DPM-UNet: A Mamba-Based Network with Dynamic Perception Feature Enhancement for Medical Image Segmentation
Shangyu Xu, Xiaohang Liu, Hongsheng Lei, Bin Hui · Sensors · 2025
In medical image segmentation, effective integration of global and local features is crucial. Current methods struggle to simultaneously model long-range dependencies and fine local details. Convolutional Neural Networks (CNNs) excel at extracting local features but are limited by their local receptive fields for capturing long-range dependencies. While global self-attention mechanisms (e.g., in Transformers) can capture long-range spatial relationships, their quadratic computational complexity incurs high costs for high-resolution medical images. To address these limitations, State Space Models (SSMs), which maintain linear complexity while effectively establishing long-range dependencies, have been introduced to visual tasks. Leveraging the advantages of SSMs, this paper proposes DPM-UNet. The network employs a Dual-path Residual Fusion Module (DRFM) at shallow layers to extract local detailed features and a DPMamba Module at deep layers to model global semantic information, achieving effective local global feature fusion. A Multi-scale Aggregation Attention Network (MAAN) is further incorporated to enhance multi-scale representations. The proposed method collaboratively captures local details, long-range dependencies, and multi-scale information in medical images. Experiments on three public datasets demonstrate that DPM-UNet outperforms existing methods across multiple evaluation metrics.