creation of image databases, as well as picture archiving and communication systems. These repositories now contain images from a diverse range of modalities, multidimensional (three-dimensional or time-varying) images, as well as co-aligned multimodality images. These image collections offer the opportunity for evidence-based diagnosis, teaching, and research; for these applications, there is a requirement for appropriate methods to search the collections for images that have characteristics similar to the case(s) of interest. Content-based image retrieval (CBIR) is an image search technique that complements the conventional text-based retrieval of images by using visual features, such as color, texture, and shape, as search criteria. Medical CBIR is an established field of study that is beginning to realize promise when applied to multidimensional and multimodality medical data. In this paper, we present a review of state-of-the-art medical CBIR approaches in five main categories: two-dimensional image retrieval, retrieval of images with three or more dimensions, the use of nonimage data to enhance the retrieval, multimodality image retrieval, and retrieval from diverse datasets. We use these categories as a framework for discussing the state of the art, focusing on the characteristics and modalities of the information used during medical image retrieval.
Automated Segmentation of Prostate MR Images Using Prior Knowledge Enhanced Random Walker
vPSNR: a visualization-aware image fidelity metric tailored for diagnostic imaging
Lundström, Claes
International Journal of Computer Assisted Radiology and Surgery2013Journal Article, cited 0 times
Website
Algorithm Development
Image compression
Purpose Often, the large amounts of data generated in diagnostic imaging cause overload problems for IT systems and radiologists. This entails a need of effective use of data reduction beyond lossless levels, which, in turn, underlines the need to measure and control the image fidelity. Existing image fidelity metrics, however, fail to fully support important requirements from a modern clinical context: support for high-dimensional data, visualization awareness, and independence from the original data.
Methods We propose an image fidelity metric, called the visual peak signal-to-noise ratio (vPSNR), fulfilling the three main requirements. A series of image fidelity tests on CT data sets is employed. The impact of visualization transform (grayscale window) on diagnostic quality of irreversibly compressed data sets is evaluated through an observer-based study. In addition, several tests were performed demonstrating the benefits, limitations, and characteristics of vPSNR in different data reduction scenarios.
Results The visualization transform has a significant impact on diagnostic quality, and the vPSNR is capable of representing this effect. Moreover, the tests establish that the vPSNR is broadly applicable.
Conclusions vPSNR fills a gap not served by existing image fidelity metrics, relevant for the clinical context. While vPSNR alone cannot fulfill all image fidelity needs, it can be a useful complement in a wide range of scenarios.
Imaging descriptors improve the predictive power of survival models for glioblastoma patients
BACKGROUND: Because effective prediction of survival time can be highly beneficial for the treatment of glioblastoma patients, the relationship between survival time and multiple patient characteristics has been investigated. In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model. METHODS: The subjects in this study were 82 glioblastoma patients for whom clinical features as well as MR imaging exams were made available by The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Twenty-six imaging features in the available MR scans were assessed by radiologists from the TCGA Glioma Phenotype Research Group. We used multivariate Cox proportional hazards regression to construct 2 survival models: one that used 3 clinical features (age, gender, and KPS) as the covariates and 1 that used both the imaging features and the clinical features as the covariates. Then, we used 2 measures to compare the predictive performance of these 2 models: area under the receiver operating characteristic curve for the 1-year survival threshold and overall concordance index. To eliminate any positive performance estimation bias, we used leave-one-out cross-validation. RESULTS: The performance of the model based on both clinical and imaging features was higher than the performance of the model based on only the clinical features, in terms of both area under the receiver operating characteristic curve (P < .01) and the overall concordance index (P < .01). CONCLUSIONS: Imaging features assessed using a controlled lexicon have additional predictive value compared with clinical features when predicting survival time in glioblastoma patients.
Phase I trial of preoperative chemoradiation plus sorafenib for high-risk extremity soft tissue sarcomas with dynamic contrast-enhanced MRI correlates
Meyer, Janelle M
Perlewitz, Kelly S
Hayden, James B
Doung, Yee-Cheen
Hung, Arthur Y
Vetto, John T
Pommier, Rodney F
Mansoor, Atiya
Beckett, Brooke R
Tudorica, Alina
Clinical Cancer Research2013Journal Article, cited 41 times
Website
Soft tissue sarcoma
Image segmentation on GPGPUs: a cellular automata-based approach
Compression is increasingly used in medical applications to enable efficient and universally accessible electronic health records. However, lossy compression introduces artifacts that can alter diagnostic accuracy, interfere with image processing algorithms and cause liability issues in cases of diagnostic errors. Compression guidelines were introduced to mitigate these issues and foster the use of modern compression algorithms with diagnostic imaging. However, these guidelines are usually defined as maximum compression ratios for each imaging protocol and do not take compressibility variations due to image content into account. In this paper we have evaluated the compressibility of thousands of computed tomography slices of an anthropomorphic thoracic phantom acquired with different parameters. We have shown that exposure, slice thickness and reconstruction filters have a significant impact on compressibility suggesting that guidelines based solely on compression ratios may be inadequate.
Texture classification of lung computed tomography images
Current development of algorithms in computer-aided diagnosis (CAD) scheme is growing rapidly to assist the radiologist in medical image interpretation. Texture analysis of computed tomography (CT) scans is one of important preliminary stage in the computerized detection system and classification for lung cancer. Among different types of images features analysis, Haralick texture with variety of statistical measures has been used widely in image texture description. The extraction of texture feature values is essential to be used by a CAD especially in classification of the normal and abnormal tissue on the cross sectional CT images. This paper aims to compare experimental results using texture extraction and different machine leaning methods in the classification normal and abnormal tissues through lung CT images. The machine learning methods involve in this assessment are Artificial Immune Recognition System (AIRS), Naive Bayes, Decision Tree (J48) and Backpropagation Neural Network. AIRS is found to provide high accuracy (99.2%) and sensitivity (98.0%) in the assessment. For experiments and testing purpose, publicly available datasets in the Reference Image Database to Evaluate Therapy Response (RIDER) are used as study cases.
ROC curves for low-dose CT in the National Lung Screening Trial
Pinsky, P. F.
Gierada, D. S.
Nath, H.
Kazerooni, E. A.
Amorosa, J.
J Med ScreenJournal of medical screening2013Journal Article, cited 4 times
Website
NLST
lung
LDCT
Cancer Screening
The National Lung Screening Trial (NLST) reported a 20% reduction in lung cancer specific mortality using low-dose chest CT (LDCT) compared with chest radiograph (CXR) screening. The high number of false positive screens with LDCT (around 25%) raises concerns. NLST radiologists reported LDCT screens as either positive or not positive, based primarily on the presence of a 4+ mm non-calcified lung nodule (NCN). They did not explicitly record a propensity score for lung cancer. However, by using maximum NCN size, or alternatively, radiologists' recommendations for diagnostic follow-up categorized hierarchically, surrogate propensity scores (PSSZ and PSFR) were created. These scores were then used to compute ROC curves, which determine possible operating points of sensitivity versus false positive rate (1-Specificity). The area under the ROC curve (AUC) was 0.934 and 0.928 for PSFR and PSSZ, respectively; the former was significantly greater than the latter. With the NLST definition of a positive screen, sensitivity and specificity of LDCT was 93.1% and 76.5%, respectively. With cutoffs based on PSFR, a specificity of 92.4% could be achieved while only lowering sensitivity to 86.9%. Radiologists using LDCT have good predictive ability; the optimal operating point for sensitivity and specificity remains to be determined.
National lung screening trial: variability in nodule detection rates in chest CT studies
Pinsky, P. F.
Gierada, D. S.
Nath, P. H.
Kazerooni, E.
Amorosa, J.
RadiologyRadiology2013Journal Article, cited 43 times
Website
NLST
lung
LDCT
Cancer Screening
PURPOSE: To characterize the variability in radiologists' interpretations of computed tomography (CT) studies in the National Lung Screening Trial (NLST) (including assessment of false-positive rates [FPRs] and sensitivity), to examine factors that contribute to variability, and to evaluate trade-offs between FPRs and sensitivity among different groups of radiologists. MATERIALS AND METHODS: The HIPAA-compliant NLST was approved by the institutional review board at each screening center; all participants provided informed consent. NLST radiologists reported overall screening results, nodule-specific findings, and recommendations for diagnostic follow-up. A noncalcified nodule of 4 mm or larger constituted a positive screening result. The FPR was defined as the rate of positive screening examinations in participants without a cancer diagnosis within 1 year. Descriptive analyses and mixed-effects models were utilized. The average odds ratio (OR) for a false-positive result across all pairs of radiologists was used as a measure of variability. RESULTS: One hundred twelve radiologists at 32 screening centers each interpreted 100 or more NLST CT studies, interpreting 72 160 of 75 126 total NLST CT studies in aggregate. The mean FPR for radiologists was 28.7% +/- 13.7 (standard deviation), with a range of 3.8%-69.0%. The model yielded an average OR of 2.49 across all pairs of radiologists and an OR of 1.83 for pairs within the same screening center. Mean FPRs were similar for academic versus nonacademic centers (27.9% and 26.7%, respectively) and for centers inside (25.0%) versus outside (28.7%) the U.S. "histoplasmosis belt." Aggregate sensitivity was 96.5% for radiologists with FPRs higher than the median (27.1%), compared with 91.9% for those with FPRs lower than the median (P = .02). CONCLUSION: There was substantial variability in radiologists' FPRs. Higher FPRs were associated with modestly higher sensitivity.
Exploring relationships between multivariate radiological phenotypes and genetic features: A case-study in Glioblastoma using the Cancer Genome Atlas
Rao, Arvind
2013Conference Proceedings, cited 0 times
Radiogenomics
Magnetic resonance spectroscopy as an early indicator of response to anti-angiogenic therapy in patients with recurrent glioblastoma: RTOG 0625/ACRIN 6677
Background. The prognosis for patients with recurrent glioblastoma remains poor. The purpose of this study was to assess the potential role of MR spectroscopy as an early indicator of response to anti-angiogenic therapy.
Methods. Thirteen patients with recurrent glioblastoma were enrolled in RTOG 0625/ACRIN 6677, a prospective multicenter trial in which bevacizumab was used in combination with either temozolomide or irinotecan. Patients were scanned prior to treatment and at specific timepoints during the treatment regimen. Postcontrast T1-weighted MRI was used to assess 6-month progression-free survival. Spectra from the enhancing tumor and peritumoral regions were defined on the postcontrast T1-weighted images. Changes in the concentration ratios of N-acetylaspartate/creatine (NAA/Cr), choline-containing compounds (Cho)/Cr, and NAA/Cho were quantified in comparison with pretreatment values.
Results. NAA/Cho levels increased and Cho/Cr levels decreased within enhancing tumor at 2 weeks relative to pretreatment levels (P = .048 and P = .016, respectively), suggesting a possible antitumor effect of bevacizumab with cytotoxic chemotherapy. Nine of the 13 patients were alive and progression free at 6 months. Analysis of receiver operating characteristic curves for NAA/Cho changes in tumor at 8 weeks revealed higher levels in patients progression free at 6 months (area under the curve = 0.85), suggesting that NAA/Cho is associated with treatment response. Similar results were observed for receiver operating characteristic curve analyses against 1-year survival. In addition, decreased Cho/Cr and increased NAA/Cr and NAA/Cho in tumor periphery at 16 weeks posttreatment were associated with both 6-month progression-free survival and 1-year survival.
Conclusion. Changes in NAA and Cho by MR spectroscopy may potentially be useful as imaging biomarkers in assessing response to anti-angiogenic treatment.
3D medical image denoising using 3D block matching and low-rank matrix completion
3D Denoising as one of the most significant tools in medical imaging was studied in the literature. However, most existing 3D medical data denoising algorithms have assumed the additive white Gaussian noise. In this work, we propose an efficient 3D medical data denoising method that can handle a noise mixture of various types. Our method is based on modified 2D Adaptive Rood Pattern Search (ARPS) [1] and low-rank matrix completion as follows. In our method, a noisy 3D data is processed in blockwise manner, for each processed 3D block we find similar 3D blocks in 3D data, where we use overlapped 3D patches to further lower the computation complexity. The 3D blocks then will stack together and unreliable voxels will be replaced using fast matrix completion method [2]. Experimental results show that the proposed method is able to robustly denoise the mixed noise from 3D medical data.
Lung nodule detection using fuzzy clustering and support vector machines
Sivakumar, S
Chandrasekar, C
International Journal of Engineering and Technology2013Journal Article, cited 43 times
Website
Algorithm Development
Computer Aided Detection (CADe)
Computed Tomography (CT)
LUNG
Machine Learning
Lung cancer is the primary cause of tumor deaths for both sexes in most countries. Lung nodule, an abnormality which leads to lung cancer is detected by various medical imaging techniques like X-ray, Computerized Tomography (CT), etc. Detection of lung nodules is a challenging task since the nodules are commonly attached to the blood vessels. Many studies have shown that early diagnosis is the most efficient way to cure this disease. This paper aims to develop an efficient lung nodule detection scheme by performing nodule segmentation through fuzzy based clustering models; classification by using a machine learning technique called Support Vector Machine (SVM). This methodology uses three different types of kernels among these RBF kernel gives better class performance.
Supervised Machine Learning Approach Utilizing Artificial Neural Networks for Automated Prostate Zone Segmentation in Abdominal MR images
Clinically relevant modeling of tumor growth and treatment response
Yankeelov, Thomas E
Atuegwu, Nkiruka
Hormuth, David
Weis, Jared A
Barnes, Stephanie L
Miga, Michael I
Rericha, Erin C
Quaranta, Vito
Science Translational Medicine2013Journal Article, cited 70 times
Website
Algorithm Development
Diffusion-weighted MRI
Dynamic Contrast-Enhanced (DCE)-MRI
Positron emission tomography (PET)
BREAST
Models
Current mathematical models of tumor growth are limited in their clinical application because they require input data that are nearly impossible to obtain with sufficient spatial resolution in patients even at a single time point--for example, extent of vascularization, immune infiltrate, ratio of tumor-to-normal cells, or extracellular matrix status. Here we propose the use of emerging, quantitative tumor imaging methods to initialize a new generation of predictive models. In the near future, these models could be able to forecast clinical outputs, such as overall response to treatment and time to progression, which will provide opportunities for guided intervention and improved patient care.
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Human cancers exhibit strong phenotypic differences that can be visualized noninvasively by medical imaging. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. Here we present a radiomic analysis of 440 features quantifying tumour image intensity, shape and texture, which are extracted from computed tomography data of 1,019 patients with lung or head-and-neck cancer. We find that a large number of radiomic features have prognostic power in independent data sets of lung and head-and-neck cancer patients, many of which were not identified as significant before. Radiogenomics analysis reveals that a prognostic radiomic signature, capturing intratumour heterogeneity, is associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.
Test–Retest Reproducibility Analysis of Lung CT Image Features
Quantitative size, shape, and texture features derived from computed tomographic (CT) images may be useful as predictive, prognostic, or response biomarkers in non-small cell lung cancer (NSCLC). However, to be useful, such features must be reproducible, non-redundant, and have a large dynamic range. We developed a set of quantitative three-dimensional (3D) features to describe segmented tumors and evaluated their reproducibility to select features with high potential to have prognostic utility. Thirty-two patients with NSCLC were subjected to unenhanced thoracic CT scans acquired within 15 min of each other under an approved protocol. Primary lung cancer lesions were segmented using semi-automatic 3D region growing algorithms. Following segmentation, 219 quantitative 3D features were extracted from each lesion, corresponding to size, shape, and texture, including features in transformed spaces (laws, wavelets). The most informative features were selected using the concordance correlation coefficient across test–retest, the biological range and a feature independence measure. There were 66 (30.14 %) features with concordance correlation coefficient ≥ 0.90 across test–retest and acceptable dynamic range. Of these, 42 features were non-redundant after grouping features with R2Bet ≥ 0.95. These reproducible features were found to be predictive of radiological prognosis. The area under the curve (AUC) was 91 % for a size-based feature and 92 % for the texture features (runlength, laws). We tested the ability of image features to predict a radiological prognostic score on an independent NSCLC (39 adenocarcinoma) samples, the AUC for texture features (runlength emphasis, energy) was 0.84 while the conventional size-based features (volume, longest diameter) was 0.80. Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range. Thus making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.
Reproducible research is a growing movement among scientists, but the tools for creating sustainable software to support the computational side of research are still in their infancy and are typically only being used by scientists with expertise in computer programming and system administration. Docker is a new platform developed for the DevOps community that enables the easy creation and management of consistent computational environments. This article describes how we have applied it to computational science and suggests that it could be a powerful tool for reproducible research.
The Quantitative Imaging Network: NCI's Historical Perspective and Planned Goals
Clarke, Laurence P.
Nordstrom, Robert J.
Zhang, Huiming
Tandon, Pushpa
Zhang, Yantian
Redmond, George
Farahani, Keyvan
Kelloff, Gary
Henderson, Lori
Shankar, Lalitha
Deye, James
Capala, Jacek
Jacobs, Paula
Transl OncolTranslational oncology2014Journal Article, cited 0 times
Website
QIN
Automated Medical Image Modality Recognition by Fusion of Visual and Text Information
NCI Workshop Report: Clinical and Computational Requirements for Correlating Imaging Phenotypes with Genomics Signatures
Colen, Rivka
Foster, Ian
Gatenby, Robert
Giger, Mary Ellen
Gillies, Robert
Gutman, David
Heller, Matthew
Jain, Rajan
Madabhushi, Anant
Madhavan, Subha
Napel, Sandy
Rao, Arvind
Saltz, Joel
Tatum, James
Verhaak, Roeland
Whitman, Gary
Transl OncolTranslational oncology2014Journal Article, cited 39 times
Website
Multi-modal imaging
Radiogenomics
Radiomics
TCGA-GBM
TCGA-BRCA
Pathomics
The National Cancer Institute (NCI) Cancer Imaging Program organized two related workshops on June 26-27, 2013, entitled "Correlating Imaging Phenotypes with Genomics Signatures Research" and "Scalable Computational Resources as Required for Imaging-Genomics Decision Support Systems." The first workshop focused on clinical and scientific requirements, exploring our knowledge of phenotypic characteristics of cancer biological properties to determine whether the field is sufficiently advanced to correlate with imaging phenotypes that underpin genomics and clinical outcomes, and exploring new scientific methods to extract phenotypic features from medical images and relate them to genomics analyses. The second workshop focused on computational methods that explore informatics and computational requirements to extract phenotypic features from medical images and relate them to genomics analyses and improve the accessibility and speed of dissemination of existing NIH resources. These workshops linked clinical and scientific requirements of currently known phenotypic and genotypic cancer biology characteristics with imaging phenotypes that underpin genomics and clinical outcomes. The group generated a set of recommendations to NCI leadership and the research community that encourage and support development of the emerging radiogenomics research field to address short-and longer-term goals in cancer research.
Imaging genomic mapping of an invasive MRI phenotype predicts patient outcome and metabolic dysfunction: a TCGA glioma phenotype research group project
Colen, Rivka R
Vangel, Mark
Wang, Jixin
Gutman, David A
Hwang, Scott N
Wintermark, Max
Jain, Rajan
Jilwan-Nicolas, Manal
Chen, James Y
Raghavan, Prashant
Holder, C. A.
Rubin, D.
Huang, E.
Kirby, J.
Freymann, J.
Jaffe, C. C.
Flanders, A.
TCGA Glioma Phenotype Research Group
Zinn, P. O.
BMC Medical Genomics2014Journal Article, cited 47 times
Website
TCGA-GBM
Radiomics
Radiogenomics
Computer Aided Detection (CADe)
Magnetic Resonance Imaging (MRI)
BACKGROUND: Invasion of tumor cells into adjacent brain parenchyma is a major cause of treatment failure in glioblastoma. Furthermore, invasive tumors are shown to have a different genomic composition and metabolic abnormalities that allow for a more aggressive GBM phenotype and resistance to therapy. We thus seek to identify those genomic abnormalities associated with a highly aggressive and invasive GBM imaging-phenotype. METHODS: We retrospectively identified 104 treatment-naive glioblastoma patients from The Cancer Genome Atlas (TCGA) whom had gene expression profiles and corresponding MR imaging available in The Cancer Imaging Archive (TCIA). The standardized VASARI feature-set criteria were used for the qualitative visual assessments of invasion. Patients were assigned to classes based on the presence (Class A) or absence (Class B) of statistically significant invasion parameters to create an invasive imaging signature; imaging genomic analysis was subsequently performed using GenePattern Comparative Marker Selection module (Broad Institute). RESULTS: Our results show that patients with a combination of deep white matter tracts and ependymal invasion (Class A) on imaging had a significant decrease in overall survival as compared to patients with absence of such invasive imaging features (Class B) (8.7 versus 18.6 months, p < 0.001). Mitochondrial dysfunction was the top canonical pathway associated with Class A gene expression signature. The MYC oncogene was predicted to be the top activation regulator in Class A. CONCLUSION: We demonstrate that MRI biomarker signatures can identify distinct GBM phenotypes associated with highly significant survival differences and specific molecular pathways. This study identifies mitochondrial dysfunction as the top canonical pathway in a very aggressive GBM phenotype. Thus, imaging-genomic analyses may prove invaluable in detecting novel targetable genomic pathways.
Glioblastoma: Imaging Genomic Mapping Reveals Sex-specific Oncogenic Associations of Cell Death
Colen, Rivka R
Wang, Jixin
Singh, Sanjay K
Gutman, David A
Zinn, Pascal O
RadiologyRadiology2014Journal Article, cited 36 times
Website
TCGA-GBM
Radiogenomics
PURPOSE: To identify the molecular profiles of cell death as defined by necrosis volumes at magnetic resonance (MR) imaging and uncover sex-specific molecular signatures potentially driving oncogenesis and cell death in glioblastoma (GBM). MATERIALS AND METHODS: This retrospective study was HIPAA compliant and had institutional review board approval, with waiver of the need to obtain informed consent. The molecular profiles for 99 patients (30 female patients, 69 male patients) were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Volumes of necrosis at MR imaging were extracted. Differential gene expression profiles were obtained in those patients (including male and female patients separately) with high versus low MR imaging volumes of tumor necrosis. Ingenuity Pathway Analysis was used for messenger RNA-microRNA interaction analysis. A histopathologic data set (n = 368; 144 female patients, 224 male patients) was used to validate the MR imaging findings by assessing the amount of cell death. A connectivity map was used to identify therapeutic agents potentially targeting sex-specific cell death in GBM. RESULTS: Female patients showed significantly lower volumes of necrosis at MR imaging than male patients (6821 vs 11 050 mm(3), P = .03). Female patients, unlike male patients, with high volumes of necrosis at imaging had significantly shorter survival (6.5 vs 14.5 months, P = .01). Transcription factor analysis suggested that cell death in female patients with GBM is associated with MYC, while that in male patients is associated with TP53 activity. Additionally, a group of therapeutic agents that can potentially be tested to target cell death in a sex-specific manner was identified. CONCLUSION: The results of this study suggest that cell death in GBM may be driven by sex-specific molecular pathways.
A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors
Breast cancer today is the leading cause of death amongstcancer patients inflicting women around the world. Breast cancer is themost common cancer in women worldwide. It is also the principle cause ofdeath from cancer among women globally. Early detection of this diseasecan greatly enhance the chances of long-term survival of breast cancervictims. Classification of cancer data helps widely in detection of the dis-ease and it can be achieved using many techniques such as Perceptronwhich is an Artificial Neural Network (ANN) classification technique.In this paper, we proposed a new hybrid algorithm by combining theperceptron algorithm and the feature extraction algorithm after apply-ing the Scale Invariant Feature Transform (SIFT) algorithm in orderto classify magnetic resonance imaging (MRI) breast cancer images. Theproposed algorithm is called breast MRI cancer classifier (BMRICC) andit has been tested tested on 281 MRI breast images (138 abnormal and143 normal). The numerical results of the general performance of theBMRICC algorithm and the comparasion results between it and other 5benchmark classifiers show that, the BMRICC algorithm is a promisingalgorithm and its performance is better than the other algorithms.
Multisite Image Data Collection and Management Using the RSNA Image Sharing Network
Erickson, Bradley J
Fajnwaks, Patricio
Langer, Steve G
Perry, John
Transl OncolTranslational oncology2014Journal Article, cited 3 times
Website
Algorithm Development
Image de-identification
The execution of a multisite trial frequently includes image collection. The Clinical Trials Processor (CTP) makes removal of protected health information highly reliable. It also provides reliable transfer of images to a central review site. Trials using central review of imaging should consider using CTP for handling image data when a multisite trial is being designed.
A comparison of two methods for estimating DCE-MRI parameters via individual and cohort based AIFs in prostate cancer: A step towards practical implementation
Fedorov, Andriy
Fluckiger, Jacob
Ayers, Gregory D
Li, Xia
Gupta, Sandeep N
Tempany, Clare
Mulkern, Robert
Yankeelov, Thomas E
Fennessy, Fiona M
Magnetic resonance imaging2014Journal Article, cited 30 times
Website
Algorithm Development
PROSTATE
Dynamic Contrast-Enhanced (DCE)-MRI
Multi-parametric Magnetic Resonance Imaging, and specifically Dynamic Contrast Enhanced (DCE) MRI, play increasingly important roles in detection and staging of prostate cancer (PCa). One of the actively investigated approaches to DCE MRI analysis involves pharmacokinetic (PK) modeling to extract quantitative parameters that may be related to microvascular properties of the tissue. It is well-known that the prescribed arterial blood plasma concentration (or Arterial Input Function, AIF) input can have significant effects on the parameters estimated by PK modeling. The purpose of our study was to investigate such effects in DCE MRI data acquired in a typical clinical PCa setting. First, we investigated how the choice of a semi-automated or fully automated image-based individualized AIF (iAIF) estimation method affects the PK parameter values; and second, we examined the use of method-specific averaged AIF (cohort-based, or cAIF) as a means to attenuate the differences between the two AIF estimation methods. Two methods for automated image-based estimation of individualized (patient-specific) AIFs, one of which was previously validated for brain and the other for breast MRI, were compared. cAIFs were constructed by averaging the iAIF curves over the individual patients for each of the two methods. Pharmacokinetic analysis using the Generalized kinetic model and each of the four AIF choices (iAIF and cAIF for each of the two image-based AIF estimation approaches) was applied to derive the volume transfer rate (K(trans)) and extravascular extracellular volume fraction (ve) in the areas of prostate tumor. Differences between the parameters obtained using iAIF and cAIF for a given method (intra-method comparison) as well as inter-method differences were quantified. The study utilized DCE MRI data collected in 17 patients with histologically confirmed PCa. Comparison at the level of the tumor region of interest (ROI) showed that the two automated methods resulted in significantly different (p<0.05) mean estimates of ve, but not of K(trans). Comparing cAIF, different estimates for both ve, and K(trans) were obtained. Intra-method comparison between the iAIF- and cAIF-driven analyses showed the lack of effect on ve, while K(trans) values were significantly different for one of the methods. Our results indicate that the choice of the algorithm used for automated image-based AIF determination can lead to significant differences in the values of the estimated PK parameters. K(trans) estimates are more sensitive to the choice between cAIF/iAIF as compared to ve, leading to potentially significant differences depending on the AIF method. These observations may have practical consequences in evaluating the PK analysis results obtained in a multi-site setting.
Glioblastoma Multiforme: Exploratory Radiogenomic Analysis by Using Quantitative Image Features
Gevaert, Olivier
Mitchell, Lex A
Achrol, Achal S
Xu, Jiajing
Echegaray, Sebastian
Steinberg, Gary K
Cheshier, Samuel H
Napel, Sandy
Zaharchuk, Greg
Plevritis, Sylvia K
RadiologyRadiology2014Journal Article, cited 151 times
Website
TCGA-GBM
Radiomics
Radiomic features
Radiogenomics
IDH mutation
Glioblastoma Multiforme (GBM)
VASARI
Computer Aided Detection (CADe)
Purpose: To derive quantitative image features from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create radiogenomic maps associating these features with various molecular data.
Materials and Methods: Clinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the Cancer Genome Atlas and the Cancer Imaging Archive after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn, and quantitative image features were derived from these ROIs. Robust quantitative image features were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest analysis. The robust features were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust image features were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust image features by using the Spearman correlation test to create radiogenomic maps and to link robust image features with molecular pathways.
Results: Eighteen image features passed the robustness analysis and were further analyzed for the three types of ROIs, for a total of 54 image features. Three enhancement features were significantly correlated with survival, 77 significant correlations were found between robust quantitative features and the VASARI feature set, and seven image features were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link image features with gene expression modules and allowed linkage of 56% (30 of 54) of the image features with biologic processes.
Conclusion: Radiogenomic approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.
Projected outcomes using different nodule sizes to define a positive CT lung cancer screening examination
Background Computed tomography (CT) screening for lung cancer has been associated with a high frequency of false positive results because of the high prevalence of indeterminate but usually benign small pulmonary nodules. The acceptability of reducing false-positive rates and diagnostic evaluations by increasing the nodule size threshold for a positive screen depends on the projected balance between benefits and risks.
Methods We examined data from the National Lung Screening Trial (NLST) to estimate screening CT performance and outcomes for scans with nodules above the 4 mm NLST threshold used to classify a CT screen as positive. Outcomes assessed included screening results, subsequent diagnostic tests performed, lung cancer histology and stage distribution, and lung cancer mortality. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the different nodule size thresholds. All statistical tests were two-sided.
Results In 64% of positive screens (11 598/18 141), the largest nodule was 7 mm or less in greatest transverse diameter. By increasing the threshold, the percentages of lung cancer diagnoses that would have been missed or delayed and false positives that would have been avoided progressively increased, for example from 1.0% and 15.8% at a 5 mm threshold to 10.5% and 65.8% at an 8 mm threshold, respectively. The projected reductions in postscreening follow-up CT scans and invasive procedures also increased as the threshold was raised. Differences across nodules sizes for lung cancer histology and stage distribution were small but statistically significant. There were no differences across nodule sizes in survival or mortality.
Conclusion Raising the nodule size threshold for a positive screen would substantially reduce false-positive CT screenings and medical resource utilization with a variable impact on screening outcomes.
Brain Tumor Detection using Curvelet Transform and Support Vector Machine
Gupta, Bhawna
Tiwari, Shamik
International Journal of Computer Science and Mobile Computing2014Journal Article, cited 8 times
Website
Web based tools for visualizing imaging data and development of XNATView, a zero footprint image viewer
Gutman, David A
Dunn Jr, William D
Cobb, Jake
Stoner, Richard M
Kalpathy-Cramer, Jayashree
Erickson, Bradley
Frontiers in Neuroinformatics2014Journal Article, cited 12 times
Website
Algorithm Development
XNAT
DICOM
Advances in web technologies now allow direct visualization of imaging data sets without necessitating the download of large file sets or the installation of software. This allows centralization of file storage and facilitates image review and analysis. XNATView is a light framework recently developed in our lab to visualize DICOM images stored in The Extensible Neuroimaging Archive Toolkit (XNAT). It consists of a PyXNAT-based framework to wrap around the REST application programming interface (API) and query the data in XNAT. XNATView was developed to simplify quality assurance, help organize imaging data, and facilitate data sharing for intra- and inter-laboratory collaborations. Its zero-footprint design allows the user to connect to XNAT from a web browser, navigate through projects, experiments, and subjects, and view DICOM images with accompanying metadata all within a single viewing instance.
A novel computer-aided detection system for pulmonary nodule identification in CT images
Computer-aided detection (CADe) of pulmonary nodules from computer tomography (CT) scans is critical for assisting radiologists to identify lung lesions at an early stage. In this paper, we propose a novel approach for CADe of lung nodules using a two-stage vector quantization (VQ) scheme. The first-stage VQ aims to extract lung from the chest volume, while the second-stage VQ is designed to extract initial nodule candidates (INCs) within the lung volume. Then rule-based expert filtering is employed to prune obvious FPs from INCs, and the commonly-used support vector machine (SVM) classifier is adopted to further reduce the FPs. The proposed system was validated on 100 CT scans randomly selected from the 262 scans that have at least one juxta-pleural nodule annotation in the publicly available database - Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The two-stage VQ only missed 2 out of the 207 nodules at agreement level 1, and the INCs detection for each scan took about 30 seconds in average. Expert filtering reduced FPs more than 18 times, while maintaining a sensitivity of 93.24%. As it is trivial to distinguish INCs attached to pleural wall versus not on wall, we investigated the feasibility of training different SVM classifiers to further reduce FPs from these two kinds of INCs. Experiment results indicated that SVM classification over the entire set of INCs was in favor of, where the optimal operating of our CADe system achieved a sensitivity of 89.4% at a specificity of 86.8%.
Approaches to uncovering cancer diagnostic and prognostic molecular signatures
Hong, Shengjun
Huang, Yi
Cao, Yaqiang
Chen, Xingwei
Han, Jing-Dong J
Molecular & Cellular Oncology2014Journal Article, cited 2 times
Website
Algorithm Development
The recent rapid development of high-throughput technology enables the study of molecular signatures for cancer diagnosis and prognosis at multiple levels, from genomic and epigenomic to transcriptomic. These unbiased large-scale scans provide important insights into the detection of cancer-related signatures. In addition to single-layer signatures, such as gene expression and somatic mutations, integrating data from multiple heterogeneous platforms using a systematic approach has been proven to be particularly effective for the identification of classification markers. This approach not only helps to uncover essential driver genes and pathways in the cancer network that are responsible for the mechanisms of cancer development, but will also lead us closer to the ultimate goal of personalized cancer therapy.
Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge
Huang, W.
Li, X.
Chen, Y.
Li, X.
Chang, M. C.
Oborski, M. J.
Malyarenko, D. I.
Muzi, M.
Jajamovich, G. H.
Fedorov, A.
Tudorica, A.
Gupta, S. N.
Laymon, C. M.
Marro, K. I.
Dyvorne, H. A.
Miller, J. V.
Barbodiak, D. P.
Chenevert, T. L.
Yankeelov, T. E.
Mountz, J. M.
Kinahan, P. E.
Kikinis, R.
Taouli, B.
Fennessy, F.
Kalpathy-Cramer, J.
Translational oncologyTransl Oncol2014Journal Article, cited 60 times
Website
QIN Breast DCE-MRI
DCE-MRI
Pharmacokinetic analysis of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) time-course data allows estimation of quantitative parameters such as K (trans) (rate constant for plasma/interstitium contrast agent transfer), v e (extravascular extracellular volume fraction), and v p (plasma volume fraction). A plethora of factors in DCE-MRI data acquisition and analysis can affect accuracy and precision of these parameters and, consequently, the utility of quantitative DCE-MRI for assessing therapy response. In this multicenter data analysis challenge, DCE-MRI data acquired at one center from 10 patients with breast cancer before and after the first cycle of neoadjuvant chemotherapy were shared and processed with 12 software tools based on the Tofts model (TM), extended TM, and Shutter-Speed model. Inputs of tumor region of interest definition, pre-contrast T1, and arterial input function were controlled to focus on the variations in parameter value and response prediction capability caused by differences in models and associated algorithms. Considerable parameter variations were observed with the within-subject coefficient of variation (wCV) values for K (trans) and v p being as high as 0.59 and 0.82, respectively. Parameter agreement improved when only algorithms based on the same model were compared, e.g., the K (trans) intraclass correlation coefficient increased to as high as 0.84. Agreement in parameter percentage change was much better than that in absolute parameter value, e.g., the pairwise concordance correlation coefficient improved from 0.047 (for K (trans)) to 0.92 (for K (trans) percentage change) in comparing two TM algorithms. Nearly all algorithms provided good to excellent (univariate logistic regression c-statistic value ranging from 0.8 to 1.0) early prediction of therapy response using the metrics of mean tumor K (trans) and k ep (=K (trans)/v e, intravasation rate constant) after the first therapy cycle and the corresponding percentage changes. The results suggest that the interalgorithm parameter variations are largely systematic, which are not likely to significantly affect the utility of DCE-MRI for assessment of therapy response.
Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor
Jain, R.
Poisson, L. M.
Gutman, D.
Scarpace, L.
Hwang, S. N.
Holder, C. A.
Wintermark, M.
Rao, A.
Colen, R. R.
Kirby, J.
Freymann, J.
Jaffe, C. C.
Mikkelsen, T.
Flanders, A.
RadiologyRadiology2014Journal Article, cited 86 times
Website
Radiogenomics
VASARI
BRAIN
Genomics
Glioblastoma
Magnetic Resonance Imaging (MRI)
PURPOSE: To correlate patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. MATERIALS AND METHODS: An institutional review board waiver was obtained for this HIPAA-compliant retrospective study. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging. Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests. RESULTS: Worsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBVNER), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBVNER and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBVNER marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBVNER as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBVNER, age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). CONCLUSION: Patients with high rCBVNER and NER crossing the midline and those with high rCBVNER and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBVNER provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.
Radiological Atlas for Patient Specific Model Generation
The paper presents the development of a radiological atlas employed in an abdomen patient specific model verification.
After a patient specific model introduction, the development of a radiological atlas is discussed.
Unprocessed database, containing DICOM images and radiological diagnosis presented. This database is processed manually to retrieve the required information. Organs and pathologies are determined and each study is tagged with specific labels, e.g. ‘liver normal’, ‘liver tumor’, ‘liver cancer’, ‘spleen normal’, ‘spleen absence’, etc. Selected structures are additionally segmented. Masks are stored as gold standard.
Web service based network system is provided to permit PACS-driven retrieval of image data matching desired criteria. Image series as well as ground truth images may be retrieved for benchmark or model-development purposes. The database is evaluated.
eFis: A Fuzzy Inference Method for Predicting Malignancy of Small Pulmonary Nodules
Predicting malignancy of small pulmonary nodules from computer tomography scans is a difficult and important problem to diagnose lung cancer. This paper presents a rule based fuzzy inference method for predicting malignancy rating of small pulmonary nodules. We use the nodule characteristics provided by Lung Image Database Consortium dataset to determine malignancy rating. The proposed fuzzy inference method uses outputs of ensemble classifiers and rules from radiologist agreements on the nodules. The results are evaluated over classification accuracy performance and compared with single classifier methods. We observed that the preliminary results are very promising and system is open to development.
The quest for'diagnostically lossless' medical image compression: a comparative study of objective quality metrics for compressed medical images
Kowalik-Urbaniak, Ilona
Brunet, Dominique
Wang, Jiheng
Koff, David
Smolarski-Koff, Nadine
Vrscay, Edward R
Wallace, Bill
Wang, Zhou
2014Conference Proceedings, cited 0 times
Image Compression
BRAIN
JPEG2000
Computed Tomography (CT)
Our study, involving a collaboration with radiologists (DK,NSK) as well as a leading international developer of medical imaging software (AGFA), is primarily concerned with improved methods of assessing the diagnostic quality of compressed medical images and the investigation of compression artifacts resulting from JPEG and JPEG2000. In this work, we compare the performances of the Structural Similarity quality measure (SSIM), MSE/PSNR, compression ratio CR and JPEG quality factor Q, based on experimental data collected in two experiments involving radiologists. An ROC and Kolmogorov-Smirnov analysis indicates that compression ratio is not always a good indicator of visual quality. Moreover, SSIM demonstrates the best performance, i.e., it provides the closest match to the radiologists' assessments. We also show that a weighted Youden index and curve fitting method can provide SSIM and MSE thresholds for acceptable compression ratios.
