MSCD-VM-UNet: A Vision Mamba Combining Multi-Scale Global and Local Feature Extraction With Cross-Domain Feature Fusion for Medical Image Segmentation

Accurate segmentation of tissues and lesions is essential for diagnosis and treatment. State Space Models (SSMs) have gained attention for their linear complexity and ability to model long-range dependencies. However, the existing Mamba architecture relies on direct skip connections, which limits its ability to integrate multi-scale and multi-level features and handle boundary details effectively. To address these limitations, we propose the MSCD-VM-UNet architecture, which incorporates three novel modules: the Spatial Group Multi-Scale Attention Module (SGMAM), the Cross-Domain Feature Fusion Module (CDFFM), and the Attention-Based Feature Injection Module (ABFIM). The SGMAM captures multi-scale global and local information and adaptively adjusts feature importance to highlight key regions while suppressing noise. The CDFFM enhances boundary and detail handling by aligning semantic features from both the frequency and spatial domains. The ABFIM utilizes attention mechanisms to adaptively fuse and weigh features from different scales and semantics, promoting feature collaboration and improving the model's robustness in complex tasks. Experiments on multiple datasets show that these modules significantly enhance the accuracy of MSCD-VM-UNet, setting a new benchmark for medical image segmentation.