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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

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.

Copied from the Kaggle site: 

You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the flagship manuscript (published or pre-published) resulting from the challenge as well as the following three manuscripts:

 Baid, U., et al.,
Localtab Group


Localtab
activetrue
titleData Access

Data Access

Tcia license 4 international

Note to curators! This macro is for collections that are restricted due to facial reconstruction possibility.

Tcia head license access


Data TypeDownload all or Query/FilterLicense

Images and Segmentations (NIfTI, 1.4 TB)

11.731 GB

Complete dataset all tasks

Tcia button generator

complete data on faspex


(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

Tcia cc by 4







BraTS 2021 Training set (NIfTI, XX GB )


(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

Tcia cc by 4

BraTS 2021 Validation set (NIfTI, XX GB)


(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

Tcia cc by 4










BraTS Task2 Radiogenomics Classifier task Training images (DICOM, GB)

  1. link to faspex for nifti
  2. link to GDC/PDC if the detail are there ( e.g. TCGA)
  3. dicom-->nifti→dicom also on Faspex

( is this open license stuff?)

BraTS Task2 Radiogenomics Classifier task molecular marker table (JSON/XLS/CSV spreadsheet, MB)

  1. download JSON/XLS/CSV spreadsheet  or 
  2. if external link that would go below not in this table

( is this open license stuff?)

BraTS 2021 Classifier Training Task
Brand new data files not elsewhere on TCIA

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 

CC by 4.0




Clinical data (CSV)



Tcia button generator



Tcia cc by 4

Tcia restricted license

Feature matrices (format, ##GB)

link or attachment

Tcia cc by 4

ID Crosswalk between BraTS ID and TCIA ID (xlsx, 79 kB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/133073473/BraTS2021_MappingToTCIA.xlsx?api=v2

BraTS2021_MappingToTCIA



Tcia cc by 4

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

Nci_crdc additional resources


Note to curators! Below are examples for what to do with other external resources/links that don't fit into the above categories.

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.

  • Software / Code on Github
  • Genomics data in DbGAP
  • Genomics data in Gene Expression Omnibus
  • RSNA or kaggle link to molecular marker table if they only provide us a link

TCIA Collections Used (in part) in these analyses:

Source Data TypeDownloadLicense
DICOM Used in BraTS 2021 Segmentation Training set from 

CPTAC-GBMTCGA-GBMTCGA-LGGACRIN-FMISO-Brain (ACRIN 6684) IvyGAP ,UPENN-GBM

Tcia restricted license

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBMTCGA-GBMTCGA-LGGACRIN-FMISO-Brain (ACRIN 6684)IvyGAP

DICOM Used in BraTS 2021 Classifier Training set from 

CPTAC-GBMTCGA-GBM , IvyGAPUPENN-GBM

Tcia restricted license

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBMTCGA-GBMTCGA-LGGACRIN-FMISO-Brain (ACRIN 6684)IvyGAP

DICOM Used in BraTS 2021 Segmentation Validation set from 

CPTAC-GBMTCGA-GBMTCGA-LGGIvyGAPUPENN-GBM

Tcia restricted license

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBMTCGA-GBMTCGA-LGGACRIN-FMISO-Brain (ACRIN 6684)IvyGAP

DICOM Used in BraTS 2021 Classifier Validation set from 

CPTAC-GBMTCGA-GBM ,  IvyGAPUPENN-GBM

Tcia restricted license

To get data used in BraTS-2021 please request the following Collections in your Agreement:

CPTAC-GBMTCGA-GBMTCGA-LGGACRIN-FMISO-Brain (ACRIN 6684)IvyGAP


new trapd-lnk files of only already-used UCSF-PDGM files

(Download and apply the IBM-Aspera-Connect plugin to your browser to retrieve this faspex package) 



Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:

<these get filled in as groups cite the dataset in their papers related to analysis of the 2021 task 1 & task2 using this Collection's DOI>




Note:

Notes about Image Registration:

  • Transformation matrices DICOM to NIfTI are not available.
Seg tasks are x/y/z 240/240/155 and all Radiogenomics Classifier
  • 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).


Localtab
titleDetailed Description

Detailed Description

Image Statistics

Radiology Image StatisticsPathology Image Statistics

Modalities

Modalities

MR, Segmentations

Number of Patients

2,040

Number of Studies


Number of Series

7,131

Number of Images


Images Size (GB)11.731


  •  the "challenge test set dataset" is sequestered on synapse.org as a tarball of nii & seg & age & diagnosis. Not available. Please see <website> their website for more detail.

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:

Source Data TypeDownloadLicense


"The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/."

Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

Tcia limited license policy


DOI goes here. Create using Datacite with information from Collection Approval form

44 authors, most with ORCiD. 

Info
titleData Citation
Info
titlePublication Citation

U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification arXiv:2107.02314, 2021. 

Info
titleAcknowledgement

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 


Info
titleAcknowledgement

"The results <published or shown> here are in whole or part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/."


Info
titleTCIA Citation
Info
titlePublication Citation

You are free to use and/or refer to the BraTS datasets in your own research, provided that you always cite the flagship manuscript (published or pre-published) resulting from the challenge as well as the following three manuscripts:

  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).
  1. The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification
, arXiv:
  1. (Version 2). arXiv. DOI: 10.48550/arXiv.2107.02314
, 2021.


  1. Menze, B. H., Jakab, A., Bauer, S., Kalpathy-Cramer, J., Farahani, K., Kirby, J., Burren
, Y., Porz
  1. , 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.,
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
  1. … 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

  2. 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.
1109
  1. 1038/
tmi.2014.2377694
  1. sdata.2017.117




Info
titleTCIA 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.

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

In addition, please be specific and also cite the following datasets that were part of this Challenge:

  1. 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
  2. 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
  3. 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
  4. 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 
  5. 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) [Dataset].  The Cancer Imaging Archive.  https://10.7937/tcia.bdgf-8v37 
  6. 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
  7. .org/10.7937/TCIA.709X-DN49
  8. .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.


Localtab
titleVersions

Version

X

1 (Current): Updated

yyyy

2023/

mm/dd

copy Access tab table here

<< One or two sentences about what you changed since last version.  No note required for version 1. >> 

05/23