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  • A DICOM dataset for evaluation of medical image de-identification (Pseudo-PHI-DICOM-Data)

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Summary

We developed a multi-modality DICOM image dataset that can be used to evaluate the performance of automated de-identification pipelines and protocols. Previously de-identified radiology cases (426) were selected from the Cancer Imaging Archive (TCIA) to use as a validation dataset. The set includes CT, MRI, PET, and radiograph images of most body parts and from various imaging system vendors.  The DICOM Standard Security and System Profile was used to create the validation image dataset along with audit logs from TCIA curation of the images. Synthetic PHI/PII and standardized patient IDs were added to DICOM tags in the validation image dataset to mimic non-de-identified images. The validation test dataset and associated de-identified test dataset for 5% of 426 subjects are being released with this publication. This paper describes the validation image dataset creation process, location of associated tables and datasets, and guides for using the dataset. We believe this is the first multi-modality image validation dataset available to the public for use in testing automated image de-identification algorithms.

Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:

  • Hospital/Institution Name city, state, country - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.

  • Continue with any names from additional submitting sites if collection consists of more that one.

Data Access

Data TypeDownload all or Query/Filter

Images,  (DICOM, XX.X GB)

CR, CT, DX, MG, MR, PT

   

(Download requires the NBIA Data Retriever)

Buttons are not populated until collection is released.

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Detailed Description

Image Statistics


Modalities

CR, CT, DX, MG, MR, PT

Number of Patients

17

Number of Studies

17

Number of Series

20

Number of Images

1823

Images Size (GB)

Citations & Data Usage Policy

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:

Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution-NonCommercial 4.0 International License under which it has been published. Attribution should include references to the following citations:

Data Citation

Rutherford, M., Mun, S.K., Levine, B., Bennett, W.C., Smith, K., Farmer, P., Jarosz, J., Wagner, U., Farahani, K., Prior, F. (2020). Data from MIDI. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/s17z-r072 (draft, not active).

Publication Citation

We ask on the proposal form if they have ONE traditional publication they'd like users to cite.

Acknowledgement

Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.

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

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 yyyy/mm/dd

Data TypeDownload all or Query/Filter
Images (DICOM, xx.x GB)

   

(Requires NBIA Data Retriever.)

Buttons are not populated until collection is released.



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