Unsupervised Multimodal Surface Registration with Geometric Deep Learning

TL;DR

GeoMorph is a geometric deep-learning framework for unsupervised cortical surface registration that improves alignment accuracy and deformation smoothness, with potential applications in neuroscience research.

Contribution

It introduces a novel deep-learning approach combining graph convolutions and deep-discrete registration with regularization for cortical surface alignment.

Findings

Outperforms existing deep-learning registration methods

Achieves smoother and more accurate surface alignments

Shows competitive performance with classical frameworks

Abstract

This paper introduces GeoMorph, a novel geometric deep-learning framework designed for image registration of cortical surfaces. The registration process consists of two main steps. First, independent feature extraction is performed on each input surface using graph convolutions, generating low-dimensional feature representations that capture important cortical surface characteristics. Subsequently, features are registered in a deep-discrete manner to optimize the overlap of common structures across surfaces by learning displacements of a set of control points. To ensure smooth and biologically plausible deformations, we implement regularization through a deep conditional random field implemented with a recurrent neural network. Experimental results demonstrate that GeoMorph surpasses existing deep-learning methods by achieving improved alignment with smoother deformations. Furthermore, GeoMorph exhibits competitive performance compared to classical frameworks. Such versatility and robustness suggest strong potential for various neuroscience applications.