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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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titlePublication Citation

Coming soon.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, (2017) [In Press]

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  • Original DICOM Image Data - 108 subjects (8.5 GB)
  • Processed NIFTI images with segmentations and radiomic features
    • 65 subjects (536 MB)
    • 43 subjects (366 MB) to be used for BRATS 2017 Test Data Set – Available by helpdesk request only until completion of challenge