De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

Journal of Medical Internet Research
Yeon Uk JeongWoo Hyun Shim

Abstract

High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data. According to the Health Insurance Portability and Accountability Act (HIPAA) privacy rules, full-face photographic images and any comparable image are direct identifiers and considered as protected health information. Moreover, the General Data Protection Regulation (GDPR) categorizes facial images as biometric data and stipulates that special restrictions should be placed on the processing of biometric data. This study aimed to develop software that can remove the header information from Digital Imaging and Communications in Medicine (DICOM) format files and facial features (eyes, nose, and ears) at the 2D sliced-image level to anonymize personal information in medical images. A total of 240 cranial magnetic resonance (MR) images were used to train the deep learning model (144, 48, and 48 for the training, validation, and test sets, respectively, from the Alzheimer's Disease Neuroimaging Initiative [ADNI] database). To overcome the small samp...Continue Reading

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Citations

Jun 20, 2021·Journal of Medical Imaging and Radiation Oncology·Phillip ChlapAnnette Haworth

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Software Mentioned

MRI
Deface
Deface Azure
Open Access Series of Imaging Studies ( OASIS )
OASIS
MRI viewer
AFNI

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