Multi-scale network for medical image segmentation integrated with edge perception

Abstract

Precise medical image segmentation plays a crucial role in early disease diagnosis, yet existing methods struggle with complex backgrounds and ambiguous boundaries. To overcome these issues, a multi-scale network integrated with edge perception (MENet) is proposed in this paper. Firstly, an edge-related module is introduced to extract and feedback edge features, enhancing the overall feature representation. Secondly, a frequency-domain enhancement module is developed to dynamically amplify critical frequency bands, improving lesion morphology modeling while preserving global contextual representations. Thirdly, a multi-scale feature fusion module is constructed to achieve effective integration of features across different levels by leveraging the channel attention mechanism. Finally, a multi-scale aggregation loss function is designed to supervise segmentation and edge detection tasks. Experiments are conducted on Synapse, ACDC, CVC-ClinicDB and BUSI datasets. MENet achieves 84.36%, 92.40%, 95.34% and 81.24% on mDice individually. HD95 is 14.75 mm on Synapse. mIoU is 91.23% on CVC-ClinicDB and 72.64% on BUSI. It can be demonstrated that MENet consistently outperforms traditional models, baseline variants, and recent methods in terms of segmentation accuracy.