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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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active | true |
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title | Data Access |
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| 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) | | Images and Segmentations (NIfTI, zip) | | Click the Versions tab for more info about data releases. |
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title | Detailed Description |
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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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| 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: Public collection license |
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| 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 |
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title | Publication Citation |
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| 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 |
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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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| Data Type | Download all or Query/Filter |
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Images and Segmentations (DICOM, 5.0 GB) | | Images (NIfTI, zip) | |
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