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DICOM-GLIOMA-SEG

DICOM-Glioma-SEG | DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets

DOI: 10.7937/TCIA.2018.ow6ce3ml | Data Citation Required | Analysis Result

Cancer Types Location Subjects Related Collections Size Supporting Data Updated
Glioblastoma, Low Grade Glioma Brain 167 34.32GB Tumor segmentations 2020/04/30

Summary

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.
 

Data Access

Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a TCIA Restricted License Agreement to help@cancerimagingarchive.net before accessing the data.

Be sure to “request dataset” with these : DICOM-Glioma-SEG, TCGA-GBM, and TCGA-LGG in your Agreement on page 1 so that we can process your request efficiently. Complete all pages.

Version 1: Updated 2020/04/30

Title Data Type Format Access Points Subjects Studies Series Images License
Segmentations CT, MR DICOM
Download requires NBIA Data Retriever
102 191 2,290 255,625 TCIA Restricted
DCMQI Metadata JSON, TXT, and ZIP CC BY 4.0
TCGA key mapping CSV CC BY 4.0

Collections Used In This Analysis Result

Title Data Type Format Access Points Subjects Studies Series Images License
Corresponding Original MR Images from TCGA-LGG images MR DICOM 108 110 432 28,295 TCIA Restricted
Corresponding Original MR Images from TCGA-GBM images MR DICOM 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

Beers, A., Gerstner, E., Rosen, B., Clunie, D., Pieper, S., Fedorov, A., & Kalpathy-Cramer, J. (2018). DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2018.ow6ce3ml

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-LGG collection [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF

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. https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q

Detailed Description

*For TCGA-GBM patient TCGA-06-0192, there were 2 studies.

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 TCIA’s Helpdesk.

Publication Citation

Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017). Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data, 4(1). https://doi.org/10.1038/sdata.2017.117 https://www.nature.com/articles/sdata2017117

TCIA 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. (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. In Journal of Digital Imaging (Vol. 26, Issue 6, pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7

Collections Used In This Analysis Result

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