Abstract: Segmentation and parcellation of the thalamus is an important step in
providing volumetric assessment of the impact of disease on brain
structures. Conventionally, segmentation is carried out on T1-weighted
magnetic resonance (MR) images and nuclear parcellation using
diffusion weighted MR images. We present the first fully automatic
method that incorporates both tissue contrasts and several derived
features to first segment and then parcellate the thalamus. We
incorporate fractional anisotrophy, fiber orientation from the 5D
Knutsson representation of the principal eigenvectors, and
connectivity between the thalamus and the cortical lobes, as features.
Combining these multiple information sources allows us to identify
discriminating dimensions and thus parcellate the thalamic nuclei. A
hierarchical random forest framework with a multidimensional feature
per voxel, first distinguishes thalamus from background, and then
separates each group of thalamic nuclei. Using a leave one out
cross-validation on 12 subjects we have a mean Dice score of 0.805 and
0.799 for the left and right thalami, respectively. We also report
overlap for the thalamic nuclear groups.
Paper (pdf)
Poster (pdf)