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Showing content from https://pubmed.ncbi.nlm.nih.gov/33208302/ below:

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

. 2020 Dec 10;22(12):e22739. doi: 10.2196/22739. De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology

Affiliations

¹ Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
² Human Research Protection Center, Asan Institute of Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
³ Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁴ Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁵ Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

PMID: 33208302
PMCID: PMC7759440
DOI: 10.2196/22739

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

Yeon Uk Jeong et al. J Med Internet Res. 2020.

. 2020 Dec 10;22(12):e22739. doi: 10.2196/22739. Affiliations

¹ Department of Medical Science, Asan Medical Institute of Convergence Science and Technology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
² Human Research Protection Center, Asan Institute of Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
³ Division of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁴ Department of Information Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
⁵ Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.

PMID: 33208302
PMCID: PMC7759440
DOI: 10.2196/22739

Item in Clipboard

Abstract

Background: 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.

Objective: 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.

Methods: 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 sample size problem, we used a data augmentation technique to create 576 images per epoch. We used attention-gated U-net for the basic structure of our deep learning model. To validate the performance of the software, we adapted an external test set comprising 100 cranial MR images from the Open Access Series of Imaging Studies (OASIS) database.

Results: The facial features (eyes, nose, and ears) were successfully detected and anonymized in both test sets (48 from ADNI and 100 from OASIS). Each result was manually validated in both the 2D image plane and the 3D-rendered images. Furthermore, the ADNI test set was verified using Microsoft Azure's face recognition artificial intelligence service. By adding a user interface, we developed and distributed (via GitHub) software named "Deface program" for medical images as an open-source project.

Conclusions: We developed deep learning-based software for the anonymization of MR images that distorts the eyes, nose, and ears to prevent facial identification of the subject in reconstructed 3D images. It could be used to share medical big data for secondary research while making both data providers and recipients compliant with the relevant privacy regulations.

Keywords: GDPR; HIPAA; de-identification; deep learning; facial feature detection; medical image; personal information protection; privacy protection.

©Yeon Uk Jeong, Soyoung Yoo, Young-Hak Kim, Woo Hyun Shim. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 10.12.2020.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures
Figure 1

Process of (A) developing the…

Figure 1

Process of (A) developing the facial feature detector, which is a deep learning…

Figure 1

Process of (A) developing the facial feature detector, which is a deep learning model that can detect the eyes, nose, and ears in 3-dimensional (3D) magnetic resonance (MR) images, and (B) distorting the facial features in nonanonymized cranial MR images.

Figure 2

3-dimensional (3D) volume rendering of…

Figure 2

3-dimensional (3D) volume rendering of magnetic resonance images (MRI), showing the raw and…

Figure 2

3-dimensional (3D) volume rendering of magnetic resonance images (MRI), showing the raw and distorted images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) .