Summary
This dataset includes brain MRI scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., T1, T1c, T2, T2-FLAIR) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their MGMT promoter methylation status. These scans are a collection of data from existing TCIA collections, but also cases provided by individual institutions and willing to share with a cc-by license.The BraTS dataset describes a retrospective collection of brain tumor structural mpMRI scans of 2,040 patients (1,480 here), acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. The 4 structural mpMRI scans included in the BraTS challenge describe a) native (T1) and b) post-contrast T1-weighted (T1Gd (Gadolinium)), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, acquired with different protocols and various scanners from multiple institutions. Furthermore, data on the O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is provided as a binary label. Notably, MGMT is a DNA repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response.
It is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status.
A note about available TCIA data which were converted for use in this Challenge
Dr. Bakas's group has provided brain-extracted challenge TRAINING data in NIfTI that do not pose DUA-level risk of potential facial reidentification, and segmentations to go with them. This group has provided the brain-extracted challenge TEST data in NIfTI, and segmentations to go with them (here and here, available upon request through the helpdesk). If you want the original DICOM-format files, then as part of page 1 of this Agreement form TCIA needs to have the citation(s) for the (TCGA-GBM, CPTAC-GBM, Ivy GAP, TCGA-LGG, UPENN-GBM, UCSF-PDGM, ACRIN-FMISO-Brain) filled in. These earlier data pose enough reidentification risk that TCIA must keep them behind an explicit usage agreement follow this restriction.
BraTS 2021 Classification task data includes DICOM→ NIfTI→ dcm files, brain extracted and registered data files that do not strictly adhere to the DICOM standard. These TRAINING and VALIDATION data are available at the link below.
Please also note that specificity of the exact series in DICOM became which exact volume in NIfTI has, unfortunately, been lost to time but the available lists below (in most cases, by Collection, TCIA Patient ID, Study date) represent our best effort at reconstructing the BraTS input imaging.
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
- Data used in this publication were obtained as part of the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID (syn25829067).
Data Access
| Data Type | Download all or Query/Filter | License |
|---|---|---|
| Challenge data (both tasks, 142 GB, *.nii.gz or *.dcm) | (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | |
ID Crosswalk between BraTS ID and TCIA ID (xlsx, 79 kB) |
Click the Versions tab for more info about data releases.
| Source Data Type | Download | License |
|---|---|---|
| Original corresponding DICOM used in BraTS 2021 Segmentation Training set from CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,UPENN-GBM | ||
| Original corresponding DICOM used in BraTS 2021 MGMT Classifier Training set from | ||
| Original corresponding DICOM used in BraTS 2021 Segmentation Validation set from CPTAC-GBM , TCGA-GBM , TCGA-LGG , IvyGAP , UPENN-GBM | ||
| Original corresponding DICOM used in BraTS 2021 MGMT Classifier Validation set from | ||
Original corresponding imaging from UCSF-PDGM v1 | (Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) | CC BY 4.0 |
Additional Resources for this Dataset
The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.
- Imaging Data Commons (IDC) (Imaging Data)
- Genomic Data Commons (GDC) (Genomic, Digitized Histopathology & Clinical Data)
- Proteomic Data Commons (PDC) (Proteomic & Clinical Data)
The following external resources have been made available by the data submitters. These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.
IvyGAP provides access to additional resources for this data:
Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:
Detailed Description
Image Statistics | Radiology Image Statistics |
|---|---|
Modalities | MR, Segmentations |
Number of Patients | 1,480 |
Number of Studies | |
Number of Series | 7,131 |
Number of Images | 407,245 |
| Images Size (GB) | 140 |
NOTE: The "challenge test set dataset" is sequestered on synapse.org (Project SynID: syn25829067). Please see their site for more detail.
NOTE: Segmentation task nifti: Number of Images 7,131 (Seg) , Images Size (GB)12 (Seg)
NOTE: Classification task nifti+DICOM: Number of Images 400,114 (Class), Images Size (GB) 128 (Class)
The data used in BraTS Challenges often have some overlap with other TCIA Collections, cases, and series. Some filters for handling these, so that you can work with statistically not-duplicated images, include these below:
- Manifest of case identifiers between BraTS and TCIA, NOTE: includes new series files with no TCIA equivalent: BraTS2021_MappingToTCIA.xlsx
- Spreadsheet list of cases and series used in prior year BraTS Challenges may also refer to these:
- Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS)
- Multimodal Brain Tumor Segmentation Challenge 2019
- Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection (BraTS-TCGA-GBM)
- Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection (BraTS-TCGA-LGG)
- Spreadsheet list of new (NIfTI) series files with no TCIA DICOM equivalent: NotPreviouslyInTCIA.csv
You might find these splits useful to navigate accidental duplication while making superset cohorts. These were processed for the BraTS Collection: NOTE: complete this form TCIA Restricted to request access; the TCIA original data may contain risk of
Segmentation Task (Training sets) BraTS2021_TCIAderived_Seg-Task-Training.tcia
- Classification Task (Training sets) BraTS2021_TCIAderived_Class-Task-Training.tcia
- Segmentation Task (Validation sets) BraTS2021_TCIAderived_Seg-Task-Validation.tcia
- Classification Task (Validation sets) BraTS2021_TCIAderived_Class-Task-Validation.tcia
- We didn't split the UCSF-PDGM v1 data by BraTS task, but excerpted series in 299 cases are here as a faspex package: BraTS2021_UCSF-PDGMv1
Notes about Image Registration:
- Transformation matrices DICOM to NIfTI are not available.