Performance Analysis of Denoising in MR Images with Double Density Dual Tree Complex Wavelets, Curvelets and NonSubsampled Contourlet Transforms
Krishnakumar, V
Parthiban, Latha
2014Journal Article, cited 0 times
RIDER Breast MRI
Digital images are extensively used by the medical doctors during different stages of disease diagnosis and treatment process. In the medical field, noise occurs in an image during two phases: acquisition and transmission. During the acquisition phase, noise is induced into an image, due to manufacturing defects, improper functioning of internal components, minute component failures and manual handling errors of the electronic scanning devices such as PECT/SPECT, MRI/CT scanners. Nowadays, healthcare organizations are beginning to consider cloud computing solutions for managing and sharing huge volume of medical data. This leads to the possibility of transmitting different types of medical data including CT, MR images, patient details and much more information through internet. Due to the presence of noise in the transmission channel, some unwanted signals are added to the transmitted medical data. Image denoising algorithms are employed to reduce the unwanted modifications of the pixels in an image. In this paper, the performance of denoising methods with two dimensional transformations of nonsubsampled contourlets (NSCT), curvelets, double density dual tree complex wavelets (DD-DTCWT) are compared and analysed using the image quality measures such as peak signal to noise ratio, root mean square error, structural similarity index. In this paper, 200 MR images of brain (3T MRI scan), heart and breast are selected for testing the noise reduction techniques with above transformations. The results shows that the NSCT gives good PSNR values for random and impulse noises. DD-DTCWT has good noise suppressing capability for speckle and Rician noises. Both NSCT and DD-DTCWT copes well in images affected by poisson noises. The best PSNR value obtained for salt and pepper and additive white Guassian noises are 21.29 and 56.45 respectively. For speckle noises, DD-DTCWT gives 33.46 and it is better than NSCT and curvelet. The values 33.50 and 33.56 are the top PSNRs of NSCT and DD-DTCWT for poisson noises.
A visual analytics approach using the exploration of multidimensional feature spaces for content-based medical image retrieval
Kumar, Ashnil
Nette, Falk
Klein, Karsten
Fulham, Michael
Kim, Jinman
IEEE Journal of Biomedical and Health Informatics2014Journal Article, cited 27 times
Website
LIDC-IDRI
Content based medical image retrieval
Combining Generative Models for Multifocal Glioma Segmentation and Registration
In this paper, we propose a new method for simultaneously segmenting brain scans of glioma patients and registering these scans to a normal atlas. Performing joint segmentation and registration for brain tumors is very challenging when tumors include multifocal masses and have complex shapes with heterogeneous textures. Our approach grows tumors for each mass from multiple seed points using a tumor growth model and modifies a normal atlas into one with tumors and edema using the combined results of grown tumors. We also generate a tumor shape prior via the random walk with restart, utilizing multiple tumor seeds as initial foreground information. We then incorporate this shape prior into an EM framework which estimates the mapping between the modified atlas and the scans, posteriors for each tissue labels, and the tumor growth model parameters. We apply our method to the BRATS 2013 leaderboard dataset to evaluate segmentation performance. Our method shows the best performance among all participants.
Radiogenomic Analysis of Breast Cancer: Luminal B Molecular Subtype Is Associated with Enhancement Dynamics at MR Imaging
Mazurowski, Maciej A
Zhang, Jing
Grimm, Lars J
Yoon, Sora C
Silber, James I
RadiologyRadiology2014Journal Article, cited 88 times
Website
TCGA-BRCA
Radiogenomics
Computer Aided Detection (CADe)
Classification
Purpose: To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features.
Materials and Methods: Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial).
Results: There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype.
Conclusion: The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma. (C) RSNA, 2014
Computer-extracted MR imaging features are associated with survival in glioblastoma patients
Mazurowski, Maciej A
Zhang, Jing
Peters, Katherine B
Hobbs, Hasan
Journal of Neuro-Oncology2014Journal Article, cited 33 times
Website
Segmentation
Cox regression
MRI
Automatic survival prognosis in glioblastoma (GBM) could result in improved treatment planning for the patient. The purpose of this research is to investigate the association of survival in GBM patients with tumor features in pre-operative magnetic resonance (MR) images assessed using a fully automatic computer algorithm. MR imaging data for 68 patients from two US institutions were used in this study. The images were obtained from the Cancer Imaging Archive. A fully automatic computer vision algorithm was applied to segment the images and extract eight imaging features from the MRI studies. The features included tumor side, proportion of enhancing tumor, proportion of necrosis, T1/FLAIR ratio, major axis length, minor axis length, tumor volume, and thickness of enhancing margin. We constructed a multivariate Cox proportional hazards regression model and used a likelihood ratio test to establish whether the imaging features are prognostic of survival. We also evaluated the individual prognostic value of each feature through multivariate analysis using the multivariate Cox model and univariate analysis using univariate Cox models for each feature. We found that the automatically extracted imaging features were predictive of survival (p = 0.031). Multivariate analysis of individual features showed that two individual features were predictive of survival: proportion of enhancing tumor (p = 0.013), and major axis length (p = 0.026). Univariate analysis indicated the same two features as significant (p = 0.021, and p = 0.017 respectively). We conclude that computer-extracted MR imaging features can be used for survival prognosis in GBM patients.
Automatic rectum limit detection by anatomical markers correlation
Namías, R
D’Amato, JP
Del Fresno, M
Vénere, M
Computerized Medical Imaging and Graphics2014Journal Article, cited 1 times
Website
CT Colonography
Several diseases take place at the end of the digestive system. Many of them can be diagnosed by means of different medical imaging modalities together with computer aided detection (CAD) systems. These CAD systems mainly focus on the complete segmentation of the digestive tube. However, the detection of limits between different sections could provide important information to these systems.
In this paper we present an automatic method for detecting the rectum and sigmoid colon limit using a novel global curvature analysis over the centerline of the segmented digestive tube in different imaging modalities. The results are compared with the gold standard rectum upper limit through a validation scheme comprising two different anatomical markers: the third sacral vertebra and the average rectum length. Experimental results in both magnetic resonance imaging (MRI) and computed tomography colonography (CTC) acquisitions show the efficacy of the proposed strategy in automatic detection of rectum limits. The method is intended for application to the rectum segmentation in MRI for geometrical modeling and as contextual information source in virtual colonoscopies and CAD systems. (C) 2014 Elsevier Ltd. All rights reserved.
Image Processing and Classification Techniques for Early Detection of Lung Cancer for Preventive Health Care: A Survey
Naresh, Prashant
Shettar, Rajashree
Int. J. of Recent Trends in Engineering & Technology2014Journal Article, cited 6 times
Website
Addition of MR imaging features and genetic biomarkers strengthens glioblastoma survival prediction in TCGA patients
Nicolasjilwan, Manal
Hu, Ying
Yan, Chunhua
Meerzaman, Daoud
Holder, Chad A
Gutman, David
Jain, Rajan
Colen, Rivka
Rubin, Daniel L
Zinn, Pascal O
Hwang, Scott N
Raghavan, Prashant
Hammoud, Dima A
Scarpace, Lisa M
Mikkelsen, Tom
Chen, James
Gevaert, Olivier
Buetow, Kenneth
Freymann, John
Kirby, Justin
Flanders, Adam E
Wintermark, Max
Journal of Neuroradiology2014Journal Article, cited 49 times
Website
TCGA-GBM
Glioblastoma Multiforme (GBM)
Radiogenomics
PURPOSE: The purpose of our study was to assess whether a model combining clinical factors, MR imaging features, and genomics would better predict overall survival of patients with glioblastoma (GBM) than either individual data type. METHODS: The study was conducted leveraging The Cancer Genome Atlas (TCGA) effort supported by the National Institutes of Health. Six neuroradiologists reviewed MRI images from The Cancer Imaging Archive (http://cancerimagingarchive.net) of 102 GBM patients using the VASARI scoring system. The patients' clinical and genetic data were obtained from the TCGA website (http://www.cancergenome.nih.gov/). Patient outcome was measured in terms of overall survival time. The association between different categories of biomarkers and survival was evaluated using Cox analysis. RESULTS: The features that were significantly associated with survival were: (1) clinical factors: chemotherapy; (2) imaging: proportion of tumor contrast enhancement on MRI; and (3) genomics: HRAS copy number variation. The combination of these three biomarkers resulted in an incremental increase in the strength of prediction of survival, with the model that included clinical, imaging, and genetic variables having the highest predictive accuracy (area under the curve 0.679+/-0.068, Akaike's information criterion 566.7, P<0.001). CONCLUSION: A combination of clinical factors, imaging features, and HRAS copy number variation best predicts survival of patients with GBM.
Medical image retrieval using hybrid wavelet network classifier
Nowadays the amount of imaging data is rapidly increasing with the widespread dissemination of picture archiving in medical systems. Effective image retrieval systems are required to manage these complex and large image databases. Indexing medical images become, for clinical applications, an essential and effective tool which assists the monitoring in diagnosis therapeutic. CBIR (Content Based Image Retrieval) is one of the possible solutions to manage effectively these bases. In order to achieve this application, we have to ensure these key tasks: indexing medical images and classification. Accordingly to accomplish this work, medical images are indexed and classified using wavelet network classifier (WNC) based on fast wavelet transform (FWT) for its robustness and for its pertinent results in the classification domain.
Fast and robust methods for non-rigid registration of medical images
Swift Pre Rendering Volumetric Visualization of Magnetic Resonance Cardiac Images based on Isosurface Technique
Patel, Nikhilkumar P
Parmar, Shankar K
Jain, Kavindra R
Procedia Technology2014Journal Article, cited 0 times
Website
CHEST
Segmentation
Algorithm Development
Magnetic Resonance imaging (MRI) is a medical imaging procedure which uses strong magnetic fields and radio waves to produce cross sectional images of organs and internal structures of the body. Three dimensional (3D) models of CT is available and it has been practiced by almost all radiologists for pre-diagnosis. But in MRI still there is a scope for researcher to improvise a 3D model. Two dimensional images are taken from different viewpoints to reconstruct them in 3D, which is known as rendering process. In this paper, we have proposed a rendering concept for Medical (cardiac MRI) images based on iso values and number of marching cubes. Designer can place colors and textures over the 3D model to make it look realistic. This makes it easier for people to observe and visualize a substance in a better sense. The algorithm basically works on triangulation methods with various iso value and different combination of marching cube pairs. As a result of an application of marching cube concept, volumetric data (voxels) is generated. Voxels are then arranged and projected to visualize a 3D scene. Approximate processing time for various iso values are also compared in this paper.
Short-and long-term lung cancer risk associated with noncalcified nodules observed on low-dose CT
Pinsky, Paul F
Nath, P Hrudaya
Gierada, David S
Sonavane, Sushil
Szabo, Eva
Cancer prevention research2014Journal Article, cited 10 times
Website
NLST
LUNG
Nodule classification
Prostate segmentation: An efficient convex optimization approach with axial symmetry using 3-D TRUS and MR images
Qiu, Wu
Yuan, Jing
Ukwatta, Eranga
Sun, Yue
Rajchl, Martin
Fenster, Aaron
Medical Imaging, IEEE Transactions on2014Journal Article, cited 58 times
Website
QIN PROSTATE
3D-SCoBeP: 3D medical image registration using sparse coding and belief propagation
Roozgard, Aminmohammad
Barzigar, Nafise
Verma, Pramode
Cheng, Samuel
International Journal of Diagnostic Imaging2014Journal Article, cited 4 times
Website
LIDC-IDRI
A new 2.5 D representation for lymph node detection using random sets of deep convolutional neural network observations
Roth, Holger R
Lu, Le
Seff, Ari
Cherry, Kevin M
Hoffman, Joanne
Wang, Shijun
Liu, Jiamin
Turkbey, Evrim
Summers, Ronald M
2014Conference Proceedings, cited 192 times
Website
CT Lymph Nodes
Computer Aided Detection (CADe)
*Algorithms
Computer Simulation
Data Interpretation
Statistical
Humans
Imaging
Three-Dimensional/*methods
Lymph Nodes/*diagnostic imaging
Lymphatic Diseases/*diagnostic imaging
*Models
Statistical
Neural Networks
Computer
Pattern Recognition
Automated/*methods
Radiographic Image Enhancement/methods
Radiographic Image Interpretation
Computer-Assisted/*methods
Reproducibility of Results
Sensitivity and Specificity
Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards -100% sensitivity at the cost of high FP levels (-40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.
Visual Interpretation with Three-Dimensional Annotations (VITA): Three-Dimensional Image Interpretation Tool for Radiological Reporting
This paper introduces a software framework called Visual Interpretation with Three-Dimensional Annotations (VITA) that is able to automatically generate three-dimensional (3D) visual summaries based on radiological annotations made during routine exam reporting. VITA summaries are in the form of rotating 3D volumes where radiological annotations are highlighted to place important clinical observations into a 3D context. The rendered volume is produced as a Digital Imaging and Communications in Medicine (DICOM) object and is automatically added to the study for archival in Picture Archiving and Communication System (PACS). In addition, a video summary (e.g., MPEG4) can be generated for sharing with patients and for situations where DICOM viewers are not readily available to referring physicians. The current version of VITA is compatible with ClearCanvas; however, VITA can work with any PACS workstation that has a structured annotation implementation (e.g., Extendible Markup Language, Health Level 7, Annotation and Image Markup) and is able to seamlessly integrate into the existing reporting workflow. In a survey with referring physicians, the vast majority strongly agreed that 3D visual summaries improve the communication of the radiologists' reports and aid communication with patients.
2d view aggregation for lymph node detection using a shallow hierarchy of linear classifiers
Enlarged lymph nodes (LNs) can provide important information for cancer diagnosis, staging, and measuring treatment reactions, making automated detection a highly sought goal. In this paper, we propose a new algorithm representation of decomposing the LN detection problem into a set of 2D object detection subtasks on sampled CT slices, largely alleviating the curse of dimensionality issue. Our 2D detection can be effectively formulated as linear classification on a single image feature type of Histogram of Oriented Gradients (HOG), covering a moderate field-of-view of 45 by 45 voxels. We exploit both simple pooling and sparse linear fusion schemes to aggregate these 2D detection scores for the final 3D LN detection. In this manner, detection is more tractable and does not need to perform perfectly at instance level (as weak hypotheses) since our aggregation process will robustly harness collective information for LN detection. Two datasets (90 patients with 389 mediastinal LNs and 86 patients with 595 abdominal LNs) are used for validation. Cross-validation demonstrates 78.0% sensitivity at 6 false positives/volume (FP/vol.) (86.1% at 10 FP/vol.) and 73.1% sensitivity at 6 FP/vol. (87.2% at 10 FP/vol.), for the mediastinal and abdominal datasets respectively. Our results compare favorably to previous state-of-the-art methods.
A STUDY ON IMAGE DENOISING FOR LUNG CT SCAN IMAGES
Sivakumar, S
Chandrasekar, C
International Journal of Emerging Technologies in Computational and Applied Sciences2014Journal Article, cited 1 times
Website
LIDC-IDRI
Image denoising
Computed Tomography (CT)
Medical imaging is the technique and process used to create images of the human body for clinical purposes and diagnosis. Medical imaging is often perceived to designate the set of techniques that non- invasively produce images of the internal aspect of the body. The x-ray computed tomographic (CT) scanner has made it possible to detect the presence of lesions of very low contrast. The noise in the reconstructed CT images is significantly reduced through the use of efficient x-ray detectors and electronic processing. The CT reconstruction technique almost completely eliminates the superposition of anatomic structures, leading to a reduction of "structural" noise. It is the random noise in a CT image that ultimately limits the ability of the radiologist to discriminate between two regions of different density. Because of its unpredictable nature, such noise cannot be completely eliminated from the image and will always lead to some uncertainty in the interpretation of the image. The noise present in the images may appear as additive or multiplicative components and the main purpose of denoising is to remove these noisy components while preserving the important signal as much as possible. In this paper we analyzed the denoising filters such as Mean, Median, Midpoint, Wiener filters and the three more modified filter approaches for the Lung CT scan images to remove the noise present in the images and compared by the quality parameters.
Survival analysis of pre-operative GBM patients by using quantitative image features
This paper concerns a preliminary study of the relationship between survival time of both overall and progression free survival, and multiple imaging features of patients with glioblastoma. Simulation results showed that specific imaging features were found to have significant prognostic value to predict survival time in glioblastoma patients.
Low-complexity atlas-based prostate segmentation by combining global, regional, and local metrics
Xie, Qiuliang
Ruan, Dan
Medical Physics2014Journal Article, cited 15 times
Website
QIN PROSTATE
PURPOSE: To improve the efficiency of atlas-based segmentation without compromising accuracy, and to demonstrate the validity of the proposed method on MRI-based prostate segmentation application. METHODS: Accurate and efficient automatic structure segmentation is an important task in medical image processing. Atlas-based methods, as the state-of-the-art, provide good segmentation at the cost of a large number of computationally intensive nonrigid registrations, for anatomical sites/structures that are subject to deformation. In this study, the authors propose to utilize a combination of global, regional, and local metrics to improve the accuracy yet significantly reduce the number of required nonrigid registrations. The authors first perform an affine registration to minimize the global mean squared error (gMSE) to coarsely align each atlas image to the target. Subsequently, a target-specific regional MSE (rMSE), demonstrated to be a good surrogate for dice similarity coefficient (DSC), is used to select a relevant subset from the training atlas. Only within this subset are nonrigid registrations performed between the training images and the target image, to minimize a weighted combination of gMSE and rMSE. Finally, structure labels are propagated from the selected training samples to the target via the estimated deformation fields, and label fusion is performed based on a weighted combination of rMSE and local MSE (lMSE) discrepancy, with proper total-variation-based spatial regularization. RESULTS: The proposed method was applied to a public database of 30 prostate MR images with expert-segmented structures. The authors' method, utilizing only eight nonrigid registrations, achieved a performance with a median/mean DSC of over 0.87/0.86, outperforming the state-of-the-art full-fledged atlas-based segmentation approach of which the median/mean DSC was 0.84/0.82 when applying to their data set. CONCLUSIONS: The proposed method requires a fixed number of nonrigid registrations, independent of atlas size, providing desirable scalability especially important for a large or growing atlas. When applied to prostate segmentation, the method achieved better performance to the state-of-the-art atlas-based approaches, with significant improvement in computation efficiency. The proposed rationale of utilizing jointly global, regional, and local metrics, based on the information characteristic and surrogate behavior for registration and fusion subtasks, can be extended naturally to similarity metrics beyond MSE, such as correlation or mutual information types.
A fully automatic extraction of magnetic resonance image features in glioblastoma patients
Zhang, Jing
Barboriak, Daniel P
Hobbs, Hasan
Mazurowski, Maciej A
Medical Physics2014Journal Article, cited 21 times
Website
TCGA-GBM
BRAIN
Glioblastoma Multiforme (GBM)
Algorithm Development
PURPOSE: Glioblastoma is the most common malignant brain tumor. It is characterized by low median survival time and high survival variability. Survival prognosis for glioblastoma is very important for optimized treatment planning. Imaging features observed in magnetic resonance (MR) images were shown to be a good predictor of survival. However, manual assessment of MR features is time-consuming and can be associated with a high inter-reader variability as well as inaccuracies in the assessment. In response to this limitation, the authors proposed and evaluated a computer algorithm that extracts important MR image features in a fully automatic manner. METHODS: The algorithm first automatically segmented the available volumes into a background region and four tumor regions. Then, it extracted ten features from the segmented MR imaging volumes, some of which were previously indicated as predictive of clinical outcomes. To evaluate the algorithm, the authors compared the extracted features for 73 glioblastoma patients to the reference standard established by manual segmentation of the tumors. RESULTS: The experiments showed that their algorithm was able to extract most of the image features with moderate to high accuracy. High correlation coefficients between the automatically extracted value and reference standard were observed for the tumor location, minor and major axis length as well as tumor volume. Moderately high correlation coefficients were also observed for proportion of enhancing tumor, proportion of necrosis, and thickness of enhancing margin. The correlation coefficients for all these features were statistically significant (p < 0.0001). CONCLUSIONS: The authors proposed and evaluated an algorithm that, given a set of MR volumes of a glioblastoma patient, is able to extract MR image features that correlate well with their reference standard. Future studies will evaluate how well the computer-extracted features predict survival.
A generalized framework for medical image classification and recognition
Abedini, M
Codella, NCF
Connell, JH
Garnavi, R
Merler, M
Pankanti, S
Smith, JR
Syeda-Mahmood, T
IBM Journal of Research and DevelopmentIbm J Res Dev2015Journal Article, cited 19 times
Website
Classification
Machine Learning
In this work, we study the performance of a two-stage ensemble visual machine learning framework for classification of medical images. In the first stage, models are built for subsets of features and data, and in the second stage, models are combined. We demonstrate the performance of this framework in four contexts: 1) The public ImageCLEF (Cross Language Evaluation Forum) 2013 medical modality recognition benchmark, 2) echocardiography view and mode recognition, 3) dermatology disease recognition across two datasets, and 4) a broad medical image dataset, merged from multiple data sources into a collection of 158 categories covering both general and specific medical concepts-including modalities, body regions, views, and disease states. In the first context, the presented system achieves state-of-art performance of 82.2% multiclass accuracy. In the second context, the system attains 90.48% multiclass accuracy. In the third, state-of-art performance of 90% specificity and 90% sensitivity is obtained on a small standardized dataset of 200 images using a leave-one-out strategy. For a larger dataset of 2,761 images, 95% specificity and 98% sensitivity is obtained on a 20% held-out test set. Finally, in the fourth context, the system achieves sensitivity and specificity of 94.7% and 98.4%, respectively, demonstrating the ability to generalize over domains.
Self-organizing Approach to Learn a Level-set Function for Object Segmentation in Complex Background Environments
Boundary extraction for object region segmentation is one of the most challenging tasks in image processing and computer vision areas. The complexity of large variations in the appearance of the object and the background in a typical image causes the performance degradation of existing segmentation algorithms. One of the goals of computer vision studies is to produce algorithms to segment object regions to produce accurate object boundaries that can be utilized in feature extraction and classification.
This dissertation research considers the incorporation of prior knowledge of intensity/color of objects of interest within segmentation framework to enhance the performance of object region and boundary extraction of targets in unconstrained environments. The information about intensity/color of object of interest is taken from small patches as seeds that are fed to learn a neural network. The main challenge is accounting for the projection transformation between the limited amount of prior information and the appearance of the real object of interest in the testing data. We address this problem by the use of a Self-organizing Map (SOM) which is an unsupervised learning neural network. The segmentation process is achieved by the construction of a local fitted image level-set cost function, in which, the dynamic variable is a Best Matching Unit (BMU) coming from the SOM map.
The proposed method is demonstrated on the PASCAL 2011 challenging dataset, in which, images contain objects with variations of illuminations, shadows, occlusions and clutter. In addition, our method is tested on different types of imagery including thermal, hyperspectral, and medical imagery. Metrics illustrate the effectiveness and accuracy of the proposed algorithm in improving the efficiency of boundary extraction and object region detection.
In order to reduce computational time, a lattice Boltzmann Method (LBM) convergence criteria is used along with the proposed self-organized active contour model for producing faster and effective segmentation. The lattice Boltzmann method is utilized to evolve the level-set function rapidly and terminate the evolution of the curve at the most optimum region. Experiments performed on our test datasets show promising results in terms of time and quality of the segmentation when compared to other state-of-the-art learning-based active contour model approaches. Our method is more than 53% faster than other state-of-the-art methods. Research is in progress to employ Time Adaptive Self- Organizing Map (TASOM) for improved segmentation and utilize the parallelization property of the LBM to achieve real-time segmentation.
Application of Fuzzy c-means and Neural networks to categorize tumor affected breast MR Images
Anand, Shruthi
Vinod, Viji
Rampure, Anand
International Journal of Applied Engineering Research2015Journal Article, cited 4 times
Website
TCGA-BRCA
Machine learning
Special Section Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned
Armato, Samuel G
Hadjiiski, Lubomir
Tourassi, Georgia D
Drukker, Karen
Giger, Maryellen L
Li, Feng
Redmond, George
Farahani, Keyvan
Kirby, Justin S
Clarke, Laurence P
Journal of Medical Imaging2015Journal Article, cited 20 times
Website
The purpose of this work is to describe the LUNGx Challenge for the computerized classification of lung nodules on diagnostic computed tomography (CT) scans as benign or malignant and report the performance of participants' computerized methods along with that of six radiologists who participated in an observer study performing the same Challenge task on the same dataset. The Challenge provided sets of calibration and testing scans, established a performance assessment process, and created an infrastructure for case dissemination and result submission. Ten groups applied their own methods to 73 lung nodules (37 benign and 36 malignant) that were selected to achieve approximate size matching between the two cohorts. Area under the receiver operating characteristic curve (AUC) values for these methods ranged from 0.50 to 0.68; only three methods performed statistically better than random guessing. The radiologists' AUC values ranged from 0.70 to 0.85; three radiologists performed statistically better than the best-performing computer method. The LUNGx Challenge compared the performance of computerized methods in the task of differentiating benign from malignant lung nodules on CT scans, placed in the context of the performance of radiologists on the same task. The continued public availability of the Challenge cases will provide a valuable resource for the medical imaging research community.
Automatic Design of Window Operators for the Segmentation of the Prostate Gland in Magnetic Resonance Images
Image denoising is a well documented part of Image processing. It has always posed a problem for researchers and there is no dearth of solutions extended. Obtaining a denoised and perfectly similar image after application of processes represents a mirage that has been chased a lot. In this paper, we attempt to combine the effects of block least mean square algorithm (BLMS) to maximizes the Peak Signal to Noise Ratio (PSNR), along with singular valued decomposition (SVD), so as to achieve results that bring us closer to our aim of perfect reconstruction. The results showed that the combination of these methods provides easy computation, coupled with efficiency and as such is an effective way of approaching the problem.
Medical Physics2015Journal Article, cited 4 times
Website
Algorithm Development
Image registration
PROSTATE
Magnetic Resonance Imaging (MRI)
PURPOSE: T2-weighted magnetic resonance imaging (MRI) is commonly used for anatomical visualization in the pelvis area, such as the prostate, with high soft-tissue contrast. MRI can also provide functional information such as diffusion-weighted imaging (DWI) which depicts the molecular diffusion processes in biological tissues. The combination of anatomical and functional imaging techniques is widely used in oncology, e.g., for prostate cancer diagnosis and staging. However, acquisition-specific distortions as well as physiological motion lead to misalignments between T2 and DWI and consequently to a reduced diagnostic value. Image registration algorithms are commonly employed to correct for such misalignment. METHODS: The authors compare the performance of five state-of-the-art nonrigid image registration techniques for accurate image fusion of DWI with T2. RESULTS: Image data of 20 prostate patients with cancerous lesions or cysts were acquired. All registration algorithms were validated using intensity-based as well as landmark-based techniques. CONCLUSIONS: The authors' results show that the "fast elastic image registration" provides most accurate results with a target registration error of 1.07 +/- 0.41 mm at minimum execution times of 11 +/- 1 s.
Automated feature extraction in brain tumor by magnetic resonance imaging using gaussian mixture models
Chaddad, Ahmad
Journal of Biomedical Imaging2015Journal Article, cited 29 times
Website
Radiomics
High-Throughput Quantification of Phenotype Heterogeneity Using Statistical Features
Chaddad, Ahmad
Tanougast, Camel
Advances in Bioinformatics2015Journal Article, cited 5 times
Website
Radiomics
Classification
Glioblastoma Multiforme (GBM)
Support Vector Machine (SVM)
Naïve Bayes (NB)
Machine Learning
Magnetic Resonance Imaging (MRI)
Radiomic features
Radiogenomics
TCGA-GBM
Statistical features are widely used in radiology for tumor heterogeneity assessment using magnetic resonance (MR) imaging technique. In this paper, feature selection based on decision tree is examined to determine the relevant subset of glioblastoma (GBM) phenotypes in the statistical domain. To discriminate between active tumor (vAT) and edema/invasion (vE) phenotype, we selected the significant features using analysis of variance (ANOVA) with p value < 0.01. Then, we implemented the decision tree to define the optimal subset features of phenotype classifier. Naive Bayes (NB), support vector machine (SVM), and decision tree (DT) classifier were considered to evaluate the performance of the feature based scheme in terms of its capability to discriminate vAT from vE. Whole nine features were statistically significant to classify the vAT from vE with p value < 0.01. Feature selection based on decision tree showed the best performance by the comparative study using full feature set. The feature selected showed that the two features Kurtosis and Skewness achieved a highest range value of 58.33-75.00% accuracy classifier and 73.88-92.50% AUC. This study demonstrated the ability of statistical features to provide a quantitative, individualized measurement of glioblastoma patient and assess the phenotype progression.
Automatic detection of spiculation of pulmonary nodules in computed tomography images
Prognostic Imaging Biomarkers in Glioblastoma: Development and Independent Validation on the Basis of Multiregion and Quantitative Analysis of MR Images
Cui, Yi
Tha, Khin Khin
Terasaka, Shunsuke
Yamaguchi, Shigeru
Wang, Jeff
Kudo, Kohsuke
Xing, Lei
Shirato, Hiroki
Li, Ruijiang
RadiologyRadiology2015Journal Article, cited 45 times
Website
TCGA-GBM
Computer Aided Diagnosis (CADx)
Segmentation
PURPOSE: To develop and independently validate prognostic imaging biomarkers for predicting survival in patients with glioblastoma on the basis of multiregion quantitative image analysis. MATERIALS AND METHODS: This retrospective study was approved by the local institutional review board, and informed consent was waived. A total of 79 patients from two independent cohorts were included. The discovery and validation cohorts consisted of 46 and 33 patients with glioblastoma from the Cancer Imaging Archive (TCIA) and the local institution, respectively. Preoperative T1-weighted contrast material-enhanced and T2-weighted fluid-attenuation inversion recovery magnetic resonance (MR) images were analyzed. For each patient, we semiautomatically delineated the tumor and performed automated intratumor segmentation, dividing the tumor into spatially distinct subregions that demonstrate coherent intensity patterns across multiparametric MR imaging. Within each subregion and for the entire tumor, we extracted quantitative imaging features, including those that fully capture the differential contrast of multimodality MR imaging. A multivariate sparse Cox regression model was trained by using TCIA data and tested on the validation cohort. RESULTS: The optimal prognostic model identified five imaging biomarkers that quantified tumor surface area and intensity distributions of the tumor and its subregions. In the validation cohort, our prognostic model achieved a concordance index of 0.67 and significant stratification of overall survival by using the log-rank test (P = .018), which outperformed conventional prognostic factors, such as age (concordance index, 0.57; P = .389) and tumor volume (concordance index, 0.59; P = .409). CONCLUSION: The multiregion analysis presented here establishes a general strategy to effectively characterize intratumor heterogeneity manifested at multimodality imaging and has the potential to reveal useful prognostic imaging biomarkers in glioblastoma.
Segmentação Automática de Candidatos a Nódulos Pulmonares em Imagens de Tomografia Computadorizada
Este trabalho apresenta um algoritmo para segmentação automática de candidatos a nódulos pulmonares em imagens de Tomografia Computadorizada do tórax. A metodologia empregada inclui aquisição das imagens, eliminação de ruídos, segmentação do parênquima pulmonar e segmentação dos candidatos a nódulos pulmonares. O uso do filtro wiener e a aplicação do limiar ideal garante ao algoritmo uma melhora significativa nos resultados, permitindo detectar um maior número de nódulos nas imagens. Os testes foram realizados utilizando um conjunto de imagens da base LIDC-IDRI, contendo 708 nódulos. Os resultados do teste mostraram que o algoritmo localizou 93,08% dos nódulos considerados.
This paper presents an algorithm for automatic segmentation of pulmonary nodules candidates in chest computed tomography images. The methodology includes acquisition images, noise elimination, segmentation of pulmonary parenchyma and segmentation pulmonary nodules candidates. The use of the filter wiener and the application of ideal threshold ensures to the algorithm a significant improvement in results, allowing to detect a greater number of nodules on the images. The tests were conducted using a set of images of the base LIDC-IDRI, containing 708 nodules. The test results showed that the algorithm located 93.08% of the nodules considered.
Computer-aided detection of lung nodules using outer surface features
Demir, Önder
Yılmaz Çamurcu, Ali
Bio-Medical Materials and EngineeringBio-Med Mater Eng2015Journal Article, cited 28 times
Website
LIDC-IDRI
Computed Tomography (CT)
Computer Aided Detection (CADe)
LUNG
Classification
In this study, a computer-aided detection (CAD) system was developed for the detection of lung nodules in computed tomography images. The CAD system consists of four phases, including two-dimensional and three-dimensional preprocessing phases. In the feature extraction phase, four different groups of features are extracted from volume of interests: morphological features, statistical and histogram features, statistical and histogram features of outer surface, and texture features of outer surface. The support vector machine algorithm is optimized using particle swarm optimization for classification. The CAD system provides 97.37% sensitivity, 86.38% selectivity, 88.97% accuracy and 2.7 false positive per scan using three groups of classification features. After the inclusion of outer surface texture features, classification results of the CAD system reaches 98.03% sensitivity, 87.71% selectivity, 90.12% accuracy and 2.45 false positive per scan. Experimental results demonstrate that outer surface texture features of nodule candidates are useful to increase sensitivity and decrease the number of false positives in the detection of lung nodules in computed tomography images.
Local Wavelet Pattern: A New Feature Descriptor for Image Retrieval in Medical CT Databases
Dubey, Shiv Ram
Singh, Satish Kumar
Singh, Rajat Kumar
IEEE Trans Image Process2015Journal Article, cited 52 times
Website
wavelet
Computed Tomography (CT)
A new image feature description based on the local wavelet pattern (LWP) is proposed in this paper to characterize the medical computer tomography (CT) images for content-based CT image retrieval. In the proposed work, the LWP is derived for each pixel of the CT image by utilizing the relationship of center pixel with the local neighboring information. In contrast to the local binary pattern that only considers the relationship between a center pixel and its neighboring pixels, the presented approach first utilizes the relationship among the neighboring pixels using local wavelet decomposition, and finally considers its relationship with the center pixel. A center pixel transformation scheme is introduced to match the range of center value with the range of local wavelet decomposed values. Moreover, the introduced local wavelet decomposition scheme is centrally symmetric and suitable for CT images. The novelty of this paper lies in the following two ways: 1) encoding local neighboring information with local wavelet decomposition and 2) computing LWP using local wavelet decomposed values and transformed center pixel values. We tested the performance of our method over three CT image databases in terms of the precision and recall. We also compared the proposed LWP descriptor with the other state-of-the-art local image descriptors, and the experimental results suggest that the proposed method outperforms other methods for CT image retrieval.
Performance Analysis of Prediction Methods for Lossless Image Compression
Performance analysis of several state-of-the-art prediction approaches is performed for lossless image compression. To provide this analysis special models of edges are presented: bound-oriented and gradient-oriented approaches. Several heuristic assumptions are proposed for considered intra- and inter-component predictors using determined edge models. Numerical evaluation using image test sets with various statistical features confirms obtained heuristic assumptions.
A Content-Based-Image-Retrieval Approach for Medical Image Repositories
Glioblastoma (GBM) is the most common and most aggressive primary malignant tumor of the central nervous system. Recently, researchers concluded that the "one-size-fits-all" approach for treatment of GBM is no longer valid and research should be directed toward more personalized and patient-tailored treatment protocols. Identification of the molecular and genomic pathways underlying GBM is essential for achieving this personalized and targeted therapeutic approach. Imaging genomics represents a new era as a noninvasive surrogate for genomic and molecular profile identification. This article discusses the basics of imaging genomics of GBM, its role in treatment decision-making, and its future potential in noninvasive genomic identification.
Diffusion MRI quality control and functional diffusion map results in ACRIN 6677/RTOG 0625: a multicenter, randomized, phase II trial of bevacizumab and chemotherapy in recurrent glioblastoma
Ellingson, Benjamin M
Kim, Eunhee
Woodworth, Davis C
Marques, Helga
Boxerman, Jerrold L
Safriel, Yair
McKinstry, Robert C
Bokstein, Felix
Jain, Rajan
Chi, T Linda
Sorensen, A Gregory
Gilbert, Mark R
Barboriak, Daniel P
Int J Oncol2015Journal Article, cited 27 times
Website
ACRIN-DSC-MR-Brain
ACRIN 6677
BRAIN
Glioblastoma Multiforme (GBM)
Magnetic Resonance Imaging (MRI)
Functional diffusion mapping (fDM) is a cancer imaging technique that quantifies voxelwise changes in apparent diffusion coefficient (ADC). Previous studies have shown value of fDMs in bevacizumab therapy for recurrent glioblastoma multiforme (GBM). The aim of the present study was to implement explicit criteria for diffusion MRI quality control and independently evaluate fDM performance in a multicenter clinical trial (RTOG 0625/ACRIN 6677). A total of 123 patients were enrolled in the current multicenter trial and signed institutional review board-approved informed consent at their respective institutions. MRI was acquired prior to and 8 weeks following therapy. A 5-point QC scoring system was used to evaluate DWI quality. fDM performance was evaluated according to the correlation of these metrics with PFS and OS at the first follow-up time-point. Results showed ADC variability of 7.3% in NAWM and 10.5% in CSF. A total of 68% of patients had usable DWI data and 47% of patients had high quality DWI data when also excluding patients that progressed before the first follow-up. fDM performance was improved by using only the highest quality DWI. High pre-treatment contrast enhancing tumor volume was associated with shorter PFS and OS. A high volume fraction of increasing ADC after therapy was associated with shorter PFS, while a high volume fraction of decreasing ADC was associated with shorter OS. In summary, DWI in multicenter trials are currently of limited value due to image quality. Improvements in consistency of image quality in multicenter trials are necessary for further advancement of DWI biomarkers.
Computer-aided detection of Pulmonary Nodules based on SVM in thoracic CT images
Eskandarian, Parinaz
Bagherzadeh, Jamshid
2015Conference Proceedings, cited 12 times
Website
LIDC-IDRI
Computer-Aided diagnosis of Solitary Pulmonary Nodules using the method of X-ray CT images is the early detection of lung cancer. In this study, a computer-aided system for detection of pulmonary nodules on CT scan based support vector machine classifier is provided for the diagnosis of solitary pulmonary nodules. So at the first step, by data mining techniques, volume of data are reduced. Then divided by the area of the chest, the suspicious nodules are identified and eventually nodules are detected. In comparison with the threshold-based methods, support vector machine classifier to classify more accurately describes areas of the lungs. In this study, the false positive rate is reduced by combination of threshold with support vector machine classifier. Experimental results based on data from 147 patients with lung LIDC image database show that the proposed system is able to obtained sensitivity of 89.9% and false positive of 3.9 per scan. In comparison to previous systems, the proposed system demonstrates good performance.
LCD-OpenPACS: sistema integrado de telerradiologia com auxílio ao diagnóstico de nódulos pulmonares em exames de tomografia computadorizada
Automatic Segmentation of Colon in 3D CT Images and Removal of Opacified Fluid Using Cascade Feed Forward Neural Network
Gayathri Devi, K
Radhakrishnan, R
Computational and Mathematical Methods in Medicine2015Journal Article, cited 5 times
Website
CT Colonography
Radiomics: Images are more than pictures, they are data
Gillies, Robert J
Kinahan, Paul E
Hricak, Hedvig
RadiologyRadiology2015Journal Article, cited 694 times
Website
Radiomics
Imaging features
BRAIN
LUNG
PROSTATE
BLADDER
BREAST
In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma
Grove, Olya
Berglund, Anders E
Schabath, Matthew B
Aerts, Hugo JWL
Dekker, Andre
Wang, Hua
Velazquez, Emmanuel Rios
Lambin, Philippe
Gu, Yuhua
Balagurunathan, Yoganand
Eikman, E.
Gatenby, Robert A
Eschrich, S
Gillies, Robert J
PLoS One2015Journal Article, cited 87 times
Website
Algorithm Development
LungCT-Diagnosis
LUNG
Segmentation
Classification
Two CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity (feature 1: convexity) and intratumor density variation (feature 2: entropy ratio) in routinely obtained diagnostic CT scans. The developed quantitative features were analyzed in two independent cohorts (cohort 1: n = 61; cohort 2: n = 47) of patients diagnosed with primary lung adenocarcinoma, retrospectively curated to include imaging and clinical data. Preoperative chest CTs were segmented semi-automatically. Segmented tumor regions were further subdivided into core and boundary sub-regions, to quantify intensity variations across the tumor. Reproducibility of the features was evaluated in an independent test-retest dataset of 32 patients. The proposed metrics showed high degree of reproducibility in a repeated experiment (concordance, CCC>/=0.897; dynamic range, DR>/=0.92). Association with overall survival was evaluated by Cox proportional hazard regression, Kaplan-Meier survival curves, and the log-rank test. Both features were associated with overall survival (convexity: p = 0.008; entropy ratio: p = 0.04) in Cohort 1 but not in Cohort 2 (convexity: p = 0.7; entropy ratio: p = 0.8). In both cohorts, these features were found to be descriptive and demonstrated the link between imaging characteristics and patient survival in lung adenocarcinoma.
