  The thalamus is a subcortical gray matter structure that plays a key role in
relaying sensory and motor signals within the brain. Its nuclei can atrophy or
otherwise be affected by neurological disease and injuries including mild
traumatic brain injury. Segmenting both the thalamus and its nuclei is
challenging because of the relatively low contrast within and around the
thalamus in conventional magnetic resonance (MR) images. This paper explores
imaging features to determine key tissue signatures that naturally cluster,
from which we can parcellate thalamic nuclei. Tissue contrasts include
T1-weighted and T2-weighted images, MR diffusion measurements including FA,
mean diffusivity, Knutsson coefficients that represent fiber orientation, and
synthetic multi-TI images derived from FGATIR and T1-weighted images. After
registration of these contrasts and isolation of the thalamus, we use the
uniform manifold approximation and projection (UMAP) method for dimensionality
reduction to produce a low-dimensional representation of the data within the
thalamus. Manual labeling of the thalamus provides labels for our UMAP
embedding from which k nearest neighbors can be used to label new unseen voxels
in that same UMAP embedding. N -fold cross-validation of the method reveals
comparable performance to state-of-the-art methods for thalamic parcellation.
