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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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This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-GBM collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.

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Localtab Group



Localtab
activetrue
titleData Access

Data Access

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

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

 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

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

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

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

Please contact the helpdesk to request access to these files.


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

Collections Used in this Third Party Analysis
Below is a list of the Collections used in these analyses:




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 

Public collection license

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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