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Abstract #4307

Fast and Efficient Diffusion-based Thalamic Segmentation Using Spectral Clustering

Debottama Das1, Charles Iglehart1, Ali Bilgin1, and Manojkumar Saranathan2
1Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States, 2Department of Radiology, University of Massachusetts Chan Medical School, Worcester, MA, United States

Synopsis

Keywords: Segmentation, Segmentation, Thalamus, Diffusion Weighted Imaging, Nuclei Parcellation

Motivation: Current diffusion-based thalamus segmentation methods are limited by time-consuming cortical parcellation and inefficiencies in handling complex intricate small-scale fiber tract geometries.

Goal(s): Develop a fast and efficient segmentation approach to overcome the limitations of traditional methods, such as k-means clustering and lengthy parcellation processes.

Approach: We utilize THOMAS for automated thalamus masking, MSMT-CSD for enhanced ODFs, and spectral clustering to effectively capture the intricate geometry of thalamic nuclei.

Results: Tested on 15 subjects, our approach successfully segmented the thalamus into seven clusters and the pulvinar nucleus into three sub-clusters, demonstrating fast and efficient segmentation, and the ability to handle complex anatomical structures.

Impact: Fast and accurate subthalamic segmentation can enable more accurate thalamic studies and interventions, thereby improving both our understanding of brain pathology and patient outcomes in various neurological conditions

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