# Joint inference on structural and diffusion MRI for sequence-adaptive Bayesian segmentation of thalamic nuclei with probabilistic atlases

## TL;DR

This paper introduces a novel Bayesian segmentation algorithm that jointly analyzes structural and diffusion MRI data, improving the accuracy of thalamic nuclei segmentation across diverse MRI contrasts and datasets.

## Contribution

The paper presents a new hierarchical likelihood model combining Beta and Dimroth-Scheidegger-Watson distributions for dMRI, integrated with Gaussian sMRI modeling, enabling multi-modal MRI segmentation with a unified Bayesian framework.

## Key findings

- Effective joint segmentation of sMRI and dMRI data.
- Application to thalamic nuclei segmentation on HCP and ADNI datasets.
- Achieved accurate segmentation with smaller sample sizes.

## Abstract

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.