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The amygdala plays a vital role in emotional processing and exhibitsstructural diversity that necessitates fine-scale parcellation for acomprehensive understanding of its anatomico-functional correlations. DiffusionMRI tractography is an advanced imaging technique that can estimate the brain'swhite matter structural connectivity to potentially reveal the topography ofthe amygdala for studying its subdivisions. In this work, we present a deepclustering pipeline to perform automated, fine-scale parcellation of theamygdala using diffusion MRI tractography. First, we incorporate a newlyproposed deep learning approach to enable accurate segmentation of the amygdaladirectly on the dMRI data. Next, we design a novel streamline clustering-basedstructural connectivity feature for a robust representation of voxels withinthe amygdala. Finally, we improve the popular joint dimensionality reductionand k-means clustering approach to enable amygdala parcellation at a finerscale. With the proposed method, we obtain nine unique amygdala parcels.Experiments show that these parcels can be consistently identified acrosssubjects and have good correspondence to the widely used coarse-scale amygdalaparcellation.

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