Fuzzy Attention-based Geometric Algebra Convolution in Recursive Fusion Network for Medical Image Registration

Deep learning-based registration methods have significantly improved accuracy; however, they still grapple with the inherent fuzziness in medical images. Challenges such as intensity inhomogeneity, partial volume effects, and noise can distort spatial dependencies, leading to discontinuities in registration results. In this study, we introduce FuzzyGA-Net, a novel model designed to address these issues. Our approach has three key features: (i) We propose type-1 fuzzy attention (T1FA), which re-weights the feature map to mitigate the impact of uncertainty and enhance the network’s focus on the target center, thereby improving registration accuracy. (ii) Geometric Algebra Convolution is utilized throughout the network to enhance the extraction of spatial dependency cues. (iii) Two fusion strategies are employed to provide flexibility in combining information at various potential locations within the network, optimizing overall performance. Experimental results demonstrate that FuzzyGA-Net achieves the highest registration accuracy compared to state-of-the-art methods, while maintaining the smoothness of the deformation field. Our method shows significant improvement in quantitative metrics and exhibits strong generalization capabilities, suggesting its broad applicability in medical image registration.