The Burdenko Glioblastoma Progression Dataset (BGPD) is a systematic data collection from 180 patients with primary glioblastoma treated at the Burdenko National Medical Research Center of Neurosurgery between 2014 and 2020.
For each patient, the dataset includes imaging studies conducted for radiotherapy planning and follow-up studies. The radiotherapy studies consist of 4 MRI sequences (T1, T1C, T2, FLAIR),...
Accurate deformable registration between CT and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high radiation doses is highly desirable for assessing changes in organ-at-risk (OAR) locations and shapes at treatment. The objective of this dataset is to provide a means of evaluating the performance of CT-to-CBCT deformable registration and auto-segmentation algorithms for delineating OARs. The...
The detection of breast cancer metastases to lymph nodes is of great prognostic value for patient treatment. Using machine learning to detect metastatic breast cancer to lymph nodes can increase efficiency of pathologist diagnosis and ultimately ensure patients are accurately staged for prospective treatment. This dataset allows for the objective comparison of breast cancer metastases detection algorithms.
The...
The Breast-Diagnosis collection contains cases that are high-risk normals, DCIS, fibroids and lobular carcinomas. Each case has 3 or more distinct MR pulse sequences from a Phillips 1.5 T (usual sequences are labeled T2, STIR and BLISS but may occasionally include other pulse sequences and digital mammogram of tumor specimen). Multiple time point studies on the same patient are possible.
The following is relevant...
Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.
Here,...
Here we release a brain cancer MRI dataset with the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset consists of 47 subjects. A total of 244 lesions were collected with annotations. The dataset contains original patient MRI images (in DICOM format), radiation therapy structure data (in DICOM and NRRD format), code, and clinical information. First,...
This collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (CRT) followed by adjuvant chemotherapy. Two MRI exams are included for each patient: within 90 days following CRT completion and at progression (determined clinically, and based on a combination of clinical performance and/or imaging findings,...
Purpose: To provide an annotated data set of oncologic PET/CT studies for the development and training of machine learning methods and to help address the limited availability of publicly available high-quality training data for PET/CT image analysis projects. This data can also be used for machine learning challenges, which is exemplified in the autoPET MICCAI 2022 competition: https://autopet.grand-challenge.org/.
Data:...
Breast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery....
The Munich AML Morphology Dataset contains 18,365 expert-labeled single-cell images taken from peripheral blood smears of 100 patients diagnosed with Acute Myeloid Leukemia at Munich University Hospital between 2014 and 2017, as well as 100 patients without signs of hematological malignancy. Image acquisition was done using a M8 digital microscope / scanner (Precipoint GmbH, Freising, Germany) at 100-fold optical magnification...
