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  • RSNA-ASNR-MICCAI-BraTS-2021

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  • Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  • Continue with any names from additional submitting sites if collection consists of more that one.

  • Copied from Kaggle site: "Data used in this publication were obtained as part of the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID (syn25829067)." 

Clinical data (CSV)

Localtab Group


Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB

Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the NBIA Data Retriever

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



batch2 by case ID

Data TypeDownload all or Query/FilterLicense

Images and Segmentations (NIfTI, 1.4 TB)


Complete dataset

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 Segmentation  Training set in batches of XXX PatientID Images, Segmentations (NIfTI, 1.4 TB ) from CPTAC



  1. batch1 (ID PP to QQ) (XX GB)

    Tcia button generator

    batch1


  2. batch2 (ID XX to YY) (XX GB)

    Tcia button generator

    batch2



  3.  (and so on)



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

Tcia cc by 4

BraTS 2021 Segmentation Validation set in batches of XXX PatientID Images, (maybe not Segmentations?)  (NIfTI, 1.4 TB)

Tcia button generator

batch1

Tcia button generator


  1. Tcia button generator

    NIfTI derived from Ivy GAP sources



  2. Tcia button generator

    NIfTI derived from CPTAC-GBM sources


  3. (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 images (DICOM?, GB)

  1. link to faspex for nifti
  2. link to GDC/PDC if the detail are there

( 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?)

2021 Segmentation Validation set in batches of XXX PatientID  Segmentations  (NIfTI, 1.4 TB)

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

TCIA Restricted (special permission beyond defacing DUA required to be fleshed out later)

BraTS 2021 Segmentation 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




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 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

Images from TCGA-LGG that have been transformed for use in this challenge  - 108 Subjects (DICOM, 8.5 GB)
Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/24282668/doiJNLP-JAMS4RFq.tcia?api=v2

TCGA-LGG batch1 source series

Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever . 

Tcia restricted license

)



Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/24282666/doiJNLP-QoOaKUdn.tcia?api=v2

TCGA-GBM batch1 source series

Tcia restricted license

Images from Ivy GAP that have been transformed for use in this challenge- XXX subjects (DICOM, XX GB)
Tcia button generator

Ivy GAP batch1 source series

Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the  NBIA Data Retriever

Tcia restricted license

CPTAC-GBM DICOM

Tcia restricted license

UCSF-PDGM DICOM (note Evan didn't give this to us  –  only nifti so far) UPENN-GBM DICOM ( others)

Transformation matrices DICOM to NII (zip, XXMB)

Tcia button generator

Tcia cc by 4

Tcia button generator

Tcia cc by 4

Tcia restricted license

Feature matrices (format, ##GB)

link or attachment

Tcia cc by 4

Click the Versions tab for more info about data releases.

Additional Resources for this Dataset

Note to curators! Use this any time you are linking to NCI's IDC/GDC/PDC resources.  The links below are examples and will need to be tailored to point to the specific dataset (see parameters in URLS).

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:

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>



Tcia cc by 4

Tcia restricted license

Feature matrices (format, ##GB)

link or attachment

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-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

Tcia restricted license

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

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Classifier Training set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

Tcia restricted license

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

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Segmentation Validation set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

Tcia restricted license

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

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

DICOM Used in BraTS 2021 Classifier Validation set from 

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP ,

Note: Limited Access.
Download requires the NBIA Data Retriever

Tcia restricted license

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

CPTAC-GBM , TCGA-GBM , TCGA-LGG , ACRIN-FMISO-Brain (ACRIN 6684) , IvyGAP

UCSF-PDGM  DICOM

Data retriever or Faspex?

Tcia cc by 4

UPENN-GBM DICOM

Data retriever or Faspex?

Tcia cc by 4

Images from TCGA-LGG that have been transformed for use in this challenge  - 108 Subjects (DICOM, 8.5 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/24282668/doiJNLP-JAMS4RFq.tcia?api=v2

TCGA-LGG batch1 source series

Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file to your computer, which you must open with the  NBIA Data Retriever . 

Tcia restricted license

Images from TCGA-GBM that have been transformed for use in this challenge- 135 subjects (DICOM, 6 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/24282666/doiJNLP-QoOaKUdn.tcia?api=v2

TCGA-GBM batch1 source series


Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the NBIA Data Retriever

Tcia restricted license

Images from Ivy GAP that have been transformed for use in this challenge- XXX subjects (DICOM, XX GB)


Tcia button generator

Ivy GAP batch1 source series


Note: Limited Access.
Click the Download  button to save a ".tcia" manifest file, needs the  NBIA Data Retriever

Tcia restricted license


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>



  •  the "challenge test set dataset" is sequestered on synapse as a tarball of nii & seg & age & diagnosis. Not available. Please see <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:

  • Manifest of case identifiers between BraTS and TCIA
  • Spreadsheet list of cases and series used in prior year BraTS Challenges  which may refer also to these:
  • Spreadsheet list of new (DICOM and NIFTI) series files with no TCIA DICOM equivalent: 
    • Manifest of (DICOM and NIFTI) files sourced from other TCIA Collections, so you can avoid superset accidental duplication in case you want everything TCIA has, after you've gotten all of BRaTS data: 


    Localtab
    titleDetailed Description

    Detailed Description

    Image Statistics

    		Radiology Image StatisticsPathology Image Statistics

    Modalities



    Number of Patients



    Number of Studies



    Number of Series



    Number of Images



    Images Size (GB)


    •  the "challenge test set dataset" is sequestered on synapse as a tarball of nii & seg & age & diagnosis. Not available. Please see <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:

    Note: Transformation matrices DICOM to NIfTI are not available.ll Seg tasks are x/y/z 240/240/155 and all Radiogenomics Classifier files are restored to original DICOM resolution & orientation.

    Localtab
    titleDetailed Description

    Detailed Description

    Image Statistics

    		Radiology Image StatisticsPathology Image Statistics

    Modalities

    Number of Patients

    Number of Studies

    Number of Series

    Number of Images

    		Images Size (GB)


    Localtab
    titleCitations & Data Usage Policy

    Citations & Data Usage Policy

    Tcia limited license policy

    Info
    titleData Citation

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

    44 authors, most with ORCiD. 


    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

    "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

    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:

    1.  U.Baid, et al., The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification, arXiv:2107.02314, 2021.
    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). https://doi.org/10.1109/tmi.2014.2377694
    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

      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.org/10.7937/TCIA.709X-DN49


    Localtab
    titleVersions

    Version X (Current): Updated yyyy/mm/dd

    copy Access tab table here

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


    ...