Multimodal Brain Tumor Segmentation Challenge 2018
BraTS 2018 Data Request
Challenge data may be used for all purposes, provided that the challenge is appropriately referenced using the citations given at the bottom of this page.
To request the training and the validation data of the BraTS 2018 challenge, please follow the steps below:
- Create an account in CBICA's Image Processing Portal (ipp.cbica.upenn.edu) and wait for its approval. Note that a confirmation email will be sent so make sure that you also check your Spam folder.
- Once your IPP account is approved, login to ipp.cbica.upenn.edu and then click on the application "BraTS'18: Data Request", under the "MICCAI BraTS 2018" group.
- Fill in the requested details and press "Submit Job".
- Once your request is recorded, you will receive an email pointing to the "results" of your submitted job. You need to login to IPP, access the "Results.zip" file, in which you will find the file “REGISTRATION_STATUS.txt” that will provide the links to download the BraTS 2018 data. The training data will include for each subject the 4 structural modalities, ground truth segmentation labels and accompanying survival information, age, and resection status, whereas the validation data will include on the 4 modalities.
Please note that you can use CBICA's IPP to evaluate your method against the ground truth labels of the validation dataset. For this evaluation you can use the applications in IPP named: "BraTS'18 Validation Data: Segmentation Task" and "BraTS'18 Validation Data: Survival Task", to upload your segmentations and predicted survival labels, respectively. Note that the segmentations you upload must be named using only the subject ID and the extension nii.gz. Furthermore, the predicted survival data must have the subject ID in column 1 and the predicted survival values in days in column 2. Once your uploaded data are evaluated, you will receive an email pointing to a separate zip file for each of the tasks, accessible via the IPP.
You can access the BraTS 2018 challenge leaderboard here.
Feel free to send any communication related to the BraTS challenge to firstname.lastname@example.org
Please make sure that whenever you use and/or refer to the BraTS datasets in your manuscripts, you should always cite the following two papers:
 Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp Γ, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SM, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694
 Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby JS, Freymann JB, Farahani K, Davatzikos C. "Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features", Nature Scientific Data, 4:170117 (2017) DOI: 10.1038/sdata.2017.117
In addition, if the journal/conference you submit your paper does not restrict you from citing "data citations" you might also cite the following:
 Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q
 Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. "Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection", The Cancer Imaging Archive, 2017. DOI: 10.7937/K9/TCIA.2017.GJQ7R0EF