- Segmentation task image volume have been set to x=y=240 voxels by z=155 voxels.
- All Radiogenomics Classifier task files are restored to original DICOM resolution & orientation (thus volume may vary).
Citations & Data Usage Policy
Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution should include references to the following citations:
Data Citation
Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F. C., Pati, S., Prevedello, L., Rudie, J., Sako, C., Shinohara, R., Bergquist, T., Chai, R., Eddy, J., Elliott, J., Reade, W., Schaffter, T., Yu, T., Zheng, J., Davatzikos, C., Mongan, J., Hess, C., Cha, S., Villanueva-Meyer, J., Freymann, J. B., Kirby, J. S., Wiestler, B., Crivellaro, P., Colen, R. R., Kotrotsou, A., Marcus, D., Milchenko, M., Nazeri, A., Fathallah-Shaykh, H., Wiest, R., Jakab, A., Weber, M-A., Mahajan, A., Menze, B., Flanders, A E., Bakas, S., (2023) RSNA-ASNR-MICCAI-BraTS-2021 Dataset. The Cancer Imaging Archive DOI: 10.7937/jc8x-9874
Acknowledgement
"The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/."
Publication Citation
1. Baid, U., Ghodasara, S., Mohan, S., Bilello, M., Calabrese, E., Colak, E., Farahani, K., Kalpathy-Cramer, J., Kitamura, F. C., Pati, S., Prevedello, L. M., Rudie, J. D., Sako, C., Shinohara, R. T., Bergquist, T., Chai, R., Eddy, J., Elliott, J., Reade, W., Schaffter, T., Yu, T., Zheng, J., Moawad, A. W., Coelho, L. O., McDonnell, O., Miller, E., Moron, F. E., Oswood, M. C., Shih, R. Y., Siakallis, L., Bronstein, Y., Mason, J. R., Miller, A. F., Choudhary, G., Agarwal, A., Besada, C. H., Derakhshan, J. J., Diogo, M. C., Do-Dai, D D., Farage, L., Go, J. L., Hadi, M., Hill, V. B., Iv, M., Joyner, D., Lincoln, C., Lotan, E., Miyakoshi, A., Sanchez-Montano, M., Nath, J., Nguyen, X. V., Nicolas-Jilwan, M., Ortiz Jimenez, J., Ozturk, K., Petrovic, B. D., Shah, C., Shah, L. M., Sharma, M., Simsek, O., Singh, A. K., Soman, S., Statsevych, V., Weinberg, B. D., Young, R. J., Ikuta, I., Agarwal, A. K.,Cambron, S. C., Silbergleit, R., Dusoi, A., Postma, A. A., Letourneau-Guillon, L., Guzman Perez-Carrillo, G. J., Saha, A., Soni, N., Zaharchuk, G., Zohrabian, V. M., Chen, Y., Cekic, M. M., Rahman, A., Small, J. E., Sethi, V., Davatzikos, C., Mongan, J., Hess, C., Cha, S., Villanueva-Meyer, J., Freymann, J. B., Kirby, J. S., Wiestler, B., Crivellaro, P., Colen, R. R., Kotrotsou, A., Marcus, D., Milchenko, M., Nazeri, A., Fathallah-Shaykh, H., Wiest, R., Jakab, A., Weber, M-A. Mahajan ,A., Menze, B., Flanders, A. E., Bakas, S. (2021). The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (Version 2). arXiv. DOI: 10.48550/arXiv.2107.02314
You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the flagship manuscript above resulting from the challenge as well as the following two manuscripts:
Publication Citation
2. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren, Y., Porz, N., Slotboom, J., Wiest, R., Lanczi, L., Gerstner, E., Weber, M.-A., Arbel, T., Avants, B. B., Ayache, N., Buendia, P., Collins, D. L., Cordier, N., … Van Leemput, K. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). In IEEE Transactions on Medical Imaging (Vol. 34, Issue 10, pp. 1993–2024). Institute of Electrical and Electronics Engineers (IEEE). DOI: 10.1109/tmi.2014.2377694
Publication Citation
3. Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. In Scientific Data (Vol. 4, Issue 1). https://doi.org/10.1038/sdata.2017.117
TCIA Citation
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7
Additional Publication Resources:
The Collection authors suggest the below will give context to this dataset:
You are free to use and/or refer to the BraTS datasets in your own research. In addition, please be specific and also cite the following datasets that were part of this Challenge:
- Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
- Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., & Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
- Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., & Pierce, L. J. (2016). The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (Version 4) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.RNYFUYE9
- Pedano, N., Flanders, A. E., Scarpace, L., Mikkelsen, T., Eschbacher, J. M., Hermes, B., Sisneros, V., Barnholtz-Sloan, J., & Ostrom, Q. (2016). The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) (Version 3) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.L4LTD3TK
- Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., & Hess, C. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.bdgf-8v37
- Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., … Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.709X-DN49
Other Publications Using This Data
TCIA maintains a list of publications which leverage our data. If you have a manuscript you'd like to add please contact TCIA's Helpdesk.