VMambaMorph: A 3D multi-modality deformable image registration framework based on visual state space model with cross-scan module

Wang, Ziyang, Zheng, Jianqing, Lei, Yongxiang, Tao, Tianli, Zuo, Kaiwen and Zhou, Wei 2026. VMambaMorph: A 3D multi-modality deformable image registration framework based on visual state space model with cross-scan module. Presented at: 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 3-8 May 2026. ICASSP 2026 - 2026 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics Speech and Signal Processing. IEEE; 1999, pp. 8847-8851. 10.1109/icassp55912.2026.11464524

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Abstract

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 both VMambaMorph and all baseline methods are available at https://github.com/ziyangwang007/VMambaMorph.

Item Type:	Conference or Workshop Item - published (Paper)
Date Type:	Publication
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE; 1999
ISBN:	979-8-3315-6702-6
ISSN:	1520-6149
Last Modified:	08 May 2026 09:45
URI:	https://orca.cardiff.ac.uk/id/eprint/186875

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