Wang Yaping, Nie Jingxin, Yap Pew-Thian, Shi Feng, Guo Lei, Shen Dinggang
School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi Province, China.
Med Image Comput Comput Assist Interv. 2011;14(Pt 3):635-42. doi: 10.1007/978-3-642-23626-6_78.
Skull-stripping refers to the separation of brain tissue from non-brain tissue, such as the scalp, skull, and dura. In large-scale studies involving a significant number of subjects, a fully automatic method is highly desirable, since manual skull-stripping requires tremendous human effort and can be inconsistent even after sufficient training. We propose in this paper a robust and effective method that is capable of skull-stripping a large number of images accurately with minimal dependence on the parameter setting. The key of our method involves an initial skull-stripping by co-registration of an atlas, followed by a refinement phase with a surface deformation scheme that is guided by prior information obtained from a set of real brain images. Evaluation based on a total of 831 images, consisting of normal controls (NC) and patients with mild cognitive impairment (MCI) or Alzheimer's Disease (AD), from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database indicates that our method performs favorably at a consistent overall overlap rate of approximately 98% when compared with expert results. The software package will be made available to the public to facilitate neuroimaging studies.
颅骨剥离是指将脑组织与非脑组织（如头皮、颅骨和硬脑膜）分离。在涉及大量受试者的大规模研究中，全自动方法非常可取，因为手动颅骨剥离需要巨大的人力，即使经过充分训练也可能不一致。我们在本文中提出了一种稳健且有效的方法，该方法能够以最少的参数设置依赖准确地对大量图像进行颅骨剥离。我们方法的关键包括通过图谱配准进行初始颅骨剥离，随后通过由从一组真实脑图像获得的先验信息引导的表面变形方案进行细化阶段。基于来自阿尔茨海默病神经影像学倡议（ADNI）数据库的总共831张图像（包括正常对照（NC）以及轻度认知障碍（MCI）或阿尔茨海默病（AD）患者）进行的评估表明，与专家结果相比，我们的方法在约98%的一致总体重叠率下表现良好。该软件包将向公众提供，以促进神经影像学研究。
