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DPM-UNet: A Mamba-Based Network with Dynamic Perception Feature Enhancement for Medical Image Segmentation.

Shangyu Xu^1,2,3, Xiaohang Liu^1,2,3, Hongsheng Lei^2,3

¹Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China.

Sensors (Basel, Switzerland)

|November 27, 2025

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This study introduces DPM-UNet for medical image segmentation, effectively integrating local and global features. The novel approach enhances accuracy by capturing fine details and long-range dependencies, outperforming existing methods.

Area of Science:

Medical Imaging
Computer Vision
Artificial Intelligence

Background:

Effective medical image segmentation requires integrating both local and global features.
Existing methods like CNNs struggle with long-range dependencies, while Transformers have high computational costs.
State Space Models (SSMs) offer a solution with linear complexity for long-range dependency modeling.

Purpose of the Study:

To propose DPM-UNet, a novel network for medical image segmentation.
To effectively fuse local and global features using SSMs and other modules.
To improve the modeling of long-range dependencies and multi-scale information.

Main Methods:

Developed DPM-UNet incorporating a Dual-path Residual Fusion Module (DRFM) for local features.

Keywords:

Mamba local global feature fusion medical image segmentation multi-scale feature

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Utilized a DPMamba Module in deep layers for global semantic information and feature fusion.

Integrated a Multi-scale Aggregation Attention Network (MAAN) to enhance multi-scale representations.

Main Results:

DPM-UNet demonstrated superior performance in medical image segmentation across three public datasets.
The method effectively captured local details, long-range dependencies, and multi-scale information.
Outperformed existing state-of-the-art methods based on multiple evaluation metrics.

Conclusions:

DPM-UNet offers an effective solution for medical image segmentation by balancing local and global feature extraction.
The proposed architecture leverages SSMs to efficiently model long-range dependencies.
The findings suggest DPM-UNet as a promising advancement for medical image analysis tasks.