# Multimodal connectivity-based cortical segmentation with graph neural networks

## TL;DR

This paper explores using graph neural networks to automatically segment brain regions from MRI scans, showing that combining different types of MRI data improves accuracy.

## Contribution

The novel contribution is the use of multimodal MRI data with graph neural networks for cortical segmentation, demonstrating competitive performance.

## Key findings

- Graph Attention Networks (GAT) achieved Dice scores comparable to non-graph methods for brain segmentation.
- Combining structural and diffusion MRI data improved segmentation performance compared to using only structural MRI.
- GNN-based and FreeSurfer segmentations showed similar predictive power for demographic/clinical data.

## Abstract

Due to the significant amount of time and expertise needed for manual segmentation of the brain cortex from magnetic resonance imaging (MRI) data, there is a substantial need for efficient and accurate algorithms to replace the need for human involvement. In this work, we explore the capabilities of Graph Neural Networks (GNNs) to segment the brain surface based on structural brain connectivity. We train three different GNN architectures, the Graph Convolutional Network (GCN), the Graph Attention Network (GAT), and the Graph U-Net, and evaluate their performances when trained on silver-standard cortical region labels created by FreeSurfer. We take a multimodal approach to brain segmentation by examining the influence of the structural connectivity values inferred from diffusion MRI (dMRI) in addition to using values from structural MRI (sMRI). Our results demonstrate the utility of GNN models, particularly the GAT architecture, which achieved Dice scores competitive to those reported in the literature with non-graph methods. Additionally, structural connectivity derived from dMRI revealed significant value in improving automatic segmentation, as models trained on combined attributes from dMRI and sMRI outperformed those trained only on sMRI. Finally, we compared the GNN-based and the FreeSurfer segmentations in their ability to predict demographic/clinical data, where neither of the two approaches was statistically significantly superior to the other.