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RSNA-ASNR-MICCAI-BraTS-2021 | RSNA-ASNR-MICCAI-BraTS-2021

DOI: 10.7937/jc8x-9874 | Page Accessibility: Public | Analysis Result

Analysis Result Snapshot
Cancer Types Location Subjects Related Collections Supporting Data Updated
Glioma Brain 1,480 Tumor segmentations 08/25/2023

Summary

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.

Dr. Bakas’s group here provides brain-extracted Segmentation task BraTS 2021 challenge TRAINING and VALIDATION set data in NIfTI that do not pose DUA-level risk of potential facial reidentification, and segmentations to go with them.
This group has provided some of the brain-extracted BraTS challenge TEST data in NIfTI, and segmentations to go with them (here and here, from the 2018 challenge, request through [email protected]).
This group here provides brain-extracted Classification task BraTS 2021 challenge TRAINING and VALIDATION set data includes DICOM→ NIfTI→ dcm files, registered to original orientation, data files that do not strictly adhere to the DICOM standard. BraTS 2021 Classification challenge TEST files are unavailable at this time.
You may want the original corresponding DICOM-format files drawn from TCIA Collections; please note that these original data are not brain-extracted and may pose enough reidentification risk that TCIA must keep them behind an explicit usage agreement.

Please also note that specificity of which exact series in DICOM became which exact volume in NIfTI has, unfortunately, been lost to time but the available lists below represent our best effort at reconstructing the link to the BraTS source files.

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

Click the Versions tab for more info about data releases.

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to [email protected] before accessing the data. Be sure to include “RSNA-ASNR-MICCAI-BraTS-2021 DOI: 10.7937/jc8x-9874” in the COLLECTION section of your form to assure the request is processed appropriately. 

Title Data Type Format Access Points Studies Series Images License
Challenge data NIFTI and DCM CC BY 4.0
ID Crosswalk between BraTS ID and TCIA ID XLSX CC BY 4.0
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 SEG, MR 7,131 407,245 TCIA Restricted
Original corresponding DICOM used in BraTS 2021 MGMT Classifier Training set from CPTAC-GBM , TCGA-GBM , IvyGAP , UPENN-GBM TCIA Restricted
Original corresponding DICOM used in BraTS 2021 Segmentation Validation set from CPTAC-GBM , TCGA-GBM , TCGA-LGG , IvyGAP , UPENN-GBM TCIA Restricted
Original corresponding DICOM used in BraTS 2021 MGMT Classifier Validation set from CPTAC-GBM , TCGA-GBM , IvyGAP , UPENN-GBM TCIA Restricted
Original corresponding imaging from UCSF-PDGM v1 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.

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.

Detailed Description

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)
Segmentation labels of the different glioma sub-regions considered for evaluation are the “enhancing tumor” (ET), the “tumor core” (TC), and the “whole tumor” (WT). The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to “healthy” white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (NCR) parts of the tumor. The appearance of NCR is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edematous/invaded tissue (ED), which is typically depicted by hyper-intense signal in FLAIR. The provided segmentation labels have values of 1 for NCR, 2 for ED, 4 for ET, and 0 for everything else.
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:

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

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 

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

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

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/.”

Other Publications Using This Data

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Collections Used In This Analysis Result

Collections Used In This Analysis Result