Joint Segmentation of Thalamic Nuclei from a Population of Diffusion Tensor MR Images

Abstract. 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.

• Type: Conference
• Year: 2008
• Where: MICCAI
• Authors: Ulas Ziyan, Carl-Fredrik Westin
• Keywords: Medical Imaging, MICCAI 2008, Thalamic Nuclei, Unsupervised Segmentation Methods, Unsupervised Segmentation Tools