Ziyan Ulas, Westin Carl-Fredrik
MIT Computer Science and Artificial Intelligence Lab, Cambridge MA, USA.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):279-86. doi: 10.1007/978-3-540-85988-8_34.
Several recent studies explored the use of unsupervised segmentation methods for segmenting thalamic nuclei from diffusion tensor images. These methods provide a plausible segmentation on individual subjects; however, they do not address the problem of consistently identifying the same functional areas in a population. The lack of correspondence between the segmented nuclei make it more difficult to use the results from the unsupervised segmentation tools for morphometry. In this paper we present a novel segmentation algorithm to automatically segment the gray matter nuclei while ensuring consistency between subjects in a population. This new algorithm, referred to as Consistency Clustering, finds correspondence between the nuclei as the segmentation is achieved through a single model for the whole population, similar to the brain atlases experts use to identify thalamic nuclei.
最近的几项研究探索了使用无监督分割方法从扩散张量图像中分割丘脑核。这些方法在个体受试者上提供了合理的分割；然而，它们没有解决在群体中一致识别相同功能区域的问题。分割出的核之间缺乏对应关系使得使用无监督分割工具的结果进行形态测量变得更加困难。在本文中，我们提出了一种新颖的分割算法，可自动分割灰质核，同时确保群体中受试者之间的一致性。这种新算法称为一致性聚类，通过为整个群体使用单一模型来实现分割，从而找到核之间的对应关系，类似于大脑图谱专家用于识别丘脑核的方法。
