Implementation and validation of face de-identification (de-facing) in ADNI4

IF 13 1区 医学 Q1 CLINICAL NEUROLOGY Alzheimer's & Dementia Pub Date : 2024-10-11 DOI:10.1002/alz.14303
Christopher G. Schwarz, Mark Choe, Stephanie Rossi, Sandhitsu R. Das, Ranjit Ittyerah, Evan Fletcher, Pauline Maillard, Baljeet Singh, Danielle J. Harvey, Ian B. Malone, Lloyd Prosser, Matthew L. Senjem, Leonard C. Matoush, Chadwick P. Ward, Carl M. Prakaashana, Susan M. Landau, Robert A. Koeppe, JiaQie Lee, Charles DeCarli, Michael W. Weiner, Clifford R. Jack Jr., William J. Jagust, Paul A. Yushkevich, Duygu Tosun, for the Alzheimer's Disease Neuroimaging Initiative
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Abstract

INTRODUCTION

Recent technological advances have increased the risk that de-identified brain images could be re-identified from face imagery. The Alzheimer's Disease Neuroimaging Initiative (ADNI) is a leading source of publicly available de-identified brain imaging, who quickly acted to protect participants’ privacy.

METHODS

An independent expert committee evaluated 11 face-deidentification (“de-facing”) methods and selected four for formal testing.

RESULTS

Effects of de-facing on brain measurements were comparable across methods and sufficiently small to recommend de-facing in ADNI. The committee ultimately recommended mri_reface for advantages in reliability, and for some practical considerations. ADNI leadership approved the committee's recommendation, beginning in ADNI4.

DISCUSSION

ADNI4 de-faces all applicable brain images before subsequent pre-processing, analyses, and public release. Trained analysts inspect de-faced images to confirm complete face removal and complete non-modification of brain. This paper details the history of the algorithm selection process and extensive validation, then describes the production workflows for de-facing in ADNI.

Highlights

  • ADNI is implementing “de-facing” of MRI and PET beginning in ADNI4.
  • “De-facing” alters face imagery in brain images to help protect privacy.
  • Four algorithms were extensively compared for ADNI and mri_reface was chosen.
  • Validation confirms mri_reface is robust and effective for ADNI sequences.
  • Validation confirms mri_reface negligibly affects ADNI brain measurements.

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在 ADNI4 中实施和验证人脸去标识化(去脸谱化)。
引言最近的技术进步增加了从人脸图像中重新识别去身份化大脑图像的风险。阿尔茨海默病神经成像计划(ADNI)是公开提供去身份化大脑图像的主要来源,他们迅速采取行动保护参与者的隐私:一个独立的专家委员会评估了 11 种人脸识别("去脸谱化")方法,并选择了四种方法进行正式测试:结果:去脸部识别对大脑测量的影响在各种方法中不相上下,而且影响很小,因此建议在 ADNI 中采用去脸部识别方法。出于可靠性方面的优势和一些实际考虑,委员会最终推荐使用 mri_reface。ADNI 领导层批准了委员会的建议,从 ADNI4.Discussion 开始:ADNI4 在进行后续预处理、分析和公开发布之前,会对所有适用的大脑图像进行去表面化处理。训练有素的分析师会对去脸图像进行检查,以确认脸部是否完全去除,大脑是否完全未修改。本文详细介绍了算法选择过程和广泛验证的历史,然后介绍了 ADNI.Highlights 中去脸部的生产工作流程:ADNI 从 ADNI4 开始对 MRI 和 PET 实施 "去脸谱化"。"去脸谱化 "改变了大脑图像中的人脸图像,有助于保护隐私。ADNI 广泛比较了四种算法,最终选择了 mri_reface。验证结果表明,mri_reface 对于 ADNI 序列是稳健有效的。验证证实 mri_reface 对 ADNI 脑部测量的影响可以忽略不计。
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来源期刊
Alzheimer's & Dementia
Alzheimer's & Dementia 医学-临床神经学
CiteScore
14.50
自引率
5.00%
发文量
299
审稿时长
3 months
期刊介绍: Alzheimer's & Dementia is a peer-reviewed journal that aims to bridge knowledge gaps in dementia research by covering the entire spectrum, from basic science to clinical trials to social and behavioral investigations. It provides a platform for rapid communication of new findings and ideas, optimal translation of research into practical applications, increasing knowledge across diverse disciplines for early detection, diagnosis, and intervention, and identifying promising new research directions. In July 2008, Alzheimer's & Dementia was accepted for indexing by MEDLINE, recognizing its scientific merit and contribution to Alzheimer's research.
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