Smooth extrapolation of unknown anatomy via statistical shape models
Graph-based segmentation methods such as the random walker (RW) are known to be computationally expensive. For high resolution images, user interaction with the algorithm is significantly affected. This paper introduces a novel seeding approach for graph-based segmentation that reduces computation time. Instead of marking foreground and background pixels, the user roughly marks the object boundary forming separate regions. The image pixels are then grouped into a hierarchy of increasingly large layers based on their distance from these markings. Next, foreground and background seeds are automatically generated according to the hierarchical layers of each region. The highest layers of the hierarchy are ignored leading to a significant graph reduction. Finally, validation experiments based on multiple automatically generated input seeds were carried out on a variety of medical images. Results show a significant gain in time for high resolution images using the new approach.
Prediction of clinical phenotypes in invasive breast carcinomas from the integration of radiomics and genomics data
Guo, Wentian
Li, Hui
Zhu, Yitan
Lan, Li
Yang, Shengjie
Drukker, Karen
Morris, Elizabeth
Burnside, Elizabeth
Whitman, Gary
Giger, Maryellen L
Ji, Y.
TCGA Breast Phenotype Research Group
Journal of Medical Imaging2015Journal Article, cited 57 times
Website
TCGA-BRCA
Breast
Radiogenomics
Genomic and radiomic imaging profiles of invasive breast carcinomas from The Cancer Genome Atlas and The Cancer Imaging Archive were integrated and a comprehensive analysis was conducted to predict clinical outcomes using the radiogenomic features. Variable selection via LASSO and logistic regression were used to select the most-predictive radiogenomic features for the clinical phenotypes, including pathological stage, lymph node metastasis, and status of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Higher AUCs were obtained in the prediction of pathological stage, ER, and PR status than for lymph node metastasis and HER2 status. Overall, the prediction performances by genomics alone, radiomics alone, and combined radiogenomics features showed statistically significant correlations with clinical outcomes; however, improvement on the prediction performance by combining genomics and radiomics data was not found to be statistically significant, most likely due to the small sample size of 91 cancer cases with 38 radiomic features and 144 genomic features.
A tool for lung nodules analysis based on segmentation and morphological operation
Somatic mutations associated with MRI-derived volumetric features in glioblastoma
Gutman, David A
Dunn Jr, William D
Grossmann, Patrick
Cooper, Lee AD
Holder, Chad A
Ligon, Keith L
Alexander, Brian M
Aerts, Hugo JWL
Neuroradiology2015Journal Article, cited 45 times
Website
Radiomics
BRAIN
Glioblastoma Multiforme (GBM)
Magnetic Resonance Imaging (MRI)
INTRODUCTION: MR imaging can noninvasively visualize tumor phenotype characteristics at the macroscopic level. Here, we investigated whether somatic mutations are associated with and can be predicted by MRI-derived tumor imaging features of glioblastoma (GBM). METHODS: Seventy-six GBM patients were identified from The Cancer Imaging Archive for whom preoperative T1-contrast (T1C) and T2-FLAIR MR images were available. For each tumor, a set of volumetric imaging features and their ratios were measured, including necrosis, contrast enhancing, and edema volumes. Imaging genomics analysis assessed the association of these features with mutation status of nine genes frequently altered in adult GBM. Finally, area under the curve (AUC) analysis was conducted to evaluate the predictive performance of imaging features for mutational status. RESULTS: Our results demonstrate that MR imaging features are strongly associated with mutation status. For example, TP53-mutated tumors had significantly smaller contrast enhancing and necrosis volumes (p = 0.012 and 0.017, respectively) and RB1-mutated tumors had significantly smaller edema volumes (p = 0.015) compared to wild-type tumors. MRI volumetric features were also found to significantly predict mutational status. For example, AUC analysis results indicated that TP53, RB1, NF1, EGFR, and PDGFRA mutations could each be significantly predicted by at least one imaging feature. CONCLUSION: MRI-derived volumetric features are significantly associated with and predictive of several cancer-relevant, drug-targetable DNA mutations in glioblastoma. These results may shed insight into unique growth characteristics of individual tumors at the macroscopic level resulting from molecular events as well as increase the use of noninvasive imaging in personalized medicine.
OPTIMISING DELINEATION ACCURACY OF TUMOURS IN PET FOR RADIOTHERAPY PLANNING USING BLIND DECONVOLUTION
Guvenis, A
Koc, A
Radiation Protection DosimetryRadiat Prot Dosim2015Journal Article, cited 3 times
Website
Algorithm Development
Computer Assisted Detection (CAD)
Segmentation
Positron Emission Tomography (PET)
Phantom
Positron emission tomography (PET) imaging has been proven to be useful in radiotherapy planning for the determination of the metabolically active regions of tumours. Delineation of tumours, however, is a difficult task in part due to high noise levels and the partial volume effects originating mainly from the low camera resolution. The goal of this work is to study the effect of blind deconvolution on tumour volume estimation accuracy for different computer-aided contouring methods. The blind deconvolution estimates the point spread function (PSF) of the imaging system in an iterative manner in a way that the likelihood of the given image being the convolution output is maximised. In this way, the PSF of the imaging system does not need to be known. Data were obtained from a NEMA NU-2 IQ-based phantom with a GE DSTE-16 PET/CT scanner. The artificial tumour diameters were 13, 17, 22, 28 and 37 mm with a target/background ratio of 4:1. The tumours were delineated before and after blind deconvolution. Student's two-tailed paired t-test showed a significant decrease in volume estimation error (p < 0.001) when blind deconvolution was used in conjunction with computer-aided delineation methods. A manual delineation confirmation demonstrated an improvement from 26 to 16 % for the artificial tumour of size 37 mm while an improvement from 57 to 15 % was noted for the small tumour of 13 mm. Therefore, it can be concluded that blind deconvolution of reconstructed PET images may be used to increase tumour delineation accuracy.
Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities
Itakura, Haruka
Achrol, Achal S
Mitchell, Lex A
Loya, Joshua J
Liu, Tiffany
Westbroek, Erick M
Feroze, Abdullah H
Rodriguez, Scott
Echegaray, Sebastian
Azad, Tej D
Science Translational Medicine2015Journal Article, cited 90 times
Website
TCGA-GBM
MRI
radiomic features
Quantitative imaging in radiation oncology: An emerging science and clinical service
A low cost approach for brain tumor segmentation based on intensity modeling and 3D Random Walker
Kanas, Vasileios G
Zacharaki, Evangelia I
Davatzikos, Christos
Sgarbas, Kyriakos N
Megalooikonomou, Vasileios
Biomedical Signal Processing and Control2015Journal Article, cited 15 times
Website
Algorithm Development
BRAIN
Objective
Magnetic resonance imaging (MRI) is the primary imaging technique for evaluation of the brain tumor progression before and after radiotherapy or surgery. The purpose of the current study is to exploit conventional MR modalities in order to identify and segment brain images with neoplasms.
Methods
Four conventional MR sequences, namely, T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid attenuation inversion recovery, are combined with machine learning techniques to extract global and local information of brain tissues and model the healthy and neoplastic imaging profiles. Healthy tissue clustering, outlier detection and geometric and spatial constraints are applied to perform a first segmentation which is further improved by a modified multiparametric Random Walker segmentation method. The proposed framework is applied on clinical data from 57 brain tumor patients (acquired by different scanners and acquisition parameters) and on 25 synthetic MR images with tumors. Assessment is performed against expert-defined tissue masks and is based on sensitivity analysis and Dice coefficient.
Results
The results demonstrate that the proposed multiparametric framework differentiates neoplastic tissues with accuracy similar to most current approaches while it achieves lower computational cost and higher degree of automation.
Conclusion
This study might provide a decision-support tool for neoplastic tissue segmentation, which can assist in treatment planning for tumor resection or focused radiotherapy.
Malignancy prediction by using characteristic-based fuzzy sets: A preliminary study
Computer-aided detection applications has managed to make significant contributions to medical world in today's technology. In this study, the detection of brain tumors in magnetic resonance images was performed. This study proposes a computer aided detection system that is based on morphological reconstruction and rule based detection of tumors that using the morphological features of the regions of interest. The steps involved in this study are: the pre-processing stage, the segmentation stage, the stage of identification of the region of interest and the stage of detection of tumors. With these methods applied on 497 magnetic resonance image slices of 10 patients, the performance of the computer aided detection system is achieved 84,26% accuracy.
Lung Nodule Classification Using Deep Features in CT Images
Kumar, Devinder
Wong, Alexander
Clausi, David A
2015Conference Proceedings, cited 114 times
Website
LIDC-IDRI
Computer Aided Diagnosis (CADx)
Early detection of lung cancer can help in a sharp decrease in the lung cancer mortality rate, which accounts for more than 17% percent of the total cancer related deaths. A large number of cases are encountered by radiologists on a daily basis for initial diagnosis. Computer-aided diagnosis (CAD) systems can assist radiologists by offering a "second opinion" and making the whole process faster. We propose a CAD system which uses deep features extracted from an auto encoder to classify lung nodules as either malignant or benign. We use 4303 instances containing 4323 nodules from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy of 75.01% with a sensitivity of 83.35% and false positive of 0.39/patient over a 10 fold cross validation.
Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans
The malignancy of lung nodules is most often detected by analyzing changes of the nodule diameter in follow-up scans. A recent study showed that comparing the volume or the mass of a nodule over time is much more significant than comparing the diameter. Since the survival rate is higher when the disease is still in an early stage it is important to detect the growth rate as soon as possible. However manual segmentation of a volume is time-consuming. Whereas there are several well evaluated methods for the segmentation of solid nodules, less work is done on subsolid nodules which actually show a higher malignancy rate than solid nodules. In this work we present a fast, semi-automatic method for segmentation of subsolid nodules. As minimal user interaction the method expects a user-drawn stroke on the largest diameter of the nodule. First, a threshold-based region growing is performed based on intensity analysis of the nodule region and surrounding parenchyma. In the next step the chest wall is removed by a combination of a connected component analyses and convex hull calculation. Finally, attached vessels are detached by morphological operations. The method was evaluated on all nodules of the publicly available LIDC/IDRI database that were manually segmented and rated as non-solid or part-solid by four radiologists (Dataset 1) and three radiologists (Dataset 2). For these 59 nodules the Jaccard index for the agreement of the proposed method with the manual reference segmentations was 0.52/0.50 (Dataset 1/Dataset 2) compared to an inter-observer agreement of the manual segmentations of 0.54/0.58 (Dataset 1/Dataset 2). Furthermore, the inter-observer agreement using the proposed method (i.e. different input strokes) was analyzed and gave a Jaccard index of 0.74/0.74 (Dataset 1/Dataset 2). The presented method provides satisfactory segmentation results with minimal observer effort in minimal time and can reduce the inter-observer variability for segmentation of subsolid nodules in clinical routine.
Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme
Lee, Joonsang
Narang, Shivali
Martinez, Juan
Rao, Ganesh
Rao, Arvind
PLoS One2015Journal Article, cited 14 times
Website
TCGA-GBM
Glioblastoma
Radiomics
Magnetic Resonance Imaging (MRI)
One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (</=12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in Glioblastoma multiforme.
Associating spatial diversity features of radiologically defined tumor habitats with epidermal growth factor receptor driver status and 12-month survival in glioblastoma: methods and preliminary investigation
Lee, Joonsang
Narang, Shivali
Martinez, Juan J
Rao, Ganesh
Rao, Arvind
Journal of Medical Imaging2015Journal Article, cited 15 times
Website
TCGA-GBM
Radiogenomics
Radiomics
Magnetic Resonance Imaging (MRI)
We analyzed the spatial diversity of tumor habitats, regions with distinctly different intensity characteristics of a tumor, using various measurements of habitat diversity within tumor regions. These features were then used for investigating the association with a 12-month survival status in glioblastoma (GBM) patients and for the identification of epidermal growth factor receptor (EGFR)-driven tumors. T1 postcontrast and T2 fluid attenuated inversion recovery images from 65 GBM patients were analyzed in this study. A total of 36 spatial diversity features were obtained based on pixel abundances within regions of interest. Performance in both the classification tasks was assessed using receiver operating characteristic (ROC) analysis. For association with 12-month overall survival, area under the ROC curve was 0.74 with confidence intervals [0.630 to 0.858]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.59 and 0.75, respectively. For the identification of EGFR-driven tumors, the area under the ROC curve (AUC) was 0.85 with confidence intervals [0.750 to 0.945]. The sensitivity and specificity at the optimal operating point ([Formula: see text]) on the ROC were 0.76 and 0.83, respectively. Our findings suggest that these spatial habitat diversity features are associated with these clinical characteristics and could be a useful prognostic tool for magnetic resonance imaging studies of patients with GBM.
Patient-specific biomechanical model as whole-body CT image registration tool
Li, Mao
Miller, Karol
Joldes, Grand Roman
Doyle, Barry
Garlapati, Revanth Reddy
Kikinis, Ron
Wittek, Adam
Medical Image Analysis2015Journal Article, cited 15 times
Website
Image registration
patient-specific biomechanical model
non-linear finite element analysis
Fuzzy-c means
Hausdorff distance
Magnetic Resonance Imaging (MRI)
Computed Tomography (CT)
finite-element model
BRAIN
mechanical-properties
nonrigid registration
Whole-body computed tomography (CT) image registration is important for cancer diagnosis, therapy planning and treatment. Such registration requires accounting for large differences between source and target images caused by deformations of soft organs/tissues and articulated motion of skeletal structures. The registration algorithms relying solely on image processing methods exhibit deficiencies in accounting for such deformations and motion. We propose to predict the deformations and movements of body organs/tissues and skeletal structures for whole-body CT image registration using patient-specific non-linear biomechanical modelling. Unlike the conventional biomechanical modelling, our approach for building the biomechanical models does not require time-consuming segmentation of CT scans to divide the whole body into non-overlapping constituents with different material properties. Instead, a Fuzzy C-Means (FCM) algorithm is used for tissue classification to assign the constitutive properties automatically at integration points of the computation grid. We use only very simple segmentation of the spine when determining vertebrae displacements to define loading for biomechanical models. We demonstrate the feasibility and accuracy of our approach on CT images of seven patients suffering from cancer and aortic disease. The results confirm that accurate whole-body CT image registration can be achieved using a patient-specific non-linear biomechanical model constructed without time-consuming segmentation of the whole-body images. (C) 2015 Elsevier B.V. All rights reserved.
Automatic Classification of Normal and Cancer Lung CT Images Using Multiscale AM-FM Features
Magdy, Eman
Zayed, Nourhan
Fakhr, Mahmoud
International Journal of Biomedical Imaging2015Journal Article, cited 6 times
Website
Computer-aided diagnostic (CAD) systems provide fast and reliable diagnosis for medical images. In this paper, CAD system is proposed to analyze and automatically segment the lungs and classify each lung into normal or cancer. Using 70 different patients' lung CT dataset, Wiener filtering on the original CT images is applied firstly as a preprocessing step. Secondly, we combine histogram analysis with thresholding and morphological operations to segment the lung regions and extract each lung separately. Amplitude-Modulation Frequency-Modulation (AM-FM) method thirdly, has been used to extract features for ROIs. Then, the significant AM-FM features have been selected using Partial Least Squares Regression (PLSR) for classification step. Finally, K-nearest neighbour (KNN), support vector machine (SVM), naive Bayes, and linear classifiers have been used with the selected AM-FM features. The performance of each classifier in terms of accuracy, sensitivity, and specificity is evaluated. The results indicate that our proposed CAD system succeeded to differentiate between normal and cancer lungs and achieved 95% accuracy in case of the linear classifier.
Automatic Electronic Cleansing in Computed Tomography Colonography Images using Domain Knowledge
Manjunath, KN
Siddalingaswamy, PC
Prabhu, GK
Asian Pacific Journal of Cancer Prevention2015Journal Article, cited 0 times
CT Colonography
Determining the variability of lesion size measurements from ct patient data sets acquired under “no change” conditions
McNitt-Gray, Michael F
Kim, Grace Hyun
Zhao, Binsheng
Schwartz, Lawrence H
Clunie, David
Cohen, Kristin
Petrick, Nicholas
Fenimore, Charles
Lu, ZQ John
Buckler, Andrew J
Transl OncolTranslational oncology2015Journal Article, cited 0 times
RIDER Lung CT
Comparison of Automatic Seed Generation Methods for Breast Tumor Detection Using Region Growing Technique
Segmentation of Pulmonary Nodules in Computed Tomography Using a Regression Neural Network Approach and its Application to the Lung Image Database Consortium and Image Database Resource Initiative Dataset
Messay, Temesguen
Hardie, Russell C
Tuinstra, Timothy R
Medical Image Analysis2015Journal Article, cited 55 times
Website
LIDC-IDRI
Computed Tomography (CT)
Automatic segmentation
Computer Aided Diagnosis (CADx)
Semi-automatic segmentation
Automated Brain Lesion Detection and Segmentation Using Magnetic Resonance Images
Brain tumors detection and segmentation in MR images: Gabor wavelet vs. statistical features
Nabizadeh, Nooshin
Kubat, Miroslav
Computers & Electrical Engineering2015Journal Article, cited 85 times
Website
BRAIN
Magnetic Resonance Imaging (MRI)
Automated recognition of brain tumors in magnetic resonance images (MRI) is a difficult procedure owing to the variability and complexity of the location, size, shape, and texture of these lesions. Because of intensity similarities between brain lesions and normal tissues, some approaches make use of multi-spectral anatomical MRI scans. However, the time and cost restrictions for collecting multi-spectral MRI scans and some other difficulties necessitate developing an approach that can detect tumor tissues using a single-spectral anatomical MRI images. In this paper, we present a fully automatic system, which is able to detect slices that include tumor and, to delineate the tumor area. The experimental results on single contrast mechanism demonstrate the efficacy of our proposed technique in successfully segmenting brain tumor tissues with high accuracy and low computational complexity. Moreover, we include a study evaluating the efficacy of statistical features over Gabor wavelet features using several classifiers. This contribution fills the gap in the literature, as is the first to compare these sets of features for tumor segmentation applications. (C) 2015 Elsevier Ltd. All rights reserved.
Automatic Classification of Brain MRI Images Using SVM and Neural Network Classifiers
Computer Aided Diagnosis (CAD) is a technique where diagnosis is performed in an automatic way. This work has developed a CAD system for automatically classifying the given brain Magnetic Resonance Imaging (MRI) image into ‘tumor affected’ or ‘tumor not affected’. The input image is preprocessed using wiener filter and Contrast Limited Adaptive Histogram Equalization (CLAHE). The image is then quantized and aggregated to get a reduced image data. The reduced image is then segmented into four regions such as gray matter, white matter, cerebrospinal fluid and high intensity tumor cluster using Fuzzy C Means (FCM) algorithm. The tumor region is then extracted using the intensity metric. A contour is evolved over the identified tumor region using Active Contour model (ACM) to extract exact tumor segment. Thirty five features including Gray Level Co-occurrence Matrix (GLCM) features, Gray Level Run Length Matrix features (GLRL), statistical features and shape based features are extracted from the tumor region. Neural network and Support Vector Machine (SVM) classifiers are trained using these features. Results indicate that Support vector machine classifier with quadratic kernel function performs better than Radial Basis Function (RBF) kernel function and neural network classifier with fifty hidden nodes performs better than twenty five hidden nodes. It is also evident from the result that average running time of FCM is less when used on reduced image data.
Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine
Navaei-Lavasani, Saeedeh
Fathi-Kazerooni, Anahita
Saligheh-Rad, Hamidreza
Gity, Masoumeh
Frontiers in Biomedical Technologies2015Journal Article, cited 4 times
Website
BREAST-DIAGNOSIS
machine learning
Machine Learning methods for Quantitative Radiomic Biomarkers
Radiomics extracts and mines large number of medical imaging features quantifying tumor phenotypic characteristics. Highly accurate and reliable machine-learning approaches can drive the success of radiomic applications in clinical care. In this radiomic study, fourteen feature selection methods and twelve classification methods were examined in terms of their performance and stability for predicting overall survival. A total of 440 radiomic features were extracted from pre-treatment computed tomography (CT) images of 464 lung cancer patients. To ensure the unbiased evaluation of different machine-learning methods, publicly available implementations along with reported parameter configurations were used. Furthermore, we used two independent radiomic cohorts for training (n = 310 patients) and validation (n = 154 patients). We identified that Wilcoxon test based feature selection method WLCX (stability = 0.84 +/- 0.05, AUC = 0.65 +/- 0.02) and a classification method random forest RF (RSD = 3.52%, AUC = 0.66 +/- 0.03) had highest prognostic performance with high stability against data perturbation. Our variability analysis indicated that the choice of classification method is the most dominant source of performance variation (34.21% of total variance). Identification of optimal machine-learning methods for radiomic applications is a crucial step towards stable and clinically relevant radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor-phenotypic characteristics in clinical practice.
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head &Neck cancer
Parmar, C.
Leijenaar, R. T.
Grossmann, P.
Rios Velazquez, E.
Bussink, J.
Rietveld, D.
Rietbergen, M. M.
Haibe-Kains, B.
Lambin, P.
Aerts, H. J.
2015Journal Article, cited 0 times
Radiomics
Radiomics provides a comprehensive quantification of tumor phenotypes by extracting and mining large number of quantitative image features. To reduce the redundancy and compare the prognostic characteristics of radiomic features across cancer types, we investigated cancer-specific radiomic feature clusters in four independent Lung and Head &Neck (H) cancer cohorts (in total 878 patients). Radiomic features were extracted from the pre-treatment computed tomography (CT) images. Consensus clustering resulted in eleven and thirteen stable radiomic feature clusters for Lung and H cancer, respectively. These clusters were validated in independent external validation cohorts using rand statistic (Lung RS = 0.92, p < 0.001, H RS = 0.92, p < 0.001). Our analysis indicated both common as well as cancer-specific clustering and clinical associations of radiomic features. Strongest associations with clinical parameters: Prognosis Lung CI = 0.60 +/- 0.01, Prognosis H CI = 0.68 +/- 0.01; Lung histology AUC = 0.56 +/- 0.03, Lung stage AUC = 0.61 +/- 0.01, H HPV AUC = 0.58 +/- 0.03, H stage AUC = 0.77 +/- 0.02. Full utilization of these cancer-specific characteristics of image features may further improve radiomic biomarkers, providing a non-invasive way of quantifying and monitoring tumor phenotypic characteristics in clinical practice.
Intelligent texture feature extraction and indexing for MRI image retrieval using curvelet and PCA with HTF
Rajakumar, K
Muttan, S
Deepa, G
Revathy, S
Priya, B Shanmuga
Advances in Natural and Applied Sciences2015Journal Article, cited 0 times
Website
Radiomics
Content based image retrieval (CBIR)
Magnetic Resonance Imaging (MRI)
BRAIN
BREAST
PROSTATE
PHANTOM
MATLAB
With the development of multimedia network technology and the rapid increase of image application, Content Based Image Retrieval (CBIR) has become the most active area in image retrieval system. The fields of application of CBIR are becoming more and more exhaustive and wide. Most traditional image retrieval systems usually use color, texture, shape and spatial relationship. At present texture features play a very important role in computer vision and pattern recognition, especially in describing the content of images. Most texture image retrieval systems are providing retrieval result with insufficient retrieval accuracy. We address this problem, by using curvelet with PCA using Haralick Texture Feature (HTF) based image retrieval system is proposed in this paper. The combined approach of curvelet and PCA using HTF has produced better results than other proposed techniques.
Multi-fractal detrended texture feature for brain tumor classification
We propose a novel non-invasive brain tumor type classification using Multi-fractal Detrended Fluctuation Analysis (MFDFA) [1] in structural magnetic resonance (MR) images. This preliminary work investigates the efficacy of the MFDFA features along with our novel texture feature known as multi-fractional Brownian motion (mBm) [2]in classifying (grading) brain tumors as High Grade (HG) and Low Grade (LG). Based on prior performance, Random Forest (RF) [3] is employed for tumor grading using two different datasets such as BRATS-2013 [4] and BRATS-2014 [5]. Quantitative scores such as precision, recall, accuracy are obtained using the confusion matrix. On an average 90% precision and 85% recall from the inter-dataset cross-validation confirm the efficacy of the proposed method.
A scheme for patient study retrieval from 3D brain MR volumes
The paper presents a pipeline for case retrieval in magnetic resonance (MR) brain volumes acquired from biomedical image sensors. The framework proposed in this paper, inputs a patient study consisting of MR brain image slices and outputs similar patient case studies present in the brain MR volume database. Query slice pertains to a new case and the output slices belong to the previous case histories stored in the database. The framework could be of immense help to the medical practitioners. It might prove to be a useful diagnostic aid for the medical expert and also serve as a teaching aid for students and researchers in the medical field. Apart from diagnosis, radiologists can use the tumor location to past case studies relevant to the present patient study, which can aid in the treatment of the patients. Similarity distance employed in this work is the three dimensional Hausdorff distance which is significant as it takes into account the spatial location of the tumors. The preliminary results are encouraging and therefore the scheme could be adapted to various modalities and pathologies.
Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial
Background. The study goal was to determine whether changes in relative cerebral blood volume (rCBV) derived from dynamic susceptibility contrast (DSC) MRI are predictive of overall survival (OS) in patients with recurrent glioblastoma multiforme (GBM) when measured 2, 8, and 16 weeks after treatment initiation.
Methods. Patients with recurrent GBM (37/123) enrolled in ACRIN 6677/RTOG 0625, a multicenter, randomized, phase II trial of bevacizumab with irinotecan or temozolomide, consented to DSC-MRI plus conventional MRI, 21 with DSC-MRI at baseline and at least 1 postbaseline scan. Contrast-enhancing regions of interest were determined semi-automatically using pre- and postcontrast T1-weighted images. Mean tumor rCBV normalized to white matter (nRCBV) and standardized rCBV (sRCBV) were determined for these regions of interest. The OS rates for patients with positive versus negative changes from baseline in nRCBV and sRCBV were compared using Wilcoxon rank-sum and Kaplan-Meier survival estimates with log-rank tests.
Results. Patients surviving at least 1 year (OS-1) had significantly larger decreases in nRCBV at week 2 (P=.0451) and sRCBV at week 16 (P=.014). Receiver operating characteristic analysis found the percent changes of nRCBV and sRCBV at week 2 and sRCBV at week 16, but not rCBV data at week 8, to be good prognostic markers for OS-1. Patients with positive change from baseline rCBV had significantly shorter OS than those with negative change at both week 2 and week 16 (P=.0015 and P=.0067 for nRCBV and P=.0251 and P=.0004 for sRCBV, respectively).
Conclusions. Early decreases in rCBV are predictive of improved survival in patients with recurrent GBM treated with bevacizumab.
An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy
Computer-aided detection and diagnosis (CAD) has been widely investigated to improve radiologists' diagnostic accuracy in detecting and characterizing lung disease, as well as to assist with the processing of increasingly sizable volumes of imaging. Lung segmentation is a requisite preprocessing step for most CAD schemes. This paper proposes a parameter-free lung segmentation algorithm with the aim of improving lung nodule detection accuracy, focusing on juxtapleural nodules. A bidirectional chain coding method combined with a support vector machine (SVM) classifier is used to selectively smooth the lung border while minimizing the over-segmentation of adjacent regions. This automated method was tested on 233 computed tomography (CT) studies from the lung imaging database consortium (LIDC), representing 403 juxtapleural nodules. The approach obtained a 92.6% re-inclusion rate. Segmentation accuracy was further validated on 10 randomly selected CT series, finding a 0.3% average over-segmentation ratio and 2.4% under-segmentation rate when compared to manually segmented reference standards done by an expert. (C) 2014 Elsevier Ltd. All rights reserved.
Radiogenomics of clear cell renal cell carcinoma: preliminary findings of The Cancer Genome Atlas–Renal Cell Carcinoma (TCGA–RCC) Imaging Research Group
Shinagare, Atul B
Vikram, Raghu
Jaffe, Carl
Akin, Oguz
Kirby, Justin
Huang, Erich
Freymann, John
Sainani, Nisha I
Sadow, Cheryl A
Bathala, Tharakeswara K
Rubin, D. L.
Oto, A.
Heller, M. T.
Surabhi, V. R.
Katabathina, V.
Silverman, S. G.
Abdominal imaging2015Journal Article, cited 47 times
Website
TCGA-RCC
Radiogenomics
TCGA-KIRC
Clear cell renal cell carcinoma (ccRCC)
PURPOSE: To investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC). MATERIALS AND METHODS: This multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34-79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%-33%, 34%-66% or >66%), growth pattern (endophytic, <50% exophytic, or >/=50% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed. RESULTS: Median tumor size was 49 mm (range 14-162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had >/=50% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson's chi (2) test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann-Whitney U test). CONCLUSIONS: BAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.
A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques
Sivakumar, S
Chandrasekar, C
International Journal of Algorithms Design and Analysis2015Journal Article, cited 0 times
LIDC-IDRI
Iterative Probabilistic Voxel Labeling: Automated Segmentation for Analysis of The Cancer Imaging Archive Glioblastoma Images
Steed, TC
Treiber, JM
Patel, KS
Taich, Z
White, NS
Treiber, ML
Farid, N
Carter, BS
Dale, AM
Chen, CC
American Journal of Neuroradiology2015Journal Article, cited 12 times
Website
Algorithm Development
Glioblastoma Multiforme (GBM)
BACKGROUND AND PURPOSE: Robust, automated segmentation algorithms are required for quantitative analysis of large imaging datasets. We developed an automated method that identifies and labels brain tumor-associated pathology by using an iterative probabilistic voxel labeling using k-nearest neighbor and Gaussian mixture model classification. Our purpose was to develop a segmentation method which could be applied to a variety of imaging from The Cancer Imaging Archive. MATERIALS AND METHODS: Images from 2 sets of 15 randomly selected subjects with glioblastoma from The Cancer Imaging Archive were processed by using the automated algorithm. The algorithm-defined tumor volumes were compared with those segmented by trained operators by using the Dice similarity coefficient. RESULTS: Compared with operator volumes, algorithm-generated segmentations yielded mean Dice similarities of 0.92 +/- 0.03 for contrast-enhancing volumes and 0.84 +/- 0.09 for FLAIR hyperintensity volumes. These values compared favorably with the means of Dice similarity coefficients between the operator-defined segmentations: 0.92 +/- 0.03 for contrast-enhancing volumes and 0.92 +/- 0.05 for FLAIR hyperintensity volumes. Robust segmentations can be achieved when only postcontrast T1WI and FLAIR images are available. CONCLUSIONS: Iterative probabilistic voxel labeling defined tumor volumes that were highly consistent with operator-defined volumes. Application of this algorithm could facilitate quantitative assessment of neuroimaging from patients with glioblastoma for both research and clinical indications.
A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme
In Glioblastoma Multiforme (GBM) image-derived features ("radiomics") could help in individualizing patient management. Simple geometric features of tumors (necrosis, edema, active tumor) and first-order statistics in Magnetic Resonance Imaging (MRI) are used in clinical practice. However, these features provide limited characterization power because they do not incorporate spatial information and thus cannot differentiate patterns. The aim of this work is to develop and evaluate a methodological framework dedicated to building a prognostic model based on heterogeneity textural features of multimodal MRI sequences (T1. T1-contrast. T2 and FLAIR) in GBM. The proposed workflow consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting image features such as heterogeneity metrics and iii) building a prognostic model by selecting, ranking and combining optimal features through machine learning (Support Vector Machine). This framework was applied to 40 histologically proven GBM patients with the endpoint being overall survival (OS) classified as above or below the median survival (15 months). The models combining features from a maximum of two modalities were evaluated using leave-one-out cross-validation (LOOCV). A classification accuracy of 90% (sensitivity 85%, specificity 95%) was obtained by combining features from T1 pre-contrast and T1 post-contrast sequences. Our results suggest that several textural features in each MR sequence have prognostic value in GBM. (C) 2015 AGBM. Published by Elsevier Masson SAS. All rights reserved.
Prognostic value of multimodal MRI tumor features in Glioblastoma multiforme using textural features analysis
Upadhaya, Taman
Morvan, Yannick
Stindel, Eric
Reste, Le
Hatt, Mathieu
2015Conference Proceedings, cited 12 times
Website
TCGA-GBM
Radiomics
BRAIN
Support Vector Machine (SVM)
Image-derived features (“radiomics”) are increasingly being considered for patient management in (neuro)oncology and radiotherapy. In Glioblastoma multiforme (GBM), simple features are often used by clinicians in clinical practice, such as the size of the tumor or the relative sizes of the necrosis and active tumor. First order statistics provide a limited characterization power because they do not incorporate spatial information and thus cannot differentiate patterns. In this work, we present the methodological framework for building a prognostic model based on heterogeneity textural features of multimodal MRI sequences (T1, T1-contrast, T2 and FLAIR) in GBM. The proposed workflow consists in i) registering the available 3D multimodal MR images and segmenting the tumor volume, ii) extracting image features such as heterogeneity metrics and shape indices, iii) building a prognostic model using Support Vector Machine by selecting, ranking and combining optimal features. We present preliminary results obtained for the classification of 40 patients into short (≤ 15 months) or long (> 15 months) overall survival, validated using leave-one-out cross-validation. Our results suggest that several textural features in each MR sequence have prognostic value in GBM, classification accuracy of 90% (sensitivity 85%, specificity 95%) being obtained by combining both T1 sequences. Future work will consist in i) adding more patients for validation using training and testing groups, ii) considering additional features, iii) building a fully multimodal MRI model by combining features from more than two sequences, iv) consider survival as a continuous variable and v) combine image-derived features with clinical and histopatholoigcal data to build an even more accurate model.
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities
This study aims at developing a joint FDG-PET and MRI texture-based model for the early evaluation of lung metastasis risk in soft-tissue sarcomas (STSs). We investigate if the creation of new composite textures from the combination of FDG-PET and MR imaging information could better identify aggressive tumours. Towards this goal, a cohort of 51 patients with histologically proven STSs of the extremities was retrospectively evaluated. All patients had pre-treatment FDG-PET and MRI scans comprised of T1-weighted and T2-weighted fat-suppression sequences (T2FS). Nine non-texture features (SUV metrics and shape features) and forty-one texture features were extracted from the tumour region of separate (FDG-PET, T1 and T2FS) and fused (FDG-PET/T1 and FDG-PET/T2FS) scans. Volume fusion of the FDG-PET and MRI scans was implemented using the wavelet transform. The influence of six different extraction parameters on the predictive value of textures was investigated. The incorporation of features into multivariable models was performed using logistic regression. The multivariable modeling strategy involved imbalance-adjusted bootstrap resampling in the following four steps leading to final prediction model construction: (1) feature set reduction; (2) feature selection; (3) prediction performance estimation; and (4) computation of model coefficients. Univariate analysis showed that the isotropic voxel size at which texture features were extracted had the most impact on predictive value. In multivariable analysis, texture features extracted from fused scans significantly outperformed those from separate scans in terms of lung metastases prediction estimates. The best performance was obtained using a combination of four texture features extracted from FDG-PET/T1 and FDGPET/T2FS scans. This model reached an area under the receiver-operating characteristic curve of 0.984 +/- 0.002, a sensitivity of 0.955 +/- 0.006, and a specificity of 0.926 +/- 0.004 in bootstrapping evaluations. Ultimately, lung metastasis risk assessment at diagnosis of STSs could improve patient outcomes by allowing better treatment adaptation.
Fully automatic GBM segmentation in the TCGA-GBM dataset: Prognosis and correlation with VASARI features
Velazquez, Emmanuel Rios
Meier, Raphael
Dunn Jr, William D
Alexander, Brian
Wiest, Roland
Bauer, Stefan
Gutman, David A
Reyes, Mauricio
Aerts, Hugo JWL
Sci RepScientific reports2015Journal Article, cited 42 times
Website
TCGA-GBM
VASARI
Magnetic Resonance Imaging (MRI)
Segmentation
Radiomics
Reproducible definition and quantification of imaging biomarkers is essential. We evaluated a fully automatic MR-based segmentation method by comparing it to manually defined sub-volumes by experienced radiologists in the TCGA-GBM dataset, in terms of sub-volume prognosis and association with VASARI features. MRI sets of 109 GBM patients were downloaded from the Cancer Imaging archive. GBM sub-compartments were defined manually and automatically using the Brain Tumor Image Analysis (BraTumIA). Spearman's correlation was used to evaluate the agreement with VASARI features. Prognostic significance was assessed using the C-index. Auto-segmented sub-volumes showed moderate to high agreement with manually delineated volumes (range (r): 0.4 - 0.86). Also, the auto and manual volumes showed similar correlation with VASARI features (auto r = 0.35, 0.43 and 0.36; manual r = 0.17, 0.67, 0.41, for contrast-enhancing, necrosis and edema, respectively). The auto-segmented contrast-enhancing volume and post-contrast abnormal volume showed the highest AUC (0.66, CI: 0.55-0.77 and 0.65, CI: 0.54-0.76), comparable to manually defined volumes (0.64, CI: 0.53-0.75 and 0.63, CI: 0.52-0.74, respectively). BraTumIA and manual tumor sub-compartments showed comparable performance in terms of prognosis and correlation with VASARI features. This method can enable more reproducible definition and quantification of imaging based biomarkers and has potential in high-throughput medical imaging research.
Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative
Wang, Weisheng
Luo, Jiawei
Yang, Xuedong
Lin, Hongli
Academic Radiology2015Journal Article, cited 5 times
Website
LIDC-IDRI
RATIONALE AND OBJECTIVES: The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) is the largest publicly available computed tomography (CT) image reference data set of lung nodules. In this article, a comprehensive data analysis of the data set and a uniform data model are presented with the purpose of facilitating potential researchers to have an in-depth understanding to and efficient use of the data set in their lung cancer-related investigations. MATERIALS AND METHODS: A uniform data model was designed for representation and organization of various types of information contained in different source data files. A software tool was developed for the processing and analysis of the database, which 1) automatically aligns and graphically displays the nodule outlines marked manually by radiologists onto the corresponding CT images; 2) extracts diagnostic nodule characteristics annotated by radiologists; 3) calculates a variety of nodule image features based on the outlines of nodules, including diameter, volume, and degree of roundness, and so forth; 4) integrates all the extracted nodule information into the uniform data model and stores it in a common and easy-to-access data format; and 5) analyzes and summarizes various feature distributions of nodules in several different categories. Using this data processing and analysis tool, all 1018 CT scans from the data set were processed and analyzed for their statistical distribution. RESULTS: The information contained in different source data files with different formats was extracted and integrated into a new and uniform data model. Based on the new data model, the statistical distributions of nodules in terms of nodule geometric features and diagnostic characteristics were summarized. In the LIDC/IDRI data set, 2655 nodules >/=3 mm, 5875 nodules <3 mm, and 7411 non-nodules are identified, respectively. Among the 2655 nodules, 1) 775, 488, 481, and 911 were marked by one, two, three, or four radiologists, respectively; 2) most of nodules >/=3 mm (85.7%) have a diameter <10.0 mm with the mean value of 6.72 mm; and 3) 10.87%, 31.4%, 38.8%, 16.4%, and 2.6% of nodules were assessed with a malignancy score of 1, 2, 3, 4, and 5, respectively. CONCLUSIONS: This study demonstrates the usefulness of the proposed software tool to the potential users for an in-depth understanding of the LIDC/IDRI data set, therefore likely to be beneficial to their future investigations. The analysis results also demonstrate the distribution diversity of nodules characteristics, therefore being useful as a reference resource for assessing the performance of a new and existing nodule detection and/or segmentation schemes.
