This dataset contains DICOM-SEG (DSO) conversions of the Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection and Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection analysis datasets.
The MR volumes and segmentations provided in the original segmentation datasets (T1 pre-contrast, T1 post-contrast, T2, FLAIR) are in NIfTI format, co-registered to an atlas space, and re-sampled to 1mm isotropic resolution. This dataset contains DICOM-SEG versions of the same segmentations, transformed back into the original patient resolutions and orientations found in the TCIA’s TCGA-GBM and TCGA-LGG datasets. This allows users to extract features from MR sequences without introducing interpolation artifacts from isotropic resampling.
The process for creating these DSO objects is as follows. Patient data from the original NIfTI datasets were registered and resampled from isotropic space to patient space and resolution using 3DSlicer’s BRAINSFit module. The affine transformation files from these registrations are used to register and resample both the semi-automatic and automatic NIfTI segmentations into the spaces of each original MR DICOM dataset. These transformed NIfTI segmentations are then converted into DICOM-SEG datasets using the software package dcmqi. Because each MR sequence has a unique patient space and resolution, the resulting dataset contains four DSO segmentations for each original NIfTI segmentation.
Included in this dataset are the converted DSO volumes, DSO metadata values used in the DSO conversion program dcmqi, and affine transformation files from isotropic space to the original patient space saved in ITK format. Original patient DICOM volumes are also available for download below. A key is provided that maps individual DSO objects to their corresponding DICOM Series UID, to facilitate easier data analysis.
Click the Download button to save a ".tcia" manifest file to your computer, which you must open with the NBIA Data Retriever
|Data Type||Download all or Query/Filter|
|TCGA-LGG images - 108 subjects (DICOM, 8.5 GB)|
|TCGA-GBM images - 135 subjects (DICOM, 6 GB)|
|Segmentations - 3,278 files (ZIP, 42.1 MB)|
|TCGA key mapping (CSV)|
Please contact email@example.com with any questions regarding usage.
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 firstname.lastname@example.org. Please be sure to acknowledge both this data set and TCIA in publications by including the following citations in your work:
Andrew Beers, Elizabeth Gerstner, Bruce Rosen, David Clunie, Steve Pieper, Andrey Fedorov, Jayashree Kalpathy-Cramer. (2018) DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2018.ow6ce3ml
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. (2013) The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6 pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
In addition to the dataset citation above, please be sure to cite the following if you utilize these data in your research:
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). https://www.nature.com/articles/sdata2017117