Budin François, Zeng Donglin, Ghosh Arpita, Bullitt Elizabeth
CASILab, CB#7062, 217 Wing E, Department of Surgery, University of North Carolina, NC 27514, USA.
Med Image Anal. 2008 Jun;12(3):229-39. doi: 10.1016/j.media.2007.10.008. Epub 2007 Nov 6.
In the United States it is not allowed to make public any patient-specific information without the patient's consent. This ruling has led to difficulty for those interested in sharing three-dimensional (3D) images of the head and brain since a patient's face might be recognized from a 3D rendering of the skin surface. Approaches employed to date have included brain stripping and total removal of the face anterior to a cut plane, each of which lose potentially important anatomical information about the skull surface, air sinuses, and orbits. This paper describes a new approach that involves (a) definition of a plane anterior to which the face lies, and (b) an adjustable level of deformation of the skin surface anterior to that plane. On the basis of a user performance study using forced choices, we conclude that approximately 30% of individuals are at risk of recognition from 3D renderings of unaltered images and that truncation of the face below the level of the nose does not preclude facial recognition. Removal of the face anterior to a cut plane may interfere with accurate registration and may delete important anatomical information. Our new method alters little of the underlying anatomy and does not prevent effective registration into a common coordinate system. Although the methods presented here were not fully effective (one subject was consistently recognized under the forced choice study design even at the maximum deformation level employed) this paper may point a way toward solution of a difficult problem that has received little attention in the literature.
在美国,未经患者同意,禁止公开任何特定患者的信息。这项规定给那些想要分享头部和大脑三维(3D)图像的人带来了困难,因为从皮肤表面的3D渲染图中可能会识别出患者的面部。迄今为止采用的方法包括脑部剥离以及在切割平面之前完全去除面部,这两种方法都会丢失有关颅骨表面、鼻窦和眼眶的潜在重要解剖信息。本文描述了一种新方法,该方法包括:(a)定义面部所在平面之前的一个平面,以及(b)该平面之前皮肤表面的可调节变形程度。基于一项使用强制选择的用户性能研究,我们得出结论,大约30%的人有从未改变图像的3D渲染图中被识别出来的风险,并且在鼻子水平以下截断面部并不能排除面部识别。在切割平面之前去除面部可能会干扰精确配准,并可能删除重要的解剖信息。我们的新方法几乎不改变基础解剖结构,也不妨碍有效地注册到一个公共坐标系中。尽管这里提出的方法并不完全有效(在强制选择研究设计中,即使在采用的最大变形水平下,仍有一名受试者始终被识别出来),但本文可能为解决一个在文献中很少受到关注的难题指明了一条道路。