Domain-agnostic segmentation of thalamic nuclei from joint structural
  and diffusion MRI
Henry F. J. Tregidgo Sonja Soskic Mark D. Olchanyi Juri Althonayan Benjamin Billot Chiara Maffei Polina Golland Anastasia Yendiki Daniel C. Alexander Martina Bocchetta Jonathan D. Rohrer Juan Eugenio Iglesias
The human thalamus is a highly connected subcortical grey-matter structure
within the brain. It comprises dozens of nuclei with different function and
connectivity, which are affected differently by disease. For this reason, there
is growing interest in studying the thalamic nuclei in vivo with MRI. Tools are
available to segment the thalamus from 1 mm T1 scans, but the contrast of the
lateral and internal boundaries is too faint to produce reliable segmentations.
Some tools have attempted to incorporate information from diffusion MRI in the
segmentation to refine these boundaries, but do not generalise well across
diffusion MRI acquisitions. Here we present the first CNN that can segment
thalamic nuclei from T1 and diffusion data of any resolution without retraining
or fine tuning. Our method builds on a public histological atlas of the
thalamic nuclei and silver standard segmentations on high-quality diffusion
data obtained with a recent Bayesian adaptive segmentation tool. We combine
these with an approximate degradation model for fast domain randomisation
during training. Our CNN produces a segmentation at 0.7 mm isotropic
resolution, irrespective of the resolution of the input. Moreover, it uses a
parsimonious model of the diffusion signal at each voxel (fractional anisotropy
and principal eigenvector) that is compatible with virtually any set of
directions and b-values, including huge amounts of legacy data. We show results
of our proposed method on three heterogeneous datasets acquired on dozens of
different scanners. An implementation of the method is publicly available at
https://freesurfer.net/fswiki/ThalamicNucleiDTI.