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PLETHORA

PleThora | Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines

DOI: 10.7937/tcia.2020.6c7y-gq39 | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Lung Lung 402 30.58MB Thoracic segmentations, pleural effusion segmentations, image features 2020/07/28

Summary

Automated or semi-automated algorithms intended for chest CT analyses typically require the creation of a 3D map of the thoracic volume as their initial step. Identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. However, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs – yet this is exactly the circumstance for which pipeline analyses would be most useful.

To address this need, we have created PleThora, a dataset of pleural effusion and thoracic cavity segmentations in subjects with diseased lungs. PleThora consists of left and right thoracic cavity segmentations delineated on 402 CT scans from The Cancer Imaging Archive NSCLC-Radiomics collection as well as separate segmentations labeling pleural effusions alone. Thoracic cavity segmentations include lung parenchyma, tumor, atelectasis, adhesions, and effusion. PleThora is a tool for medical image preprocessing and segmentation researchers to build and compare approaches for region-of-interest identification and analysis.

The thoracic cavity segmentations were generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student, and revised by a radiation oncologist or a radiologist.  Pleural effusion segmentations were manually delineated by a medical student and revised by a radiologist. Expert GTV segmentations already provided by the NSCLC-Radiomics collection helped inform our segmentations, and areas of the effusion that overlap with GTVs are not included. Researchers interested in discriminating between GTV and effusion using imaging biomarker inputs may find our pleural effusion segmentations useful, especially when paired with the GTV segmentations provided in the NSCLC-Radiomics collection.

Tabular data are also provided, including GTV, thorax, and effusion volumes (in cm3), tumor location, and image metadata. Additionally, we standardized a train/test split for training deep learning algorithms with the thoracic cavity segmentations.

Note: These segmentations use the RPI orientation, but the original DICOM files are oriented using the RAI convention.  As a result, some tools such as ITK-SNAP will not render the segmentations in the correct orientation when visualized.  The authors of these data suggest using software like Mango (http://ric.uthscsa.edu/mango/) or to convert to DICOM files to NIfTI with software like dcm2niix (https://github.com/rordenlab/dcm2niix) to address this issue.

Data Access

Version 3: Updated 2020/07/28

Version 3 changes:

2D U-Net

  • Incorrectly reported the 2D U-Net achieved segmentations with Dice similarity coefficients of 0.90 and 0.94 for left and right lungs.
  • The performances should be 0.94 and 0.94. 

3D U-Net

  • Incorrectly reported the 3D U-Net achieved segmentations with Dice similarity coefficients of 0.82 and 0.94 for left and right lungs.
  • The performances should be 0.95 and 0.96.

Data Dictionary 

Added Auto-MS Thorax DSC description.

Title Data Type Format Access Points Subjects Studies Series Images License
Thoracic Segmentations 402 402 CC BY 3.0
Pleural Effusion Segmentations NIFTI and ZIP CC BY 3.0
Segmentation Features and Image Metadata CSV CC BY 3.0
Baseline UNet 2D Summary PDF CC BY 3.0
Baseline UNet 3D Summary PDF CC BY 3.0
Data Dictionary DOCX CC BY 3.0

Collections Used In This Analysis Result

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding Original CT Images from NSCLC-Radiomics CT DICOM 402 402 402 48,568 CC BY 3.0

Citations & Data Usage Policy

Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:

Data Citation

Kiser, K.J., Ahmed, S., Stieb, S.M., Mohamed, A.S.R., Elhalawani, H., Park, P.Y.S., Doyle, N.S., Wang, B.J., Barman, A., Fuller, C.D., Giancardo, L. (2020). Data from the Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines (PleThora) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2020.6c7y-gq39 .

Data Citation

Aerts, H. J. W. L., Wee, L., Rios Velazquez, E., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M. M., Leemans, C. R., Dekker, A., Quackenbush, J., Gillies, R. J., & Lambin, P. (2019). Data From NSCLC-Radiomics (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2015.PF0M9REI

Detailed Description

All NIfTI files have been compressed for convenience (.nii.gz)

Note: These segmentations use the RPI orientation, but the original DICOM files are oriented using the RAI convention.  As a result, some tools such as ITK-SNAP will not render the segmentations in the correct orientation when visualized.  The authors of these data suggest using software like Mango (http://ric.uthscsa.edu/mango/) or to convert to DICOM files to NIfTI with software like dcm2niix (https://github.com/rordenlab/dcm2niix) to address this issue.

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • University of Texas M.D. Anderson Cancer Center,  Houston, TX,  USA – Special thanks to Kendall Kiser, MS Biomedical Informatics, from the Department of Radiation Oncology.
  • The University of Texas Health Science Center School of Biomedical Informatics,  Houston, TX,  USA
  • John P. and Kathrine G. McGovern Medical School, Houston, TX. Department of Diagnostic and Interventional Imaging.

Publications Using This Data

TCIA maintains a list of publications which leverage our data. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.

