# A cross-center smoothness prior for variational Bayesian brain tissue segmentation

## TL;DR

This paper introduces a smoothness prior for brain tissue segmentation in MRI that leverages cross-center data to improve unsupervised Bayesian segmentation, addressing generalization issues caused by acquisition differences.

## Contribution

It proposes a novel cross-center smoothness prior for unsupervised Bayesian segmentation, enhancing generalization across different medical centers without requiring labeled data.

## Key findings

- The prior improves segmentation smoothness consistency across centers.
- The semi-supervised model eliminates the need for manual cluster interpretation.
- The approach enhances cross-center tissue segmentation accuracy.

## Abstract

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.