{"id":5526,"date":"2023-09-04T03:01:28","date_gmt":"2023-09-04T03:01:28","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/pseudo-phi-dicom-data\/"},"modified":"2023-09-13T11:55:51","modified_gmt":"2023-09-13T11:55:51","slug":"pseudo-phi-dicom-data","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/pseudo-phi-dicom-data\/","title":{"rendered":"PSEUDO-PHI-DICOM-DATA"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Various"],"citations":[4329,4330,2925],"collection_doi":"10.7937\/s17z-r072","collection_download_info":"Click the Versions tab for more info about data releases.\nPlease contact help@cancerimagingarchive.net<\/a>\u00a0 with any questions regarding usage.","collection_downloads":[4880,4881,4882,4883],"full_export":"

Summary<\/h1>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.\u00a0 The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. \u00a0Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted.\u00a0 This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms. \u00a0<\/p>

We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms.\u00a0DICOM image information objects were selected from datasets published in TCIA.\u00a0 Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams.\u00a0 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, and 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<\/span><\/h3>

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.\u00a0 Original data came from multiple institutions and multiple TCIA image collections.<\/p>