Publication Citation

Kiser, K.J., Barman, A., Stieb, S., Fuller, C.D., Giancardo, L., 2021. Novel Autosegmentation Spatial Similarity Metrics Capture the Time Required to Correct Segmentations Better Than Traditional Metrics in a Thoracic Cavity Segmentation Workflow. J Digit Imaging. https://doi.org/10.1007/s10278-021-00460-3 
PMID: 34027588  PMCID: PMC8329111
(2020 medRxiv preprint doi): https://doi.org/10.1101/2020.05.14.20102103.

TCIA Citation

Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Acknowledgement

Swiss Cancer League (BIL KLS-4300-08-2017).

Acknowledgement

Learning Healthcare Award funded by the UTHealth Center for Clinical and Translational Science (CCTS).

Acknowledgement

NIH grant UL1TR003167.

Acknowledgement

National Institutes of Health (NIH) National Institute for Dental and Craniofacial Research Establishing Outcome Measures Award (1R01DE025248/R56DE025248) and an Academic Industrial Partnership Grant (R01DE028290)

Acknowledgement

National Cancer Institute (NCI) Early Phase Clinical Trials in Imaging and Image-Guided Interventions Program  (1R01CA218148)

Acknowledgement

NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672)

Acknowledgement

NIH/NCI Head and Neck Specialized Programs of Research Excellence (SPORE) Developmental Research Program Award (P50 CA097007)

Acknowledgement

National Science Foundation (NSF), Division of Mathematical Sciences, Joint NIH/NSF Initiative on Quantitative Approaches to Biomedical Big Data (QuBBD) Grant (NSF 1557679)

Acknowledgement

NSF Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) standard grant (NSF 1933369) a National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant  (R25EB025787-01)

Acknowledgement

NIH Big Data to Knowledge (BD2K) Program of the NCI Early Stage Development of Technologies in Biomedical Computing, Informatics, and Big Data Science Award (1R01CA214825).

Acknowledgement

Direct infrastructure support was provided by the multidisciplinary Stiefel Oropharyngeal Research Fund of the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer and the Cancer Center Support Grant (P30CA016672) and the MD Anderson Program in Image-guided Cancer Therapy.

Acknowledgement

Direct industry grant support, honoraria, and travel funding from Elekta AB

Previous Versions

Version 2: Updated 2020/06/26

Version 2 changes:

  • The dataset is now named “PleThora” for “Pleural effusion and thoracic cavity segmentations in diseased lungs.”
  • All NIfTI files have been compressed for convenience (.nii à .nii.gz)
  • All thoracic cavity primary reviewer segmentations have been renamed from “lungMask_edit.nii” to “[CaseID]_thor_cav_primary_reviewer.nii.gz” to more specifically identify each file’s contents and avoid confusion.
  • Eighty-six thoracic cavity secondary reviewer segmentations have been added. These are named “[CaseID]_thor_cav_secondary_reviewer.nii.gz.”
  • Interobserver variability analysis between primary and secondary reviewer thoracic cavity segmentations revealed four cases in which interobserver agreement was anomalously lower than all other cases. These cases were manually re-reviewed by another physician. In three cases (LUNG1-026, LUNG1-157, and LUNG1-354) it was deemed that the secondary reviewer’s segmentation excluded structures that should have been included. These were corrected. In one case (LUNG-088) it was determined that the primary reviewer segmentation included a large (400 cm3) nodal conglomerate. Our original thoracic cavity segmentation definition did not intend to include nodal conglomerates, so for consistency’s sake we corrected the primary reviewer segmentation accordingly. However, the segmentation with the nodal conglomerate is still valuable, so we provide it as well and name it “LUNG1-088_thor_cav_primary_reviewer_with_nodal_conglomerate.nii”
  • We manually reviewed the pleural effusion segmentations of the primary physician reviewer and determined that in many cases the reviewer had not been sufficiently careful. Therefore, all 78 primary reviewer segmentations were re-reviewed by another physician and corrected as necessary. They are now re-submitted as “[CaseID]_effusion_first_reviewer.nii.gz”
  • Seventy-eight pleural effusion secondary reviewer segmentations have been added. These are named “[CaseID]_effusion_second_reviewer.nii.gz.”
  • Fifteen pleural effusion tertiary reviewer segmentations have been added. These are named “[CaseID]_effusion_third_reviewer.nii.gz.”
  • We add two documents that describe baseline performances for 2D and 3D U-Net segmentation algorithms and define a reproducible train/test split.
  • Data Dictionary: we provide a data dictionary to describe the meanings of column names in the “Thorax and Pleural Effusion Segmentation Metadata” spreadsheet.

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding Original CT Images from NSCLC-Radiomics DICOM
Thoracic Segmentations NIFTI
Pleural Effusion Segmentations NIFTI
Segmentation Features and Image Metadata CSV
Baseline UNet 2D Summary PDF
Baseline UNet 3D Summary PDF
Data Dictionary DOCX

Version 1: Updated 2020/04/03

Title Data Type Format Access Points Subjects Studies Series Images License
Thoracic Segmentations NIFTI
Pleural Effusion Segmentations NIFTI
Segmentation Features and Image Metadata CSV
Corresponding Original CT Images from NSCLC-Radiomics DICOM

Collections Used In This Analysis Result

Related Collections