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

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Summary

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) Low Grade Glioma (LGG) 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-LGG 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.


Data Access

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

Data TypeDownload all or Query/Filter
Images - 108 Subjects (DICOM, 8.5 GB)

Processed images with segmentations and radiomic features - 65 subjects (NIFTI, 536 MB)

BRATS 2018 Test Data Set - 43 subjects (NIFTI, 366 MB)

Please contact helpdesk to request access to these files

help@cancerimagingarchive.net

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

Detailed Description

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

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

Citations & Data Usage Policy 

These collections are freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. Questions may be directed to help@cancerimagingarchive.net. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:

Data Citation

Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Martin Rozycki, Justin Kirby, John Freymann, Keyvan Farahani, and Christos Davatzikos. (2017) Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

TCIA Citation

Trinity Urban, Erik Ziegler, Steve Pieper, Justin Kirby, Daniel Rukas, Britney Beardmore, Bhanusupriya Somarouthu, Evren Ozkan, Gustavo Lelis, Brenda Fevrier-Sullivan, Samarth Nandekar, Andrew Beers, Carl Jaffe, John Freymann, David Clunie, Gordon J. Harris, Jayashree Kalpathy-Cramer. Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting. The Cancer Imaging Archive. doi: 10.7937/TCIA.2019.yk0gm1eb

In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:

Publication Citation

Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello, Michel Rozycki, Justin S Kirby, John B Freymann, Keyvan Farahani, Christos Davatzikos. "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 (2017).

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.

Version 1 (Current): 2019/07/11

Data TypeDownload all or Query/Filter
Images - 108 subjects (DICOM, 8.5 GB)

Processed images with segmentations and radiomic features - 65 subjects (NIFTI, 536 MB)

BRATS 2018 Test Data Set - 43 subjects (NIFTI, 366 MB)

Please contact helpdesk to request access to these files

help@cancerimagingarchive.net

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