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Summary
Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
Spyridon Bakas & Sarthak Pati, Ph.D., Center for Biomedical Image Computing & Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Data Access
Data Type | Download all or Query/Filter |
---|---|
Images (NIfTI, 4.7 GB) | |
Segmentations (NIfTI, 2.7 GB) | |
Quantitative Results (XLSX/TXT/CSV, 34.3 MB) |
Click the Versions tab for more info about data releases.
Please contact help@cancerimagingarchive.net with any questions regarding usage.
Detailed Description
The data comprises of expert segmentation labels from each institution (i.e. 34 subjects from both UPenn and CWRU, with a total of 37), along with the corresponding co-registered and skull-stripped structural MRI scans in the space they were created (i.e., SRI for UPenn and MNI for CWRU), and the expert segmentation labels for the 31 common subjects co-registered in the SRI atlas. For brevity, we have included the corresponding SRI and MNI anatomical atlas files that we employed, the complete set of extracted radiomic features per subject for each of the 31 included subjects, along with the parameters used for the radiomic feature extraction and the correlation analysis results for identifying robust radiomic features, and finally, the identified robust 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
Pati, S., Verma, R., Akbari, H., Bilello, M., Hill, V.B., Sako, C., Correa, R., Beig, N., Venet, L., Thakur, S., Serai, P., Ha, S.M., Shinohara, R.T., Tiwari, P., Bakas, S. (2020). Data from the Multi-Institutional Paired Expert Segmentations and Radiomic Features of the Ivy GAP Dataset. DOI: https://doi.org/10.7937/9j41-7d44.
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. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7
Grant Acknowledgement
- National Institutes of Health (NIH) under award number NCI:U01CA242871
- Department of Defense (DoD) Peer Reviewed Cancer Research Program (W81XWH-18-1-0404)
- Dana Foundation David Mahoney Neuroimaging Grant, the CCCC Brain Tumor Pilot Award
- CWRU Technology Validation Start-Up Fund (CTP)
- The V Foundation Translational Research Award.
The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH, U.S. Department of Veterans Affairs, the DoD, or the United States Government.
Other Publications Using This Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
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