VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module
<|reference_start|>VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module: 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. To overcome the complex motion and structure on multi-modality images, we further propose a fine-tune recursive registration framework. 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 with all baseline methods is available on GitHub.<|reference_end|>
@article{wang2024vmambamorph:,
    title={VMambaMorph: a Multi-Modality Deformable Image Registration Framework
  based on Visual State Space Model with Cross-Scan Module},
    author={Ziyang Wang, Jian-Qing Zheng, Chao Ma, Tao Guo},
    journal={arXiv preprint arXiv:2404.05105},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05105},
    primaryClass={cs.CV}
}