This is the home page for the Medical Imaging De-Identification Initiative (MIDI) projectTask Group.

MIDI

Mission

The mission of MIDI is to document strategies and best practices in Drive consensus, provide guidance, and promote best practices for medical image de-identification ID for various imaging informatics applications in academic and industrial researchsecondary sharing of imaging data with an emphasis on DICOM.

MIDI

Task Group

The MIDI projects described below focus on medical imaging in radiology and the DICOM standard, as the most basic, and yet the most prevalent need or use case.  Future extensions of these projects could address imaging de-ID in other domains such as digital pathology (whole slide imaging) and multi-spectral imaging.  There will be many NCI use cases for these projects, including IDC, potentially TCIA, SBIR imaging de-ID contract product validation, benchmarking of de-ID community tools through challenges, and promotion of data sharing in the research community.

Project 1: Medical Image De-ID Assessment (MIDIA) Reference Datasets

• Goal: To design and create a comprehensive imaging dataset using real patient image data, anonymized, and injected with artificial PHI and identifiable information in conventional and non-conventional DICOM fields in the image file header and in the pixel data.
• Collaborators: Fred Prior, PhD (TCIA/UAMS) and David Clunie, MD (PixelMed)
• Project period: June-Aug 2020
• Funding source: Possibly through existing CBIIT contracts in Imaging Informatics for Precision Medicine, including a DICOM contract with D. Clunie.
• Current status: Based on preliminary, and separate, discussions with Fred Prior, David Clunie, they have been interested in developing image de-ID validation datasets. However, to date, no concerted effort has been made. 

Project 2: MIDI Tools and Pipeline

• Goals: To integrate best of the class image de-ID tools developed by Google Health for scalable applications, and to develop a pipeline for assessment (validation) and application of de-identification tools to imaging datasets.
• Collaborators: Deloitte Consulting
• Project period: June 2020 – October 2020
• Funding source: CBIIT funds through the GCP STRIDES program
• Current status: CIT and GCP STRIDES have confirmed the availability of GCP STRIDES mechanism toward Professional Services Development.  CBIIT has approved the availability of STRIDES credits to complete this project.  There is a possibility of co-funding by NHLBI as they have expressed interest in this project.

Project 3: MIDI Working Group

Task Group is chaired by David Clunie, M.B.B.S. The group first started meeting in July 2021 and met monthly until November 2022.

The group is charged with the following:

• Issuing consensus-based guidelines on best practices in medical image de-identification for secondary sharing of imaging data with emphasis on DICOM
• Making recommendations on criteria and resources for performance evaluation of image de-identification tools
• Providing guidelines for image de-identification (de-ID) using automated vs. manual, cloud-based vs. local approaches, portability, interoperability, scalability
• Recommending mitigation measures in case of privacy breach

MIDI Task Group Members

• David Clunie, MBBS, PixelMed, Inc., Chair
• Kathy Andriole, PhD, Mass General Brigham, Radiology
• Brian Bialecki, ACR Data Science Institute
• Brad Erickson, MD, PhD, Mayo Clinic, Radiology
• TJ Fitzgerald, MD, UMass, Radiation Oncology
• Adam Flanders, MD, Thomas Jefferson University, Radiology
• Luke Geneslaw, Memorial Sloan Kettering, Pathology
• Judy Wawira Gichoya, MD, Emory University Radiology
• David Gutman, MD, PhD, Emory University, Psychiatry, Digital Pathology
• Justin Kirby, BS, Frederick National Lab
• Steve Moore, Washington University, Radiology
• John Perry, former RSNA contractor
• Fred Prior, PhD, University of Arkansas for Medical Sciences
• Tony Seibert, PhD, UC Davis
• Adam Taylor, Sage Bionetworks, Fluorescence microscopy
• Wyatt Tellis, PhD, UCSF, Radiology
• Ying Xiao, PhD, UPenn, Radiation Oncology

MIDI Steering Committee

The MIDI Steering Committee provides oversight to the MIDI TG, provides input on the alignment with CBIIT/NCI and other NIH IC interests in image de-ID, and considers related activities such as a workshop, a challenge, or engagement of stakeholders. So far, the MIDI Steering Committee has met once, in July 2021.

MIDI Steering Committee Members

• Keyvan Farahani (NCI)
• Debra Babcock (NINDS)
• Valerie Cotton (NICHD)
• Kerry Goetz (NEI)
• John Hsiao (NIA)
• Kris Kandarpa (NIBIB)
• James Luo (NHLBI)
• Alex Rosenthal (NIAID)
• Ben Xu (NIAAA)