A Reliability-Guided Fusion and Dynamic Dual-Domain Mask-Modulated Shearlet Network for Medical Image Segmentation

Deep learning methods are becoming increasingly important for medical image segmentation. However, previous approaches often performed poorly in spatial-frequency-domain fusion and feature fusion, leading to inaccurate lesion localization, hazy boundaries, and low accuracy. This paper proposes a reliability-guided fusion and dynamic dual-domain mask-modulated Shearlet network for medical image segmentation (RF-DMSNet for short). Its two primary components are the reliability-guided multi-scale feature fusion module (RGMF) and the dynamic dual-domain mask with a coordinate attention-modulated Shearlet operator (DAMS). The former module exhibits reliability in both the channel and spatial dimensions, allowing adaptive weighted fusion to increase feature quality. Meanwhile, the latter uses a dynamic dual-domain mask and coordinate attention to improve lesion boundary and location segmentation performance. The frequency-domain re-enhancement module (FREM) and critical feature guided module (CFGM) are designed using DAMS. These two modules work together to optimize a unified, detailed enhancement module (DEM), enabling progressive refinement of segmentation predictions from coarse to fine. Extensive experimental results demonstrate that RF-DMSNet achieves superior segmentation performance compared to state-of-the-art approaches for medical image segmentation.