  In the last decade, convolutional neural networks (ConvNets) have dominated
and achieved state-of-the-art performances in a variety of medical imaging
applications. However, the performances of ConvNets are still limited by
lacking the understanding of long-range spatial relations in an image. The
recently proposed Vision Transformer (ViT) for image classification uses a
purely self-attention-based model that learns long-range spatial relations to
focus on the relevant parts of an image. Nevertheless, ViT emphasizes the
low-resolution features because of the consecutive downsamplings, result in a
lack of detailed localization information, making it unsuitable for image
registration. Recently, several ViT-based image segmentation methods have been
combined with ConvNets to improve the recovery of detailed localization
information. Inspired by them, we present ViT-V-Net, which bridges ViT and
ConvNet to provide volumetric medical image registration. The experimental
results presented here demonstrate that the proposed architecture achieves
superior performance to several top-performing registration methods.