Multicenter imaging outcomes study of The Cancer Genome Atlas glioblastoma patient cohort: imaging predictors of overall and progression-free survival
Despite an aggressive therapeutic approach, the prognosis for most patients with glioblastoma (GBM) remains poor. The aim of this study was to determine the significance of preoperative MRI variables, both quantitative and qualitative, with regard to overall and progression-free survival in GBM.We retrospectively identified 94 untreated GBM patients from the Cancer Imaging Archive who had pretreatment MRI and corresponding patient outcomes and clinical information in The Cancer Genome Atlas. Qualitative imaging assessments were based on the Visually Accessible Rembrandt Images feature-set criteria. Volumetric parameters were obtained of the specific tumor components: contrast enhancement, necrosis, and edema/invasion. Cox regression was used to assess prognostic and survival significance of each image.Univariable Cox regression analysis demonstrated 10 imaging features and 2 clinical variables to be significantly associated with overall survival. Multivariable Cox regression analysis showed that tumor-enhancing volume (P = .03) and eloquent brain involvement (P < .001) were independent prognostic indicators of overall survival. In the multivariable Cox analysis of the volumetric features, the edema/invasion volume of more than 85 000 mm3 and the proportion of enhancing tumor were significantly correlated with higher mortality (Ps = .004 and .003, respectively).Preoperative MRI parameters have a significant prognostic role in predicting survival in patients with GBM, thus making them useful for patient stratification and endpoint biomarkers in clinical trials.
A 3D semi-automated co-segmentation method for improved tumor target delineation in 3D PET/CT imaging
The planning of radiotherapy is increasingly based on multi-modal imaging techniques such as positron emission tomography (PET)-computed tomography (CT), since PET/CT provides not only anatomical but also functional assessment of the tumor. In this work, we propose a novel co-segmentation method, utilizing both the PET and CT images, to localize the tumor. The method constructs the segmentation problem as minimization of a Markov random field model, which encapsulates features from both imaging modalities. The minimization problem can then be solved by the maximum flow algorithm, based on graph cuts theory. The proposed tumor delineation algorithm was validated in both a phantom, with a high-radiation area, and in patient data. The obtained results show significant improvement compared to existing segmentation methods, with respect to various qualitative and quantitative metrics.
Effect of color visualization and display hardware on the visual assessment of pseudocolor medical images
Zabala-Travers, Silvina
Choi, Mina
Cheng, Wei-Chung
Badano, Aldo
Medical Physics2015Journal Article, cited 4 times
Website
Magnetic Resonance Imaging (MRI)
Image processing
PURPOSE: Even though the use of color in the interpretation of medical images has increased significantly in recent years, the ad hoc manner in which color is handled and the lack of standard approaches have been associated with suboptimal and inconsistent diagnostic decisions with a negative impact on patient treatment and prognosis. The purpose of this study is to determine if the choice of color scale and display device hardware affects the visual assessment of patterns that have the characteristics of functional medical images. METHODS: Perfusion magnetic resonance imaging (MRI) was the basis for designing and performing experiments. Synthetic images resembling brain dynamic-contrast enhanced MRI consisting of scaled mixtures of white, lumpy, and clustered backgrounds were used to assess the performance of a rainbow ("jet"), a heated black-body ("hot"), and a gray ("gray") color scale with display devices of different quality on the detection of small changes in color intensity. The authors used a two-alternative, forced-choice design where readers were presented with 600 pairs of images. Each pair consisted of two images of the same pattern flipped along the vertical axis with a small difference in intensity. Readers were asked to select the image with the highest intensity. Three differences in intensity were tested on four display devices: a medical-grade three-million-pixel display, a consumer-grade monitor, a tablet device, and a phone. RESULTS: The estimates of percent correct show that jet outperformed hot and gray in the high and low range of the color scales for all devices with a maximum difference in performance of 18% (confidence intervals: 6%, 30%). Performance with hot was different for high and low intensity, comparable to jet for the high range, and worse than gray for lower intensity values. Similar performance was seen between devices using jet and hot, while gray performance was better for handheld devices. Time of performance was shorter with jet. CONCLUSIONS: Our findings demonstrate that the choice of color scale and display hardware affects the visual comparative analysis of pseudocolor images. Follow-up studies in clinical settings are being considered to confirm the results with patient images.
Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities
Zayed, Nourhan
Elnemr, Heba A
International Journal of Biomedical Imaging2015Journal Article, cited 30 times
Website
SPIE-AAPM Lung CT Challenge
Segmentation
Classification
The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.
Two-stage fusion set selection in multi-atlas-based image segmentation
Conventional multi-atlas-based segmentation demands pairwise full-fledged registration between each atlas image and the target image, which leads to high computational cost and poses great challenge in the new era of big data. On the other hand, only the most relevant atlases should contribute to final label fusion. In this work, we introduce a two-stage fusion set selection method by first trimming the atlas collection into an augmented subset based on a low-cost registration and the preliminary relevance metric, followed by a further refinement based on a full-fledged registration and the corresponding relevance metric. A statistical inference model is established to relate the preliminary and the refined relevance metrics, and a proper augmented subset size is derived based on it. Empirical evidence supported the inference model, and end-to-end performance assessment demonstrated the proposed scheme to be computationally efficient without compromising segmentation accuracy.
A statistical method for lung tumor segmentation uncertainty in PET images based on user inference
Deciphering Genomic Underpinnings of Quantitative MRI-based Radiomic Phenotypes of Invasive Breast Carcinoma
Zhu, Yitan
Li, Hui
Guo, Wentian
Drukker, Karen
Lan, Li
Giger, Maryellen L
Ji, Yuan
Sci RepScientific reports2015Journal Article, cited 52 times
Website
TCGA-BRCA
Radiogenomics
Magnetic Resonance Imaging (MRI)
BREAST
Magnetic Resonance Imaging (MRI) has been routinely used for the diagnosis and treatment of breast cancer. However, the relationship between the MRI tumor phenotypes and the underlying genetic mechanisms remains under-explored. We integrated multi-omics molecular data from The Cancer Genome Atlas (TCGA) with MRI data from The Cancer Imaging Archive (TCIA) for 91 breast invasive carcinomas. Quantitative MRI phenotypes of tumors (such as tumor size, shape, margin, and blood flow kinetics) were associated with their corresponding molecular profiles (including DNA mutation, miRNA expression, protein expression, pathway gene expression and copy number variation). We found that transcriptional activities of various genetic pathways were positively associated with tumor size, blurred tumor margin, and irregular tumor shape and that miRNA expressions were associated with the tumor size and enhancement texture, but not with other types of radiomic phenotypes. We provide all the association findings as a resource for the research community (available at http://compgenome.org/Radiogenomics/). These findings pave potential paths for the discovery of genetic mechanisms regulating specific tumor phenotypes and for improving MRI techniques as potential non-invasive approaches to probe the cancer molecular status.
Robust Computer-Aided Detection of Pulmonary Nodules from Chest Computed Tomography
Detection of pulmonary nodules in chest computed tomography scans play an important role in the early diagnosis of lung cancer. A simple yet effective computer-aided detection system is developed to distinguish pulmonary nodules in chest CT scans. The proposed system includes feature extraction, normalization, selection and classification steps. One hundred forty-nine gray level statistical features are extracted from selected regions of interest. A min-max normalization method is used followed by sequential forward feature selection technique with logistic regression model used as criterion function that selected an optimal set of five features for classification. The classification step was done using nearest neighbor and support vector machine (SVM) classifiers with separate training and testing sets. Several measures to evaluate the system performance were used including the area under ROC curve (AUC), sensitivity, specificity, precision, accuracy, F1 score and Cohen-k factor. Excellent performance with high sensitivity and specificity is reported using data from two reference datasets as compared to previous work.
Adaptive Enhancement Technique for Cancerous Lung Nodule in Computed Tomography Images
AbuBaker, Ayman A
International Journal of Engineering and Technology2016Journal Article, cited 1 times
Website
LIDC-IDRI
Computed Tomography (CT)
Computer Aided Detection (CADe)
Diagnosis the Computed Tomography Images (CT-Images) may take a lot of time by the radiologist. This will increase the radiologist fatigue and may miss some of the cancerous lung nodule lesions. Therefore, an adaptive local enhancement Computer Aided Diagnosis (CAD) system is proposed. The proposed technique is design to enhance the suspicious cancerous regions in the CT-Images. The visual characteristics of the cancerous lung nodules in the CT-Images was the main criteria in designing this technique. The new approach is divided into two phases, pre-processing phase and image enhancement phase. The image noise reduction, thresholding process, and extraction the lung regions are considered as a pre-processing phase. Whereas, the new adaptive local enhancement method for the CTImages were implemented as a second phase. The proposed algorithm is tested and evaluated on 42 normal and cancerous lung nodule CT-Images. As a result, this new approach can efficiently enhance the cancerous lung nodules by 25% comparing with the original images.
Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
Aerts, Hugo JWL
Grossmann, Patrick
Tan, Yongqiang
Oxnard, Geoffrey G
Rizvi, Naiyer
Schwartz, Lawrence H
Zhao, Binsheng
Sci RepScientific reports2016Journal Article, cited 40 times
Website
RIDER
radiomics
NSCLC
lung
Medical imaging plays a fundamental role in oncology and drug development, by providing a non-invasive method to visualize tumor phenotype. Radiomics can quantify this phenotype comprehensively by applying image-characterization algorithms, and may provide important information beyond tumor size or burden. In this study, we investigated if radiomics can identify a gefitinib response-phenotype, studying high-resolution computed-tomography (CT) imaging of forty-seven patients with early-stage non-small cell lung cancer before and after three weeks of therapy. On the baseline-scan, radiomic-feature Laws-Energy was significantly predictive for EGFR-mutation status (AUC = 0.67, p = 0.03), while volume (AUC = 0.59, p = 0.27) and diameter (AUC = 0.56, p = 0.46) were not. Although no features were predictive on the post-treatment scan (p > 0.08), the change in features between the two scans was strongly predictive (significant feature AUC-range = 0.74-0.91). A technical validation revealed that the associated features were also highly stable for test-retest (mean +/- std: ICC = 0.96 +/- 0.06). This pilot study shows that radiomic data before treatment is able to predict mutation status and associated gefitinib response non-invasively, demonstrating the potential of radiomics-based phenotyping to improve the stratification and response assessment between tyrosine kinase inhibitors (TKIs) sensitive and resistant patient populations.
Tumor Lesion Segmentation from 3D PET Using a Machine Learning Driven Active Surface
Increased robustness in reference region model analysis of DCE MRI using two‐step constrained approaches
Ahmed, Zaki
Levesque, Ives R
Magnetic Resonance in Medicine2016Journal Article, cited 1 times
Website
DCE-MRI
Algorithm development
QIN Breast DCE-MRI
Quantitative assessment of colorectal morphology: Implications for robotic colonoscopy
Alazmani, A
Hood, A
Jayne, D
Neville, A
Culmer, P
Medical engineering & physics2016Journal Article, cited 11 times
Website
CT COLONOGRAPHY
Segmentation
This paper presents a method of characterizing the distribution of colorectal morphometrics. It uses three-dimensional region growing and topological thinning algorithms to determine and visualize the luminal volume and centreline of the colon, respectively. Total and segmental lengths, diameters, volumes, and tortuosity angles were then quantified. The effects of body orientations on these parameters were also examined. Variations in total length were predominately due to differences in the transverse colon and sigmoid segments, and did not significantly differ between body orientations. The diameter of the proximal colon was significantly larger than the distal colon, with the largest value at the ascending and cecum segments. The volume of the transverse colon was significantly the largest, while those of the descending colon and rectum were the smallest. The prone position showed a higher frequency of high angles and consequently found to be more torturous than the supine position. This study yielded a method for complete segmental measurements of healthy colorectal anatomy and its tortuosity. The transverse and sigmoid colons were the major determinant in tortuosity and morphometrics between body orientations. Quantitative understanding of these parameters may potentially help to facilitate colonoscopy techniques, accuracy of polyp spatial distribution detection, and design of novel endoscopic devices.
Breast Cancer Response Prediction in Neoadjuvant Chemotherapy Treatment Based on Texture Analysis
Ammar, Mohammed
Mahmoudi, Saïd
Stylianos, Drisis
Procedia Computer Science2016Journal Article, cited 2 times
Website
QIN Breast DCE-MRI
texture analysis
Computer Aided Diagnosis (CADx)
BREAST
MRI modality is one of the most usual techniques used for diagnosis and treatment planning of breast cancer. The aim of this study is to prove that texture based feature techniques such as co-occurrence matrix features extracted from MRI images can be used to quantify response of tumor treatment. To this aim, we use a dataset composed of two breast MRI examinations for 9 patients. Three of them were responders and six non responders. The first exam was achieved before the initiation of the treatment (baseline). The later one was done after the first cycle of the chemo treatment (control). A set of selected texture parameters have been selected and calculated for each exam. These selected parameters are: Cluster Shade, dissimilarity, entropy, homogeneity. The p-values estimated for the pathologic complete responders pCR and non pathologic complete responders pNCR patients prove that homogeneity (P-value=0.027) and cluster shade (P-value=0.0013) are the more relevant parameters related to pathologic complete responders pCR.
Imaging Genomics in Glioblastoma Multiforme: A Predictive Tool for Patients Prognosis, Survival, and Outcome
Anil, Rahul
Colen, Rivka R
Magnetic Resonance Imaging Clinics of North America2016Journal Article, cited 3 times
Website
Radiogenomics
Glioblastoma Multiforme (GBM)
The integration of imaging characteristics and genomic data has started a new trend in approach toward management of glioblastoma (GBM). Many ongoing studies are investigating imaging phenotypical signatures that could explain more about the behavior of GBM and its outcome. The discovery of biomarkers has played an adjuvant role in treating and predicting the outcome of patients with GBM. Discovering these imaging phenotypical signatures and dysregulated pathways/genes is needed and required to engineer treatment based on specific GBM manifestations. Characterizing these parameters will establish well-defined criteria so researchers can build on the treatment of GBM through personal medicine.
Lung nodule detection using 3D convolutional neural networks trained on weakly labeled data
Anirudh, Rushil
Thiagarajan, Jayaraman J
Bremer, Timo
Kim, Hyojin
2016Conference Proceedings, cited 33 times
Website
Convolutional Neural Network (CNN)
LUNG
Brain tumour classification using two-tier classifier with adaptive segmentation technique
Anitha, V
Murugavalli, S
IET Computer VisionIet Comput Vis2016Journal Article, cited 46 times
Website
TCGA-GBM
Radiomics
BRAIN
Texture features
Magnetic resonance imaging (MRI)
A brain tumour is a mass of tissue that is structured by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Human investigation is the routine technique for brain MRI tumour detection and tumours classification. Interpretation of images is based on organised and explicit classification of brain MRI and also various techniques have been proposed. Information identified with anatomical structures and potential abnormal tissues which are noteworthy to treat are given by brain tumour segmentation on MRI, the proposed system uses the adaptive pillar K-means algorithm for successful segmentation and the classification methodology is done by the two-tier classification approach. In the proposed system, at first the self-organising map neural network trains the features extracted from the discrete wavelet transform blend wavelets and the resultant filter factors are consequently trained by the K-nearest neighbour and the testing process is also accomplished in two stages. The proposed two-tier classification system classifies the brain tumours in double training process which gives preferable performance over the traditional classification method. The proposed system has been validated with the support of real data sets and the experimental results showed enhanced performance.
Imaging genomics in cancer research: limitations and promises
Bai, Harrison X
Lee, Ashley M
Yang, Li
Zhang, Paul
Davatzikos, Christos
Maris, John M
Diskin, Sharon J
The British journal of radiology2016Journal Article, cited 28 times
Website
Radiogenomics
GLISTRboost: Combining Multimodal MRI Segmentation, Registration, and Biophysical Tumor Growth Modeling with Gradient Boosting Machines for Glioma Segmentation.
Bakas, S.
Zeng, K.
Sotiras, A.
Rathore, S.
Akbari, H.
Gaonkar, B.
Rozycki, M.
Pati, S.
Davatzikos, C.
Brainlesion2016Journal Article, cited 49 times
Website
Algorithm Development
Challenge
Segmentation
BRAIN
BraTS
Lower-grade glioma (LGG)
Glioblastoma Multiforme (GBM)
We present an approach for segmenting low- and high-grade gliomas in multimodal magnetic resonance imaging volumes. The proposed approach is based on a hybrid generative-discriminative model. Firstly, a generative approach based on an Expectation-Maximization framework that incorporates a glioma growth model is used to segment the brain scans into tumor, as well as healthy tissue labels. Secondly, a gradient boosting multi-class classification scheme is used to refine tumor labels based on information from multiple patients. Lastly, a probabilistic Bayesian strategy is employed to further refine and finalize the tumor segmentation based on patient-specific intensity statistics from the multiple modalities. We evaluated our approach in 186 cases during the training phase of the BRAin Tumor Segmentation (BRATS) 2015 challenge and report promising results. During the testing phase, the algorithm was additionally evaluated in 53 unseen cases, achieving the best performance among the competing methods.
A New Adaptive-Weighted Fusion Rule for Wavelet based PET/CT Fusion
Barani, R
Sumathi, M
International Journal of Signal Processing, Image Processing and Pattern Recognition2016Journal Article, cited 1 times
Website
RIDER Lung PET-CT
Image fusion
In recent years the Wavelet Transform (WT) had an important role in various applications of signal and image processing. In Image Processing, WT is more useful in many domains like image denoising, feature segmentation, compression, restoration, image fusion, etc. In WT based image fusion, initially the source images are decomposed into approximation and detail coefficients and followed by combining the coefficients using the suitable fusion rules. The resultant fused image is reconstructed by applying
inverse WT on the combined coefficients. This paper proposes a new adaptive fusion rule for combining the approximation coefficients of CT and PET images. The Excellency of the proposed fusion rule is stamped by measuring the image information metrics, EOG, SD and ENT on the decomposed approximation coefficients. On the other hand, the detail coefficients are combined using several existing fusion rules. The resultant fused images are quantitatively analyzed using the non-reference image quality, image fusion and error metrics. The analysis declares that the newly proposed fusion rule is more suitable for
extracting the complementary information from CT and PET images and also produces the fused image which is rich in content with good contrast and sharpness.
Pulmonary nodule detection using a cascaded SVM classifier
Automatic detection of lung nodules from chest CT has been researched intensively over the last decades resulting also in several commercial products. However, solutions are adopted only slowly into daily clinical routine as many current CAD systems still potentially miss true nodules while at the same time generating too many false positives (FP). While many earlier approaches had to rely on rather few cases for development, larger databases become now available and can be used for algorithmic development. In this paper, we address the problem of lung nodule detection via a cascaded SVM classifier. The idea is to sequentially perform two classification tasks in order to select from an extremely large pool of potential candidates the few most likely ones. As the initial pool is allowed to contain thousands of candidates, very loose criteria could be applied during this pre-selection. In this way, the chances that a true nodule is falsely rejected as a candidate are reduced significantly. The final algorithm is trained and tested on the full LIDC/IDRI database. Comparison is done against two previously published CAD systems. Overall, the algorithm achieved sensitivity of 0.859 at 2.5 FP/volume where the other two achieved sensitivity values of 0.321 and 0.625, respectively. On low dose data sets, only slight increase in the number of FP/volume was observed, while the sensitivity was not affected.
G-DOC Plus–an integrative bioinformatics platform for precision medicine
Bhuvaneshwar, Krithika
Belouali, Anas
Singh, Varun
Johnson, Robert M
Song, Lei
Alaoui, Adil
Harris, Michael A
Clarke, Robert
Weiner, Louis M
Gusev, Yuriy
BMC Bioinformatics2016Journal Article, cited 14 times
Website
TCGA
REMBRANDT
Bioinformatics
Cloud computing
Precision medicine
Using computer‐extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage
Selección de un algoritmo para la clasificación de Nódulos Pulmonares Solitarios
Castro, Arelys Rivero
Correa, Luis Manuel Cruz
Lezcano, Jeffrey Artiles
Revista Cubana de Informática Médica2016Journal Article, cited 0 times
Website
LIDC-IDRI
Radiomic analysis of multi-contrast brain MRI for the prediction of survival in patients with glioblastoma multiforme
Chaddad, Ahmad
Desrosiers, Christian
Toews, Matthew
2016Conference Proceedings, cited 11 times
Website
Radiomics
BRAIN
Glioblastoma Multiforme (GBM)
Machine Learning
Magnetic Resonance Imaging (MRI)
Image texture features are effective at characterizing the microstructure of cancerous tissues. This paper proposes predicting the survival times of glioblastoma multiforme (GBM) patients using texture features extracted in multi-contrast brain MRI images. Texture features are derived locally from contrast enhancement, necrosis and edema regions in T1-weighted post-contrast and fluid-attenuated inversion-recovery (FLAIR) MRIs, based on the gray-level co-occurrence matrix representation. A statistical analysis based on the Kaplan-Meier method and log-rank test is used to identify the texture features related with the overall survival of GBM patients. Results are presented on a dataset of 39 GBM patients. For FLAIR images, four features (Energy, Correlation, Variance and Inverse of Variance) from contrast enhancement regions and a feature (Homogeneity) from edema regions were shown to be associated with survival times (p-value <; 0.01). Likewise, in T1-weighted images, three features (Energy, Correlation, and Variance) from contrast enhancement regions were found to be useful for predicting the overall survival of GBM patients. These preliminary results show the advantages of texture analysis in predicting the prognosis of GBM patients from multi-contrast brain MRI.
Phenotypic characterization of glioblastoma identified through shape descriptors
This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.
GBM heterogeneity characterization by radiomic analysis of phenotype anatomical planes
Glioblastoma multiforme (GBM) is the most common malignant primary tumor of the central nervous system, characterized among other traits by rapid metastatis. Three tissue phenotypes closely associated with GBMs, namely, necrosis (N), contrast enhancement (CE), and edema/invasion (E), exhibit characteristic patterns of texture heterogeneity in magnetic resonance images (MRI). In this study, we propose a novel model to characterize GBM tissue phenotypes using gray level co-occurrence matrices (GLCM) in three anatomical planes. The GLCM encodes local image patches in terms of informative, orientation-invariant texture descriptors, which are used here to sub-classify GBM tissue phenotypes. Experiments demonstrate the model on MRI data of 41 GBM patients, obtained from the cancer genome atlas (TCGA). Intensity-based automatic image registration is applied to align corresponding pairs of fixed T1˗weighted (T1˗WI) post-contrast and fluid attenuated inversion recovery (FLAIR) images. GBM tissue regions are then segmented using the 3D Slicer tool. Texture features are computed from 12 quantifier functions operating on GLCM descriptors, that are generated from MRI intensities within segmented GBM tissue regions. Various classifier models are used to evaluate the effectiveness of texture features for discriminating between GBM phenotypes. Results based on T1-WI scans showed a phenotype classification accuracy of over 88.14%, a sensitivity of 85.37% and a specificity of 96.1%, using the linear discriminant analysis (LDA) classifier. This model has the potential to provide important characteristics of tumors, which can be used for the sub-classification of GBM phenotypes.
Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients
GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients were analyzed. Phenotypes vN, vAT, and vE were segmented in a preprocessing step using 3D Slicer for rigid registration by T1-weighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes were segmented using 3D Slicer tools. Texture features were extracted from GLCM of GBM phenotypes. Thereafter, Kruskal-Wallis test was employed to select the significant features. Robust predictive GBM features were identified and underwent numerous classifier analyses to distinguish phenotypes. Kaplan-Meier analysis was also performed to determine the relationship, if any, between phenotype texture features and survival rate. The simulation results showed that the 22 texture features were significant with p value < 0.05. GBM phenotype discrimination based on texture features showed the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference were statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population.
Quantitative evaluation of robust skull stripping and tumor detection applied to axial MR images
Chaddad, Ahmad
Tanougast, Camel
Brain Informatics2016Journal Article, cited 28 times
Website
TCGA-GBM
To isolate the brain from non-brain tissues using a fully automatic method may be affected by the presence of radio frequency non-homogeneity of MR images (MRI), regional anatomy, MR sequences, and the subjects of the study. In order to automate the brain tumor (Glioblastoma) detection, we proposed a novel approach of skull stripping for axial slices derived from MRI. Then, the brain tumor was detected using multi-level threshold segmentation based on histogram analysis. Skull-stripping method, was applied by adaptive morphological operations approach. This is considered an empirical threshold by calculation of the area of brain tissue, iteratively. It was employed on the registration of non-contrast T1-weighted (T1-WI) and its corresponding fluid attenuated inversion recovery sequence. Then, we used multi-thresholding segmentation (MTS) method which is proposed by Otsu. We calculated the performance metrics based on the similarity coefficients for patients (n = 120) with tumor. The adaptive algorithm of skull stripping and MTS of segmented tumors were achieved efficient in preliminary results with 92 and 80 % of Dice similarity coefficient and 0.3 and 25.8 % of false negative rate, respectively. The adaptive skull stripping algorithm provides robust skull-stripping results, and the tumor area for medical diagnosis was determined by MTS.
Incremental Prognostic Value of ADC Histogram Analysis over MGMT Promoter Methylation Status in Patients with Glioblastoma
Choi, Yoon Seong
Ahn, Sung Soo
Kim, Dong Wook
Chang, Jong Hee
Kang, Seok-Gu
Kim, Eui Hyun
Kim, Se Hoon
Rim, Tyler Hyungtaek
Lee, Seung-Koo
RadiologyRadiology2016Journal Article, cited 18 times
Website
Radiogenomics
Glioblastoma Multiforme (GBM)
Purpose To investigate the incremental prognostic value of apparent diffusion coefficient (ADC) histogram analysis over oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status in patients with glioblastoma and the correlation between ADC parameters and MGMT status. Materials and Methods This retrospective study was approved by institutional review board, and informed consent was waived. A total of 112 patients with glioblastoma were divided into training (74 patients) and test (38 patients) sets. Overall survival (OS) and progression-free survival (PFS) was analyzed with ADC parameters, MGMT status, and other clinical factors. Multivariate Cox regression models with and without ADC parameters were constructed. Model performance was assessed with c index and receiver operating characteristic curve analyses for 12- and 16-month OS and 12-month PFS in the training set and validated in the test set. ADC parameters were compared according to MGMT status for the entire cohort. Results By using ADC parameters, the c indices and diagnostic accuracies for 12- and 16-month OS and 12-month PFS in the models showed significant improvement, with the exception of c indices in the models for PFS (P < .05 for all) in the training set. In the test set, the diagnostic accuracy was improved by using ADC parameters and was significant, with the 25th and 50th percentiles of ADC for 16-month OS (P = .040 and P = .047) and the 25th percentile of ADC for 12-month PFS (P = .026). No significant correlation was found between ADC parameters and MGMT status. Conclusion ADC histogram analysis had incremental prognostic value over MGMT promoter methylation status in patients with glioblastoma. ((c)) RSNA, 2016 Online supplemental material is available for this article.
ST3GAL1-associated transcriptomic program in glioblastoma tumor growth, invasion, and prognosis
BACKGROUND: Cell surface sialylation is associated with tumor cell invasiveness in many cancers. Glioblastoma is the most malignant primary brain tumor and is highly infiltrative. ST3GAL1 sialyltransferase gene is amplified in a subclass of glioblastomas, and its role in tumor cell self-renewal remains unexplored. METHODS: Self-renewal of patient glioma cells was evaluated using clonogenic, viability, and invasiveness assays. ST3GAL1 was identified from differentially expressed genes in Peanut Agglutinin-stained cells and validated in REMBRANDT (n = 390) and Gravendeel (n = 276) clinical databases. Gene set enrichment analysis revealed upstream processes. TGFbeta signaling on ST3GAL1 transcription was assessed using chromatin immunoprecipitation. Transcriptome analysis of ST3GAL1 knockdown cells was done to identify downstream pathways. A constitutively active FoxM1 mutant lacking critical anaphase-promoting complex/cyclosome ([APC/C]-Cdh1) binding sites was used to evaluate ST3Gal1-mediated regulation of FoxM1 protein. Finally, the prognostic role of ST3Gal1 was determined using an orthotopic xenograft model (3 mice groups comprising nontargeting and 2 clones of ST3GAL1 knockdown in NNI-11 [8 per group] and NNI-21 [6 per group]), and the correlation with patient clinical information. All statistical tests on patients' data were two-sided; other P values below are one-sided. RESULTS: High ST3GAL1 expression defines an invasive subfraction with self-renewal capacity; its loss of function prolongs survival in a mouse model established from mesenchymal NNI-11 (P < .001; groups of 8 in 3 arms: nontargeting, C1, and C2 clones of ST3GAL1 knockdown). ST3GAL1 transcriptomic program stratifies patient survival (hazard ratio [HR] = 2.47, 95% confidence interval [CI] = 1.72 to 3.55, REMBRANDT P = 1.92 x 10(-)(8); HR = 2.89, 95% CI = 1.94 to 4.30, Gravendeel P = 1.05 x 10(-)(1)(1)), independent of age and histology, and associates with higher tumor grade and T2 volume (P = 1.46 x 10(-)(4)). TGFbeta signaling, elevated in mesenchymal patients, correlates with high ST3GAL1 (REMBRANDT gliomacor = 0.31, P = 2.29 x 10(-)(1)(0); Gravendeel gliomacor = 0.50, P = 3.63 x 10(-)(2)(0)). The transcriptomic program upon ST3GAL1 knockdown enriches for mitotic cell cycle processes. FoxM1 was identified as a statistically significantly modulated gene (P = 2.25 x 10(-)(5)) and mediates ST3Gal1 signaling via the (APC/C)-Cdh1 complex. CONCLUSIONS: The ST3GAL1-associated transcriptomic program portends poor prognosis in glioma patients and enriches for higher tumor grades of the mesenchymal molecular classification. We show that ST3Gal1-regulated self-renewal traits are crucial to the sustenance of glioblastoma multiforme growth.
Reproducing 2D breast mammography images with 3D printed phantoms
Primary lung tumor segmentation from PET–CT volumes with spatial–topological constraint
Cui, Hui
Wang, Xiuying
Lin, Weiran
Zhou, Jianlong
Eberl, Stefan
Feng, Dagan
Fulham, Michael
International Journal of Computer Assisted Radiology and Surgery2016Journal Article, cited 14 times
Website
RIDER Phantom PET–CT
LUNG
Radiogenomics of glioblastoma: a pilot multi-institutional study to investigate a relationship between tumor shape features and tumor molecular subtype
Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval
Deep, G
Kaur, L
Gupta, S
Engineering Science and Technology, an International Journal2016Journal Article, cited 9 times
Website
LIDC-IDRI
Algorithm Development
Computed Tomography (CT)
Magnetic resonance imaging (MRI)
Texture features
Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval
Deep, G.
Kaur, L.
Gupta, S.
Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization2016Journal Article, cited 3 times
Website
Algorithm Development
LIDC-IDRI
This paper proposes a new pattern-based feature called local mesh ternary pattern for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the greyscale relationship between the centre pixel and its surrounding neighbours in two-dimensional (2D) local region of an image, whereas the proposed method encodes the greyscale relationship among the neighbours for a given centre pixel with three selected directions of mesh patterns which is generated from 2D image. The novelty of the proposed method is that it uses ternary patterns from mesh patterns of an image to encode more spatial structure information which leads to better retrieval. The experiments have been carried out for proving the worth of proposed algorithm on three different types of benchmarked biomedical databases; (i) computed tomography (CT) scanned lung image databases named as LIDC-IDRI-CT and VIA/I–ELCAP-CT, (ii) brain magnetic resonance imaging (MRI) database named as OASIS-MRI. The results demonstrate that the proposed method yields better performance in terms of average retrieval precision and average retrieval rate over state-of-the-art feature extraction techniques like LBP, LTP, local mesh pattern, etc.
Predicting response before initiation of neoadjuvant chemotherapy in breast cancer using new methods for the analysis of dynamic contrast enhanced MRI (DCE MRI) data
The pharmacokinetic parameters derived from dynamic contrast enhanced (DCE) MRI have shown promise as biomarkers for tumor response to therapy. However, standard methods of analyzing DCE MRI data (Tofts model) require high temporal resolution, high signal-to-noise ratio (SNR), and the Arterial Input Function (AIF). Such models produce reliable biomarkers of response only when a therapy has a large effect on the parameters. We recently reported a method that solves the limitations, the Linear Reference Region Model (LRRM). Similar to other reference region models, the LRRM needs no AIF. Additionally, the LRRM is more accurate and precise than standard methods at low SNR and slow temporal resolution, suggesting LRRM-derived biomarkers could be better predictors. Here, the LRRM, Non-linear Reference Region Model (NRRM), Linear Tofts model (LTM), and Non-linear Tofts Model (NLTM) were used to estimate the RKtrans between muscle and tumor (or the Ktrans for Tofts) and the tumor kep,TOI for 39 breast cancer patients who received neoadjuvant chemotherapy (NAC). These parameters and the receptor statuses of each patient were used to construct cross-validated predictive models to classify patients as complete pathological responders (pCR) or non-complete pathological responders (non-pCR) to NAC. Model performance was evaluated using area under the ROC curve (AUC). The AUC for receptor status alone was 0.62, while the best performance using predictors from the LRRM, NRRM, LTM, and NLTM were AUCs of 0.79, 0.55, 0.60, and 0.59 respectively. This suggests that the LRRM can be used to predict response to NAC in breast cancer.
Development of a nomogram combining clinical staging with 18F-FDG PET/CT image features in non-small-cell lung cancer stage I–III
Desseroit, Marie-Charlotte
Visvikis, Dimitris
Tixier, Florent
Majdoub, Mohamed
Perdrisot, Rémy
Guillevin, Rémy
Le Rest, Catherine Cheze
Hatt, Mathieu
European journal of nuclear medicine and molecular imaging2016Journal Article, cited 34 times
Website
RIDER Lung PET-CT
3D Slicer
Texture features
18F-FDG PET/CT
Non-Small Cell Lung Cancer (NSCLC)
MedCalc
Kaplan-Meier curve
Assessing the Effects of Software Platforms on Volumetric Segmentation of Glioblastoma
Dunn, William D Jr
Aerts, Hugo J W L
Cooper, Lee A
Holder, Chad A
Hwang, Scott N
Jaffe, Carle C
Brat, Daniel J
Jain, Rajan
Flanders, Adam E
Zinn, Pascal O
Colen, Rivka R
Gutman, David A
J Neuroimaging Psychiatry Neurol2016Journal Article, cited 0 times
Website
Radiogenomics
Magnetic resonance imaging (MRI)
Segmentation
TCGA
3D Slicer
BRAIN
Background: Radiological assessments of biologically relevant regions in glioblastoma have been associated with genotypic characteristics, implying a potential role in personalized medicine. Here, we assess the reproducibility and association with survival of two volumetric segmentation platforms and explore how methodology could impact subsequent interpretation and analysis. Methods: Post-contrast T1- and T2-weighted FLAIR MR images of 67 TCGA patients were segmented into five distinct compartments (necrosis, contrast-enhancement, FLAIR, post contrast abnormal, and total abnormal tumor volumes) by two quantitative image segmentation platforms - 3D Slicer and a method based on Velocity AI and FSL. We investigated the internal consistency of each platform by correlation statistics, association with survival, and concordance with consensus neuroradiologist ratings using ordinal logistic regression. Results: We found high correlations between the two platforms for FLAIR, post contrast abnormal, and total abnormal tumor volumes (spearman's r(67) = 0.952, 0.959, and 0.969 respectively). Only modest agreement was observed for necrosis and contrast-enhancement volumes (r(67) = 0.693 and 0.773 respectively), likely arising from differences in manual and automated segmentation methods of these regions by 3D Slicer and Velocity AI/FSL, respectively. Survival analysis based on AUC revealed significant predictive power of both platforms for the following volumes: contrast-enhancement, post contrast abnormal, and total abnormal tumor volumes. Finally, ordinal logistic regression demonstrated correspondence to manual ratings for several features. Conclusion: Tumor volume measurements from both volumetric platforms produced highly concordant and reproducible estimates across platforms for general features. As automated or semi-automated volumetric measurements replace manual linear or area measurements, it will become increasingly important to keep in mind that measurement differences between segmentation platforms for more detailed features could influence downstream survival or radio genomic analyses.
Decision forests for learning prostate cancer probability maps from multiparametric MRI
Computer aided diagnosis is starting to be implemented broadly in the diagnosis and
detection of many varieties of abnormities acquired during various imaging procedures.
The main aim of the CAD systems is to increase the accuracy and decrease the time of
diagnoses, while the general achievement for CAD systems are to find the place of nodules
and to determine the characteristic features of the nodule. As lung cancer is one of the fatal
and leading cancer types, there has been plenty of studies for the usage of the CAD
systems to detect lung cancer. Yet, the CAD systems need to be developed a lot in order to
identify the different shapes of nodules, lung segmentation and to have higher level of
sensitivity, specifity and accuracy. This challenge is the motivation of this study in
implementation of CAD system for lung cancer detection. In the study, LIDC database is
used which comprises of an image set of lung cancer thoracic documented CT scans. The
presented CAD system consists of CT image reading, image pre-processing, segmentation,
feature extraction and classification steps. To avoid losing important features, the CT
images were read as a raw form in DICOM file format. Then, filtration and enhancement
techniques were used as an image processing. Otsu’s algorithm, edge detection and
morphological operations are applied for the segmentation, following the feature
extractions step. Finally, support vector machine with Gaussian RBF is utilized for the
classification step which is widely used as a supervised classifier.
Computational Challenges and Collaborative Projects in the NCI Quantitative Imaging Network
Farahani, Keyvan
Kalpathy-Cramer, Jayashree
Chenevert, Thomas L
Rubin, Daniel L
Sunderland, John J
Nordstrom, Robert J
Buatti, John
Hylton, Nola
Tomography2016Journal Article, cited 2 times
Website
Radiomics
Quantitative Imaging Network (QIN)
The Quantitative Imaging Network (QIN) of the National Cancer Institute (NCI) conducts research in development and validation of imaging tools and methods for predicting and evaluating clinical response to cancer therapy. Members of the network are involved in examining various imaging and image assessment parameters through network-wide cooperative projects. To more effectively use the cooperative power of the network in conducting computational challenges in benchmarking of tools and methods and collaborative projects in analytical assessment of imaging technologies, the QIN Challenge Task Force has developed policies and procedures to enhance the value of these activities by developing guidelines and leveraging NCI resources to help their administration and manage dissemination of results. Challenges and Collaborative Projects (CCPs) are further divided into technical and clinical CCPs. As the first NCI network to engage in CCPs, we anticipate a variety of CCPs to be conducted by QIN teams in the coming years. These will be aimed to benchmark advanced software tools for clinical decision support, explore new imaging biomarkers for therapeutic assessment, and establish consensus on a range of methods and protocols in support of the use of quantitative imaging to predict and assess response to cancer therapy.
Signal intensity analysis of ecological defined habitat in soft tissue sarcomas to predict metastasis development
Magnetic Resonance Imaging (MRI) is the standard of care in the clinic for diagnosis and follow up of Soft Tissue Sarcomas (STS) which presents an opportunity to explore the heterogeneity inherent in these rare tumors. Tumor heterogeneity is a challenging problem to quantify and has been shown to exist at many scales, from genomic to radiomic, existing both within an individual tumor, between tumors from the same primary in the same patient and across different patients. In this paper, we propose a method which focuses on spatially distinct sub-regions or habitats in the diagnostic MRI of patients with STS by using pixel signal intensity. Habitat characteristics likely represent areas of differing underlying biology within the tumor, and delineation of these differences could provide clinically relevant information to aid in selecting a therapeutic regimen (chemotherapy or radiation). To quantify tumor heterogeneity, first we assay intra-tumoral segmentations based on signal intensity and then build a spatial mapping scheme from various MRI modalities. Finally, we predict clinical outcomes, using in this paper the appearance of distant metastasis - the most clinically meaningful endpoint. After tumor segmentation into high and low signal intensities, a set of quantitative imaging features based on signal intensity is proposed to represent variation in habitat characteristics. This set of features is utilized to predict metastasis in a cohort of STS patients. We show that this framework, using only pre-therapy MRI, predicts the development of metastasis in STS patients with 72.41% accuracy, providing a starting point for a number of clinical hypotheses.
DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research
Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM((R))) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.
HEVC/H.265 is the most interesting and cutting-edge topic in the world of digital video compression, allowing to reduce by half the required bandwidth in comparison with the previous H.264 standard. Telemedicine services and in general any medical video application can benefit from the video encoding advances. However, the HEVC is computationally expensive to implement. In this paper a method for reducing the HEVC complexity in the medical environment is proposed. The sequences that are typically processed in this context contain several homogeneous regions. Leveraging these regions, it is possible to simplify the HEVC flow while maintaining a high-level quality. In comparison with the HM16.2 standard, the encoding time is reduced up to 75%, with a negligible quality loss. Moreover, the algorithm is straightforward to implement in any hardware platform.
Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy
Firmino, Macedo
Angelo, Giovani
Morais, Higor
Dantas, Marcel R
Valentim, Ricardo
BioMedical Engineering OnLine2016Journal Article, cited 63 times
Website
LIDC-IDRI
Computer Aided Detection (CADe)
Computer Aided Diagnosis (CADx)
LUNG
Computed Tomography (CT)
BACKGROUND: CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. METHODS: The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. RESULTS: The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. CONCLUSIONS: From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer.
Computer-aided nodule assessment and risk yield risk management of adenocarcinoma: the future of imaging?
Lung nodule detection in CT images using deep convolutional neural networks
Golan, Rotem
Jacob, Christian
Denzinger, Jörg
2016Conference Proceedings, cited 26 times
Website
LIDC-IDRI
Radiomics
Computer Aided Detection (CADe)
Computed Tomography (CT)
Early detection of lung nodules in thoracic Computed Tomography (CT) scans is of great importance for the successful diagnosis and treatment of lung cancer. Due to improvements in screening technologies, and an increased demand for their use, radiologists are required to analyze an ever increasing amount of image data, which can affect the quality of their diagnoses. Computer-Aided Detection (CADe) systems are designed to assist radiologists in this endeavor. Here, we present a CADe system for the detection of lung nodules in thoracic CT images. Our system is based on (1) the publicly available Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) database, which contains 1018 thoracic CT scans with nodules of different shape and size, and (2) a deep Convolutional Neural Network (CNN), which is trained, using the back-propagation algorithm, to extract valuable volumetric features from the input data and detect lung nodules in sub-volumes of CT images. Considering only those test nodules that have been annotated by four radiologists, our CADe system achieves a sensitivity (true positive rate) of 78.9% with 20 false positives (FPs) per scan, or a sensitivity of 71.2% with 10 FPs per scan. This is achieved without using any segmentation or additional FP reduction procedures, both of which are commonly used in other CADe systems. Furthermore, our CADe system is validated on a larger number of lung nodules compared to other studies, which increases the variation in their appearance, and therefore, makes their detection by a CADe system more challenging.
Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique
Greenspan, Hayit
van Ginneken, Bram
Summers, Ronald M
IEEE Transactions on Medical Imaging2016Journal Article, cited 395 times
Website
Pancreas-CT
CT Lymph Nodes
Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in Glioblastoma
Grossmann, Patrick
Gutman, David A
Dunn, William D
Holder, Chad A
Aerts, Hugo JWL
BMC Cancer2016Journal Article, cited 21 times
Website
TCGA-GBM
Radiomics
Magnetic Resonance Imaging (MRI)
Background
Glioblastoma (GBM) tumors exhibit strong phenotypic differences that can be quantified using magnetic resonance imaging (MRI), but the underlying biological drivers of these imaging phenotypes remain largely unknown. An Imaging-Genomics analysis was performed to reveal the mechanistic associations between MRI derived quantitative volumetric tumor phenotype features and molecular pathways.
Methods
One hundred fourty one patients with presurgery MRI and survival data were included in our analysis. Volumetric features were defined, including the necrotic core (NE), contrast-enhancement (CE), abnormal tumor volume assessed by post-contrast T1w (tumor bulk or TB), tumor-associated edema based on T2-FLAIR (ED), and total tumor volume (TV), as well as ratios of these tumor components. Based on gene expression where available (n = 91), pathway associations were assessed using a preranked gene set enrichment analysis. These results were put into context of molecular subtypes in GBM and prognostication.
Results
Volumetric features were significantly associated with diverse sets of biological processes (FDR < 0.05). While NE and TB were enriched for immune response pathways and apoptosis, CE was associated with signal transduction and protein folding processes. ED was mainly enriched for homeostasis and cell cycling pathways. ED was also the strongest predictor of molecular GBM subtypes (AUC = 0.61). CE was the strongest predictor of overall survival (C-index = 0.6; Noether test, p = 4x10−4).
Conclusion
GBM volumetric features extracted from MRI are significantly enriched for information about the biological state of a tumor that impacts patient outcomes. Clinical decision-support systems could exploit this information to develop personalized treatment strategies on the basis of noninvasive imaging.
Using Deep Learning for Pulmonary Nodule Detection & Diagnosis
Gruetzemacher, Richard
Gupta, Ashish
2016Conference Paper, cited 0 times
LIDC-IDRI
A generalized graph reduction framework for interactive segmentation of large images
Gueziri, Houssem-Eddine
McGuffin, Michael J
Laporte, Catherine
Computer Vision and Image UnderstandingComput Vis Image Und2016Journal Article, cited 5 times
Website
Algorithm Development
Segmentation
Computer Aided Detection (CADe)
The speed of graph-based segmentation approaches, such as random walker (RW) and graph cut (GC), depends strongly on image size. For high-resolution images, the time required to compute a segmentation based on user input renders interaction tedious. We propose a novel method, using an approximate contour sketched by the user, to reduce the graph before passing it on to a segmentation algorithm such as RW or GC. This enables a significantly faster feedback loop. The user first draws a rough contour of the object to segment. Then, the pixels of the image are partitioned into "layers" (corresponding to different scales) based on their distance from the contour. The thickness of these layers increases with distance to the contour according to a Fibonacci sequence. An initial segmentation result is rapidly obtained after automatically generating foreground and background labels according to a specifically selected layer; all vertices beyond this layer are eliminated, restricting the segmentation to regions near the drawn contour. Further foreground background labels can then be added by the user to refine the segmentation. All iterations of the graph-based segmentation benefit from a reduced input graph, while maintaining full resolution near the object boundary. A user study with 16 participants was carried out for RW segmentation of a multi-modal dataset of 22 medical images, using either a standard mouse or a stylus pen to draw the contour. Results reveal that our approach significantly reduces the overall segmentation time compared with the status quo approach (p < 0.01). The study also shows that our approach works well with both input devices. Compared to super-pixel graph reduction, our approach provides full resolution accuracy at similar speed on a high-resolution benchmark image with both RW and GC segmentation methods. However, graph reduction based on super-pixels does not allow interactive correction of clustering errors. Finally, our approach can be combined with super-pixel clustering methods for further graph reduction, resulting in even faster segmentation. (C) 2016 Elsevier Inc. All rights reserved.
Appropriate Contrast Enhancement Measures for Brain and Breast Cancer Images
Gupta, Suneet
Porwal, Rabins
International Journal of Biomedical Imaging2016Journal Article, cited 10 times
Website
BRAIN
BREAST
Image Enhancement/methods
Medical imaging systems often produce images that require enhancement, such as improving the image contrast as they are poor in contrast. Therefore, they must be enhanced before they are examined by medical professionals. This is necessary for proper diagnosis and subsequent treatment. We do have various enhancement algorithms which enhance the medical images to different extents. We also have various quantitative metrics or measures which evaluate the quality of an image. This paper suggests the most appropriate measures for two of the medical images, namely, brain cancer images and breast cancer images.
Publishing descriptions of non-public clinical datasets: proposed guidance for researchers, repositories, editors and funding organisations
Hrynaszkiewicz, Iain
Khodiyar, Varsha
Hufton, Andrew L
Sansone, Susanna-Assunta
Research Integrity and Peer Review2016Journal Article, cited 8 times
Website
Open science
Sharing of experimental clinical research data usually happens between individuals or research groups rather than via public repositories, in part due to the need to protect research participant privacy. This approach to data sharing makes it difficult to connect journal articles with their underlying datasets and is often insufficient for ensuring access to data in the long term. Voluntary data sharing services such as the Yale Open Data Access (YODA) and Clinical Study Data Request (CSDR) projects have increased accessibility to clinical datasets for secondary uses while protecting patient privacy and the legitimacy of secondary analyses but these resources are generally disconnected from journal articles-where researchers typically search for reliable information to inform future research. New scholarly journal and article types dedicated to increasing accessibility of research data have emerged in recent years and, in general, journals are developing stronger links with data repositories. There is a need for increased collaboration between journals, data repositories, researchers, funders, and voluntary data sharing services to increase the visibility and reliability of clinical research. Using the journal Scientific Data as a case study, we propose and show examples of changes to the format and peer-review process for journal articles to more robustly link them to data that are only available on request. We also propose additional features for data repositories to better accommodate non-public clinical datasets, including Data Use Agreements (DUAs).
Computer-aided grading of gliomas based on local and global MRI features
Hsieh, Kevin Li-Chun
Lo, Chung-Ming
Hsiao, Chih-Jou
Computer Methods and Programs in Biomedicine2016Journal Article, cited 13 times
Website
TCGA-GBM
TCGA-LGG
Radiomics
BACKGROUND AND OBJECTIVES: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors. METHODS: The acquired image database for the CAD performance evaluation was composed of 34 glioblastomas and 73 diffuse lower-grade gliomas. In each case, tissues enclosed in a delineated tumor area were analyzed according to their gray-scale intensities on MRI scans. Four histogram moment features describing the global gray-scale distributions of gliomas tissues and 14 textural features were used to interpret local correlations between adjacent pixel values. With a logistic regression model, the individual feature set and a combination of both feature sets were used to establish the malignancy prediction model. RESULTS: Performances of the CAD system using global, local, and the combination of both image feature sets achieved accuracies of 76%, 83%, and 88%, respectively. Compared to global features, the combined features had significantly better accuracy (p = 0.0213). With respect to the pathology results, the CAD classification obtained substantial agreement kappa = 0.698, p < 0.001. CONCLUSIONS: Numerous proposed image features were significant in distinguishing glioblastomas from lower-grade gliomas. Combining them further into a malignancy prediction model would be promising in providing diagnostic suggestions for clinical use.
A neural network approach to lung nodule segmentation
Computed tomography (CT) imaging is a sensitive and specific lung cancer screening tool for the high-risk population and shown to be promising for detection of lung cancer. This study proposes an automatic methodology for detecting and segmenting lung nodules from CT images. The proposed methods begin with thorax segmentation, lung extraction and reconstruction of the original shape of the parenchyma using morphology operations. Next, a multi-scale hessian-based vesselness filter is applied to extract lung vasculature in lung. The lung vasculature mask is subtracted from the lung region segmentation mask to extract 3D regions representing candidate pulmonary nodules. Finally, the remaining structures are classified as nodules through shape and intensity features which are together used to train an artificial neural network. Up to 75% sensitivity and 98% specificity was achieved for detection of lung nodules in our testing dataset, with an overall accuracy of 97.62%±0.72% using 11 selected features as input to the neural network classifier, based on 4-fold cross-validation studies. Receiver operator characteristics for identifying nodules revealed an area under curve of 0.9476.
The Impact of Arterial Input Function Determination Variations on Prostate Dynamic Contrast-Enhanced Magnetic Resonance Imaging Pharmacokinetic Modeling: A Multicenter Data Analysis Challenge
Huang, Wei
Chen, Yiyi
Fedorov, Andriy
Li, Xia
Jajamovich, Guido H
Malyarenko, Dariya I
Aryal, Madhava P
LaViolette, Peter S
Oborski, Matthew J
O'Sullivan, Finbarr
Tomography: a journal for imaging research2016Journal Article, cited 21 times
Website
QIN PROSTATE
Integrative analysis of diffusion-weighted MRI and genomic data to inform treatment of glioblastoma
Jajamovich, Guido H
Valiathan, Chandni R
Cristescu, Razvan
Somayajula, Sangeetha
Journal of Neuro-Oncology2016Journal Article, cited 4 times
Website
TCGA-GBM
Radiogenomics
Classification
Gene expression profiling from glioblastoma (GBM) patients enables characterization of cancer into subtypes that can be predictive of response to therapy. An integrative analysis of imaging and gene expression data can potentially be used to obtain novel biomarkers that are closely associated with the genetic subtype and gene signatures and thus provide a noninvasive approach to stratify GBM patients. In this retrospective study, we analyzed the expression of 12,042 genes for 558 patients from The Cancer Genome Atlas (TCGA). Among these patients, 50 patients had magnetic resonance imaging (MRI) studies including diffusion weighted (DW) MRI in The Cancer Imaging Archive (TCIA). We identified the contrast enhancing region of the tumors using the pre- and post-contrast T1-weighted MRI images and computed the apparent diffusion coefficient (ADC) histograms from the DW-MRI images. Using the gene expression data, we classified patients into four molecular subtypes, determined the number and composition of genes modules using the gap statistic, and computed gene signature scores. We used logistic regression to find significant predictors of GBM subtypes. We compared the predictors for different subtypes using Mann-Whitney U tests. We assessed detection power using area under the receiver operating characteristic (ROC) analysis. We computed Spearman correlations to determine the associations between ADC and each of the gene signatures. We performed gene enrichment analysis using Ingenuity Pathway Analysis (IPA). We adjusted all p values using the Benjamini and Hochberg method. The mean ADC was a significant predictor for the neural subtype. Neural tumors had a significantly lower mean ADC compared to non-neural tumors ([Formula: see text]), with mean ADC of [Formula: see text] and [Formula: see text] for neural and non-neural tumors, respectively. Mean ADC showed an area under the ROC of 0.75 for detecting neural tumors. We found eight gene modules in the GBM cohort. The mean ADC was significantly correlated with the gene signature related with dendritic cell maturation ([Formula: see text], [Formula: see text]). Mean ADC could be used as a biomarker of a gene signature associated with dendritic cell maturation and to assist in identifying patients with neural GBMs, known to be resistant to aggressive standard of care.
Cloud-based NoSQL open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research
Tumor volume estimation, as well as accurate and reproducible borders segmentation in medical images, are important in the diagnosis, staging, and assessment of response to cancer therapy. The goal of this study was to demonstrate the feasibility of a multi-institutional effort to assess the repeatability and reproducibility of nodule borders and volume estimate bias of computerized segmentation algorithms in CT images of lung cancer, and to provide results from such a study. The dataset used for this evaluation consisted of 52 tumors in 41 CT volumes (40 patient datasets and 1 dataset containing scans of 12 phantom nodules of known volume) from five collections available in The Cancer Imaging Archive. Three academic institutions developing lung nodule segmentation algorithms submitted results for three repeat runs for each of the nodules. We compared the performance of lung nodule segmentation algorithms by assessing several measurements of spatial overlap and volume measurement. Nodule sizes varied from 29 mul to 66 ml and demonstrated a diversity of shapes. Agreement in spatial overlap of segmentations was significantly higher for multiple runs of the same algorithm than between segmentations generated by different algorithms (p < 0.05) and was significantly higher on the phantom dataset compared to the other datasets (p < 0.05). Algorithms differed significantly in the bias of the measured volumes of the phantom nodules (p < 0.05) underscoring the need for assessing performance on clinical data in addition to phantoms. Algorithms that most accurately estimated nodule volumes were not the most repeatable, emphasizing the need to evaluate both their accuracy and precision. There were considerable differences between algorithms, especially in a subset of heterogeneous nodules, underscoring the recommendation that the same software be used at all time points in longitudinal studies.
The analysis of Magnetic Resonance Image has an important role in definite detection of Brain Tumor. The shape, location and size of tumor are examined by Radiology specialist to diagnose and plan treatment. In the intense work pace, it's not possible to get results quickly. At this scheme, unnoticed information can be recovered by an image processing algorithm. In this study, at database images which are collected from REMBRANT were cleared from noise, transformed with Karhunen Loeve Transform to gray level and segmented with Pott's Markov Random Field Model. This hybrid algorithm minimizes the data loss, contrast and noise problems. After segmentation stage, shape and statistical analysis are performed to get feature vector about Region of Interest. The images are classified as existing tumor or not existing tumor. The algorithm can recognize the presence of tumor with 100% and tumor's area with 95% accuracy. The results are reported to help the specialists.
“Radiotranscriptomics”: A synergy of imaging and transcriptomics in clinical assessment
Katrib, Amal
Hsu, William
Bui, Alex
Xing, Yi
Quantitative Biology2016Journal Article, cited 0 times
Radiogenomics
A joint intensity and edge magnitude-based multilevel thresholding algorithm for the automatic segmentation of pathological MR brain images
Kaur, Taranjit
Saini, Barjinder Singh
Gupta, Savita
Neural Computing and Applications2016Journal Article, cited 1 times
Website
Radiomics
BraTS
Preliminary Detection and Analysis of Lung Cancer on CT images using MATLAB: A Cost-effective Alternative
Khan, Md Daud Hossain
Ahmed, Mansur
Bach, Christian
Journal of Biomedical Engineering and Medical Imaging2016Journal Article, cited 0 times
LUNG
MATLAB
Computer Aided Detection (CADe)
Non-Small Cell Lung Cancer (NSCLC)
Computed Tomography (CT)
Cancer is the second leading cause of death worldwide. Lung cancer possesses the highest mortality, with non-small cell lung cancer (NSCLC) being its most prevalent subtype of lung cancer. Despite gradual reduction in incidence, approximately 585720 new cancer patients were diagnosed in 2014, with majority from low-and-middleincome countries (LMICs). Limited availability of diagnostic equipment, poorly trained medical staff, late revelation of symptoms and classification of the exact lung cancer subtype and overall poor patient access to medical providers result in late or terminal stage diagnosis and delay of treatment. Therefore, the need for an economic, simple, fast computed image-processing system to aid decisions regarding staging and resection, especially for LMICs is clearly imminent. In this study, we developed a preliminary program using MATLAB that accurately detects cancer cells in CT images of lungs of affected patients, measures area of region of interest (ROI) or tumor mass and helps determine nodal spread. A preset value for nodal spread was used, which can be altered accordingly.
Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning
Korfiatis, Panagiotis
Kline, Timothy L
Erickson, Bradley J
Tomography2016Journal Article, cited 16 times
Website
BraTS
Magnetic Resonance Imaging (MRI)
FLAIR
Convolutional Neural Network (CNN)
Segmentation
We present a deep convolutional neural network application based on autoencoders aimed at segmentation of increased signal regions in fluid-attenuated inversion recovery magnetic resonance imaging images. The convolutional autoencoders were trained on the publicly available Brain Tumor Image Segmentation Benchmark (BRATS) data set, and the accuracy was evaluated on a data set where 3 expert segmentations were available. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to provide the ground truth for comparison, and Dice coefficient, Jaccard coefficient, true positive fraction, and false negative fraction were calculated. The proposed technique was within the interobserver variability with respect to Dice, Jaccard, and true positive fraction. The developed method can be used to produce automatic segmentations of tumor regions corresponding to signal-increased fluid-attenuated inversion recovery regions.
An Level Set Evolution Morphology Based Segmentation of Lung Nodules and False Nodule Elimination by 3D Centroid Shift and Frequency Domain DC Constant Analysis
Krishnamurthy, Senthilkumar
Narasimhan, Ganesh
Rengasamy, Umamaheswari
International Journal of u- and e- Service, Science and Technology2016Journal Article, cited 0 times
Website
LIDC-IDRI
Segmentation
LUNG
Classification
A Level Set Evolution with Morphology (LSEM) based segmentation algorithm is proposed in this work to segment all the possible lung nodules from a series of CT scan images. All the segmented nodule candidates were not cancerous in nature. Initially the vessels and calcifications were also segmented as nodule candidates. The structural feature analysis was carried out to remove the vessels. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule’s resultant position did not usually deviate. The calcifications were eliminated by frequency domain analysis.
DC constant of nodule candidates were computed in frequency domain. The nodule candidates with high DC constant value could be the calcifications as the calcification patterns were homogeneous in nature. This algorithm was applied on a database of 40 patient cases with 58 malignant nodules. The algorithms proposed in this paper precisely detected 55 malignant nodules and failed to detect 3 with a sensitivity of 95%. Further,
this algorithm correctly eliminated 778 tissue clusters that were initially segmented as nodules, however, 79 non-malignant tissue clusters were detected as malignant nodules.
Therefore, the false positive of this algorithm was 1.98 per patient.
Three-dimensional lung nodule segmentation and shape variance analysis to detect lung cancer with reduced false positives
Krishnamurthy, Senthilkumar
Narasimhan, Ganesh
Rengasamy, Umamaheswari
Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine2016Journal Article, cited 17 times
Website
LIDC-IDRI
Algorithms
Analysis of Variance
Humans
Imaging
Three-Dimensional/*methods
LUNG
Radiographic Image Interpretation
Computer-Assisted/*methods
Tomography
X-Ray Computed/*methods
Computed Tomography (CT)
juxta-pleural nodule
morphology processing
shape feature extraction
three-dimensional segmentation
The three-dimensional analysis on lung computed tomography scan was carried out in this study to detect the malignant lung nodules. An automatic three-dimensional segmentation algorithm proposed here efficiently segmented the tissue clusters (nodules) inside the lung. However, an automatic morphological region-grow segmentation algorithm that was implemented to segment the well-circumscribed nodules present inside the lung did not segment the juxta-pleural nodule present on the inner surface of wall of the lung. A novel edge bridge and fill technique is proposed in this article to segment the juxta-pleural and pleural-tail nodules accurately. The centroid shift of each candidate nodule was computed. The nodules with more centroid shift in the consecutive slices were eliminated since malignant nodule's resultant position did not usually deviate. The three-dimensional shape variation and edge sharp analyses were performed to reduce the false positives and to classify the malignant nodules. The change in area and equivalent diameter was more for malignant nodules in the consecutive slices and the malignant nodules showed a sharp edge. Segmentation was followed by three-dimensional centroid, shape and edge analysis which was carried out on a lung computed tomography database of 20 patient with 25 malignant nodules. The algorithms proposed in this article precisely detected 22 malignant nodules and failed to detect 3 with a sensitivity of 88%. Furthermore, this algorithm correctly eliminated 216 tissue clusters that were initially segmented as nodules; however, 41 non-malignant tissue clusters were detected as malignant nodules. Therefore, the false positive of this algorithm was 2.05 per patient.
Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network
Le, Trong-Ngoc
Bao, Pham The
Huynh, Hieu Trung
BioMed Research International2016Journal Article, cited 5 times
Website
LIVER
Magnetic Resonance Imaging (MRI)
Computer Aided Detection (CADe)
Segmentation
Algorithm Development
Objective. Our objective is to develop a computerized scheme for liver tumor segmentation in MR images. Materials and Methods. Our proposed scheme consists of four main stages. Firstly, the region of interest (ROI) image which contains the liver tumor region in the T1-weighted MR image series was extracted by using seed points. The noise in this ROI image was reduced and the boundaries were enhanced. A 3D fast marching algorithm was applied to generate the initial labeled regions which are considered as teacher regions. A single hidden layer feedforward neural network (SLFN), which was trained by a noniterative algorithm, was employed to classify the unlabeled voxels. Finally, the postprocessing stage was applied to extract and refine the liver tumor boundaries. The liver tumors determined by our scheme were compared with those manually traced by a radiologist, used as the "ground truth." Results. The study was evaluated on two datasets of 25 tumors from 16 patients. The proposed scheme obtained the mean volumetric overlap error of 27.43% and the mean percentage volume error of 15.73%. The mean of the average surface distance, the root mean square surface distance, and the maximal surface distance were 0.58 mm, 1.20 mm, and 6.29 mm, respectively.
Texture feature ratios from relative CBV maps of perfusion MRI are associated with patient survival in glioblastoma
Lee, J
Jain, R
Khalil, K
Griffith, B
Bosca, R
Rao, G
Rao, A
American Journal of Neuroradiology2016Journal Article, cited 27 times
Website
TCGA-GBM
Texture analysis
BACKGROUND AND PURPOSE: Texture analysis has been applied to medical images to assist in tumor tissue classification and characterization. In this study, we obtained textural features from parametric (relative CBV) maps of dynamic susceptibility contrast-enhanced MR images in glioblastoma and assessed their relationship with patient survival. MATERIALS AND METHODS: MR perfusion data of 24 patients with glioblastoma from The Cancer Genome Atlas were analyzed in this study. One- and 2D texture feature ratios and kinetic textural features based on relative CBV values in the contrast-enhancing and nonenhancing lesions of the tumor were obtained. Receiver operating characteristic, Kaplan-Meier, and multivariate Cox proportional hazards regression analyses were used to assess the relationship between texture feature ratios and overall survival. RESULTS: Several feature ratios are capable of stratifying survival in a statistically significant manner. These feature ratios correspond to homogeneity (P = .008, based on the log-rank test), angular second moment (P = .003), inverse difference moment (P = .013), and entropy (P = .008). Multivariate Cox proportional hazards regression analysis showed that homogeneity, angular second moment, inverse difference moment, and entropy from the contrast-enhancing lesion were significantly associated with overall survival. For the nonenhancing lesion, skewness and variance ratios of relative CBV texture were associated with overall survival in a statistically significant manner. For the kinetic texture analysis, the Haralick correlation feature showed a P value close to .05. CONCLUSIONS: Our study revealed that texture feature ratios from contrast-enhancing and nonenhancing lesions and kinetic texture analysis obtained from perfusion parametric maps provide useful information for predicting survival in patients with glioblastoma.
MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays
Li, Hui
Zhu, Yitan
Burnside, Elizabeth S
Drukker, Karen
Hoadley, Katherine A
Fan, Cheng
Conzen, Suzanne D
Whitman, Gary J
Sutton, Elizabeth J
Net, Jose M
RadiologyRadiology2016Journal Article, cited 103 times
Website
TCGA-Breast-Radiogenomics
radiomics
radiogenomics
Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set
Li, Hui
Zhu, Yitan
Burnside, Elizabeth S
Huang, Erich
Drukker, Karen
Hoadley, Katherine A
Fan, Cheng
Conzen, Suzanne D
Zuley, Margarita
Net, Jose M
npj Breast Cancer2016Journal Article, cited 63 times
Website
TCGA-BRCA
Radiomics
breast cancer
Biomechanical model for computing deformations for whole‐body image registration: A meshless approach
Li, Mao
Miller, Karol
Joldes, Grand Roman
Kikinis, Ron
Wittek, Adam
International Journal for Numerical Methods in Biomedical Engineering2016Journal Article, cited 13 times
Website
Algorithm Development
Fuzzy C-means clustering (FCM)
Segmentation
Computed Tomography (CT)
Machine Learning
Computational Identification of Tumor Anatomic Location Associated with Survival in 2 Large Cohorts of Human Primary Glioblastomas
Liu, T T
Achrol, A S
Mitchell, L A
Du, W A
Loya, J J
Rodriguez, S A
Feroze, A
Westbroek, E M
Yeom, K W
Stuart, J M
Chang, S D
Harsh, G R 4th
Rubin, D L
American Journal of Neuroradiology2016Journal Article, cited 6 times
Website
TCGA-GBM
Radiomics
Radiogenomics
Classification
BACKGROUND AND PURPOSE: Tumor location has been shown to be a significant prognostic factor in patients with glioblastoma. The purpose of this study was to characterize glioblastoma lesions by identifying MR imaging voxel-based tumor location features that are associated with tumor molecular profiles, patient characteristics, and clinical outcomes. MATERIALS AND METHODS: Preoperative T1 anatomic MR images of 384 patients with glioblastomas were obtained from 2 independent cohorts (n = 253 from the Stanford University Medical Center for training and n = 131 from The Cancer Genome Atlas for validation). An automated computational image-analysis pipeline was developed to determine the anatomic locations of tumor in each patient. Voxel-based differences in tumor location between good (overall survival of >17 months) and poor (overall survival of <11 months) survival groups identified in the training cohort were used to classify patients in The Cancer Genome Atlas cohort into 2 brain-location groups, for which clinical features, messenger RNA expression, and copy number changes were compared to elucidate the biologic basis of tumors located in different brain regions. RESULTS: Tumors in the right occipitotemporal periventricular white matter were significantly associated with poor survival in both training and test cohorts (both, log-rank P < .05) and had larger tumor volume compared with tumors in other locations. Tumors in the right periatrial location were associated with hypoxia pathway enrichment and PDGFRA amplification, making them potential targets for subgroup-specific therapies. CONCLUSIONS: Voxel-based location in glioblastoma is associated with patient outcome and may have a potential role for guiding personalized treatment.
Magnetic resonance perfusion image features uncover an angiogenic subgroup of glioblastoma patients with poor survival and better response to antiangiogenic treatment
Background. In previous clinical trials, antiangiogenic therapies such as bevacizumab did not show efficacy in patients with newly diagnosed glioblastoma (GBM). This may be a result of the heterogeneity of GBM, which has a variety of imaging-based phenotypes and gene expression patterns. In this study, we sought to identify a phenotypic subtype of GBM patients who have distinct tumor-image features and molecular activities and who may benefit from antiangiogenic therapies.Methods. Quantitative image features characterizing subregions of tumors and the whole tumor were extracted from preoperative and pretherapy perfusion magnetic resonance (MR) images of 117 GBM patients in 2 independent cohorts. Unsupervised consensus clustering was performed to identify robust clusters of GBM in each cohort. Cox survival and gene set enrichment analyses were conducted to characterize the clinical significance and molecular pathway activities of the clusters. The differential treatment efficacy of antiangiogenic therapy between the clusters was evaluated.Results. A subgroup of patients with elevated perfusion features was identified and was significantly associated with poor patient survival after accounting for other clinical covariates (P values <.01; hazard ratios > 3) consistently found in both cohorts. Angiogenesis and hypoxia pathways were enriched in this subgroup of patients, suggesting the potential efficacy of antiangiogenic therapy. Patients of the angiogenic subgroups pooled from both cohorts, who had chemotherapy information available, had significantly longer survival when treated with antiangiogenic therapy (log-rank P=.022).Conclusions. Our findings suggest that an angiogenic subtype of GBM patients may benefit from antiangiogenic therapy with improved overall survival.
Effect of Imaging Parameter Thresholds on MRI Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer Subtypes
Lo, Wei-Ching
Li, Wen
Jones, Ella F
Newitt, David C
Kornak, John
Wilmes, Lisa J
Esserman, Laura J
Hylton, Nola M
PLoS One2016Journal Article, cited 7 times
Website
TCGA-BRCA
Magnetic Resonance Imaging (MRI)
Radiomics
Radiogenomics
Automatic lung nodule classification with radiomics approach
Ma, Jingchen
Wang, Qian
Ren, Yacheng
Hu, Haibo
Zhao, Jun
2016Conference Proceedings, cited 10 times
Website
LUNG
Classification
Computer Aided Detection (CADe)
Computer Aided Diagnosis (CADx)
Lung cancer is the first killer among the cancer deaths. Malignant lung nodules have extremely high mortality while some of the benign nodules don't need any treatment .Thus, the accuracy of diagnosis between benign or malignant nodules diagnosis is necessary. Notably, although currently additional invasive biopsy or second CT scan in 3 months later may help radiologists to make judgments, easier diagnosis approaches are imminently needed. In this paper, we propose a novel CAD method to distinguish the benign and malignant lung cancer from CT images directly, which can not only improve the efficiency of rumor diagnosis but also greatly decrease the pain and risk of patients in biopsy collecting process. Briefly, according to the state-of-the-art radiomics approach, 583 features were used at the first step for measurement of nodules' intensity, shape, heterogeneity and information in multi-frequencies. Further, with Random Forest method, we distinguish the benign nodules from malignant nodules by analyzing all these features. Notably, our proposed scheme was tested on all 79 CT scans with diagnosis data available in The Cancer Imaging Archive (TCIA) which contain 127 nodules and each nodule is annotated by at least one of four radiologists participating in the project. Satisfactorily, this method achieved 82.7% accuracy in classification of malignant primary lung nodules and benign nodules. We believe it would bring much value for routine lung cancer diagnosis in CT imaging and provide improvement in decision-support with much lower cost.
An improved method of colon segmentation in computed tomography colonography images using domain knowledge
Manjunath, KN
Siddalingaswamy, PC
Gopalakrishna Prabhu, K
2016Journal Article, cited 0 times
CT Colonography
colon
Tumor Growth in the Brain: Complexity and Fractality
Tumor growth is a complex process characterized by uncontrolled cell proliferation and invasion of neighboring tissues. The understanding of these phenomena is of vital importance to establish appropriate diagnosis and therapy strategies and starts with the evaluation of their complexity with suitable descriptors produced by scaling analysis. There has been considerable effort in the evaluation of fractal dimension as a suitable parameter to describe differences between normal and pathological tissues, and it has been used for brain tumor grading with great success. In the present work, several contributions, which exploit scaling analysis in the context of brain tumors, are reviewed. These include very promising results in tumor segmentation, grading, and therapy monitoring. Emphasis is done on scaling analysis techniques applicable to multifractal systems, proposing new descriptors to advance the understanding of tumor growth dynamics in brain. These techniques serve as a starting point to develop innovative practical growth models for therapy simulation and optimization, drug delivery, and the evaluation of related neurological disorders.
Predicting outcomes in glioblastoma patients using computerized analysis of tumor shape: preliminary data
Glioblastoma (GBM) is the most common primary brain tumor characterized by very poor survival. However, while some patients survive only a few months, some might live for multiple years. Accurate prognosis of survival and stratification of patients allows for making more personalized treatment decisions and moves treatment of GBM one step closer toward the paradigm of precision medicine. While some molecular biomarkers are being investigated, medical imaging remains significantly underutilized for prognostication in GBM. In this study, we investigated whether computer analysis of tumor shape can contribute toward accurate prognosis of outcomes. Specifically, we implemented applied computer algorithms to extract 5 shape features from magnetic resonance imaging (MRI) for 22 GBM patients. Then, we determined whether each one of the features can accurately distinguish between patients with good and poor outcomes. We found that that one of the 5 analyzed features showed prognostic value of survival. The prognostic feature describes how well the 3D tumor shape fills its minimum bounding ellipsoid. Specifically, for low values (less or equal than the median) the proportion of patients that survived more than a year was 27% while for high values (higher than median) the proportion of patients with survival of more than 1 year was 82%. The difference was statistically significant (p < 0.05) even though the number of patients analyzed in this pilot study was low. We concluded that computerized, 3D analysis of tumor shape in MRI may strongly contribute to accurate prognostication and stratification of patients for therapy in GBM.
Quantitative Multiparametric MRI Features and PTEN Expression of Peripheral Zone Prostate Cancer: A Pilot Study
McCann, Stephanie M
Jiang, Yulei
Fan, Xiaobing
Wang, Jianing
Antic, Tatjana
Prior, Fred
VanderWeele, David
Oto, Aytekin
American Journal of Roentgenology2016Journal Article, cited 11 times
Website
Radiogenomics
Bolus arrival time and its effect on tissue characterization with dynamic contrast-enhanced magnetic resonance imaging
Mehrtash, Alireza
Gupta, Sandeep N
Shanbhag, Dattesh
Miller, James V
Kapur, Tina
Fennessy, Fiona M
Kikinis, Ron
Fedorov, Andriy
Journal of Medical Imaging2016Journal Article, cited 6 times
Website
QIN Prostate
Algorithm Development
Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI)
PROSTATE
BREAST
Matching the bolus arrival time (BAT) of the arterial input function (AIF) and tissue residue function (TRF) is necessary for accurate pharmacokinetic (PK) modeling of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). We investigated the sensitivity of volume transfer constant ([Formula: see text]) and extravascular extracellular volume fraction ([Formula: see text]) to BAT and compared the results of four automatic BAT measurement methods in characterization of prostate and breast cancers. Variation in delay between AIF and TRF resulted in a monotonous change trend of [Formula: see text] and [Formula: see text] values. The results of automatic BAT estimators for clinical data were all comparable except for one BAT estimation method. Our results indicate that inaccuracies in BAT measurement can lead to variability among DCE-MRI PK model parameters, diminish the quality of model fit, and produce fewer valid voxels in a region of interest. Although the selection of the BAT method did not affect the direction of change in the treatment assessment cohort, we suggest that BAT measurement methods must be used consistently in the course of longitudinal studies to control measurement variability.
Detection of Lung Cancer Nodule on CT scan Images by using Region Growing Method
Mhetre, Rajani R
Sache, Rukhsana G
International Journal of Current Trends in Engineering & Research2016Journal Article, cited 0 times
Website
LIDC-IDRI
Radiomics
Optimization Methods for Medical Image Super Resolution Reconstruction
Super-resolution (SR) concentrates on constructing a high-resolution (HR) image of a scene from two or more sets of low-resolution (LR) images of the same scene. It is the process of combining a sequence of low-resolution (LR) noisy blurred images to produce a higher-resolution image. The reconstruction of high-resolution images is computationally expensive. SR is defined to be an inverse problem that is well-known as ill-conditioned. The SR problem has been reformulated using optimization techniques to define a solution that is a close approximation of the true scene and less sensitive to errors in the observed images. This paper reviews the optimized SR reconstruction approaches and highlights its challenges and limitations. An experiment has been done to compare between bicubic, iterative back-projection (IBP), projected onto convex sets (POCS), total variation (TV) and Gradient descent via sparse representation. The experimental results show that Gradient descent via sparse representation outperforms other optimization techniques.
Extended Modality Propagation: Image Synthesis of Pathological Cases
Cordier N
Delingette H
Le M
Ayache N
IEEE Transactions on Medical Imaging2016Journal Article, cited 18 times
Website
Algorithm Development
Brain modeling
Image generation
Image segmentation
Magnetic resonance imaging (MRI)
Pathology
Training
Tumors
generative model
glioma
medical image simulation
modality synthesis
multi-atlas
patch-based
Security of Multi-frame DICOM Images Using XOR Encryption Approach
Natsheh, QN
Li, B
Gale, AG
Procedia Computer Science2016Journal Article, cited 4 times
Website
Breast-MRI-NACT-Pilot
Security
Transferring medical images using networks is subjected to a wide variety of security risks. Hence, there is a need of a robust and secure mechanism to exchange medical images over the Internet. The Digital Image and Communication in Medicine (DICOM) standard provides attributes for the header data confidentiality but not for the pixel image data. In this paper, a simple and effective encryption approach for pixel data is provided for multi-frame DICOM medical images. The main goal of the proposed approach is to reduce the encryption and decryption time of these images, using Advanced Encryption Standard (AES) where only one image is encrypted and XOR cipher for encrypting the remaining multi-frame DICOM images. The proposed algorithm is evaluated using computational time, normalized correlation, entropy, Peak-Signal-to-Noise-Ratio (PSNR) and histogram analysis. The results show that the proposed approach can reduce the encryption and decryption time and is able to ensure image confidentiality.
Big biomedical image processing hardware acceleration: A case study for K-means and image filtering
Most hospitals today are dealing with the big data problem, as they generate and store petabytes of patient records most of which in form of medical imaging, such as pathological images, CT scans and X-rays in their datacenters. Analyzing such large amounts of biomedical imaging data to enable discovery and guide physicians in personalized care is becoming an important focus of data mining and machine learning algorithms developed for biomedical Informatics (BMI). Algorithms that are developed for BMI heavily rely on complex and computationally intensive machine learning and data mining methods to learn from large data. The high processing demand of big biomedical imaging data has given rise to their implementation in high-end server platforms running software ecosystems that are optimized for dealing with large amount of data including Apache Hadoop and Apache Spark. However, efficient processing of such large amount of imaging data running computational intensive learning methods is becoming a challenging problem using state-of-the-art high performance computing server architectures. To address this challenge, in this paper, we introduce a scalable and efficient hardware acceleration method using low cost commodity FPGAs that is interfaced with a server architecture through a high speed interface. In this work we present a full end-to-end implementation of big data image processing and machine learning applications in a heterogeneous CPU+FPGA architecture. We develop the MapReduce implementation of K-means and Laplacian Filtering in Hadoop Streaming environment that allows developing mapper functions in non-Java based languages suited for interfacing with FPGA-based hardware accelerating environment. We accelerate the mapper functions through hardware+software (HW+SW) co-design. We do a full implementation of the HW+SW mappers on the Zynq FPGA platform. The results show promising kernel speedup of up to 27× for large image data sets. This translate to 7.8× and 1.8× speedup in an end-to-end Hadoop MapReduce implementation of K-mean s and Laplacian Filtering algorithm, respectively.
