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.

翻译：随着开放数据的兴起，基于常规头部结构磁共振成像（MRI）扫描生成的三维重建图像对个体的可识别性已成为日益严重的隐私问题。为保护受试者隐私，研究人员开发了多种通过模糊化、面部去除或面部重建技术对影像数据进行去识别的算法。完全移除面部结构虽能提供最佳再识别防护，但会显著影响脑形态测量等后续处理步骤。作为替代方案，用通用模板替换个体面部特征的重建方法对原始扫描图像的几何形状及灰度分布影响较小，能以更高的再识别风险和计算复杂度为代价，获得更一致的后续处理结果。本研究提出一种基于三维条件生成对抗网络的新方法，用于对已去除面部的三维T1加权扫描图像进行匿名化面部生成。为评估该去识别工具的性能，我们采用两种不同分割算法（FAST和Morphobox），对现有多种面部去除与重建工具进行了对比研究。评估指标包括：（i）对脑形态测量可重复性的影响，（ii）再识别风险，（iii）两者间的平衡，以及（iv）处理时间。本方法仅需9秒即可完成面部生成，适用于在面部去除后恢复一致的后续处理结果。