Work

SLGMA-UNet: Comprehensive Feature Aggregation With Context-Sensitive Attention for Medical Image Segmentation

Year: 2025

Type: article

Abstract: Medical image segmentation is essential for clinical diagnosis and treatment planning. Existing segmentation methods encounter challenges such as managing size variations, interpreting contextual relationships, and integrating multi-source data. This paper introduces SLGMA-UNet, an enhanced architecture that incorporates a multi-level attention system, adaptive scale processing Efficient Multi-Scale Attention( EMA), and a combined focus mechanism Spatial & Local-Global Attention(SLGA). The EMA module integrates multi-scale information, avoids channel dimensionality reduction, facilitates cross-space learning, and effectively establishes dependencies. The SLGA module balances local and global feature processing through an adaptive fusion strategy. Experiments conducted on five medical datasets, including ISIC2017, ISIC2018, CVC-ClinicDB, Kvasir-SEG, and GLAS, demonstrate that SLGMA-UNet outperforms existing models, achieving accuracy improvements of 4.2-7.8% (mean Intersection over Union, mIoU) and 3.6-6.4% (Dice Similarity Coefficient, DSC), along with enhanced edge detection and reduced errors in complex cases. Ablation studies confirm the positive and synergistic effects of the additional components. SLGMA-UNet offers a more effective solution for accurate medical image segmentation and shows promise for practical clinical applications.

Source: IEEE Access

DOI: 10.1109/access.2025.3584832

Authors: Xinghuo Ye, Na Wang

Institutions: Jiaxing Vocational Technical College, Shanghai University of International Business and Economics

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,075