Summary
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Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain. The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted. This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms. We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM image information objects were selected from datasets published in TCIA. Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams. The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation. de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification. |
Acknowledgements
We would like to acknowledge the National Cancer Institute for funding and actively participating in the project that generated the evaluation datasets being published here and the TCIA curation team, led by Ms. Geri Blake, who curated this data. Original data came from multiple institutions and multiple TCIA image collections.
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title | Data Access |
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| Data AccessData Type | Download all or Query/Filter |
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Images, (DICOM, XX.X GB) CR, CT, DX, MG, MR, PT | Download and search button currently not active. (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. |
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title | Detailed Description |
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| Detailed DescriptionImage Statistics |
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Modalities | CR, CT, DX, MG, MR, PT | Number of Patients | 21 | Number of Studies | 22 | Number of Series | 26 | Number of Images | 1836 | Images Size (MB) | 593 |
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title | Citations & Data Usage Policy |
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| Citations & Data Usage Policy Tcia license 4 international |
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Tcia license 4 noncommercial |
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| 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). |
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title | Publication Citation |
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| We ask on the proposal form if they have ONE traditional publication they'd like users to cite. |
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| Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal. |
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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 |
Other Publications Using This DataTCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk. |
Localtab |
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| Version 1 (Current): Updated yyyy/mm/ddData Type | Download all or Query/Filter |
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Images (DICOM, 593 MB) | Download and search button currently not active.(Requires NBIA Data Retriever.) |
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