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

Authors:
Ulas Ziyan;Carl-Fredrik Westin
Affiliations:
MIT Computer Science and Artificial Intelligence Lab, , Cambridge, USA;MIT Computer Science and Artificial Intelligence Lab, , Cambridge, USA and Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, , Boston, USA
Venue:
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Year:
2008

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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.