Big biomedical image processing hardware acceleration: A case study for K-means and image filtering
Neshatpour, Katayoun
Koohi, Arezou
Farahmand, Farnoud
Joshi, Rajiv
Rafatirad, Setareh
Sasan, Avesta
Homayoun, Houman
IEEE International Symposium on Circuits and Systems (ISCAS)2016Conference Paper, cited 7 times
Website
TCGA
K-means clustering
Laplacian filter
Most hospitals today are dealing with the big data problem, as they generate and store petabytes of patient records most of which in form of medical imaging, such as pathological images, CT scans and X-rays in their datacenters. Analyzing such large amounts of biomedical imaging data to enable discovery and guide physicians in personalized care is becoming an important focus of data mining and machine learning algorithms developed for biomedical Informatics (BMI). Algorithms that are developed for BMI heavily rely on complex and computationally intensive machine learning and data mining methods to learn from large data. The high processing demand of big biomedical imaging data has given rise to their implementation in high-end server platforms running software ecosystems that are optimized for dealing with large amount of data including Apache Hadoop and Apache Spark. However, efficient processing of such large amount of imaging data running computational intensive learning methods is becoming a challenging problem using state-of-the-art high performance computing server architectures. To address this challenge, in this paper, we introduce a scalable and efficient hardware acceleration method using low cost commodity FPGAs that is interfaced with a server architecture through a high speed interface. In this work we present a full end-to-end implementation of big data image processing and machine learning applications in a heterogeneous CPU+FPGA architecture. We develop the MapReduce implementation of K-means and Laplacian Filtering in Hadoop Streaming environment that allows developing mapper functions in non-Java based languages suited for interfacing with FPGA-based hardware accelerating environment. We accelerate the mapper functions through hardware+software (HW+SW) co-design. We do a full implementation of the HW+SW mappers on the Zynq FPGA platform. The results show promising kernel speedup of up to 27× for large image data sets. This translate to 7.8× and 1.8× speedup in an end-to-end Hadoop MapReduce implementation of K-mean s and Laplacian Filtering algorithm, respectively.
Efficient Colorization of Medical Imaging based on Colour Transfer Method
Uma Proposta Para Utilização De Workflows Científicos Para A Definição De Pipelines Para A Recuperação De Imagens Médicas Por Conteúdo Em Um Ambiente Distribuído
This paper presents a method for complexity reduction in medical image encoding that exploits the structure of medical images. The amount of texture detail and structure in medical images depends on the modality used to capture the image and the body part captured by that image. The proposed approach was evaluated using Computed Radiography (CR) modality, commonly known as x-ray imaging, and three body parts. The proposed method essentially reduces the number of CU partitions evaluated as well as the number of intra prediction modes for each evaluated partition. Evaluation using the HEVC reference software (HM) 16.4 and lossless intra coding shows an average reduction of 52.47% in encoding time with a negligible penalty of up to 0.22%, increase in compressed file size.
The recognizability of facial images extracted from publically available medical scans raises patient privacy concerns. This study examined how accurately facial images extracted from computed tomography (CT) scans are objectively matched with corresponding photographs of the scanned individuals. The test subjects were 128 adult Americans ranging in age from 18 to 60 years, representing both sexes and three self-identified population (ancestral descent) groups (African, European, and Hispanic). Using facial recognition software, the 2D images of the extracted facial models were compared for matches against five differently sized photo galleries. Depending on the scanning protocol and gallery size, in 6-61 % of the cases, a correct life photo match for a CT-derived facial image was the top ranked image in the generated candidate lists, even when blind searching in excess of 100,000 images. In 31-91 % of the cases, a correct match was located within the top 50 images. Few significant differences (p > 0.05) in match rates were observed between the sexes or across the three age cohorts. Highly significant differences (p < 0.01) were, however, observed across the three ancestral cohorts and between the two CT scanning protocols. Results suggest that the probability of a match between a facial image extracted from a medical scan and a photograph of the individual is moderately high. The facial image data inherent in commonly employed medical imaging modalities may need to consider a potentially identifiable form of "comparable" facial imagery and protected as such under patient privacy legislation.
An Approach Toward Automatic Classification of Tumor Histopathology of Non–Small Cell Lung Cancer Based on Radiomic Features
Patil, Ravindra
Mahadevaiah, Geetha
Dekker, Andre
Tomography: a journal for imaging research2016Journal Article, cited 2 times
Website
NSCLC-Radiomics
lung cancer
tumor histopathology
Radiomics
Lung cancer incidence and mortality in National Lung Screening Trial participants who underwent low-dose CT prevalence screening: a retrospective cohort analysis of a randomised, multicentre, diagnostic screening trial
Patz Jr, Edward F
Greco, Erin
Gatsonis, Constantine
Pinsky, Paul
Kramer, Barnett S
Aberle, Denise R
The Lancet Oncology2016Journal Article, cited 67 times
Website
NLST
lung
LDCT
Auto Diagnostics of Lung Nodules Using Minimal Characteristics Extraction Technique
Peña, Diego M
Luo, Shouhua
Abdelgader, Abdeldime
Diagnostics2016Journal Article, cited 6 times
Website
LungCT-Diagnosis
SPIE-AAPM Lung CT Challenge
Segmentation
Computer-aided detection (CAD) systems provide useful tools and an advantageous process to physicians aiming to detect lung nodules. This paper develops a method composed of four processes for lung nodule detection. The first step employs image acquisition and pre-processing techniques to isolate the lungs from the rest of the body. The second stage involves the segmentation process using a 2D algorithm to affect every layer of a scan eliminating non-informative structures inside the lungs, and a 3D blob algorithm associated with a connectivity algorithm to select possible nodule shape candidates. The combinations of these algorithms efficiently eliminate the high rates of false positives. The third process extracts eight minimal representative characteristics of the possible candidates. The final step utilizes a support vector machine for classifying the possible candidates into nodules and non-nodules depending on their features. As the objective is to find nodules bigger than 4mm, the proposed approach demonstrated quite encouraging results. Among 65 computer tomography (CT) scans, 94.23% of sensitivity and 84.75% in specificity were obtained. The accuracy of these two results was 89.19% taking into consideration that 45 scans were used for testing and 20 for training. The rate of false positives was 0.2 per scan.
Prediction of lung cancer incidence on the low-dose computed tomography arm of the National Lung Screening Trial: A dynamic Bayesian network
Petousis, Panayiotis
Han, Simon X
Aberle, Denise
Bui, Alex AT
Artificial intelligence in medicine2016Journal Article, cited 13 times
Website
NLST
Dynamic Bayesian Network
LDCT
Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings
Prasanna, Prateek
Patel, Jay
Partovi, Sasan
Madabhushi, Anant
Tiwari, Pallavi
European Radiology2016Journal Article, cited 45 times
Website
TCGA-GBM
Radiomics
Identification of biomarkers for pseudo and true progression of GBM based on radiogenomics study
Qian, Xiaohua
Tan, Hua
Zhang, Jian
Liu, Keqin
Yang, Tielin
Wang, Maode
Debinskie, Waldemar
Zhao, Weilin
Chan, Michael D
Zhou, Xiaobo
OncotargetOncotarget2016Journal Article, cited 8 times
Website
TCGA-GBM
Radiogenomics
The diagnosis for pseudoprogression (PsP) and true tumor progression (TTP) of GBM is a challenging task in clinical practices. The purpose of this study is to identify potential genetic biomarkers associated with PsP and TTP based on the clinical records, longitudinal imaging features, and genomics data. We are the first to introduce the radiogenomics approach to identify candidate genes for PsP and TTP of GBM. Specifically, a novel longitudinal sparse regression model was developed to construct the relationship between gene expression and imaging features. The imaging features were extracted from tumors along the longitudinal MRI and provided diagnostic information of PsP and TTP. The 33 candidate genes were selected based on their association with the imaging features, reflecting their relation with the development of PsP and TTP. We then conducted biological relevance analysis for 33 candidate genes to identify the potential biomarkers, i.e., Interferon regulatory factor (IRF9) and X-ray repair cross-complementing gene (XRCC1), which were involved in the cancer suppression and prevention, respectively. The IRF9 and XRCC1 were further independently validated in the TCGA data. Our results provided the first substantial evidence that IRF9 and XRCC1 can serve as the potential biomarkers for the development of PsP and TTP.
Integrative Analysis of mRNA, microRNA, and Protein Correlates of Relative Cerebral Blood Volume Values in GBM Reveals the Role for Modulators of Angiogenesis and Tumor Proliferation
Rao, Arvind
Manyam, Ganiraju
Rao, Ganesh
Jain, Rajan
Cancer Informatics2016Journal Article, cited 5 times
Website
TCGA-GBM
angiogenesis
data integration
imaging-genomics
pathway analysis
perfusion imaging
rCBV
Dynamic susceptibility contrast-enhanced magnetic resonance imaging is routinely used to provide hemodynamic assessment of brain tumors as a diagnostic as well as a prognostic tool. Recently, it was shown that the relative cerebral blood volume (rCBV), obtained from the contrast-enhancing as well as -nonenhancing portion of glioblastoma (GBM), is strongly associated with overall survival. In this study, we aim to characterize the genomic correlates (microRNA, messenger RNA, and protein) of this vascular parameter. This study aims to provide a comprehensive radiogenomic and radioproteomic characterization of the hemodynamic phenotype of GBM using publicly available imaging and genomic data from the Cancer Genome Atlas GBM cohort. Based on this analysis, we identified pathways associated with angiogenesis and tumor proliferation underlying this hemodynamic parameter in GBM.
A combinatorial radiographic phenotype may stratify patient survival and be associated with invasion and proliferation characteristics in glioblastoma
Rao, Arvind
Rao, Ganesh
Gutman, David A
Flanders, Adam E
Hwang, Scott N
Rubin, Daniel L
Colen, Rivka R
Zinn, Pascal O
Jain, Rajan
Wintermark, Max
Journal of neurosurgery2016Journal Article, cited 19 times
Website
TCGA-GBM
Radiogenomics
Radiomic features
OBJECTIVE Individual MRI characteristics (e.g., volume) are routinely used to identify survival-associated phenotypes for glioblastoma (GBM). This study investigated whether combinations of MRI features can also stratify survival. Furthermore, the molecular differences between phenotype-induced groups were investigated.
METHODS Ninety-two patients with imaging, molecular, and survival data from the TCGA (The Cancer Genome Atlas)GBM collection were included in this study. For combinatorial phenotype analysis, hierarchical clustering was used. Groups were defined based on a cutpoint obtained via tree-based partitioning. Furthermore, differential expression analysis of microRNA (miRNA) and mRNA expression data was performed using GenePattern Suite. Functional analysis of the resulting genes and miRNAs was performed using Ingenuity Pathway Analysis. Pathway analysis was performed using Gene Set Enrichment Analysis.
RESULTS Clustering analysis reveals that image-based grouping of the patients is driven by 3 features: volume-class, hemorrhage, and T1/FLAIR-envelope ratio. A combination of these features stratifies survival in a statistically significant manner. A cutpoint analysis yields a significant survival difference in the training set (median survival difference: 12 months, p = 0.004) as well as a validation set (p = 0.0001). Specifically, a low value for any of these 3 features indicates favorable survival characteristics. Differential expression analysis between cutpoint-induced groups suggests that several immune-associated (natural killer cell activity, T-cell lymphocyte differentiation) and metabolism-associated (mitochondrial activity, oxidative phosphorylation) pathways underlie the transition of this phenotype. Integrating data for mRNA and miRNA suggests the roles of several genes regulating proliferation and invasion.
CONCLUSIONS A 3-way combination of MRI phenotypes may be capable of stratifying survival in GBM. Examination of molecular processes associated with groups created by this combinatorial phenotype suggests the role of biological processes associated with growth and invasion characteristics.
Automated pulmonary nodule CT image characterization in lung cancer screening
Reeves, Anthony P
Xie, Yiting
Jirapatnakul, Artit
International Journal of Computer Assisted Radiology and Surgery2016Journal Article, cited 19 times
Website
NLST
Radiomic feature
Segmentation of candidates for pulmonary nodules based on computed tomorography
Abstract: The present work presents a methodology for automatic segmentation of pulmonary solitary nodules candidates using cellular automaton. Early detection of pulmonary solitary nodules that may become cancer is essential
for survival of patients. To assist the experts in the identification of these nodules are being developed computer aided
systems that aim to automate the work of detection and classification. The segmentation stage plays a key role in automatic detection of lung nodules, as it allows separating the image elements in regions, which have the same property or
characteristic. The methodology used in the article includes acquisition of images, noise elimination, pulmonary parenchyma segmentation and segmentation of pulmonary solitary nodules candidates. The tests were conducted using set
of images of the LIDC-IDRI base, containing 739 nodules. The test results show a sensitivity of 95.66% of the nodules.
DEMARCATE: Density-based Magnetic Resonance Image Clustering for Assessing Tumor Heterogeneity in Cancer
Saha, Abhijoy
Banerjee, Sayantan
Kurtek, Sebastian
Narang, Shivali
Lee, Joonsang
Rao, Ganesh
Martinez, Juan
Bharath, Karthik
Rao, Arvind UK
Baladandayuthapani, Veerabhadran
NeuroImage: Clinical2016Journal Article, cited 4 times
Website
TCGA-GBM
Radiomics
Radiogenomics
Semi-automatic segmentation
K-means clustering
Principal component analysis (PCA)
Tumor heterogeneity is a crucial area of cancer research wherein inter- and intra-tumor differences are investigated to assess and monitor disease development and progression, especially in cancer. The proliferation of imaging and linked genomic data has enabled us to evaluate tumor heterogeneity on multiple levels. In this work, we examine magnetic resonance imaging (MRI) in patients with brain cancer to assess image-based tumor heterogeneity. Standard approaches to this problem use scalar summary measures (e.g., intensity-based histogram statistics) that do not adequately capture the complete and finer scale information in the voxel-level data. In this paper, we introduce a novel technique, DEMARCATE (DEnsity-based MAgnetic Resonance image Clustering for Assessing Tumor hEterogeneity) to explore the entire tumor heterogeneity density profiles (THDPs) obtained from the full tumor voxel space. THDPs are smoothed representations of the probability density function of the tumor images. We develop tools for analyzing such objects under the Fisher-Rao Riemannian framework that allows us to construct metrics for THDP comparisons across patients, which can be used in conjunction with standard clustering approaches. Our analyses of The Cancer Genome Atlas (TCGA) based Glioblastoma dataset reveal two significant clusters of patients with marked differences in tumor morphology, genomic characteristics and prognostic clinical outcomes. In addition, we see enrichment of image-based clusters with known molecular subtypes of glioblastoma multiforme, which further validates our representation of tumor heterogeneity and subsequent clustering techniques.
Wwox–Brca1 interaction: role in DNA repair pathway choice
Schrock, MS
Batar, B
Lee, J
Druck, T
Ferguson, B
Cho, JH
Akakpo, K
Hagrass, H
Heerema, NA
Xia, F
Oncogene2016Journal Article, cited 12 times
Website
Radiogenomics
REMBRANDT
Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis
Song, Jiangdian
Liu, Zaiyi
Zhong, Wenzhao
Huang, Yanqi
Ma, Zelan
Dong, Di
Liang, Changhong
Tian, Jie
Sci RepScientific reports2016Journal Article, cited 14 times
Website
Radiomics
NSCLC
Lung
MR and mammographic imaging features of HER2-positive breast cancers according to hormone receptor status: a retrospective comparative study
Song, Sung Eun
Bae, Min Sun
Chang, Jung Min
Cho, Nariya
Ryu, Han Suk
Moon, Woo Kyung
Acta Radiologica2016Journal Article, cited 2 times
Website
TCGA-BRCA
Background Human epidermal growth factor receptor 2-positive (HER2+) breast cancer has two distinct subtypes according to hormone receptor (HR) status. Survival, pattern of recurrence, and treatment response differ between HR-/HER2+ and HR+/HER2+ cancers. Purpose To investigate imaging and clinicopathologic features of HER2+ cancers and their correlation with HR expression. Material and Methods Between 2011 and 2013, 252 consecutive patients with 252 surgically confirmed HER2+ cancers (125 HR- and 127 HR+) were included. Two experienced breast radiologists blinded to the clinicopathologic findings reviewed the mammograms and magnetic resonance (MR) images using the BI-RADS lexicon. Tumor kinetic features were acquired by computer-aided detection (CAD). The imaging and clinicopathologic features of 125 HR-/HER2+ cancers were compared with those of 127 HR+/HER2+ cancers. Association between the HR status and each feature was assessed. Results Multiple logistic regression analysis showed that circumscribed mass margin (odds ratio [OR], 4.73; P < 0.001), associated non-mass enhancement (NME) on MR images (OR, 3.29; P = 0.001), high histologic grade (OR, 3.89; P = 0.002), high Ki-67 index (OR, 3.06; P = 0.003), and older age (OR, 2.43; P = 0.006) remained independent indicators associated with HR-/HER2+ cancers. Between the two HER2+ subtypes, there were no differences in mammographic imaging presentations and calcification features and MR kinetic features by a CAD. Conclusion HER2+ breast cancers have different MR imaging (MRI) phenotypes and clinicopathologic feature according to HR status. MRI features related to HR and HER2 status have the potential to be used for the diagnosis and treatment decisions in HER2+ breast cancer patients.
Differential localization of glioblastoma subtype: implications on glioblastoma pathogenesis
Steed, Tyler C
Treiber, Jeffrey M
Patel, Kunal
Ramakrishnan, Valya
Merk, Alexander
Smith, Amanda R
Carter, Bob S
Dale, Anders M
Chow, LM
Chen, Clark C
OncotargetOncotarget2016Journal Article, cited 8 times
Website
TCGA-GBM
Magnetic Resonance Imaging (MRI)
BRAIN
Glioblastoma
REMBRANDT
INTRODUCTION: The subventricular zone (SVZ) has been implicated in the pathogenesis of glioblastoma. Whether molecular subtypes of glioblastoma arise from unique niches of the brain relative to the SVZ remains largely unknown. Here, we tested whether these subtypes of glioblastoma occupy distinct regions of the cerebrum and examined glioblastoma localization in relation to the SVZ. METHODS: Pre-operative MR images from 217 glioblastoma patients from The Cancer Imaging Archive were segmented automatically into contrast enhancing (CE) tumor volumes using Iterative Probabilistic Voxel Labeling (IPVL). Probabilistic maps of tumor location were generated for each subtype and distances were calculated from the centroid of CE tumor volumes to the SVZ. Glioblastomas that arose in a Genetically Modified Murine Model (GEMM) model were also analyzed with regard to SVZ distance and molecular subtype. RESULTS: Classical and mesenchymal glioblastomas were more diffusely distributed and located farther from the SVZ. In contrast, proneural and neural glioblastomas were more likely to be located in closer proximity to the SVZ. Moreover, in a GFAP-CreER; PtenloxP/loxP; Trp53loxP/loxP; Rb1loxP/loxP; Rbl1-/- GEMM model of glioblastoma where tumor can spontaneously arise in different regions of the cerebrum, tumors that arose near the SVZ were more likely to be of proneural subtype (p < 0.0001). CONCLUSIONS: Glioblastoma subtypes occupy different regions of the brain and vary in proximity to the SVZ. These findings harbor implications pertaining to the pathogenesis of glioblastoma subtypes.
Comparison of Safety Margin Generation Concepts in Image Guided Radiotherapy to Account for Daily Head and Neck Pose Variations
Stoll, Markus
Stoiber, Eva Maria
Grimm, Sarah
Debus, Jürgen
Bendl, Rolf
Giske, Kristina
PLoS One2016Journal Article, cited 2 times
Website
QIN-HEADNECK
Radiation Therapy
PURPOSE: Intensity modulated radiation therapy (IMRT) of head and neck tumors allows a precise conformation of the high-dose region to clinical target volumes (CTVs) while respecting dose limits to organs a risk (OARs). Accurate patient setup reduces translational and rotational deviations between therapy planning and therapy delivery days. However, uncertainties in the shape of the CTV and OARs due to e.g. small pose variations in the highly deformable anatomy of the head and neck region can still compromise the dose conformation. Routinely applied safety margins around the CTV cause higher dose deposition in adjacent healthy tissue and should be kept as small as possible. MATERIALS AND METHODS: In this work we evaluate and compare three approaches for margin generation 1) a clinically used approach with a constant isotropic 3 mm margin, 2) a previously proposed approach adopting a spatial model of the patient and 3) a newly developed approach adopting a biomechanical model of the patient. All approaches are retrospectively evaluated using a large patient cohort of over 500 fraction control CT images with heterogeneous pose changes. Automatic methods for finding landmark positions in the control CT images are combined with a patient specific biomechanical finite element model to evaluate the CTV deformation. RESULTS: The applied methods for deformation modeling show that the pose changes cause deformations in the target region with a mean motion magnitude of 1.80 mm. We found that the CTV size can be reduced by both variable margin approaches by 15.6% and 13.3% respectively, while maintaining the CTV coverage. With approach 3 an increase of target coverage was obtained. CONCLUSION: Variable margins increase target coverage, reduce risk to OARs and improve healthy tissue sparing at the same time.
Breast cancer molecular subtype classifier that incorporates MRI features
Sutton, Elizabeth J
Dashevsky, Brittany Z
Oh, Jung Hun
Veeraraghavan, Harini
Apte, Aditya P
Thakur, Sunitha B
Morris, Elizabeth A
Deasy, Joseph O
Journal of Magnetic Resonance Imaging2016Journal Article, cited 34 times
Website
Radiomics
Imaging features
BREAST
Machine learning
Radiogenomics
Purpose: To use features extracted from magnetic resonance (MR) images and a machine-learning method to assist in differentiating breast cancer molecular subtypes.
Materials and Methods: This retrospective Health Insurance Portability and Accountability Act (HIPAA)-compliant study received Institutional Review Board (IRB) approval. We identified 178 breast cancer patients between 2006-2011 with: 1) ERPR+ (n=95, 53.4%), ERPR-/HER2+ (n=35, 19.6%), or triple negative (TN, n=48, 27.0%) invasive ductal carcinoma (IDC), and 2) preoperative breast MRI at 1.5T or 3.0T. Shape, texture, and histogram-based features were extracted from each tumor contoured on pre- and three postcontrast MR images using in-house software. Clinical and pathologic features were also collected. Machine-learning-based (support vector machines) models were used to identify significant imaging features and to build models that predict IDC subtype. Leave-one-out cross-validation (LOOCV) was used to avoid model overfitting. Statistical significance was determined using the Kruskal-Wallis test.
Results: Each support vector machine fit in the LOOCV process generated a model with varying features. Eleven out of the top 20 ranked features were significantly different between IDC subtypes with P < 0.05. When the top nine pathologic and imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 83.4%. The combined pathologic and imaging model's accuracy for each subtype was 89.2% (ERPR+), ;63.6% (ERPR-/HER2+), and 82.5% (TN). When only the top nine imaging features were incorporated, the predictive model distinguished IDC subtypes with an overall accuracy on LOOCV of 71.2%. The combined pathologic and imaging model's accuracy for each subtype was 69.9% (ERPR+), 62.9% (ERPR-/HER2+), and 81.0% (TN).
Conclusion: We developed a machine-learning-based predictive model using features extracted from MRI that can distinguish IDC subtypes with significant predictive power.
Automated detection of glioblastoma tumor in brain magnetic imaging using ANFIS classifier
Thirumurugan, P
Ramkumar, D
Batri, K
Sundhara Raja, D
International Journal of Imaging Systems and TechnologyInternational Journal of Imaging Systems and Technology2016Journal Article, cited 3 times
Website
Algorithm Development
BRAIN
Classification
This article proposes a novel and efficient methodology for the detection of Glioblastoma tumor in brain MRI images. The proposed method consists of the following stages as preprocessing, Non-subsampled Contourlet transform (NSCT), feature extraction and Adaptive neuro fuzzy inference system classification. Euclidean direction algorithm is used to remove the impulse noise from the brain image during image acquisition process. NSCT decomposes the denoised brain image into approximation bands and high frequency bands. The features mean, standard deviation and energy are computed for the extracted coefficients and given to the input of the classifier. The classifier classifies the brain MRI image into normal or Glioblastoma tumor image based on the feature set. The proposed system achieves 99.8% sensitivity, 99.7% specificity, and 99.8% accuracy with respect to the ground truth images available in the dataset.
Prognosis classification in glioblastoma multiforme using multimodal MRI derived heterogeneity textural features: impact of pre-processing choices
Simultaneous encryption and compression of medical images based on optimized tensor compressed sensing with 3D Lorenz
Wang, Qingzhu
Chen, Xiaoming
Wei, Mengying
Miao, Zhuang
BioMedical Engineering OnLine2016Journal Article, cited 1 times
Website
LIDC-IDRI
Single NMR image super-resolution based on extreme learning machine
Wang, Zhiqiong
Xin, Junchang
Wang, Zhongyang
Tian, Shuo
Qiu, Xuejun
Physica Medica2016Journal Article, cited 0 times
Website
REMBRANDT
RIDER NEURO MRI
TCGA-GBM
TCGA-LGG
BRAIN
Introduction: The performance limitation of MRI equipment and higher resolution demand of NMR images from radiologists have formed a strong contrast. Therefore, it is important to study the super resolution algorithm suitable for NMR images, using low costs software to replace the expensive equipment-updating.
Methods and materials: Firstly, a series of NMR images are obtained from original NMR images with original noise to the lowest resolution images with the highest noise. Then, based on extreme learning machine, the mapping relation model is constructed from lower resolution NMR images with higher noise to higher resolution NMR images with lower noise in each pair of adjacent images in the obtained image sequence. Finally, the optimal mapping model is established by the ensemble way to reconstruct the higher resolution NMR images with lower noise on the basis of original resolution NMR images with original noise. Experiments are carried out by 990111 NMR brain images in datasets NITRC, REMBRANDT, RIDER NEURO MRI, TCGA-GBM and TCGA-LGG.
Results: The performance of proposed method is compared with three approaches through 7 indexes, and the experimental results show that our proposed method has a significant improvement.
Discussion: Since our method considers the influence of the noise, it has 20% higher in Peak-Signal-to-Noise-Ratio comparison. As our method is sensitive to details, and has a better characteristic retention, it has higher image quality upgrade of 15% in the additional evaluation. Finally, since extreme learning machine has a celerity learning speed, our method is 46.1% faster.
Keywords: Extreme learning machine; NMR; Single image; Super-resolution.
Evaluating long-term outcomes via computed tomography in lung cancer screening
Wu, D
Liu, R
Levitt, B
Riley, T
Baumgartner, KB
J Biom Biostat2016Journal Article, cited 0 times
NLST
LDCT
lung
Cancer Screening
Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy
Wu, Jia
Gong, Guanghua
Cui, Yi
Li, Ruijiang
Journal of Magnetic Resonance Imaging2016Journal Article, cited 43 times
Website
Algorithm Development
BREAST
PURPOSE: To predict pathological response of breast cancer to neoadjuvant chemotherapy (NAC) based on quantitative, multiregion analysis of dynamic contrast enhancement magnetic resonance imaging (DCE-MRI). MATERIALS AND METHODS: In this Institutional Review Board-approved study, 35 patients diagnosed with stage II/III breast cancer were retrospectively investigated using 3T DCE-MR images acquired before and after the first cycle of NAC. First, principal component analysis (PCA) was used to reduce the dimensionality of the DCE-MRI data with high temporal resolution. We then partitioned the whole tumor into multiple subregions using k-means clustering based on the PCA-defined eigenmaps. Within each tumor subregion, we extracted four quantitative Haralick texture features based on the gray-level co-occurrence matrix (GLCM). The change in texture features in each tumor subregion between pre- and during-NAC was used to predict pathological complete response after NAC. RESULTS: Three tumor subregions were identified through clustering, each with distinct enhancement characteristics. In univariate analysis, all imaging predictors except one extracted from the tumor subregion associated with fast washout were statistically significant (P < 0.05) after correcting for multiple testing, with area under the receiver operating characteristic (ROC) curve (AUC) or AUCs between 0.75 and 0.80. In multivariate analysis, the proposed imaging predictors achieved an AUC of 0.79 (P = 0.002) in leave-one-out cross-validation. This improved upon conventional imaging predictors such as tumor volume (AUC = 0.53) and texture features based on whole-tumor analysis (AUC = 0.65). CONCLUSION: The heterogeneity of the tumor subregion associated with fast washout on DCE-MRI predicted pathological response to NAC in breast cancer. J. Magn. Reson. Imaging 2016;44:1107-1115.
Correlation coefficient based supervised locally linear embedding for pulmonary nodule recognition
Wu, Panpan
Xia, Kewen
Yu, Hengyong
Computer Methods and Programs in Biomedicine2016Journal Article, cited 5 times
Website
Algorithm Development
Classification
LIDC-IDRI
Computer Aided Detection (CADe)
Machine Learning
BACKGROUND AND OBJECTIVE: Dimensionality reduction techniques are developed to suppress the negative effects of high dimensional feature space of lung CT images on classification performance in computer aided detection (CAD) systems for pulmonary nodule detection. METHODS: An improved supervised locally linear embedding (SLLE) algorithm is proposed based on the concept of correlation coefficient. The Spearman's rank correlation coefficient is introduced to adjust the distance metric in the SLLE algorithm to ensure that more suitable neighborhood points could be identified, and thus to enhance the discriminating power of embedded data. The proposed Spearman's rank correlation coefficient based SLLE (SC(2)SLLE) is implemented and validated in our pilot CAD system using a clinical dataset collected from the publicly available lung image database consortium and image database resource initiative (LICD-IDRI). Particularly, a representative CAD system for solitary pulmonary nodule detection is designed and implemented. After a sequential medical image processing steps, 64 nodules and 140 non-nodules are extracted, and 34 representative features are calculated. The SC(2)SLLE, as well as SLLE and LLE algorithm, are applied to reduce the dimensionality. Several quantitative measurements are also used to evaluate and compare the performances. RESULTS: Using a 5-fold cross-validation methodology, the proposed algorithm achieves 87.65% accuracy, 79.23% sensitivity, 91.43% specificity, and 8.57% false positive rate, on average. Experimental results indicate that the proposed algorithm outperforms the original locally linear embedding and SLLE coupled with the support vector machine (SVM) classifier. CONCLUSIONS: Based on the preliminary results from a limited number of nodules in our dataset, this study demonstrates the great potential to improve the performance of a CAD system for nodule detection using the proposed SC(2)SLLE.
Automatic 3D Mesh-Based Centerline Extraction from a Tubular Geometry Form
Yahya-Zoubir, Bahia
Hamami, Latifa
Saadaoui, Llies
Ouared, Rafik
Information Technology And Control2016Journal Article, cited 0 times
Website
CT Colonography
Lung cancer deaths in the National Lung Screening Trial attributed to nonsolid nodules
Yip, Rowena
Yankelevitz, David F
Hu, Minxia
Li, Kunwei
Xu, Dong Ming
Jirapatnakul, Artit
Henschke, Claudia I
RadiologyRadiology2016Journal Article, cited 0 times
NLST
Non-solid nodules
lung
Cancer Screening
Co-Segmentation Methods for Improving Tumor Target Delineation in PET-CT Images
Segmentation of gliomas in pre-operative and post-operative multimodal magnetic resonance imaging volumes based on a hybrid generative-discriminative framework
Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system
Zhu, H
Chen, H
Lin, Z
Shi, G
Lin, X
Wu, Z
Zhang, X
Zhang, X
OncotargetOncotarget2016Journal Article, cited 3 times
Website
TCGA-KIRC
PADUA scoring system
Clear cell renal cell carcinoma (ccRCC)
KIDNEY
Computed Tomography (CT)
Although the preoperative aspects and dimensions used for the PADUA scoring system were successfully applied in macroscopic clinical practice for renal tumor, the relevant molecular genetic basis remained unclear. To uncover meaningful correlations between the genetic aberrations and radiological features, we enrolled 112 patients with clear cell renal cell carcinoma (ccRCC) whose clinicopathological data, genomics data and CT data were obtained from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA). Overall PADUA score and several radiological features included in the PADUA system were assigned for each ccRCC. Despite having observed no significant association between the gene mutation frequency and the overall PADUA score, correlations between gene mutations and a few radiological features (tumor rim location and tumor size) were identified. A significant association between rim location and miRNA molecular subtypes was also observed. Survival analysis revealed that tumor size > 7 cm was significantly associated with poor survival. In addition, Gene Set Enrichment Analysis (GSEA) on mRNA expression revealed that the high PADUA score was related to numerous cancer-related networks, especially epithelial to mesenchymal transition (EMT) related pathways. This preliminary analysis of ccRCC revealed meaningful correlations between PADUA anatomical features and molecular basis including genomic aberrations and molecular subtypes.
Diffusion Weighted Magnetic Resonance Imaging Radiophenotypes and Associated Molecular Pathways in Glioblastoma
Zinn, Pascal O
Hatami, Masumeh
Youssef, Eslam
Thomas, Ginu A
Luedi, Markus M
Singh, Sanjay K
Colen, Rivka R
Neurosurgery2016Journal Article, cited 2 times
Website
TCGA-GBM
Radiogenomics
Glioblastoma Multiforme (GBM)
3D Slicer
Magnetic resonance imaging (MRI)
A review of lung cancer screening and the role of computer-aided detection
Al Mohammad, B
Brennan, PC
Mello-Thoms, C
Clinical Radiology2017Journal Article, cited 23 times
Website
LIDC-IDRI
Lung screening
Breast Cancer Diagnostic System Based on MR images Using KPCA-Wavelet Transform and Support Vector Machine
Automatic intensity windowing of mammographic images based on a perceptual metric
Albiol, Alberto
Corbi, Alberto
Albiol, Francisco
Medical Physics2017Journal Article, cited 0 times
Website
Algorithm Development
Computer Aided Diagnosis (CADx)
BI-RADS
mutual information
Mammography
Gabor filter
BREAST
Radiomic feature
PURPOSE: Initial auto-adjustment of the window level WL and width WW applied to mammographic images. The proposed intensity windowing (IW) method is based on the maximization of the mutual information (MI) between a perceptual decomposition of the original 12-bit sources and their screen displayed 8-bit version. Besides zoom, color inversion and panning operations, IW is the most commonly performed task in daily screening and has a direct impact on diagnosis and the time involved in the process. METHODS: The authors present a human visual system and perception-based algorithm named GRAIL (Gabor-relying adjustment of image levels). GRAIL initially measures a mammogram's quality based on the MI between the original instance and its Gabor-filtered derivations. From this point on, the algorithm performs an automatic intensity windowing process that outputs the WL/WW that best displays each mammogram for screening. GRAIL starts with the default, high contrast, wide dynamic range 12-bit data, and then maximizes the graphical information presented in ordinary 8-bit displays. Tests have been carried out with several mammogram databases. They comprise correlations and an ANOVA analysis with the manual IW levels established by a group of radiologists. A complete MATLAB implementation of GRAIL is available at https://github.com/TheAnswerIsFortyTwo/GRAIL. RESULTS: Auto-leveled images show superior quality both perceptually and objectively compared to their full intensity range and compared to the application of other common methods like global contrast stretching (GCS). The correlations between the human determined intensity values and the ones estimated by our method surpass that of GCS. The ANOVA analysis with the upper intensity thresholds also reveals a similar outcome. GRAIL has also proven to specially perform better with images that contain micro-calcifications and/or foreign X-ray-opaque elements and with healthy BI-RADS A-type mammograms. It can also speed up the initial screening time by a mean of 4.5 s per image. CONCLUSIONS: A novel methodology is introduced that enables a quality-driven balancing of the WL/WW of mammographic images. This correction seeks the representation that maximizes the amount of graphical information contained in each image. The presented technique can contribute to the diagnosis and the overall efficiency of the breast screening session by suggesting, at the beginning, an optimal and customized windowing setting for each mammogram.
Robust Detection of Circles in the Vessel Contours and Application to Local Probability Density Estimation
To interpret a breast MRI study, a radiologist has to examine over 1000 images, and integrate spatial and temporal information from multiple sequences. The automated detection and classification of suspicious lesions can help reduce the workload and improve accuracy. We describe a hybrid mass-detection algorithm that combines unsupervised candidate detection with deep learning-based classification. The detection algorithm first identifies image-salient regions, as well as regions that are cross-salient with respect to the contralateral breast image. We then use a convolutional neural network (CNN) to classify the detected candidates into true-positive and false-positive masses. The network uses a novel multi-channel image representation; this representation encompasses information from the anatomical and kinetic image features, as well as saliency maps. We evaluated our algorithm on a dataset of MRI studies from 171 patients, with 1957 annotated slices of malignant (59%) and benign (41%) masses. Unsupervised saliency-based detection provided a sensitivity of 0.96 with 9.7 false-positive detections per slice. Combined with CNN classification, the number of false positive detections dropped to 0.7 per slice, with 0.85 sensitivity. The multi-channel representation achieved higher classification performance compared to single-channel images. The combination of domain-specific unsupervised methods and general-purpose supervised learning offers advantages for medical imaging applications, and may improve the ability of automated algorithms to assist radiologists.
Fast wavelet based image characterization for content based medical image retrieval
A large collection of medical images surrounds health care centers and hospitals. Medical images produced by different modalities like magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and X-rays have increased incredibly with the advent of latest technologies for image acquisition. Retrieving clinical images of interest from these large data sets is a thought-provoking and demanding task. In this paper, a fast wavelet based medical image retrieval system is proposed that can aid physicians in the identification or analysis of medical images. The image signature is calculated using kurtosis and standard deviation as features. A possible use case is when the radiologist has some suspicion on diagnosis and wants further case histories, the acquired clinical images are sent (e.g. MRI images of brain) as a query to the content based medical image retrieval system. The system is tuned to retrieve the top most relevant images to the query. The proposed system is computationally efficient and more accurate in terms of the quality of retrieved images.
Analysis of Classification Methods for Diagnosis of Pulmonary Nodules in CT Images
Baboo, Capt Dr S Santhosh
Iyyapparaj, E
IOSR Journal of Electrical and Electronics Engineering2017Journal Article, cited 0 times
Website
LIDC-IDRI
Computed Tomography (CT)
LUNG
Classification
Random Forest
Computer Aided Detection (CADe)
The main aim of this work is to propose a novel Computer-aided detection (CAD) system based on a Contextual clustering combined with region growing for assisting radiologists in early identification of lung cancer from computed tomography(CT) scans. Instead of using conventional thresholding approach, this proposed work uses Contextual Clustering which yields a more accurate segmentation of the lungs from the chest volume. Following segmentation GLCM features are extracted which are then classified using three different classifiers namely Random forest, SVM and k-NN.
Detection of Brain Tumour in MRI Scan Images using Tetrolet Transform and SVM Classifier
Babu, B Shoban
Varadarajan, S
Indian Journal of Science and Technology2017Journal Article, cited 1 times
Website
REMBRANDT
Classification
Support Vector Machine (SVM)
Brain
BIOMEDICAL IMAGE RETRIEVAL USING LBWP
Babu, Joyce Sarah
Mathew, Soumya
Simon, Rini
International Research Journal of Engineering and Technology2017Journal Article, cited 0 times
Website
2017Conference Proceedings, cited 2030 times
Website
Algorithm Development
BraTS
brain
glioma
glioma sub-region segmentation
brain tumors
image mapping into colors
clinical decision support
Radiomics
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
Bakas, Spyridon
Akbari, Hamed
Sotiras, Aristeidis
Bilello, Michel
Rozycki, Martin
Kirby, Justin S.
