Facing privacy in neuroimaging: removing facial features degrades performance of image analysis methods.

ABSTRACT: BACKGROUND:Recent studies have created awareness that facial features can be reconstructed from high-resolution MRI. Therefore, data sharing in neuroimaging requires special attention to protect participants' privacy. Facial features removal (FFR) could alleviate these concerns. We assessed the impact of three FFR methods on subsequent automated image analysis to obtain clinically relevant outcome measurements in three clinical groups. METHODS:FFR was performed using QuickShear, FaceMasking, and Defacing. In 110 subjects of Alzheimer's Disease Neuroimaging Initiative, normalized brain volumes (NBV) were measured by SIENAX. In 70 multiple sclerosis patients of the MAGNIMS Study Group, lesion volumes (WMLV) were measured by lesion prediction algorithm in lesion segmentation toolbox. In 84 gl

SUBMITTER: de Sitter A 

PROVIDER: S-EPMC6957560 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature