- Faculty & Staff
- Core Faculty & Staff
- Spyridon Bakas, Ph.D.
- MICCAI 2019 BraTS
Multimodal Brain Tumor Segmentation Challenge 2019: Data
• Scope • Relevance • Tasks • Data • Evaluation • Participation Summary • Registration • Previous BraTS • People •
Data Description Overview
To register for participation and get access to the BraTS 2019 data, you can follow the instructions given at the "Registration" page.
Ample multi-institutional routine clinically-acquired pre-operative multimodal MRI scans of glioblastoma (GBM/HGG) and lower grade glioma (LGG), with pathologically confirmed diagnosis and available OS, are provided as the training, validation and testing data for this year’s BraTS challenge. Specifically, the datasets used in this year's challenge have been updated, since BraTS'18, with more routine clinically-acquired 3T multimodal MRI scans, with accompanying ground truth labels by expert board-certified neuroradiologists.
Validation data will be released on July 15, through an email pointing to the accompanying leaderboard. This, will allow participants to obtain preliminary results in unseen data and also report it in their submitted papers, in addition to their cross-validated results on the training data. The ground truth of the validation data will not be provided to the participants, but multiple submissions to the online evaluation platform (CBICA's IPP) will be allowed.
Finally, all participants will be presented with the same test data, which will be made available through email during 26 August-7 September and for a limited controlled time-window (48h), before the participants are required to upload their final results in CBICA's IPP. The top-ranked participating teams will be invited before the end of September to prepare slides for a short oral presentation of their method during the BraTS challenge.
Imaging Data Description
All BraTS multimodal scans are available as NIfTI files (.nii.gz) and describe a) native (T1) and b) post-contrast T1-weighted (T1Gd), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, and were acquired with different clinical protocols and various scanners from multiple (n=19) institutions, mentioned as data contributors here.
All the imaging datasets have been segmented manually, by one to four raters, following the same annotation protocol, and their annotations were approved by experienced neuro-radiologists. Annotations comprise the GD-enhancing tumor (ET — label 4), the peritumoral edema (ED — label 2), and the necrotic and non-enhancing tumor core (NCR/NET — label 1), as described both in the BraTS 2012-2013 TMI paper and in the latest BraTS summarizing paper (also see Fig.1). The provided data are distributed after their pre-processing, i.e. co-registered to the same anatomical template, interpolated to the same resolution (1 mm^3) and skull-stripped.
Comparison with Previous BraTS datasets
The BraTS data provided since BraTS'17 differs significantly from the data provided during the previous BraTS challenges (i.e., 2016 and backwards). The only data that have been previously used and are utilized again (during BraTS'17-'19) are the images and annotations of BraTS'12-'13, which have been manually annotated by clinical experts in the past. The data used during BraTS'14-'16 (from TCIA) have been discarded, as they described a mixture of pre- and post-operative scans and their ground truth labels have been annotated by the fusion of segmentation results from algorithms that ranked highly during BraTS'12 and '13. For BraTS'17, expert neuroradiologists have radiologically assessed the complete original TCIA glioma collections (TCGA-GBM, n=262 and TCGA-LGG, n=199) and categorized each scan as pre- or post-operative. Subsequently, all the pre-operative TCIA scans (135 GBM and 108 LGG) were annotated by experts for the various glioma sub-regions and included in this year's BraTS datasets.
This year we provide the naming convention and direct filename mapping between the data of BraTS'19, BraTS'18, BraTS'17, and the TCGA-GBM and TCGA-LGG collections, available through The Cancer Imaging Archive (TCIA).
Survival Data Description
The overall survival (OS) data, defined in days, are included in a comma-separated value (.csv) file with correspondences to the pseudo-identifiers of the imaging data. The .csv file also includes the age of patients, as well as the resection status. Note that only subjects with resection status of GTR (i.e., Gross Total Resection) will be evaluated, and you are only expected to send your predicted survival data for those subjects.
Use of Data Beyond BraTS
Participants are only allowed to use additional private data (from their own institutions) for data augmentation, if they also report results using only the BraTS'19 data and discuss any potential difference in their papers and results. This is due to our intentions to provide a fair comparison among the participating methods.
Data Usage Agreement / Citations
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:
 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
 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
 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:
 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
 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 email@example.com