A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using DMRI Tractography

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 1 - 5
• Main Authors: He, Haolin, Zhu, Ce, Zhang, Le, Liu, Yipeng, Xu, Xiao, Chen, Yuqian, Zekelman, Leo, Rushmore, Jarrett, Rathi, Yogesh, Makris, Nikos, O'Donnell, Lauren J., Zhang, Fan
• Format: Conference Proceeding
• Language: English
• Published: IEEE 27.05.2024
• Subjects: Accuracy; Amygdala; Brain; Correlation; deep clustering; Deep learning; diffusion MRI; Dimensionality reduction; Magnetic resonance imaging; parcellation; Pipelines; tractography
• ISSN: 1945-8452
• DOI: 10.1109/ISBI56570.2024.10635363

The amygdala plays a vital role in emotional processing and exhibits structural diversity that necessitates fine-scale parcellation for a comprehensive understanding of its anatomico-functional correlations. Diffusion MRI tractography is an advanced imaging technique that can estimate the brain's white matter structural connectivity to potentially reveal the topography of the amygdala for studying its subdivisions. In this work, we present a deep clustering pipeline to perform automated, fine-scale parcellation of the amygdala using diffusion MRI tractography. First, we incorporate a newly proposed deep learning approach to enable accurate segmentation of the amygdala directly on the dMRI data. Next, we design a novel streamline clustering-based structural connectivity feature for a robust representation of voxels within the amygdala. Finally, we improve the popular joint dimensionality reduction and k-means clustering approach to enable amygdala parcellation at a finer scale. With the proposed method, we obtain nine unique amygdala parcels. Experiments show that these parcels can be consistently identified across subjects and have good correspondence to the widely used coarse-scale amygdala parcellation.