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This is a sample collection of synthetic 3D Digital Reference Objects (DROs) intended for standardization of quantitative imaging feature extraction pipelines. We have developed a software toolkit for the creation of DROs with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. This collection includes objects with a range of values for the various feature categories and many combinations of these categories.
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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)
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| title | Data Access |
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| Click the Download button to save the data.
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Images and Segmentations (DICOM, 5.0 GB) | | | Images and Segmentations (NIfTI, zip) | |
Click the Versions tab for more info about data releases. TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection: |
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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. |
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| title | Citations & Data Usage Policy |
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| | Public collection license |
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| Jaggi, A., Mattonen, S. A., McNitt-Gray, M., & Napel, S. (2020). Data from the Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features Features (Version 1) [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/t062T062-8262 |
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| Jaggi, A., Mattonen, S. A., McNitt-Gray, M., & Napel, S. (2020). Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features. ( In Press) Tomography , Feb. 2020(Vol. 6, Issue 2, pp. 111–117). MDPI AG. https://doi.org/10.18383/j.tom.2019.00030 |
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| 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 |
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| - 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
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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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Images and Segmentations (DICOM, 5.0 GB) | | | Images (NIfTI, zip) | |
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