VMambaMorph: a Multi-Modality Deformable Image Registration Framework based on Visual State Space Model with Cross-Scan Module
arxiv(2024)
摘要
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.
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