18 March 2015 Thalamic nuclei segmentation in clinical 3T T1-weighted Images using high-resolution 7T shape models
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Abstract
Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is a challenging task due to their small sizes and low intra-thalamic contrast in standard T1-weighted or T2- weighted images. Previously proposed techniques rely on diffusion imaging or functional imaging. These require additional scanning and suffer from the low resolution and signal-to-noise ratio in these images. In this paper, we aim to directly segment the thalamic nuclei in standard 3T T1-weighted images using shape models. We manually delineate the structures in high-field MR images and build high resolution shape models from a group of subjects. We then investigate if the nuclei locations can be inferred from the whole thalamus. To do this, we hierarchically fit joint models. We start from the entire thalamus and fit a model that captures the relation between the thalamus and large nuclei groups. This allows us to infer the boundaries of these nuclei groups and we repeat the process until all nuclei are segmented. We validate our method in a leave-one-out fashion with seven subjects by comparing the shape-based segmentations on 3T images to the manual contours. Results we have obtained for major nuclei (dice coefficients ranging from 0.57 to 0.88 and mean surface errors from 0.29mm to 0.72mm) suggest the feasibility of using such joint shape models for localization. This may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei.
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Yuan Liu, Yuan Liu, Pierre-François D'Haese, Pierre-François D'Haese, Allen T. Newton, Allen T. Newton, Benoit M. Dawant, Benoit M. Dawant, } "Thalamic nuclei segmentation in clinical 3T T1-weighted Images using high-resolution 7T shape models", Proc. SPIE 9415, Medical Imaging 2015: Image-Guided Procedures, Robotic Interventions, and Modeling, 94150E (18 March 2015); doi: 10.1117/12.2081660; https://doi.org/10.1117/12.2081660
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