13 March 2013 Parcellation of the thalamus using diffusion tensor images and a multi-object geometric deformable model
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Proceedings Volume 8669, Medical Imaging 2013: Image Processing; 866909 (2013) https://doi.org/10.1117/12.2006119
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
The thalamus is a sub-cortical gray matter structure that relays signals between the cerebral cortex and midbrain. It can be parcellated into the thalamic nuclei which project to different cortical regions. The ability to automatically parcellate the thalamic nuclei could lead to enhanced diagnosis or prognosis in patients with some brain disease. Previous works have used diffusion tensor images (DTI) to parcellate the thalamus, using either tensor similarity or cortical connectivity as information driving the parcellation. In this paper, we propose a method that uses the diffusion tensors in a different way than previous works to guide a multiple object geometric deformable model (MGDM) for parcellation. The primary eigenvector (PEV) is used to indicate the homogeneity of fiber orientations. To remove the ambiguity due to the fact that the PEV is an orientation, we map the PEV into a 5D space known as the Knutsson space. An edge map is then generated from the 5D vector to show divisions between regions of aligned PEV's. The generalized gradient vector flow (GGVF) calculated from the edge map drives the evolution of the boundary of each nucleus. Region based force, balloon force, and curvature force are also employed to refine the boundaries. Experiments have been carried out on five real subjects. Quantitative measures show that the automated parcellation agrees with the manual delineation of an expert under a published protocol.
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Chuyang Ye, Chuyang Ye, John A. Bogovic, John A. Bogovic, Sarah H. Ying, Sarah H. Ying, Jerry L. Prince, Jerry L. Prince, } "Parcellation of the thalamus using diffusion tensor images and a multi-object geometric deformable model", Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866909 (13 March 2013); doi: 10.1117/12.2006119; https://doi.org/10.1117/12.2006119
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