CVM-fusion: parallel cross-axes mamba fusion for medical image segmentation.

OBJECTIVES: In the domain of medical image segmentation, models utilizing convolutional neural network (CNN) and Transformer have been t extensively studied and widely implemented. However, the self-attention mechanism in Transformer is incapable of adapting its focus to target structures at varying scales, resulting in discontinuities in segmentation. The objective of this study is to propose a multi-directional dynamic modeling network for medical image segmentation. METHODS: We propose a Cross-axis Mamba attention (CMA) to capture global info and establish long-range dependencies. It integrates both global context and local details, enhancing segmentation performance. We also introduce an Edge Feature Enhancement Model (EFCN) to improve edge feature detection. We evaluated the method on the ISIC2018 dataset, as well as the CVC-300 and Kvasir-SEG datasets. RESULTS: The dice similarity coefficient and intersection-over-union (IoU) metrics achieved values of 91.12 and 85.07, 90.35 and 83.43, and 94.14 and 89.62, respectively. These results outperform those of advanced models such as VM-Unet and Swin-UMamba. CONCLUSIONS: The experimental results indicate that the proposed method has good generalization ability and robustness. It also provides important support for clinical diagnosis and treatment.