Freymann, John B.
Farahani, Keyvan
Davatzikos, Christos
Scientific data2017Journal Article, cited 1036 times
Website
TCGA-GBM
TCGA-LGG
Radiomic feature
Segmentation
Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.
Multi‐site quality and variability analysis of 3D FDG PET segmentations based on phantom and clinical image data
Beichel, Reinhard R
Smith, Brian J
Bauer, Christian
Ulrich, Ethan J
Ahmadvand, Payam
Budzevich, Mikalai M
Gillies, Robert J
Goldgof, Dmitry
Grkovski, Milan
Hamarneh, Ghassan
Medical Physics2017Journal Article, cited 7 times
Website
QIN PET Phantom
PURPOSE: Radiomics utilizes a large number of image-derived features for quantifying tumor characteristics that can in turn be correlated with response and prognosis. Unfortunately, extraction and analysis of such image-based features is subject to measurement variability and bias. The challenge for radiomics is particularly acute in Positron Emission Tomography (PET) where limited resolution, a high noise component related to the limited stochastic nature of the raw data, and the wide variety of reconstruction options confound quantitative feature metrics. Extracted feature quality is also affected by tumor segmentation methods used to define regions over which to calculate features, making it challenging to produce consistent radiomics analysis results across multiple institutions that use different segmentation algorithms in their PET image analysis. Understanding each element contributing to these inconsistencies in quantitative image feature and metric generation is paramount for ultimate utilization of these methods in multi-institutional trials and clinical oncology decision making. METHODS: To assess segmentation quality and consistency at the multi-institutional level, we conducted a study of seven institutional members of the National Cancer Institute Quantitative Imaging Network. For the study, members were asked to segment a common set of phantom PET scans acquired over a range of imaging conditions as well as a second set of head and neck cancer (HNC) PET scans. Segmentations were generated at each institution using their preferred approach. In addition, participants were asked to repeat segmentations with a time interval between initial and repeat segmentation. This procedure resulted in overall 806 phantom insert and 641 lesion segmentations. Subsequently, the volume was computed from the segmentations and compared to the corresponding reference volume by means of statistical analysis. RESULTS: On the two test sets (phantom and HNC PET scans), the performance of the seven segmentation approaches was as follows. On the phantom test set, the mean relative volume errors ranged from 29.9 to 87.8% of the ground truth reference volumes, and the repeat difference for each institution ranged between -36.4 to 39.9%. On the HNC test set, the mean relative volume error ranged between -50.5 to 701.5%, and the repeat difference for each institution ranged between -37.7 to 31.5%. In addition, performance measures per phantom insert/lesion size categories are given in the paper. On phantom data, regression analysis resulted in coefficient of variation (CV) components of 42.5% for scanners, 26.8% for institutional approaches, 21.1% for repeated segmentations, 14.3% for relative contrasts, 5.3% for count statistics (acquisition times), and 0.0% for repeated scans. Analysis showed that the CV components for approaches and repeated segmentations were significantly larger on the HNC test set with increases by 112.7% and 102.4%, respectively. CONCLUSION: Analysis results underline the importance of PET scanner reconstruction harmonization and imaging protocol standardization for quantification of lesion volumes. In addition, to enable a distributed multi-site analysis of FDG PET images, harmonization of analysis approaches and operator training in combination with highly automated segmentation methods seems to be advisable. Future work will focus on quantifying the impact of segmentation variation on radiomics system performance.
Radiogenomic analysis of hypoxia pathway reveals computerized MRI descriptors predictive of overall survival in Glioblastoma
Segmentation of three-dimensional images with parametric active surfaces and topology changes
Benninghoff, Heike
Garcke, Harald
Journal of Scientific ComputingJ Sci Comput2017Journal Article, cited 1 times
Website
Algorithm Development
Segmentation
In this paper, we introduce a novel parametric finite element method for segmentation of three-dimensional images. We consider a piecewise constant version of the Mumford-Shah and the Chan-Vese functionals and perform a region-based segmentation of 3D image data. An evolution law is derived from energy minimization problems which push the surfaces to the boundaries of 3D objects in the image. We propose a parametric scheme which describes the evolution of parametric surfaces. An efficient finite element scheme is proposed for a numerical approximation of the evolution equations. Since standard parametric methods cannot handle topology changes automatically, an efficient method is presented to detect, identify and perform changes in the topology of the surfaces. One main focus of this paper are the algorithmic details to handle topology changes like splitting and merging of surfaces and change of the genus of a surface. Different artificial images are studied to demonstrate the ability to detect the different types of topology changes. Finally, the parametric method is applied to segmentation of medical 3D images.
Predicting survival time of lung cancer patients using radiomic analysis
Chaddad, Ahmad
Desrosiers, Christian
Toews, Matthew
Abdulkarim, Bassam
OncotargetOncotarget2017Journal Article, cited 4 times
Website
Radiomics
LUNG
Non Small Cell Lung Cancer (NSCLC)
Computed Tomography (CT)
Classification
Computer Assisted Diagnosis (CAD)
Objectives: This study investigates the prediction of Non-small cell lung cancer (NSCLC) patient survival outcomes based on radiomic texture and shape features automatically extracted from tumor image data.
Materials and Methods: Retrospective analysis involves CT scans of 315 NSCLC patients from The Cancer Imaging Archive (TCIA). A total of 24 image features are computed from labeled tumor volumes of patients within groups defined using NSCLC subtype and TNM staging information. Spearman's rank correlation, Kaplan-Meier estimation and log-rank tests were used to identify features related to long/short NSCLC patient survival groups. Automatic random forest classification was used to predict patient survival group from multivariate feature data. Significance is assessed at P < 0.05 following Holm-Bonferroni correction for multiple comparisons.
Results: Significant correlations between radiomic features and survival were observed for four clinical groups: (group, [absolute correlation range]): (large cell carcinoma (LCC) [0.35, 0.43]), (tumor size T2, [0.31, 0.39]), (non lymph node metastasis N0, [0.3, 0.33]), (TNM stage I, [0.39, 0.48]). Significant log-rank relationships between features and survival time were observed for three clinical groups: (group, hazard ratio): (LCC, 3.0), (LCC, 3.9), (T2, 2.5) and (stage I, 2.9). Automatic survival prediction performance (i.e. below/above median) is superior for combined radiomic features with age-TNM in comparison to standard TNM clinical staging information (clinical group, mean area-under-the-ROC-curve (AUC)): (LCC, 75.73%), (N0, 70.33%), (T2, 70.28%) and (TNM-I, 76.17%).
Conclusion: Quantitative lung CT imaging features can be used as indicators of survival, in particular for patients with large-cell-carcinoma (LCC), primary-tumor-sizes (T2) and no lymph-node-metastasis (N0).
A Fast Semi-Automatic Segmentation Tool for Processing Brain Tumor Images
Biomedical Optics Express2017Journal Article, cited 342 times
Website
Algorithm Development
low-dose CT
Convolutional Neural Network (CNN)
Image denoising
MATLAB
In order to reduce the potential radiation risk, low-dose CT has attracted an increasing attention. However, simply lowering the radiation dose will significantly degrade the image quality. In this paper, we propose a new noise reduction method for low-dose CT via deep learning without accessing original projection data. A deep convolutional neural network is here used to map low-dose CT images towards its corresponding normal-dose counterparts in a patch-by-patch fashion. Qualitative results demonstrate a great potential of the proposed method on artifact reduction and structure preservation. In terms of the quantitative metrics, the proposed method has showed a substantial improvement on PSNR, RMSE and SSIM than the competing state-of-art methods. Furthermore, the speed of our method is one order of magnitude faster than the iterative reconstruction and patch-based image denoising methods.
Bayesian Kernel Models for Statistical Genetics and Cancer Genomics
Crawford, Lorin
2017Thesis, cited 0 times
Thesis
Segmentation
Radiogenomics
Volume of high-risk intratumoral subregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma
Cui, Yi
Ren, Shangjie
Tha, Khin Khin
Wu, Jia
Shirato, Hiroki
Li, Ruijiang
European Radiology2017Journal Article, cited 10 times
Website
GBM
Algorithmic three-dimensional analysis of tumor shape in MRI improves prognosis of survival in glioblastoma: a multi-institutional study
Czarnek, Nicholas
Clark, Kal
Peters, Katherine B
Mazurowski, Maciej A
Journal of Neuro-Oncology2017Journal Article, cited 15 times
Website
TCGA-GBM
Radiomics
BRAIN
Glioblastoma Multiforme (GBM)
In this retrospective, IRB-exempt study, we analyzed data from 68 patients diagnosed with glioblastoma (GBM) in two institutions and investigated the relationship between tumor shape, quantified using algorithmic analysis of magnetic resonance images, and survival. Each patient's Fluid Attenuated Inversion Recovery (FLAIR) abnormality and enhancing tumor were manually delineated, and tumor shape was analyzed by automatic computer algorithms. Five features were automatically extracted from the images to quantify the extent of irregularity in tumor shape in two and three dimensions. Univariate Cox proportional hazard regression analysis was performed to determine how prognostic each feature was of survival. Kaplan Meier analysis was performed to illustrate the prognostic value of each feature. To determine whether the proposed quantitative shape features have additional prognostic value compared with standard clinical features, we controlled for tumor volume, patient age, and Karnofsky Performance Score (KPS). The FLAIR-based bounding ellipsoid volume ratio (BEVR), a 3D complexity measure, was strongly prognostic of survival, with a hazard ratio of 0.36 (95% CI 0.20-0.65), and remained significant in regression analysis after controlling for other clinical factors (P = 0.0061). Three enhancing-tumor based shape features were prognostic of survival independently of clinical factors: BEVR (P = 0.0008), margin fluctuation (P = 0.0013), and angular standard deviation (P = 0.0078). Algorithmically assessed tumor shape is statistically significantly prognostic of survival for patients with GBM independently of patient age, KPS, and tumor volume. This shows promise for extending the utility of MR imaging in treatment of GBM patients.
Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype?
Demerath, Theo
Simon-Gabriel, Carl Philipp
Kellner, Elias
Schwarzwald, Ralf
Lange, Thomas
Heiland, Dieter Henrik
Reinacher, Peter
Staszewski, Ori
Mast, Hansjorg
Kiselev, Valerij G
Egger, Karl
Urbach, Horst
Weyerbrock, Astrid
Mader, Irina
Neuroradiology Journal2017Journal Article, cited 5 times
Website
Radiogenomics
RIDER NEURO MRI
Magnetic resonance imaging (MRI)
Glioblastoma Multiforme (GBM)
The purpose of this study was to identify markers from perfusion, diffusion, and chemical shift imaging in glioblastomas (GBMs) and to correlate them with genetically determined and previously published patterns of structural magnetic resonance (MR) imaging. Twenty-six patients (mean age 60 years, 13 female) with GBM were investigated. Imaging consisted of native and contrast-enhanced 3D data, perfusion, diffusion, and spectroscopic imaging. In the presence of minor necrosis, cerebral blood volume (CBV) was higher (median +/- SD, 2.23% +/- 0.93) than in pronounced necrosis (1.02% +/- 0.71), pcorr = 0.0003. CBV adjacent to peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity was lower in edema (1.72% +/- 0.31) than in infiltration (1.91% +/- 0.35), pcorr = 0.039. Axial diffusivity adjacent to peritumoral FLAIR hyperintensity was lower in severe mass effect (1.08*10(-3) mm(2)/s +/- 0.08) than in mild mass effect (1.14*10(-3) mm(2)/s +/- 0.06), pcorr = 0.048. Myo-inositol was positively correlated with a marker for mitosis (Ki-67) in contrast-enhancing tumor, r = 0.5, pcorr = 0.0002. Changed CBV and axial diffusivity, even outside FLAIR hyperintensity, in adjacent normal-appearing matter can be discussed as to be related to angiogenesis pathways and to activated proliferation genes. The correlation between myo-inositol and Ki-67 might be attributed to its binding to cell surface receptors regulating tumorous proliferation of astrocytic cells.
Spatial habitats from multiparametric MR imaging are associated with signaling pathway activities and survival in glioblastoma
Dextraze, Katherine
Saha, Abhijoy
Kim, Donnie
Narang, Shivali
Lehrer, Michael
Rao, Anita
Narang, Saphal
Rao, Dinesh
Ahmed, Salmaan
Madhugiri, Venkatesh
Fuller, Clifton David
Kim, Michelle M
Krishnan, Sunil
Rao, Ganesh
Rao, Arvind
OncotargetOncotarget2017Journal Article, cited 0 times
Website
Radiomics
Glioblastoma Multiforme (GBM)
TCGA-GBM
Glioblastoma (GBM) show significant inter- and intra-tumoral heterogeneity, impacting response to treatment and overall survival time of 12-15 months. To study glioblastoma phenotypic heterogeneity, multi-parametric magnetic resonance images (MRI) of 85 glioblastoma patients from The Cancer Genome Atlas were analyzed to characterize tumor-derived spatial habitats for their relationship with outcome (overall survival) and to identify their molecular correlates (i.e., determine associated tumor signaling pathways correlated with imaging-derived habitat measurements). Tumor sub-regions based on four sequences (fluid attenuated inversion recovery, T1-weighted, post-contrast T1-weighted, and T2-weighted) were defined by automated segmentation. From relative intensity of pixels in the 3-dimensional tumor region, "imaging habitats" were identified and analyzed for their association to clinical and genetic data using survival modeling and Dirichlet regression, respectively. Sixteen distinct tumor sub-regions ("spatial imaging habitats") were derived, and those associated with overall survival (denoted "relevant" habitats) in glioblastoma patients were identified. Dirichlet regression implicated each relevant habitat with unique pathway alterations. Relevant habitats also had some pathways and cellular processes in common, including phosphorylation of STAT-1 and natural killer cell activity, consistent with cancer hallmarks. This work revealed clinical relevance of MRI-derived spatial habitats and their relationship with oncogenic molecular mechanisms in patients with GBM. Characterizing the associations between imaging-derived phenotypic measurements with the genomic and molecular characteristics of tumors can enable insights into tumor biology, further enabling the practice of personalized cancer treatment. The analytical framework and workflow demonstrated in this study are inherently scalable to multiple MR sequences.
Radiology and Enterprise Medical Imaging Extensions (REMIX)
New prognostic factor telomerase reverse transcriptase promotor mutation presents without MR imaging biomarkers in primary glioblastoma
Ersoy, Tunc F
Keil, Vera C
Hadizadeh, Dariusch R
Gielen, Gerrit H
Fimmers, Rolf
Waha, Andreas
Heidenreich, Barbara
Kumar, Rajiv
Schild, Hans H
Simon, Matthias
Neuroradiology2017Journal Article, cited 1 times
Website
Radiomics
Radiogenomics
Glioblastoma Multiforme (GBM)
REMBRANDT
TERT mutation
VASARI
Magnetic Resonance Imaging (MRI)
PURPOSE: Magnetic resonance (MR) imaging biomarkers can assist in the non-invasive assessment of the genetic status in glioblastomas (GBMs). Telomerase reverse transcriptase (TERT) promoter mutations are associated with a negative prognosis. This study was performed to identify MR imaging biomarkers to forecast the TERT mutation status. METHODS: Pre-operative MRIs of 64/67 genetically confirmed primary GBM patients (51/67 TERT-mutated with rs2853669 polymorphism) were analyzed according to Visually AcceSAble Rembrandt Images (VASARI) ( https://wiki.cancerimagingarchive.net/display/Public/VASARI+Research+Project ) imaging criteria by three radiological raters. TERT mutation and O(6)-methylguanine-DNA methyltransferase (MGMT) hypermethylation data were obtained through direct and pyrosequencing as described in a previous study. Clinical data were derived from a prospectively maintained electronic database. Associations of potential imaging biomarkers and genetic status were assessed by Fisher and Mann-Whitney U tests and stepwise linear regression. RESULTS: No imaging biomarkers could be identified to predict TERT mutational status (alone or in conjunction with TERT promoter polymorphism rs2853669 AA-allele). TERT promoter mutations were more common in patients with tumor-associated seizures as first symptom (26/30 vs. 25/37, p = 0.07); these showed significantly smaller tumors [13.1 (9.0-19.0) vs. 24.0 (16.6-37.5) all cm(3); p = 0.007] and prolonged median overall survival [17.0 (11.5-28.0) vs. 9.0 (4.0-12.0) all months; p = 0.02]. TERT-mutated GBMs were underrepresented in the extended angularis region (p = 0.03), whereas MGMT-methylated GBMs were overrepresented in the corpus callosum (p = 0.03) and underrepresented temporomesially (p = 0.01). CONCLUSION: Imaging biomarkers for prediction of TERT mutation status remain weak and cannot be derived from the VASARI protocol. Tumor-associated seizures are less common in TERT mutated glioblastomas.
Feature fusion for lung nodule classification
Farag, Amal A
Ali, Asem
Elshazly, Salwa
Farag, Aly A
International Journal of Computer Assisted Radiology and Surgery2017Journal Article, cited 3 times
Website
LIDC-IDRI
LUNG
Computed tomography (CT)
Features extraction
Gabor filter
Classification
K Nearest Neighbor (KNN)
support vector machine (SVM)
Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture
Mammograms are generally contaminated by quantum noise, degrading their visual quality and thereby the performance of the classifier in Computer-Aided Diagnosis (CAD). Hence, enhancement of mammograms is necessary to improve the visual quality and detectability of the anomalies present in the breasts. In this paper, a sigmoid based non-linear function has been applied for contrast enhancement of mammograms. The enhanced mammograms are used to define the texture of the detected anomaly using Gray Level Co-occurrence Matrix (GLCM) features. Later, a Back Propagation Artificial Neural Network (BP-ANN) is used as a classification tool for segregating the mammogram into abnormal or normal. The proposed classifier approach has reported to be the one with considerably better accuracy in comparison to other existing approaches.
Segmentation of colon and removal of opacified fluid for virtual colonoscopy
Gayathri, Devi K
Radhakrishnan, R
Rajamani, Kumar
Pattern Analysis and Applications2017Journal Article, cited 0 times
Website
CT COLONOGRAPHY
Computed Tomography (CT)
Segmentation
Image denoising
Colorectal cancer (CRC) is the third most common type of cancer. The use of techniques such as flexible sigmoidoscopy and capsule endoscopy for the screening of colorectal cancer causes physical pain and hardship to the patients. Hence, to overcome the above disadvantages, computed tomography (CT) can be employed for the identification of polyps or growth, while screening for CRC. This proposed approach was implemented to improve the accuracy and to reduce the computation time of the accurate segmentation of the colon segments from the abdominal CT images which contain anatomical organs such as lungs, small bowels, large bowels (Colon), ribs, opacified fluid and bones. The segmentation is performed in two major steps. The first step segments the air-filled colon portions by placing suitable seed points using modified 3D seeded region growing which identify and match the similar voxels by 6-neighborhood connectivity technique. The segmentation of the opacified fluid portions is done using fuzzy connectedness approach enhanced with interval thresholding. The membership classes are defined and the voxels are categorized based on the class value. Interval thresholding is performed so that the bones and opacified fluid parts may be extracted. The bones are removed by the placement of seed points as the existence of the continuity of the bone region is more in the axial slices. The resultant image containing bones is subtracted from the threshold output to segment the opacified fluid segments in all the axial slices of a dataset. Finally, concatenation of the opacified fluid with the segmented colon is performed for the 3D rendering of the segmented colon. This method was implemented in 15 datasets downloaded from TCIA and in real-time dataset in both supine and prone position and the accuracy achieved was 98.73%.
Medical Imaging Segmentation Assessment via Bayesian Approaches to Fusion, Accuracy and Variability Estimation with Application to Head and Neck Cancer
With the advancement of technology, medical imaging has become a fast growing area of research. Some imaging questions require little physician analysis, such as diagnosing a broken bone, using a 2-D X-ray image. More complicated questions, using 3-D scans, such as computerized tomography (CT), can be much more difficult to answer. For example, estimating tumor growth to evaluate malignancy; which informs whether intervention is necessary. This requires careful delineation of different structures in the image. For example, what is the tumor versus what is normal tissue; this is referred to as segmentation. Currently, the gold standard of segmentation is for a radiologist to manually trace structure edges in the 3-D image, however, this can be extremely time consuming. Additionally, manual segmentation results can differ drastically between and even within radiologists. A more reproducible, less variable, and more time efficient segmentation approach would drastically improve medical treatment. This potential, as well as the continued increase in computing power, has led to computationally intensive semiautomated segmentation algorithms. Segmentation algorithms' widespread use is limited due to difficulty in validating their performance. Fusion models, such as STAPLE, have been proposed as a way to combine multiple segmentations into a consensus ground truth; this allows for evaluation of both manual and semiautomated segmentation in relation to the consensus ground truth. Once a consensus ground truth is obtained, a multitude of approaches have been proposed for evaluating different aspects of segmentation performance; segmentation accuracy, between- and within -reader variability.
The focus of this dissertation is threefold. First, a simulation based tool is introduced to allow for the validation of fusion models. The simulation properties closely follow a real dataset, in order to ensure that they mimic reality. Second, a statistical hierarchical Bayesian fusion model is proposed, in order to estimate a consensus ground truth within a robust statistical framework. The model is validated using the simulation tool and compared to other fusion models, including STAPLE. Additionally, the model is applied to real datasets and the consensus ground truth estimates are compared across different fusion models. Third, a statistical hierarchical Bayesian performance model is proposed in order to estimate segmentation method specific accuracy, between- and within -reader variability. An extensive simulation study is performed to validate the model’s parameter estimation and coverage properties. Additionally, the model is fit to a real data source and performance estimates are summarized.
Brain tumor detection from MRI image: An approach
Ghosh, Debjyoti
Bandyopadhyay, Samir Kumar
International Journal of Applied Research2017Journal Article, cited 0 times
Website
Algorithm Development
REMBRANDT
BRAIN
Magnetic Resonance Imaging (MRI)
Segmentation
Computer Aided Detection (CADe)
A brain tumor is an abnormal growth of cells within the brain, which can be cancerous or noncancerous (benign). This paper detects different types of tumors and cancerous growth within the brain and other associated areas within the brain by using computerized methods on MRI images of a patient.
It is also possible to track the growth patterns of such tumors.
Role of Imaging in the Era of Precision Medicine
Giardino, Angela
Gupta, Supriya
Olson, Emmi
Sepulveda, Karla
Lenchik, Leon
Ivanidze, Jana
Rakow-Penner, Rebecca
Patel, Midhir J
Subramaniam, Rathan M
Ganeshan, Dhakshinamoorthy
Academic Radiology2017Journal Article, cited 12 times
Website
Radiomics
TCGA-BRCA
TCGA-RCC
Precision medicine is an emerging approach for treating medical disorders, which takes into account individual variability in genetic and environmental factors. Preventive or therapeutic interventions can then be directed to those who will benefit most from targeted interventions, thereby maximizing benefits and minimizing costs and complications. Precision medicine is gaining increasing recognition by clinicians, healthcare systems, pharmaceutical companies, patients, and the government. Imaging plays a critical role in precision medicine including screening, early diagnosis, guiding treatment, evaluating response to therapy, and assessing likelihood of disease recurrence. The Association of University Radiologists Radiology Research Alliance Precision Imaging Task Force convened to explore the current and future role of imaging in the era of precision medicine and summarized its finding in this article. We review the increasingly important role of imaging in various oncological and non-oncological disorders. We also highlight the challenges for radiology in the era of precision medicine.
Towards Image-Guided Pancreas and Biliary Endoscopy: Automatic Multi-organ Segmentation on Abdominal CT with Dense Dilated Networks
Gibson, Eli
Giganti, Francesco
Hu, Yipeng
Bonmati, Ester
Bandula, Steve
Gurusamy, Kurinchi
Davidson, Brian R
Pereira, Stephen P
Clarkson, Matthew J
Barratt, Dean C
2017Conference Proceedings, cited 14 times
Website
Pancreas-CT
Algorithm Development
Segmentation
Deep learning
Computer Aided Detection (CADe)
Intuitive Error Space Exploration of Medical Image Data in Clinical Daily Routine
Planning, guidance, and quality assurance of pelvic screw placement using deformable image registration
Goerres, J.
Uneri, A.
Jacobson, M.
Ramsay, B.
De Silva, T.
Ketcha, M.
Han, R.
Manbachi, A.
Vogt, S.
Kleinszig, G.
Wolinsky, J. P.
Osgood, G.
Siewerdsen, J. H.
Phys Med Biol2017Journal Article, cited 4 times
Website
CT Lymph Nodes
Segmentation
Percutaneous pelvic screw placement is challenging due to narrow bone corridors surrounded by vulnerable structures and difficult visual interpretation of complex anatomical shapes in 2D x-ray projection images. To address these challenges, a system for planning, guidance, and quality assurance (QA) is presented, providing functionality analogous to surgical navigation, but based on robust 3D-2D image registration techniques using fluoroscopy images already acquired in routine workflow. Two novel aspects of the system are investigated: automatic planning of pelvic screw trajectories and the ability to account for deformation of surgical devices (K-wire deflection). Atlas-based registration is used to calculate a patient-specific plan of screw trajectories in preoperative CT. 3D-2D registration aligns the patient to CT within the projective geometry of intraoperative fluoroscopy. Deformable known-component registration (dKC-Reg) localizes the surgical device, and the combination of plan and device location is used to provide guidance and QA. A leave-one-out analysis evaluated the accuracy of automatic planning, and a cadaver experiment compared the accuracy of dKC-Reg to rigid approaches (e.g. optical tracking). Surgical plans conformed within the bone cortex by 3-4 mm for the narrowest corridor (superior pubic ramus) and >5 mm for the widest corridor (tear drop). The dKC-Reg algorithm localized the K-wire tip within 1.1 mm and 1.4 degrees and was consistently more accurate than rigid-body tracking (errors up to 9 mm). The system was shown to automatically compute reliable screw trajectories and accurately localize deformed surgical devices (K-wires). Such capability could improve guidance and QA in orthopaedic surgery, where workflow is impeded by manual planning, conventional tool trackers add complexity and cost, rigid tool assumptions are often inaccurate, and qualitative interpretation of complex anatomy from 2D projections is prone to trial-and-error with extended fluoroscopy time.
Pulmonary nodule segmentation in computed tomography with deep learning
Early detection of lung cancer is essential for treating the disease. Lung nodule segmentation systems can be used together with Computer-Aided Detection (CAD) systems, and help doctors diagnose and manage lung cancer. In this work, we create a lung nodule segmentation system based on deep learning. Deep learning is a sub-field of machine learning responsible for state-of-the-art results in several segmentation datasets such as the PASCAL VOC 2012. Our model is a modified 3D U-Net, trained on the LIDC-IDRI dataset, using the intersection over union (IOU) loss function. We show our model works for multiple types of lung nodules. Our model achieves state-of-the-art performance on the LIDC test set, using nodules annotated by at least 3 radiologists and with a consensus truth of 50%.
User-centered design and evaluation of interactive segmentation methods for medical images
Segmentation of medical images is a challenging task that aims to identify a particular structure present on the image. Among the existing methods involving the user at different levels, from a fully-manual to a fully-automated task, interactive segmentation methods provide assistance to the user during the task to reduce the variability in the results and allow occasional corrections of segmentation failures. Therefore, they offer a compromise between the segmentation efficiency and the accuracy of the results. It is the user who judges whether the results are satisfactory and how to correct them during the segmentation, making the process subject to human factors. Despite the strong influence of the user on the outcomes of a segmentation task, the impact of such factors has received little attention, with the literature focusing the assessment of segmentation processes on computational performance. Yet, involving the user performance in the analysis is more representative of a realistic scenario. Our goal is to explore the user behaviour in order to improve the efficiency of interactive image segmentation processes. This is achieved through three contributions. First, we developed a method which is based on a new user interaction mechanism to provide hints as to where to concentrate the computations. This significantly improves the computation efficiency without sacrificing the quality of the segmentation. The benefits of using such hints are twofold: (i) because our contribution is based on user interaction, it generalizes to a wide range of segmentation methods, and (ii) it gives comprehensive indications about where to focus the segmentation search. The latter advantage is used to achieve the second contribution. We developed an automated method based on a multi-scale strategy to: (i) reduce the user’s workload and, (ii) improve the computational time up to tenfold, allowing real-time segmentation feedback. Third, we have investigated the effects of such improvements in computations on the user’s performance. We report an experiment that manipulates the delay induced by the computation time while performing an interactive segmentation task. Results reveal that the influence of this delay can be significantly reduced with an appropriate interaction mechanism design. In conclusion, this project provides an effective image segmentation solution that has been developed in compliance with user performance requirements. We validated our approach through multiple user studies that provided a step forward into understanding the user behaviour during interactive image segmentation.
Titre traduit
Conception et évaluation orientées utilisateur des méthodes de segmentation interactives des images médicales
Résumé traduit
La segmentation d’images consiste à identifier une structure particulière dans une image. Parmi les méthodes existantes qui impliquent l’utilisateur à différents niveaux, les méthodes de segmentation interactives fournissent un support logiciel pour assister l’utilisateur dans cette tâche, ce qui aide à réduire la variabilité des résultats et permet de corriger les erreurs occasionnelles. Ces méthodes offrent un compromis entre l’efficacité et la précision des résultats. En effet, durant la segmentation, l’utilisateur décide si les résultats sont satisfaisants et dans le cas contraire, comment les corriger, rendant le processus sujet aux facteurs humains. Malgré la forte influence qu’a l’utilisateur sur l’issue de la segmentation, l’impact de ces facteurs a reçu peu d’attention de la part de la communauté scientifique, qui souvent, réduit l’évaluation des methods de segmentation à leurs performances de calcul. Pourtant, inclure la performance de l’utilisateur lors de l’évaluation de la segmentation permet une représentation plus fidèle de la réalité. Notre but est d’explorer le comportement de l’utilisateur afin d’améliorer l’efficacité des méthodes de segmentation interactives. Cette tâche est réalisée en trois contributions. Dans un premier temps, nous avons développé un nouveau mécanisme d’interaction utilisateur qui oriente la méthode de segmentation vers les endroits de l’image où concentrer les calculs. Ceci augmente significativement l’efficacité des calculs sans atténuer la qualité de la segmentation. Il y a un double avantage à utiliser un tel mécanisme: (i) puisque notre contribution est base sur l’interaction utilisateur, l’approche est généralisable à un grand nombre de méthodes de segmentation, et (ii) ce mécanisme permet une meilleure compréhension des endroits de l’image où l’on doit orienter la recherche du contour lors de la segmentation. Ce dernier point est exploité pour réaliser la deuxième contribution. En effet, nous avons remplacé le mécanisme d’interaction par une méthode automatique basée sur une stratégie multi-échelle qui permet de: (i) réduire l’effort produit par l’utilisateur lors de la segmentation, et (ii) améliorer jusqu’à dix fois le temps de calcul, permettant une segmentation en temps-réel. Dans la troisième contribution, nous avons étudié l’effet d’une telle amélioration des performances de calculs sur l’utilisateur. Nous avons mené une expérience qui manipule les délais des calculs lors de la segmentation interactive. Les résultats révèlent qu’une conception appropriée du mécanisme d’interaction peut réduire l’effet de ces délais sur l’utilisateur. En conclusion, ce projet offer une solution interactive de segmentation d’images développée en tenant compte de la performance de l’utilisateur. Nous avons validé notre approche à travers de multiples études utilisateurs qui nous ont permis une meilleure compréhension du comportement utilisateur durant la segmentation interactive des images.
Predictive capabilities of statistical learning methods for lung nodule malignancy classification using diagnostic image features: an investigation using the Lung Image Database Consortium dataset
Background: A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings.
Method: In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement.
Results: The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334.
Conclusions: More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.
Effect of a computer-aided diagnosis system on radiologists' performance in grading gliomas with MRI
Hsieh, Kevin Li-Chun
Tsai, Ruei-Je
Teng, Yu-Chuan
Lo, Chung-Ming
PLoS One2017Journal Article, cited 0 times
Website
Algorithm Development
Computer Aided Diagnosis (CADx)
Classification
Lower-grade glioma (LGG)
Glioblastoma Multiforme (GBM)
The effects of a computer-aided diagnosis (CAD) system based on quantitative intensity features with magnetic resonance (MR) imaging (MRI) were evaluated by examining radiologists' performance in grading gliomas. The acquired MRI database included 71 lower-grade gliomas and 34 glioblastomas. Quantitative image features were extracted from the tumor area and combined in a CAD system to generate a prediction model. The effect of the CAD system was evaluated in a two-stage procedure. First, a radiologist performed a conventional reading. A sequential second reading was determined with a malignancy estimation by the CAD system. Each MR image was regularly read by one radiologist out of a group of three radiologists. The CAD system achieved an accuracy of 87% (91/105), a sensitivity of 79% (27/34), a specificity of 90% (64/71), and an area under the receiver operating characteristic curve (Az) of 0.89. In the evaluation, the radiologists' Az values significantly improved from 0.81, 0.87, and 0.84 to 0.90, 0.90, and 0.88 with p = 0.0011, 0.0076, and 0.0167, respectively. Based on the MR image features, the proposed CAD system not only performed well in distinguishing glioblastomas from lower-grade gliomas but also provided suggestions about glioma grading to reinforce radiologists' confidence rating.
A longitudinal four‐dimensional computed tomography and cone beam computed tomography dataset for image‐guided radiation therapy research in lung cancer
Hugo, Geoffrey D
Weiss, Elisabeth
Sleeman, William C
Balik, Salim
Keall, Paul J
Lu, Jun
Williamson, Jeffrey F
Medical Physics2017Journal Article, cited 8 times
Website
4D-Lung
Computed Tomography (CT)
PURPOSE: To describe in detail a dataset consisting of serial four-dimensional computed tomography (4DCT) and 4D cone beam CT (4DCBCT) images acquired during chemoradiotherapy of 20 locally advanced, nonsmall cell lung cancer patients we have collected at our institution and shared publicly with the research community. ACQUISITION AND VALIDATION METHODS: As part of an NCI-sponsored research study 82 4DCT and 507 4DCBCT images were acquired in a population of 20 locally advanced nonsmall cell lung cancer patients undergoing radiation therapy. All subjects underwent concurrent radiochemotherapy to a total dose of 59.4-70.2 Gy using daily 1.8 or 2 Gy fractions. Audio-visual biofeedback was used to minimize breathing irregularity during all fractions, including acquisition of all 4DCT and 4DCBCT acquisitions in all subjects. Target, organs at risk, and implanted fiducial markers were delineated by a physician in the 4DCT images. Image coordinate system origins between 4DCT and 4DCBCT were manipulated in such a way that the images can be used to simulate initial patient setup in the treatment position. 4DCT images were acquired on a 16-slice helical CT simulator with 10 breathing phases and 3 mm slice thickness during simulation. In 13 of the 20 subjects, 4DCTs were also acquired on the same scanner weekly during therapy. Every day, 4DCBCT images were acquired on a commercial onboard CBCT scanner. An optically tracked external surrogate was synchronized with CBCT acquisition so that each CBCT projection was time stamped with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with the half bow-tie filter. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases and reconstructed with an in-house FDK reconstruction algorithm. Errors in respiration sorting, reconstruction, and acquisition were carefully identified and corrected. DATA FORMAT AND USAGE NOTES: 4DCT and 4DCBCT images are available in DICOM format and structures through DICOM-RT RTSTRUCT format. All data are stored in the Cancer Imaging Archive (TCIA, http://www.cancerimagingarchive.net/) as collection 4D-Lung and are publicly available. DISCUSSION: Due to high temporal frequency sampling, redundant (4DCT and 4DCBCT) data at similar timepoints, oversampled 4DCBCT, and fiducial markers, this dataset can support studies in image-guided and image-guided adaptive radiotherapy, assessment of 4D voxel trajectory variability, and development and validation of new tools for image registration and motion management.
Collage CNN for Renal Cell Carcinoma Detection from CT
Advanced MRI Techniques in the Monitoring of Treatment of Gliomas
Hyare, Harpreet
Thust, Steffi
Rees, Jeremy
Current treatment options in neurology2017Journal Article, cited 11 times
Website
TCGA-GBM
glioma
OPINION STATEMENT: With advances in treatments and survival of patients with glioblastoma (GBM), it has become apparent that conventional imaging sequences have significant limitations both in terms of assessing response to treatment and monitoring disease progression. Both 'pseudoprogression' after chemoradiation for newly diagnosed GBM and 'pseudoresponse' after anti-angiogenesis treatment for relapsed GBM are well-recognised radiological entities. This in turn has led to revision of response criteria away from the standard MacDonald criteria, which depend on the two-dimensional measurement of contrast-enhancing tumour, and which have been the primary measure of radiological response for over three decades. A working party of experts published RANO (Response Assessment in Neuro-oncology Working Group) criteria in 2010 which take into account signal change on T2/FLAIR sequences as well as the contrast-enhancing component of the tumour. These have recently been modified for immune therapies, which are associated with specific issues related to the timing of radiological response. There has been increasing interest in quantification and validation of physiological and metabolic parameters in GBM over the last 10 years utilising the wide range of advanced imaging techniques available on standard MRI platforms. Previously, MRI would provide structural information only on the anatomical location of the tumour and the presence or absence of a disrupted blood-brain barrier. Advanced MRI sequences include proton magnetic resonance spectroscopy (MRS), vascular imaging (perfusion/permeability) and diffusion imaging (diffusion weighted imaging/diffusion tensor imaging) and are now routinely available. They provide biologically relevant functional, haemodynamic, cellular, metabolic and cytoarchitectural information and are being evaluated in clinical trials to determine whether they offer superior biomarkers of early treatment response than conventional imaging, when correlated with hard survival endpoints. Multiparametric imaging, incorporating different combinations of these modalities, improves accuracy over single imaging modalities but has not been widely adopted due to the amount of post-processing analysis required, lack of clinical trial data, lack of radiology training and wide variations in threshold values. New techniques including diffusion kurtosis and radiomics will offer a higher level of quantification but will require validation in clinical trial settings. Given all these considerations, it is clear that there is an urgent need to incorporate advanced techniques into clinical trial design to avoid the problems of under or over assessment of treatment response.
CASED: Curriculum Adaptive Sampling for Extreme Data Imbalance
Jesson, Andrew
Guizard, Nicolas
Ghalehjegh, Sina Hamidi
Goblot, Damien
Soudan, Florian
Chapados, Nicolas
2017Conference Proceedings, cited 18 times
Website
LIDC-IDRI
LUNA16 Challenge
Computer Aided Detection (CAD)
Segmentation
Classification
Algorithm Development
We introduce CASED, a novel curriculum sampling algorithm that facilitates the optimization of deep learning segmentation or detection models on data sets with extreme class imbalance. We evaluate the CASED learning framework on the task of lung nodule detection in chest CT. In contrast to two-stage solutions, wherein nodule candidates are first proposed by a segmentation model and refined by a second detection stage, CASED improves the training of deep nodule segmentation models (e.g. UNet) to the point where state of the art results are achieved using only a trivial detection stage. CASED improves the optimization of deep segmentation models by allowing them to first learn how to distinguish nodules from their immediate surroundings, while continuously adding a greater proportion of difficult-to-classify global context, until uniformly sampling from the empirical data distribution. Using CASED during training yields a minimalist proposal to the lung nodule detection problem that tops the LUNA16 nodule detection benchmark with an average sensitivity score of 88.35%. Furthermore, we find that models trained using CASED are robust to nodule annotation quality by showing that comparable results can be achieved when only a point and radius for each ground truth nodule are provided during training. Finally, the CASED learning framework makes no assumptions with regard to imaging modality or segmentation target and should generalize to other medical imaging problems where class imbalance is a persistent problem.
