Multimodal Brain Tumor Segmentation Challenge 2019: Registration


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BraTS 2019 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 2019 challenge, please follow the steps below:

  1. 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.
  2. Once your IPP account is approved, login to ipp.cbica.upenn.edu and then click on the application "BraTS'19: Data Request", under the "MICCAI BraTS 2019" group.
  3. Fill in the requested details and press "Submit Job".
  4. 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 2019 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 are expected to use CBICA's IPP to evaluate your method against the ground truth labels of the validation and testing datasets. 

 

You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the following three manuscripts:

[1] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, et al. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)", IEEE Transactions on Medical Imaging 34(10), 1993-2024 (2015) DOI: 10.1109/TMI.2014.2377694

[2] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J.S. Kirby, et al., "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

[3] S. Bakas, M. Reyes, A. Jakab, S. Bauer, M. Rempfler, A. Crimi, et al., "Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge", arXiv preprint arXiv:1811.02629 (2018)

In addition, if there are no restrictions imposed from the journal/conference you submit your paper about citing "Data Citations", please be specific and also cite the following:

[4] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "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

[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, et al., "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

 

Feel free to send any communication related to the BraTS challenge to brats2019@cbica.upenn.edu