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