Interactive 3D Virtual Colonoscopic Navigation For Polyp Detection From CT Images
Joseph, Jinu
Kumar, Rajesh
Chandran, Pournami S
Vidya, PV
Procedia Computer Science2017Journal Article, cited 0 times
Website
colon cancer
endoscopy
polyp
volume rendering
3D thinning
surface rendering
Dijkstra's algorithm
principal curvature
Gaussian curvature
Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma
Kanas, Vasileios G
Zacharaki, Evangelia I
Thomas, Ginu A
Zinn, Pascal O
Megalooikonomou, Vasileios
Colen, Rivka R
Computer Methods and Programs in Biomedicine2017Journal Article, cited 16 times
Website
TCGA-GBM
Radiogenomics
BRAIN
Background and objective: The O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation has been shown to be associated with improved outcomes in patients with glioblastoma (GBM) and may be a predictive marker of sensitivity to chemotherapy. However, determination of the MGMT promoter methylation status requires tissue obtained via surgical resection or biopsy. The aim of this study was to assess the ability of quantitative and qualitative imaging variables in predicting MGMT methylation status noninvasively.
Methods: A retrospective analysis of MR images from GBM patients was conducted. Multivariate prediction models were obtained by machine-learning methods and tested on data from The Cancer Genome Atlas (TCGA) database.
Results: The status of MGMT promoter methylation was predicted with an accuracy of up to 73.6%. Experimental analysis showed that the edema/necrosis volume ratio, tumor/necrosis volume ratio, edema volume, and tumor location and enhancement characteristics were the most significant variables in respect to the status of MGMT promoter methylation in GBM.
Conclusions: The obtained results provide further evidence of an association between standard preoperative MRI variables and MGMT methylation status in GBM.
A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction
Kang, E.
Min, J.
Ye, J. C.
Med Phys2017Journal Article, cited 568 times
Website
LDCT-and-Projection-data
*Radiation Dosage
Signal-To-Noise Ratio
Computed Tomography (CT)
Wavelet Analysis
Convolutional Neural Network (CNN)
Deep Learning
PURPOSE: Due to the potential risk of inducing cancer, radiation exposure by X-ray CT devices should be reduced for routine patient scanning. However, in low-dose X-ray CT, severe artifacts typically occur due to photon starvation, beam hardening, and other causes, all of which decrease the reliability of the diagnosis. Thus, a high-quality reconstruction method from low-dose X-ray CT data has become a major research topic in the CT community. Conventional model-based de-noising approaches are, however, computationally very expensive, and image-domain de-noising approaches cannot readily remove CT-specific noise patterns. To tackle these problems, we want to develop a new low-dose X-ray CT algorithm based on a deep-learning approach. METHOD: We propose an algorithm which uses a deep convolutional neural network (CNN) which is applied to the wavelet transform coefficients of low-dose CT images. More specifically, using a directional wavelet transform to extract the directional component of artifacts and exploit the intra- and inter- band correlations, our deep network can effectively suppress CT-specific noise. In addition, our CNN is designed with a residual learning architecture for faster network training and better performance. RESULTS: Experimental results confirm that the proposed algorithm effectively removes complex noise patterns from CT images derived from a reduced X-ray dose. In addition, we show that the wavelet-domain CNN is efficient when used to remove noise from low-dose CT compared to existing approaches. Our results were rigorously evaluated by several radiologists at the Mayo Clinic and won second place at the 2016 "Low-Dose CT Grand Challenge." CONCLUSIONS: To the best of our knowledge, this work is the first deep-learning architecture for low-dose CT reconstruction which has been rigorously evaluated and proven to be effective. In addition, the proposed algorithm, in contrast to existing model-based iterative reconstruction (MBIR) methods, has considerable potential to benefit from large data sets. Therefore, we believe that the proposed algorithm opens a new direction in the area of low-dose CT research.
3D multi-view convolutional neural networks for lung nodule classification
Kang, Guixia
Liu, Kui
Hou, Beibei
Zhang, Ningbo
PLoS One2017Journal Article, cited 7 times
Website
LIDC-IDRI
lung cancer
3d convolutional neural network (CNN)
Supervised Dimension-Reduction Methods for Brain Tumor Image Data Analysis
The purpose of this study was to construct a risk score for glioblastomas based on magnetic resonance imaging (MRI) data. Tumor identification requires multimodal voxel-based imaging data that are highly dimensional, and multivariate models with dimension reduction are desirable for their analysis. We propose a two-step dimension-reduction method using a radial basis function–supervised multi-block sparse principal component analysis (SMS–PCA) method. The method is first implemented through the basis expansion of spatial brain images, and the scores are then reduced through regularized matrix decomposition in order to produce simultaneous data-driven selections of related brain regions supervised by univariate composite scores representing linear combinations of covariates such as age and tumor location. An advantage of the proposed method is that it identifies the associations of brain regions at the voxel level, and supervision is helpful in the interpretation.
Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon
Kim, Ga Ram
Ku, You Jin
Cho, Soon Gu
Kim, Sei Joong
Min, Byung Soh
Annals of Surgical Treatment and Research2017Journal Article, cited 3 times
Website
TCGA-BRCA
Radiogenomics
BI-RADS
BREAST
Magnetic resonance imaging (MRI)
Gene expression profiling
Purpose: To evaluate whether the Breast Imaging Reporting and Data System (BI-RADS) MRI lexicon could reflect the genomic information of breast cancers and to suggest intuitive imaging features as biomarkers.
Methods: Matched breast MRI data from The Cancer Imaging Archive and gene expression profile from The Cancer Genome Atlas of 70 invasive breast cancers were analyzed. Magnetic resonance images were reviewed according to the BI-RADS MRI lexicon of mass morphology. The cancers were divided into 2 groups of gene clustering by gene set enrichment analysis. Clinicopathologic and imaging characteristics were compared between the 2 groups.
Results: The luminal subtype was predominant in the group 1 gene set and the triple-negative subtype was predominant in the group 2 gene set (55 of 56, 98.2% vs. 9 of 14, 64.3%). Internal enhancement descriptors were different between the 2 groups; heterogeneity was most frequent in group 1 (27 of 56, 48.2%) and rim enhancement was dominant in group 2 (10 of 14, 71.4%). In group 1, the gene sets related to mammary gland development were overexpressed whereas the gene sets related to mitotic cell division were overexpressed in group 2.
Conclusion: We identified intuitive imaging features of breast MRI associated with distinct gene expression profiles using the standard imaging variables of BI-RADS. The internal enhancement pattern on MRI might reflect specific gene expression profiles of breast cancers, which can be recognized by visual distinction.
Discovery radiomics for pathologically-proven computed tomography lung cancer prediction
Kumar, Devinder
Chung, Audrey G
Shaifee, Mohammad J
Khalvati, Farzad
Haider, Masoom A
Wong, Alexander
2017Conference Proceedings, cited 30 times
Website
LIDC-IDRI
Radiomics
Classification
LUNG
Deep convolutional neural network (DCNN)
Lung cancer is the leading cause for cancer related deaths. As such, there is an urgent need for a streamlined process that can allow radiologists to provide diagnosis with greater efficiency and accuracy. A powerful tool to do this is radiomics: a high-dimension imaging feature set. In this study, we take the idea of radiomics one step further by introducing the concept of discovery radiomics for lung cancer prediction using CT imaging data. In this study, we realize these custom radiomic sequencers as deep convolutional sequencers using a deep convolutional neural network learning architecture. To illustrate the prognostic power and effectiveness of the radiomic sequences produced by the discovered sequencer, we perform cancer prediction between malignant and benign lesions from 97 patients using the pathologically-proven diagnostic data from the LIDC-IDRI dataset. Using the clinically provided pathologically-proven data as ground truth, the proposed framework provided an average accuracy of 77.52% via 10-fold cross-validation with a sensitivity of 79.06% and specificity of 76.11%, surpassing the state-of-the art method.
Textural Analysis of Tumour Imaging: A Radiomics Approach
Conventionally, tumour characteristic are assessed by performing a biopsy. These biopsies are invasive and submissive to the problem of tumour heterogeneity. However, analysis
of imaging data may render the need for such biopsies obsolete. This master’s dissertation describes in what matter images of tumour masses can be post-processed to classify the tumours in a variety of respective clinical response classes. Tumour images obtained using both computed tomography and magnetic resonance imaging are analysed. The analysis of these images is done
using a radiomics approach. This approach will convert the imaging data into a high dimensional mineable feature space. The features considered are first-order statistics, texture features, wavelet-based features and shape parameters. Post-processing techniques applied on this feature space include k-means clustering, assessment of stability and prognostic performance and
machine learning techniques. Both random forests and neural networks are included. Results from these analyses show that the radiomics features can be correlated with different clinical response classes as well as serve as input data to create predictive models with correct prediction rates up to 63.9 % in CT and 66.0 % in MRI. Furthermore, a radiomics signature can be created
that consists of four features and is capable of predicting clinical response factors with almost the same accuracy as obtained using the entire data space.
Keywords - Radiomics, texture analysis, lung tumour, CT, brain tumour, MRI, clustering,
random forest, neural network, machine learning, radiomics signature, biopsy, tumour heterogeneity
A simple texture feature for retrieval of medical images
Lan, Rushi
Zhong, Si
Liu, Zhenbing
Shi, Zhuo
Luo, Xiaonan
Multimedia Tools and Applications2017Journal Article, cited 2 times
Website
Imaging features
Classification
Algorithm Development
Texture characteristic is an important attribute of medical images, and has been applied in many medical image applications. This paper proposes a simple approach to employ the texture features of medical images for retrieval. The developed approach first conducts image filtering to medical images using different Gabor and Schmid filters, and then uniformly partitions the filtered images into non-overlapping patches. These operations provide extensive local texture information of medical images. The bag-of-words model is finally used to obtain feature representations of the images. Compared with several existing features, the proposed one is more discriminative and efficient. Experiments on two benchmark medical CT image databases have demonstrated the effectiveness of the proposed approach.
A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme
Lao, Jiangwei
Chen, Yinsheng
Li, Zhi-Cheng
Li, Qihua
Zhang, Ji
Liu, Jing
Zhai, Guangtao
Sci RepScientific reports2017Journal Article, cited 32 times
Website
TCGA-GBM
Radiomics
Glioblastoma Multiforme (GBM)
Deep learning
Traditional radiomics models mainly rely on explicitly-designed handcrafted features from medical images. This paper aimed to investigate if deep features extracted via transfer learning can generate radiomics signatures for prediction of overall survival (OS) in patients with Glioblastoma Multiforme (GBM). This study comprised a discovery data set of 75 patients and an independent validation data set of 37 patients. A total of 1403 handcrafted features and 98304 deep features were extracted from preoperative multi-modality MR images. After feature selection, a six-deep-feature signature was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics nomogram was further presented by combining the signature and clinical risk factors such as age and Karnofsky Performance Score. Compared with traditional risk factors, the proposed signature achieved better performance for prediction of OS (C-index = 0.710, 95% CI: 0.588, 0.932) and significant stratification of patients into prognostically distinct groups (P < 0.001, HR = 5.128, 95% CI: 2.029, 12.960). The combined model achieved improved predictive performance (C-index = 0.739). Our study demonstrates that transfer learning-based deep features are able to generate prognostic imaging signature for OS prediction and patient stratification for GBM, indicating the potential of deep imaging feature-based biomarker in preoperative care of GBM patients.
4DCT imaging to assess radiomics feature stability: An investigation for thoracic cancers
Larue, Ruben THM
Van De Voorde, Lien
van Timmeren, Janna E
Leijenaar, Ralph TH
Berbée, Maaike
Sosef, Meindert N
Schreurs, Wendy MJ
van Elmpt, Wouter
Lambin, Philippe
Radiotherapy and Oncology2017Journal Article, cited 7 times
Website
RIDER Lung CT
4D-Lung
Radiomics
ESOPHAGUS
LUNG
Computed Tomography (CT)
BACKGROUND AND PURPOSE: Quantitative tissue characteristics derived from medical images, also called radiomics, contain valuable prognostic information in several tumour-sites. The large number of features available increases the risk of overfitting. Typically test-retest CT-scans are used to reduce dimensionality and select robust features. However, these scans are not always available. We propose to use different phases of respiratory-correlated 4D CT-scans (4DCT) as alternative. MATERIALS AND METHODS: In test-retest CT-scans of 26 non-small cell lung cancer (NSCLC) patients and 4DCT-scans (8 breathing phases) of 20 NSCLC and 20 oesophageal cancer patients, 1045 radiomics features of the primary tumours were calculated. A concordance correlation coefficient (CCC) >0.85 was used to identify robust features. Correlation with prognostic value was tested using univariate cox regression in 120 oesophageal cancer patients. RESULTS: Features based on unfiltered images demonstrated greater robustness than wavelet-filtered features. In total 63/74 (85%) unfiltered features and 268/299 (90%) wavelet features stable in the 4D-lung dataset were also stable in the test-retest dataset. In oesophageal cancer 397/1045 (38%) features were robust, of which 108 features were significantly associated with overall-survival. CONCLUSION: 4DCT-scans can be used as alternative to eliminate unstable radiomics features as first step in a feature selection procedure. Feature robustness is tumour-site specific and independent of prognostic value.
Comparison of novel multi-level Otsu (MO-PET) and conventional PET segmentation methods for measuring FDG metabolic tumor volume in patients with soft tissue sarcoma
Lee, Inki
Im, Hyung-Jun
Solaiyappan, Meiyappan
Cho, Steve Y
EJNMMI physics2017Journal Article, cited 0 times
Website
Soft-tissue Sarcoma
Algorithm Development
Segmentation
Spatiotemporal genomic architecture informs precision oncology in glioblastoma
Lee, Jin-Ku
Wang, Jiguang
Sa, Jason K.
Ladewig, Erik
Lee, Hae-Ock
Lee, In-Hee
Kang, Hyun Ju
Rosenbloom, Daniel S.
Camara, Pablo G.
Liu, Zhaoqi
van Nieuwenhuizen, Patrick
Jung, Sang Won
Choi, Seung Won
Kim, Junhyung
Chen, Andrew
Kim, Kyu-Tae
Shin, Sang
Seo, Yun Jee
Oh, Jin-Mi
Shin, Yong Jae
Park, Chul-Kee
Kong, Doo-Sik
Seol, Ho Jun
Blumberg, Andrew
Lee, Jung-Il
Iavarone, Antonio
Park, Woong-Yang
Rabadan, Raul
Nam, Do-Hyun
Nat Genet2017Journal Article, cited 45 times
Website
TCGA-GBM
Genomics
Precision medicine in cancer proposes that genomic characterization of tumors can inform personalized targeted therapies. However, this proposition is complicated by spatial and temporal heterogeneity. Here we study genomic and expression profiles across 127 multisector or longitudinal specimens from 52 individuals with glioblastoma (GBM). Using bulk and single-cell data, we find that samples from the same tumor mass share genomic and expression signatures, whereas geographically separated, multifocal tumors and/or long-term recurrent tumors are seeded from different clones. Chemical screening of patient-derived glioma cells (PDCs) shows that therapeutic response is associated with genetic similarity, and multifocal tumors that are enriched with PIK3CA mutations have a heterogeneous drug-response pattern. We show that targeting truncal events is more efficacious than targeting private events in reducing the tumor burden. In summary, this work demonstrates that evolutionary inference from integrated genomic analysis in multisector biopsies can inform targeted therapeutic interventions for patients with GBM.
Quality of Radiomic Features in Glioblastoma Multiforme: Impact of Semi-Automated Tumor Segmentation Software
Lee, Myungeun
Woo, Boyeong
Kuo, Michael D
Jamshidi, Neema
Kim, Jong Hyo
Korean journal of radiology2017Journal Article, cited 7 times
Website
TCGA-GBM
Radiomics
BRAIN
Magnetic Resonance Imaging (MRI)
Multiple-response regression analysis links magnetic resonance imaging features to de-regulated protein expression and pathway activity in lower grade glioma
Lehrer, Michael
Bhadra, Anindya
Ravikumar, Visweswaran
Chen, James Y
Wintermark, Max
Hwang, Scott N
Holder, Chad A
Huang, Erich P
Fevrier-Sullivan, Brenda
Freymann, John B
Rao, Arvind
Oncoscience2017Journal Article, cited 1 times
Website
TCGA-LGG
VASARI
Radiogenomics
cBioPortal
imaging-proteomics analysis
signaling pathway activity
multiple-response regression
Radiomics
Lower-grade glioma (LGG)
BACKGROUND AND PURPOSE: Lower grade gliomas (LGGs), lesions of WHO grades II and III, comprise 10-15% of primary brain tumors. In this first-of-a-kind study, we aim to carry out a radioproteomic characterization of LGGs using proteomics data from the TCGA and imaging data from the TCIA cohorts, to obtain an association between tumor MRI characteristics and protein measurements. The availability of linked imaging and molecular data permits the assessment of relationships between tumor genomic/proteomic measurements with phenotypic features. MATERIALS AND METHODS: Multiple-response regression of the image-derived, radiologist scored features with reverse-phase protein array (RPPA) expression levels generated correlation coefficients for each combination of image-feature and protein or phospho-protein in the RPPA dataset. Significantly-associated proteins for VASARI features were analyzed with Ingenuity Pathway Analysis software. Hierarchical clustering of the results of the pathway analysis was used to determine which feature groups were most strongly correlated with pathway activity and cellular functions. RESULTS: The multiple-response regression approach identified multiple proteins associated with each VASARI imaging feature. VASARI features were found to be correlated with expression of IL8, PTEN, PI3K/Akt, Neuregulin, ERK/MAPK, p70S6K and EGF signaling pathways. CONCLUSION: Radioproteomics analysis might enable an insight into the phenotypic consequences of molecular aberrations in LGGs.
Low-Dose CT streak artifacts removal using deep residual neural network
A Fully-Automatic Multiparametric Radiomics Model: Towards Reproducible and Prognostic Imaging Signature for Prediction of Overall Survival in Glioblastoma Multiforme
Li, Qihua
Bai, Hongmin
Chen, Yinsheng
Sun, Qiuchang
Liu, Lei
Zhou, Sijie
Wang, Guoliang
Liang, Chaofeng
Li, Zhi-Cheng
Sci RepScientific reports2017Journal Article, cited 9 times
Website
Radiomics
GBM
Comparison Between Radiological Semantic Features and Lung-RADS in Predicting Malignancy of Screen-Detected Lung Nodules in the National Lung Screening Trial
Li, Qian
Balagurunathan, Yoganand
Liu, Ying
Qi, Jin
Schabath, Matthew B
Ye, Zhaoxiang
Gillies, Robert J
Clinical Lung Cancer2017Journal Article, cited 3 times
Website
Lung cancer screening
Lung-RADS
National Lung Screening Trial (NLST)
Predictive
Semantic features
Evaluate the Malignancy of Pulmonary Nodules Using the 3D Deep Leaky Noisy-OR Network
Liao, Fangzhou
Liang, Ming
Li, Zhe
Hu, Xiaolin
Song, Sen
IEEE Trans Neural Netw Learn Syst2017Journal Article, cited 15 times
Website
Radiomics
LUNG
Computer Assisted Detection (CAD)
Deep Learning
Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-OR gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.
High-resolution anatomic correlation of cyclic motor patterns in the human colon: Evidence of a rectosigmoid brake
Lin, Anthony Y
Du, Peng
Dinning, Philip G
Arkwright, John W
Kamp, Jozef P
Cheng, Leo K
Bissett, Ian P
O'Grady, Gregory
American Journal of Physiology-Gastrointestinal and Liver Physiology2017Journal Article, cited 12 times
Website
CT COLONOGRAPHY
Colonic motility
High-resolution manometry
Rectosigmoid brake
Normalized Euclidean Super-Pixels for Medical Image Segmentation
We propose a super-pixel segmentation algorithm based on normalized Euclidean distance for handling the uncertainty and complexity in medical image. Benefited from the statistic characteristics, compactness within super-pixels is described by normalized Euclidean distance. Our algorithm banishes the balance factor of the Simple Linear Iterative Clustering framework. In this way, our algorithm properly responses to the lesion tissues, such as tiny lung nodules, which have a little difference in luminance with their neighbors. The effectiveness of proposed algorithm is verified in The Cancer Imaging Archive (TCIA) database. Compared with Simple Linear Iterative Clustering (SLIC) and Linear Spectral Clustering (LSC), the experiment results show that, the proposed algorithm achieves competitive performance over super-pixel segmentation in the state of art.
The Current Role of Image Compression Standards in Medical Imaging
Liu, Feng
Hernandez-Cabronero, Miguel
Sanchez, Victor
Marcellin, Michael W
Bilgin, Ali
Information2017Journal Article, cited 4 times
Website
LIDC-IDRI
TCGA-BRCA
TCGA-GBM
CT-COLONOGRAPHY
image compression
Synthetic minority image over-sampling technique: How to improve AUC for glioblastoma patient survival prediction
Real-world datasets are often imbalanced, with an important class having many fewer examples than other classes. In medical data, normal examples typically greatly outnumber disease examples. A classifier learned from imbalanced data, will tend to be very good at the predicting examples in the larger (normal) class, yet the smaller (disease) class is typically of more interest. Imbalance is dealt with at the feature vector level (create synthetic feature vectors or discard some examples from the larger class) or by assigning differential costs to errors. Here, we introduce a novel method for over-sampling minority class examples at the image level, rather than the feature vector level. Our method was applied to the problem of Glioblastoma patient survival group prediction. Synthetic minority class examples were created by adding Gaussian noise to original medical images from the minority class. Uniform local binary patterns (LBP) histogram features were then extracted from the original and synthetic image examples with a random forests classifier. Experimental results show the new method (Image SMOTE) increased minority class predictive accuracy and also the AUC (area under the receiver operating characteristic curve), compared to using the imbalanced dataset directly or to creating synthetic feature vectors.
A CADe system for nodule detection in thoracic CT images based on artificial neural network
Liu, Xinglong
Hou, Fei
Qin, Hong
Hao, Aimin
Science China Information Sciences2017Journal Article, cited 11 times
Website
LIDC-IDRI
Artificial neural network (ANN)
LUNG
Computed Tomography (CT)
computer aided detection (CADe)
Relationship between Glioblastoma Heterogeneity and Survival Time: An MR Imaging Texture Analysis
Liu, Y
Xu, X
Yin, L
Zhang, X
Li, L
Lu, H
American Journal of Neuroradiology2017Journal Article, cited 8 times
Website
TCGA-GBM
Radiomics
postcontrast TI-weighted imaging
co-occurrence matrix
run-length matrix
histogram
global spatial variations
cancer genome atlas
recursive feature-elimination–based support vector machine classifier (SVM)
Brain tumor segmentation using morphological processing and the discrete wavelet transform
Lojzim, Joshua Michael
Fries, Marcus
Journal of Young Investigators2017Journal Article, cited 0 times
Website
MATLAB
MRI
Segmentation
Brain
Harmonizing the pixel size in retrospective computed tomography radiomics studies
Mackin, Dennis
Fave, Xenia
Zhang, Lifei
Yang, Jinzhong
Jones, A Kyle
Ng, Chaan S
PLoS One2017Journal Article, cited 19 times
Website
CC-Radiomics-Phantom
Algorithm Development
image resampling
Butterworth filtering
computed tomography (CT)
hierarchical clustering
Measurement of smaller colon polyp in CT colonography images using morphological image processing
Manjunath, KN
Siddalingaswamy, PC
Prabhu, GK
International Journal of Computer Assisted Radiology and Surgery2017Journal Article, cited 1 times
Website
CT COLONOGRAPHY
ACRIN 6664
radiomics
Colon polyp
Shape descriptor
Radiogenomics of lower-grade glioma: algorithmically-assessed tumor shape is associated with tumor genomic subtypes and patient outcomes in a multi-institutional study with The Cancer Genome Atlas data
Mazurowski, Maciej A
Clark, Kal
Czarnek, Nicholas M
Shamsesfandabadi, Parisa
Peters, Katherine B
Saha, Ashirbani
Journal of Neuro-Oncology2017Journal Article, cited 8 times
Website
TCGA-LGG
Radiogenomics
Imaging features
Recent studies identified distinct genomic subtypes of lower-grade gliomas that could potentially be used to guide patient treatment. This study aims to determine whether there is an association between genomics of lower-grade glioma tumors and patient outcomes using algorithmic measurements of tumor shape in magnetic resonance imaging (MRI). We analyzed preoperative imaging and genomic subtype data from 110 patients with lower-grade gliomas (WHO grade II and III) from The Cancer Genome Atlas. Computer algorithms were applied to analyze the imaging data and provided five quantitative measurements of tumor shape in two and three dimensions. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. Patient outcomes were quantified by overall survival. We found that there is a strong association between angular standard deviation (ASD), which measures irregularity of the tumor boundary, and the IDH-1p/19q subtype (p < 0.0017), RNASeq cluster (p < 0.0002), DNA copy number cluster (p < 0.001), and the cluster of clusters (p < 0.0002). The RNASeq cluster was also associated with bounding ellipsoid volume ratio (p < 0.0005). Tumors in the IDH wild type cluster and R2 RNASeq cluster which are associated with much poorer outcomes generally had higher ASD reflecting more irregular shape. ASD also showed association with patient overall survival (p = 0.006). Shape features in MRI were strongly associated with genomic subtypes and patient outcomes in lower-grade glioma.
Transcription elongation factors represent in vivo cancer dependencies in glioblastoma
Glioblastoma is a universally lethal cancer with a median survival time of approximately 15 months. Despite substantial efforts to define druggable targets, there are no therapeutic options that notably extend the lifespan of patients with glioblastoma. While previous work has largely focused on in vitro cellular models, here we demonstrate a more physiologically relevant approach to target discovery in glioblastoma. We adapted pooled RNA interference (RNAi) screening technology for use in orthotopic patient-derived xenograft models, creating a high-throughput negative-selection screening platform in a functional in vivo tumour microenvironment. Using this approach, we performed parallel in vivo and in vitro screens and discovered that the chromatin and transcriptional regulators needed for cell survival in vivo are non-overlapping with those required in vitro. We identified transcription pause-release and elongation factors as one set of in vivo-specific cancer dependencies, and determined that these factors are necessary for enhancer-mediated transcriptional adaptations that enable cells to survive the tumour microenvironment. Our lead hit, JMJD6, mediates the upregulation of in vivo stress and stimulus response pathways through enhancer-mediated transcriptional pause-release, promoting cell survival specifically in vivo. Targeting JMJD6 or other identified elongation factors extends survival in orthotopic xenograft mouse models, suggesting that targeting transcription elongation machinery may be an effective therapeutic strategy for glioblastoma. More broadly, this study demonstrates the power of in vivo phenotypic screening to identify new classes of 'cancer dependencies' not identified by previous in vitro approaches, and could supply new opportunities for therapeutic intervention.
Volumetric brain tumour detection from MRI using visual saliency
Mitra, Somosmita
Banerjee, Subhashis
Hayashi, Yoichi
PLoS One2017Journal Article, cited 2 times
Website
MICCAI BraTS challenge
BRAIN
Computer Aided Detection (CADe)
Magnetic Resonance Imaging (MRI)
Medical image processing has become a major player in the world of automatic tumour region detection and is tantamount to the incipient stages of computer aided design. Saliency detection is a crucial application of medical image processing, and serves in its potential aid to medical practitioners by making the affected area stand out in the foreground from the rest of the background image. The algorithm developed here is a new approach to the detection of saliency in a three dimensional multi channel MR image sequence for the glioblastoma multiforme (a form of malignant brain tumour). First we enhance the three channels, FLAIR (Fluid Attenuated Inversion Recovery), T2 and T1C (contrast enhanced with gadolinium) to generate a pseudo coloured RGB image. This is then converted to the CIE L*a*b* color space. Processing on cubes of sizes k = 4, 8, 16, the L*a*b* 3D image is then compressed into volumetric units; each representing the neighbourhood information of the surrounding 64 voxels for k = 4, 512 voxels for k = 8 and 4096 voxels for k = 16, respectively. The spatial distance of these voxels are then compared along the three major axes to generate the novel 3D saliency map of a 3D image, which unambiguously highlights the tumour region. The algorithm operates along the three major axes to maximise the computation efficiency while minimising loss of valuable 3D information. Thus the 3D multichannel MR image saliency detection algorithm is useful in generating a uniform and logistically correct 3D saliency map with pragmatic applicability in Computer Aided Detection (CADe). Assignment of uniform importance to all three axes proves to be an important factor in volumetric processing, which helps in noise reduction and reduces the possibility of compromising essential information. The effectiveness of the algorithm was evaluated over the BRATS MICCAI 2015 dataset having 274 glioma cases, consisting both of high grade and low grade GBM. The results were compared with that of the 2D saliency detection algorithm taken over the entire sequence of brain data. For all comparisons, the Area Under the receiver operator characteristic (ROC) Curve (AUC) has been found to be more than 0.99 ± 0.01 over various tumour types, structures and locations.
In this work, we present a novel method to segment brain tumors using deep learning. An accurate brain tumor segmentation is key for a patient to get the right treatment and for the doctor who must perform surgery. Due to the genetic differences that exist in different patients, even between the same kind of tumor, an accurate segmentation is crucial. To beat state-of-the-art methods, we want to use technology that has provided major breakthroughs in many different areas, including segmentation, deep learning, a new area of machine learning. It is a branch of machine learning that is attempting to model high level abstractions in data. We will be using Convolutional Neural Networks, CNNs, and we will evaluate the results that we obtain comparing our method against the best results obtained from the Brain Tumor Segmentation Challenge, BRATS.
Automatic tumor segmentation in single-spectral MRI using a texture-based and contour-based algorithm
Nabizadeh, Nooshin
Kubat, Miroslav
Expert Systems with Applications2017Journal Article, cited 8 times
Website
NCI-MICCAI 2013 Challenge
MRI
Segmentation
Texture features
Regularized Winnow
Skippy greedy snake
BRAIN
Automatic detection of brain tumors in single-spectral magnetic resonance images is a challenging task. Existing techniques suffer from inadequate performance, dependence on initial assumptions, and, sometimes, the need for manual interference. The research reported in this paper seeks to reduce some of these shortcomings, and to remove others, achieving satisfactory performance at reasonable computational costs. The success of the system described here is explained by the synergy of the following aspects: (1) a broad choice of high-level features to characterize the image's texture, (2) an efficient mechanism to eliminate less useful features (3) a machine-learning technique to induce a classifier that signals the presence of a tumor-affected tissue, and (4) an improved version of the skippy greedy snake algorithm to outline the tumor's contours. The paper describes the system and reports experiments with synthetic as well as real data. (C) 2017 Elsevier Ltd. All rights reserved.
Tumor image-derived texture features are associated with CD3 T-cell infiltration status in glioblastoma
Narang, Shivali
Kim, Donnie
Aithala, Sathvik
Heimberger, Amy B
Ahmed, Salmaan
Rao, Dinesh
Rao, Ganesh
Rao, Arvind
OncotargetOncotarget2017Journal Article, cited 1 times
Website
glioma
imaging-genomics analysis
texture analysis
immune activity
TCGA-LGG
Multisite concordance of apparent diffusion coefficient measurements across the NCI Quantitative Imaging Network
Newitt, David C
Malyarenko, Dariya
Chenevert, Thomas L
Quarles, C Chad
Bell, Laura
Fedorov, Andriy
Fennessy, Fiona
Jacobs, Michael A
Solaiyappan, Meiyappan
Hectors, Stefanie
Taouli, B.
Muzi, M.
Kinahan, P. E. E.
Schmainda, K. M.
Prah, M. A.
Taber, E. N.
Kroenke, C.
Huang, W., Arlinghaus, L.
Yankeelov, T. E.
Cao, Y.
Aryal, M.
Yen, Y.-F.
Kalpathy-Cramer, J.
Shukla-Dave, A.
Fung, M.
Liang, J.
Boss, M.
Hylton, N.
Journal of Medical Imaging2017Journal Article, cited 6 times
Website
QIN
DCE-MRI
Pulmonary nodule classification with deep residual networks
Nibali, Aiden
He, Zhen
Wollersheim, Dennis
International Journal of Computer Assisted Radiology and Surgery2017Journal Article, cited 19 times
Website
LIDC-IDRI
Segmentation
Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer-aided diagnosis (CAD) systems to predict the malignancy of nodules from cropped CT images of lung nodules.
Computer-aided Diagnosis for Lung Cancer: Usefulness of Nodule Heterogeneity
Nishio, Mizuho
Nagashima, Chihiro
Academic Radiology2017Journal Article, cited 12 times
Website
SPIE LungX Challenge
Computed tomography (CT)
LUNG
Computer Aided Diagnosis (CADx)
Principal component analysis (PCA)
RATIONALE AND OBJECTIVES: To develop a computer-aided diagnosis system to differentiate between malignant and benign nodules. MATERIALS AND METHODS: Seventy-three lung nodules revealed on 60 sets of computed tomography (CT) images were analyzed. Contrast-enhanced CT was performed in 46 CT examinations. The images were provided by the LUNGx Challenge, and the ground truth of the lung nodules was unavailable; a surrogate ground truth was, therefore, constructed by radiological evaluation. Our proposed method involved novel patch-based feature extraction using principal component analysis, image convolution, and pooling operations. This method was compared to three other systems for the extraction of nodule features: histogram of CT density, local binary pattern on three orthogonal planes, and three-dimensional random local binary pattern. The probabilistic outputs of the systems and surrogate ground truth were analyzed using receiver operating characteristic analysis and area under the curve. The LUNGx Challenge team also calculated the area under the curve of our proposed method based on the actual ground truth of their dataset. RESULTS: Based on the surrogate ground truth, the areas under the curve were as follows: histogram of CT density, 0.640; local binary pattern on three orthogonal planes, 0.688; three-dimensional random local binary pattern, 0.725; and the proposed method, 0.837. Based on the actual ground truth, the area under the curve of the proposed method was 0.81. CONCLUSIONS: The proposed method could capture discriminative characteristics of lung nodules and was useful for the differentiation between malignant and benign nodules.
Medical Image Retrieval Using Vector Quantization and Fuzzy S-tree
Nowaková, Jana
Prílepok, Michal
Snášel, Václav
Journal of Medical Systems2017Journal Article, cited 33 times
Website
QIN Breast DCE-MRI
Classification
Content based image retrieval (CBIR)
The aim of the article is to present a novel method for fuzzy medical image retrieval (FMIR) using vector quantization (VQ) with fuzzy signatures in conjunction with fuzzy S-trees. In past times, a task of similar pictures searching was not based on searching for similar content (e.g. shapes, colour) of the pictures but on the picture name. There exist some methods for the same purpose, but there is still some space for development of more efficient methods. The proposed image retrieval system is used for finding similar images, in our case in the medical area - in mammography, in addition to the creation of the list of similar images - cases. The created list is used for assessing the nature of the finding - whether the medical finding is malignant or benign. The suggested method is compared to the method using Normalized Compression Distance (NCD) instead of fuzzy signatures and fuzzy S-tree. The method with NCD is useful for the creation of the list of similar cases for malignancy assessment, but it is not able to capture the area of interest in the image. The proposed method is going to be added to the complex decision support system to help to determine appropriate healthcare according to the experiences of similar, previous cases.
Memory-efficient 3D connected component labeling with parallel computing
Ohira, Norihiro
Signal, Image and Video Processing2017Journal Article, cited 0 times
Website
Phantom FDA
Algorithm Development
Image processing
labeling
memory-efficient
parallel computing
Application of Sparse-Coding Super-Resolution to 16-Bit DICOM Images for Improving the Image Resolution in MRI
Ota, Junko
Umehara, Kensuke
Ishimaru, Naoki
Ishida, Takayuki
Open Journal of Medical Imaging2017Journal Article, cited 1 times
Website
REMBRANDT
Algorithm Development
Magnetic Resonance Imaging (MRI)
super-resolution (SR) schemes
sparse-coding super resolution (ScSR)
3D PULMONARY NODULES DETECTION USING FAST MARCHING SEGMENTATION
Paing, MP
Choomchuay, S
Journal of Fundamental and Applied Sciences2017Journal Article, cited 1 times
Website
LungCT-Diagnosis
lung cancer
automated computer aided diagnosis
lung parenchyma segmentation
fast marching method
random forest classifier
A Novel End-to-End Classifier Using Domain Transferred Deep Convolutional Neural Networks for Biomedical Images
Pang, Shuchao
Yu, Zhezhou
Orgun, Mehmet A
Computer Methods and Programs in Biomedicine2017Journal Article, cited 21 times
Website
Radiomics
CT COLONOGRAPHY
Convolutional Neural Network (CNN)
Transfer learning
BACKGROUND AND OBJECTIVES: Highly accurate classification of biomedical images is an essential task in the clinical diagnosis of numerous medical diseases identified from those images. Traditional image classification methods combined with hand-crafted image feature descriptors and various classifiers are not able to effectively improve the accuracy rate and meet the high requirements of classification of biomedical images. The same also holds true for artificial neural network models directly trained with limited biomedical images used as training data or directly used as a black box to extract the deep features based on another distant dataset. In this study, we propose a highly reliable and accurate end-to-end classifier for all kinds of biomedical images via deep learning and transfer learning. METHODS: We first apply domain transferred deep convolutional neural network for building a deep model; and then develop an overall deep learning architecture based on the raw pixels of original biomedical images using supervised training. In our model, we do not need the manual design of the feature space, seek an effective feature vector classifier or segment specific detection object and image patches, which are the main technological difficulties in the adoption of traditional image classification methods. Moreover, we do not need to be concerned with whether there are large training sets of annotated biomedical images, affordable parallel computing resources featuring GPUs or long times to wait for training a perfect deep model, which are the main problems to train deep neural networks for biomedical image classification as observed in recent works. RESULTS: With the utilization of a simple data augmentation method and fast convergence speed, our algorithm can achieve the best accuracy rate and outstanding classification ability for biomedical images. We have evaluated our classifier on several well-known public biomedical datasets and compared it with several state-of-the-art approaches. CONCLUSIONS: We propose a robust automated end-to-end classifier for biomedical images based on a domain transferred deep convolutional neural network model that shows a highly reliable and accurate performance which has been confirmed on several public biomedical image datasets.
Deep learning for segmentation of brain tumors: Can we train with images from different institutions?
Atlases of the human body have many applications, includ-ing for instance the analysis of information from patient cohorts to eval-uate the distribution of tumours and metastases. We present a 3D Slicer module that simplifies the task of generating a multi-modal atlas from anatomical and functional data. It provides for a simpler evaluation of existing image and verbose patient data by integrating a database that isautomatically generated from text files and accompanies the visualization of the atlas volume. The computation of the atlas is a two step process. First, anatomical data is pairwise registered to a reference dataset withan affine initialization and a B-Spline based deformable approach. Sec-ond, the computed transformations are applied to anatomical as well as the corresponding functional data to generate both atlases. The moduleis validated with a publicly available soft tissue sarcoma dataset fromThe Cancer Imaging Archive. We show that functional data in the atlasvolume correlates with the findings from the patient database.
Detection of Lung Nodules on Medical Images by the Use of Fractal Segmentation
Rezaie, Afsaneh
Habiboghli, Ali
International Journal of Interactive Multimedia and Artificial Inteligence2017Journal Article, cited 0 times
Website
LIDC-IDRI
Radiomics
Segmentation
LUNG
Fractal
Computer Simulation of Low-dose CT with Clinical Lung Image Database: a preliminary study
Fixation devices are used in radiotherapy treatment of head and neck cancers to ensure successive treatment fractions are accurately targeted. Typical fixations usually take the form of a custom made mask that is clamped to the treatment couch and these are evident in many CT data sets as radiotherapy treatment is normally planned with the mask in place. But the fixations can make planning more difficult for certain tumor sites and are often unwanted by third parties wishing to reuse the data. Manually editing the CT images to remove the fixations is time consuming and error prone. This paper presents a fast and automatic approach that removes artifacts due to fixations in CT images without affecting pixel values representing tissue. The algorithm uses particle swarm optimisation to speed up the execution time and presents results from five CT data sets that show it achieves an average specificity of 92.01% and sensitivity of 99.39%.
Deciphering unclassified tumors of non-small-cell lung cancer through radiomics
