DOI: 10.7937/K9/TCIA.2017.KLXWJJ1Q | Data Citation Required | 406 Citations | Analysis Result
Location | Subjects | Size | Updated | |||
---|---|---|---|---|---|---|
Glioblastoma | Brain | 135 | Tumor segmentations, radiomic image features | 2017/07/17 |
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) 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.
Data Access
Version 1: Updated 2017/07/17
Title | Data Type | Format | Access Points | Subjects | License | |||
---|---|---|---|---|---|---|---|---|
Processed NIFTI images with segmentations and radiomic features | MR, Radiomic Feature, Segmentation | NIFTI, ZIP, and CSV | Download requires IBM-Aspera-Connect plugin |
102 | 607 | CC BY 3.0 | ||
BRATS 2017 Test Data Set | MR | NIFTI, ZIP, and CSV | 33 | 197 | TCIA Limited (contact Support) |
Collections Used In This Analysis Result
Title | Data Type | Format | Access Points | Subjects | License | |||
---|---|---|---|---|---|---|---|---|
Corresponding Original Images from TCGA-GBM | MR | DICOM | Requires NBIA Data Retriever |
135 | 137 | 540 | 27,754 | TCIA Restricted |
Citations & Data Usage Policy
Data Citation Required: Users must abide by the TCIA Data Usage Policy and Restrictions. Attribution must include the following citation, including the Digital Object Identifier:
Data 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 |
Detailed Description
Data resulting from this experiment is available in the following formats:
- (source data in DICOM image format)
- Processed images with segmentations (NIFTI) and radiomic features (CSV):
- Training Processed images with segmentations and radiomic features – 102 subjects (NIfTI, zip, 780 MB) and a CSV of radiomic features
- BraTS Test Data Set – 33 subjects (NIfTI, zip, 259 MB) and a CSV of radiomic features
Data resulting from this experiment is available in the following formats:
- DICOM image format
- Processed NIFTI images with segmentations and radiomic features
Related Publications
Publications by the Dataset Authors
The authors recommended the following as the best source of additional information about this dataset:
Publication 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 |
Research Community Publications
TCIA maintains a list of publications that leveraged this dataset. If you have a manuscript you’d like to add please contact TCIA’s Helpdesk.