Abstract
We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects (a total of 1,693 CT, MRI, PET, and digital X-ray images) were selected from datasets published in the Cancer Imaging Archive (TCIA). Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM Attributes to mimic typical clinical imaging exams. The DICOM Standard and TCIA curation audit logs guided the insertion of synthetic PHI into standard and non-standard DICOM data elements. A TCIA curation team tested the utility of the evaluation dataset. With this publication, the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (the result of TCIA curation) are released on TCIA in advance of a competition, sponsored by the National Cancer Institute (NCI), for algorithmic de-identification of medical image datasets. The competition will use a much larger evaluation dataset constructed in the same manner. This paper describes the creation of the evaluation datasets and guidelines for their use.
Highlights
Background & SummaryOpen access or shared research data must comply with the Health Insurance Portability and Accountability Act (HIPAA) regulations that govern patient privacy
We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms
This paper describes the creation of the evaluation datasets and guidelines for their use
Summary
Open access or shared research data must comply with the Health Insurance Portability and Accountability Act (HIPAA) regulations that govern patient privacy These regulations require the de-identification or removal of protected health information (PHI) and other personally identifiable information (PII) from datasets before they can be made publicly available. TCIA has developed image de-identification tools and protocols that combine automated and manual de-identification processes. Automated image de-identification algorithms require evaluation before they can be deployed to process data for open access. This evaluation requires a robust dataset that can be used as a part of assessing image de-identification algorithms. Researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification
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