Summary


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.

Acknowledgements

We would like to acknowledge the individuals and institutions that contributed to the development and creation of these digital reference objects:



Data Access

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Data TypeDownload all or Query/Filter

Images and Segmentations (DICOM, 5.0 GB)

 

(Requires NBIA Data Retriever .)

Images and Segmentations (NIfTI, zip)


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Third Party Analyses of this Dataset

TCIA encourages the community to publish your analyses of our datasets. Below is a list of such third party analyses published using this Collection:




Detailed Description


Image Statistics


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

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




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




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




  • 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 TypeDownload all or Query/Filter

Images and Segmentations (DICOM, 5.0 GB)

 

(Requires NBIA Data Retriever .)

Images (NIfTI, zip)