Image registration, a critical process in medical imaging, involves aligning
different sets of medical imaging data into a single unified coordinate system.
Deep learning networks, such as the Convolutional Neural Network (CNN)-based
VoxelMorph, Vision Transformer (ViT)-based TransMorph, and State Space Model
(SSM)-based MambaMorph, have demonstrated effective performance in this domain.
The recent Visual State Space Model (VMamba), which incorporates a cross-scan
module with SSM, has exhibited promising improvements in modeling global-range
dependencies with efficient computational cost in computer vision tasks. This
paper hereby introduces an exploration of VMamba with image registration, named
VMambaMorph. This novel hybrid VMamba-CNN network is designed specifically for
3D image registration. Utilizing a U-shaped network architecture, VMambaMorph
computes the deformation field based on target and source volumes. The
VMamba-based block with 2D cross-scan module is redesigned for 3D volumetric
feature processing, and a fine-grained feature extraction module is proposed
for high-dimensional feature learning. We validate VMambaMorph using a public
benchmark brain MR-CT registration dataset, comparing its performance against
current state-of-the-art methods. The results indicate that VMambaMorph
achieves competitive registration quality. The code for VMambaMorph is
available on GitHub.

VMambaMorph 是一种基于 VMamba 和 CNN 的网络，专门为 3D 图像配准设计，利用公共基准脑 MR-CT 配准数据集验证了其与当前最先进方法的竞争性配准质量。