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  • Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection (BraTS-TCGA-GBM)

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Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/FilterLicense

Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB)


Tcia button generator
urlhttps://faspex.cancerimagingarchive.net/aspera/faspex/external_deliveries/88?passcode=f800fe81c0e92dbc612be70b91d505e9a511ce0b



(Download and apply the IBM-Aspera-Connect plugin to your browser.)

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BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB)

Please contact the helpdesk to request access to these files.

TCIA Restricted

Click the Versions tab for more info about data releases.

Collections Used in this Third Party Analysis


Below is a list of the Collections used in these analyses:

Source Data TypeDownload all or Query/FilterLicense

Corresponding Original Images from TCGA-GBM - 135 subjects (DICOM, 6 GB)


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



(Requires NBIA Data Retriever.)

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Please contact help@cancerimagingarchive.net  with any questions regarding usage.


Localtab
titleDetailed Description

Detailed Description

Data resulting from this experiment is available in the following formats:

  • DICOM image format
  • Processed NIFTI images with segmentations and radiomic features


Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy 

Tcia limited license policy

Info
titleData Citation

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. DOI:  10.7937/K9/TCIA.2017.KLXWJJ1Q


Info
titlePublication Citation

Bakas S, Akbari H, Sotiras A, Bilello M, Rozycki M, Kirby J, Freymann J, Farahani K, Davatzikos C. (2017) Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features Nature Scientific Data, 4:170117 DOI:  10.1038/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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7


Other Publications Using This Data

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

  • Astaraki, M., Wang, C., Carrizo, G., Toma-Dasu, I., & Smedby, Ö. (2020). Multimodal Brain Tumor Segmentation with Normal Appearance Autoencoder. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Shenzhen, China.
  • Bhadani, S., Mitra, S., & Banerjee, S. (2020). Fuzzy volumetric delineation of brain tumor and survival prediction. Soft Computing, 24(17), 13115-13134. doi:10.1007/s00500-020-04728-8
    Chan, H.-W., Weng, Y.-T., & Huang, T.-Y. (2020). Automatic Classification of Brain Tumor Types with the MRI Scans and Histopathology Images. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries Shenzhen, China,.
  • Chen, M., Wu, Y., & Wu, J. (2020). Aggregating Multi-scale Prediction Based on 3D U-Net in Brain Tumor Segmentation. Paper presented at the Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. , Shenzhen, China.
  • Han, W.-S., & Han, I. S. (2020, October 2019). Multimodal Brain Image Segmentation and Analysis with Neuromorphic Attention-Based Learning. Paper presented at the International MICCAI Brainlesion Workshop, Shenzhen, China.
  • Sheller, M. J., Edwards, B., Reina, G. A., Martin, J., Pati, S., Kotrotsou, A., . . . Bakas, S. (2020). Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci Rep, 10(1), 12598. doi:https://doi.org/10.1038/s41598-020-69250-1
  • Thakur, S., Doshi, J., Pati, S., Rathore, S., Sako, C., Bilello, M., . . . Bakas, S. (2020). Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training. Neuroimage, 220, 117081. doi:https://doi.org/10.1016/j.neuroimage.2020.117081


Localtab
titleVersions

Version 1 (Current): 2017/07/17


Data TypeDownload all or Query/Filter
Images - 135 subjects (DICOM, 6 GB)


Note: This collection contains data that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Limited Access License to help@cancerimagingarchive.net before accessing the data.

Processed NIFTI images with segmentations and radiomic features - 102 subjects (NIFTI, 767 MB)


Download and apply the IBM-Aspera-Connect plugin to your browser to access the data.

BRATS 2018 Test Data Set - 33 subjects (NIFTI, 255 MB)

Please contact the helpdesk to request access to these files.





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