Unsupervised Multimodal Surface Registration with Geometric Deep
  Learning
Mohamed A. Suliman Logan Z. J. Williams Abdulah Fawaz Emma C. Robinson
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.