  Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed
independently in neuroimaging pipelines. However, some brain structures (e.g.,
globus pallidus, thalamus and its nuclei) can be extracted more accurately by
fusing the two modalities. Following the framework of Bayesian segmentation
with probabilistic atlases and unsupervised appearance modeling, we present
here a novel algorithm to jointly segment multi-modal sMRI/dMRI data. We
propose a hierarchical likelihood term for the dMRI defined on the unit ball,
which combines the Beta and Dimroth-Scheidegger-Watson distributions to model
the data at each voxel. This term is integrated with a mixture of Gaussians for
the sMRI data, such that the resulting joint unsupervised likelihood enables
the analysis of multi-modal scans acquired with any type of MRI contrast,
b-values, or number of directions, which enables wide applicability. We also
propose an inference algorithm to estimate the maximum-a-posteriori model
parameters from input images, and to compute the most likely segmentation.
Using a recently published atlas derived from histology, we apply our method to
thalamic nuclei segmentation on two datasets: HCP (state of the art) and ADNI
(legacy) - producing lower sample sizes than Bayesian segmentation with sMRI
alone.
