RSNA-ASNR-MICCAI-BraTS-2021 | RSNA-ASNR-MICCAI-BraTS-2021
DOI: 10.7937/jc8x-9874 | Data Citation Required | 247 Views | 8 Citations | Analysis Result
| Location | Subjects | Size | Updated | |||
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| Glioma | Brain | 1,480 | Tumor segmentations | 2023/08/25 |
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 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.A note about available TCIA data which were converted for use in this Challenge: (Training, Validation, Test)
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 via TCIA’s Helpdesk.
Data Access
Version 1: Updated 2023/08/25
| Title | Data Type | Format | Access Points | Subjects | License | |||
|---|---|---|---|---|---|---|---|---|
| Challenge data both tasks | Segmentation, MR | DICOM and NIFTI | Download requires IBM-Aspera-Connect plugin |
1,480 | 7,131 | 407,245 | CC BY 4.0 | |
| ID Crosswalk map between BraTS ID and TCIA ID | Other | XLSX | CC BY 4.0 |
Collections Used In This Analysis Result
| Title | Data Type | Format | Access Points | Subjects | 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 | CT, MR | DICOM | Requires NBIA Data Retriever |
644 | 2,224 | 3,968 | 673,833 | TCIA Restricted |
| Original corresponding DICOM used in BraTS 2021 MGMT Classifier Training set from CPTAC-GBM , TCGA-GBM , IvyGAP , UPENN-GBM | CT, MR | DICOM | Requires NBIA Data Retriever |
293 | 1,072 | 1,956 | 342,313 | TCIA Restricted |
| Original corresponding DICOM used in BraTS 2021 Segmentation Validation set from CPTAC-GBM , TCGA-GBM , TCGA-LGG , IvyGAP , UPENN-GBM | MR | DICOM | Requires NBIA Data Retriever |
130 | 312 | 716 | 104,327 | TCIA Restricted |
| Original corresponding DICOM used in BraTS 2021 MGMT Classifier Validation set from CPTAC-GBM , TCGA-GBM , IvyGAP , UPENN-GBM | MR | DICOM | Requires NBIA Data Retriever |
45 | 159 | 324 | 62,353 | TCIA Restricted |
| Original corresponding imaging from UCSF-PDGM v1 | MR | NIFTI | Requires IBM-Aspera-Connect plugin |
298 | 1,192 | 0 | 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:
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data Citation |
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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 |
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“The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/.” |
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:
- 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):
- BraTS 2018 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS’18 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms. More information can be found at http://www.med.upenn.edu/sbia/brats2018.html. This challenge utilizes subsets of The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) and The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) primary data set, and has resulted in multiple TCIA Analysis Results data sets.
- Multimodal Brain Tumor Segmentation Challenge 2019:
- From the challenge web site: https://www.med.upenn.edu/cbica/brats2019/data.htmlBraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthermore, to pinpoint the clinical relevance of this segmentation task, BraTS’19 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms. Finally, BraTS’19 intends to experimentally evaluate the uncertainty in tumor segmentations.BraTS 2019 ran in conjunction with the MICCAI 2019 conference, on Oct. 17 2019, as part of the full-day BrainLes Workshop.
- BraTS-TCGA-GBM
- BraTS-TCGA-LGG
- Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS):
- 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 as input to the BraTS Collection, and will require a Usage Agreement on file.
- 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).
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).
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication Citation |
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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 |
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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 |
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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 |
Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.