Bischoff-Grethe Amanda, Ozyurt I Burak, Busa Evelina, Quinn Brian T, Fennema-Notestine Christine, Clark Camellia P, Morris Shaunna, Bondi Mark W, Jernigan Terry L, Dale Anders M, Brown Gregory G, Fischl Bruce
Laboratory of Cognitive Imaging, Department of Psychiatry, University of California, San Diego, La Jolla, USA.
Hum Brain Mapp. 2007 Sep;28(9):892-903. doi: 10.1002/hbm.20312.
Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.
由于对受试者隐私的需求日益增加,能够对结构性磁共振图像进行去识别处理,使其不提供完整面部细节是很有必要的。开发了一个程序,该程序使用非脑结构模型来去除潜在的可识别面部特征。当呈现一幅新图像时,会为输入体积计算最优线性变换(菲施尔等人[2002年]:《神经元》33卷:341 - 355页;菲施尔等人[2004年]:《神经影像学》23卷(增刊1):S69 - S84页)。通过将所有具有非零概率为脑的体素合并,然后进行形态学膨胀来构建脑掩码。掩码外所有具有非零概率为面部特征的体素都设置为0。该算法应用于342个数据集,这些数据集包括两种不同的T1加权脉冲序列和四种不同诊断(抑郁症、阿尔茨海默病以及老年和青年对照组)。目视检查表明没有脑组织被移除。在对去脸处理对颅骨剥离影响的详细分析中,对16个数据集使用N3进行偏差校正(斯莱德等人[1998年]:《IEEE医学影像学汇刊》17卷:87 - 97页),进行去脸处理,然后使用混合分水岭算法(塞贡内等人[2004年]:《神经影像学》22卷:1060 - 1075页,在FreeSurfer中)或脑表面提取器(桑多尔和利希[1997年]:《IEEE医学影像学汇刊》16卷:41 - 54页;沙塔克等人[2001年]:《神经影像学》13卷:856 - 876页)进行颅骨剥离;去脸处理并未明显影响颅骨剥离的结果。结果表明,自动去脸算法稳健,能有效去除非脑组织,且不会过度影响所采用处理方法的结果;在某些情况下,颅骨剥离得到了改善。分析支持该算法作为一种可行的方法,可在大规模多站点项目中以最小的数据改动实现数据共享。