Summary
Acknowledgements
We would like to acknowledge the individuals and institutions that contributed to the development and creation of these digital reference objects:
- Stanford University School of Medicine, Stanford, California, USA - Akshay Jaggi B.S. and Sandy Napel PhD from the Department of Radiology
- University of California, Los Angeles School of Medicine, Los Angeles, California, USA - Michael McNitt-Gray PhD from the Department of Radiology
- The University of Western Ontario, Department of Medical Biophysics - Sarah Mattonen PhD
- The National Cancer Institute Quantitative Imaging Network (QIN)
Data Access
Click the Download button to save the data.
Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, 5.0 GB) | (Requires NBIA Data Retriever.) |
Images and Segmentations (NIfTI, zip) |
Click the Versions tab for more info about data releases.
Detailed Description
Image Statistics | |
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Modalities | CT, SEG |
Number of Participants | 32 |
Number of Studies | 32 |
Number of Series | 64 |
Number of Images | 9632 |
Images Size (GB) | 5.0 GB |
The detailed description table applies to the DICOM files only. The NIfTI data is not included in this table.
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
Jaggi, A., Mattonen, S., McNitt-Gray, M., & Napel, S. (2020). Data from the Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/t062-8262
Publication Citation
Jaggi, A., Mattonen, S., McNitt-Gray, M., Napel, S (2020). Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. (In Press) Tomography, Feb. 2020
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 Citations
- David Geffen School of Medicine at UCLA - U01CA181156
- Stanford University School of Medicine – U01CA187947 and U24CA180927
- University of Michigan - U01CA232931
- University of Washington – R50CA211270, U01CA148131
- University of South Florida - U24CA180927, U01CA200464
- Moffitt Cancer Center – U01CA143062, U01CA200464, P30CA076292
- UC San Francisco - U01CA225427
- BC Cancer Research Centre - NSERC Discovery Grant: RGPIN-2019-06467
- Columbia University- U01CA225431
- Center for Biomedical Image Computing and Analytics at the University of Pennsylvania - U24CA189523, R01NS042645
- Massachusetts General Hospital- U01CA154601, U24CA180927
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.
Version 1 (Current): Updated 2020/04/09
Data Type | Download all or Query/Filter |
---|---|
Images and Segmentations (DICOM, 5.0 GB) | (Requires NBIA Data Retriever.) |
Images (NIfTI, zip) |