3718904
doi
10.5281/zenodo.3718904
oai:zenodo.org:3718904
Bjoern Menze
Technical University of Munich (TUM), Germany
Christos Davatzikos
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
Jayashree Kalpathy-Cramer
Athinoula A. Martinos for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, USA
Keyvan Farahani
Cancer Imaging Program, National Cancer Institute (NCI), National Institutes of Health (NIH), USA
Michel Bilello
University of Pennsylvania, Philadelphia, PA, USA
Suyash Mohan
University of Pennsylvania, Philadelphia, PA, USA
John B. Freymann
The Cancer Imaging Archive (TCIA), Cancer Imaging Program, NCI, National Institutes of Health (NIH), USA
Justin S. Kirby
The Cancer Imaging Archive (TCIA), Cancer Imaging Program, NCI, National Institutes of Health (NIH), USA
Manmeet Ahluwalia
Cleveland Clinic, , Cleveland, OH, USA
Volodymyr Statsevych
Cleveland Clinic, , Cleveland, OH, USA
Raymond Huang
Brigham and Women's Hospital, Boston, MA, USA
Hassan Fathallah-Shaykh
University of Alabama at Birmingham, AL, USA
Roland Wiest
University of Bern, Switzerland
Andras Jakab
University of Debrecen, Hungary
Rivka R. Colen
University of Pittsburgh Medical Center
Aikaterini Kotrotsou
MD Anderson Cancer Center, TX, USA
Daniel Marcus
Washington University School of Medicine in St.Louis, MO, USA
Mikhail Milchenko
Washington University School of Medicine in St.Louis, MO, USA
Arash Nazeri
Washington University School of Medicine in St.Louis, MO, USA
Marc-Andre Weber
Heidelberg University, Germany
Abhishek Mahajan
Tata Memorial Center, Mumbai, India
Ujjwal Baid
Tata Memorial Center, Mumbai, India
MICCAI Brain Tumor Segmentation (BraTS) 2020 Benchmark: "Prediction of Survival and Pseudoprogression"
Spyridon Bakas
Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
info:eu-repo/semantics/openAccess
Creative Commons Attribution No Derivatives 4.0 International
https://creativecommons.org/licenses/by-nd/4.0/legalcode
MICCAI Challenges
Biomedical Challenges
MICCAI
Brain tumor
Segmentation
Glioblastoma
Glioma
Uncertainty
Survival prediction
Pseudoprogression
Recurrence
<p>This is the challenge design document for the "MICCAI Brain Tumor Segmentation (BraTS) 2020 Benchmark: 'Prediction of Survival and Pseudoprogression' ", accepted for MICCAI 2020.</p>
<p>BraTS 2020 utilizes multi-institutional MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Compared to BraTS'17-'19, this year BraTS includes both pre-operative and post-operative scans (i.e., including surgically imposed cavities) and attempts to quantify the uncertainty of the predicted segmentations. Furthermore, to pinpoint the clinical relevance of the segmentation task, BraTS’20 also focuses on 1) the prediction of patient overall survival from pre-operative scans (Task 2) and 2) the distinction between true tumor recurrence and treatment related effects on the post-operative scans (Task 3), via integrative analyses of quantitative imaging phenomic features and machine learning algorithms. Ground truth annotations are created and approved by expert neuroradiologists for every subject included in the training, validation, and testing datasets to quantitatively evaluate the predicted tumor segmentations (Task 1). Furthermore, the quantitative evaluation of the clinically-relevant tasks (i.e., overall survival (Task 2) and distinction between tumor recurrence and treatment related effects (Task 3)), is performed according to real clinical data. Participants are free to choose whether they want to focus only on one or multiple tasks.</p>
Zenodo
2020-03-20
info:eu-repo/semantics/other
3718903
1584686815.143255
9716296
md5:78aec56fb9a7be5e70127a1e409f8dc4
https://zenodo.org/records/3718904/files/MICCAIBrainTumorSegmentation(BraTS)2020Benchmark_PredictionofSurvivalandPseudoprogression.pdf
public
10.5281/zenodo.3718903
isVersionOf
doi