Data_Sheet_2_Multisite Comparison of MRI Defacing Software Across Multiple Cohorts.docx

With improvements to both scan quality and facial recognition software, there is an increased risk of participants being identified by a 3D render of their structural neuroimaging scans, even when all other personal information has been removed. To prevent this, facial features should be removed before data are shared or openly released, but while there are several publicly available software algorithms to do this, there has been no comprehensive review of their accuracy within the general population. To address this, we tested multiple algorithms on 300 scans from three neuroscience research projects, funded in part by the Ontario Brain Institute, to cover a wide range of ages (3–85 years) and multiple patient cohorts. While skull stripping is more thorough at removing identifiable features, we focused mainly on defacing software, as skull stripping also removes potentially useful information, which may be required for future analyses. We tested six publicly available algorithms (afni_refacer, deepdefacer, mri_deface, mridefacer, pydeface, quickshear), with one skull stripper (FreeSurfer) included for comparison. Accuracy was measured through a pass/fail system with two criteria; one, that all facial features had been removed and two, that no brain tissue was removed in the process. A subset of defaced scans were also run through several preprocessing pipelines to ensure that none of the algorithms would alter the resulting outputs. We found that the success rates varied strongly between defacers, with afni_refacer (89%) and pydeface (83%) having the highest rates, overall. In both cases, the primary source of failure came from a single dataset that the defacer appeared to struggle with - the youngest cohort (3–20 years) for afni_refacer and the oldest (44–85 years) for pydeface, demonstrating that defacer performance not only depends on the data provided, but that this effect varies between algorithms. While there were some very minor differences between the preprocessing results for defaced and original scans, none of these were significant and were within the range of variation between using different NIfTI converters, or using raw DICOM files.

随着扫描质量与面部识别软件（facial recognition software）的双重提升，即便已移除所有其他个人信息，参与者仍可能因结构神经影像扫描（structural neuroimaging scans）的三维渲染（3D render）被识别，相关风险显著上升。为防范此类隐私泄露风险，应在数据共享或公开发布前移除影像中的面部特征。目前虽已有多款公开可用的软件算法可实现该操作，但尚未有针对普通人群（general population）中这些算法准确性的全面综述（comprehensive review）。为填补这一研究空白，我们依托三项由安大略脑研究所（Ontario Brain Institute）部分资助的神经科学研究项目，共获取300份扫描影像，涵盖3至85岁的广泛年龄跨度以及多类患者队列（cohort），并以此测试多款算法。相较于颅骨剥离（skull stripping）可更彻底地移除可识别特征，但该操作会同时删除潜在用于未来分析的有用信息，因此本研究主要聚焦于面部去标识软件（defacing software）。我们共测试了六款公开可用的算法：afni_refacer、deepdefacer、mri_deface、mridefacer、pydeface、quickshear，并纳入一款颅骨剥离工具FreeSurfer作为对照。准确性通过双标准的通过/失败评定体系进行评估：一是所有面部特征均已被完全移除，二是处理过程中未造成脑组织（brain tissue）丢失。我们还选取部分已完成面部去标识的扫描影像，通过多款预处理流程（preprocessing pipeline）进行测试，以确保无算法会改变最终的影像输出结果。研究发现，不同面部去标识软件的成功率差异显著，总体而言afni_refacer（89%）与pydeface（83%）的成功率最高。两类算法的失败案例主要均源于某一类特定数据集：afni_refacer在最年轻的队列（3至20岁）中表现欠佳，而pydeface则在最年长的队列（44至85岁）中表现不佳，这表明面部去标识软件的性能不仅取决于输入数据的特性，且不同算法受数据影响的模式存在显著差异。尽管已完成面部去标识与原始扫描影像的预处理结果存在细微差异，但所有差异均不显著，且处于不同NIfTI转换器（NIfTI converter）间的变异范围，或使用原始DICOM文件（DICOM file）时的变异范围内。