  With the rise of open data, identifiability of individuals based on 3D
renderings obtained from routine structural magnetic resonance imaging (MRI)
scans of the head has become a growing privacy concern. To protect subject
privacy, several algorithms have been developed to de-identify imaging data
using blurring, defacing or refacing. Completely removing facial structures
provides the best re-identification protection but can significantly impact
post-processing steps, like brain morphometry. As an alternative, refacing
methods that replace individual facial structures with generic templates have a
lower effect on the geometry and intensity distribution of original scans, and
are able to provide more consistent post-processing results by the price of
higher re-identification risk and computational complexity. In the current
study, we propose a novel method for anonymised face generation for defaced 3D
T1-weighted scans based on a 3D conditional generative adversarial network. To
evaluate the performance of the proposed de-identification tool, a comparative
study was conducted between several existing defacing and refacing tools, with
two different segmentation algorithms (FAST and Morphobox). The aim was to
evaluate (i) impact on brain morphometry reproducibility, (ii)
re-identification risk, (iii) balance between (i) and (ii), and (iv) the
processing time. The proposed method takes 9 seconds for face generation and is
suitable for recovering consistent post-processing results after defacing.

Image and Video Processing;Computer Vision and Pattern Recognition