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Deformable image registration is an essential approach for medical imageanalysis.This paper introduces MambaMorph, an innovative multi-modalitydeformable registration network, specifically designed for Magnetic Resonance(MR) and Computed Tomography (CT) image alignment. MambaMorph stands out withits Mamba-based registration module and a contrastive feature learningapproach, addressing the prevalent challenges in multi-modality registration.The network leverages Mamba blocks for efficient long-range modeling andhigh-dimensional data processing, coupled with a feature extractor that learnsfine-grained features for enhanced registration accuracy. Experimental resultsshowcase MambaMorph's superior performance over existing methods in MR-CTregistration, underlining its potential in clinical applications. This workunderscores the significance of feature learning in multi-modality registrationand positions MambaMorph as a trailblazing solution in this field. The code forMambaMorph is available at: https://github.com/Guo-Stone/MambaMorph.

MambaMorph: a Mamba-based Backbone with Contrastive Feature Learning for Deformable MR-CT Registration