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  • Medical Imaging De-Identification Initiative (MIDI) - 2 presentations
  • A DICOM dataset for evaluation of medical image de-identification
    • Dr. Fred Prior (UAMS)
    • A growing number of tools and procedures claim to properly de-identify image data. Based on our decade of experience managing the Cancer Imaging Archive (TCIA) on behalf of NCI, we developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms. DICOM objects were selected from datasets published in 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. An answer key was created based on our knowledge of the placement of synthetic data and the DICOM standard’s guidelines for what actions should be taken in regard to the synthetic PHI.  A TCIA curation team tested the utility of the evaluation dataset and answer key.


Upcoming Calls

DateTentative Agenda
July 5, 20214th of July holiday - canceled
August 2, 2021
September 6, 2021Labor Day holiday - webinar may be moved or canceled
October 4, 2021
November 1, 2021Artificial Intelligence Resource (AIR) 
Dr. Brown, Dr. Harmon, Dr. Lay, Dr. Turkbey
December 6, 2021

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