HGCMorph: joint discontinuity-preserving and pose-learning via GNN and capsule networks for deformable medical images registration

This study aims to enhance medical image registration by addressing the limitations of existing approaches that rely on spatial transformations through U-Net, ConvNets, or Transformers. The objective is to develop a novel architecture that combines ConvNets, graph neural networks (GNNs), and capsule networks to improve the accuracy and efficiency of medical image registration, which can also deal with the problem of rotating registration.