Selective Guidance Network with edge and texture awareness for polyp segmentation

Abstract

Colorectal polyp segmentation plays a crucial role in preventing colorectal cancer through colonoscopic image screening. However, most existing methods overlook uncertain regions in colonoscopic images, particularly the blurred boundary areas where polyps closely resemble colon fold structures. To address this challenge, we propose the Selective Guidance Network with edge and texture awareness for polyp segmentation (SGNet). SGNet consists of three essential modules, namely the Edge and Texture Awareness Module (ETAM), the Prior Enhancement Module (PEM), and the Hierarchical Feature Fusion Module (HFFM). ETAM integrates Laplacian operators with spatial attention mechanisms to enhance feature perception, allowing for precise extraction of polyp boundaries and adaptive amplification of texture patterns. PEM strengthens multi-scale contextual perception through dilated convolutions while refining backbone features through dual prior-driven feature rectification. HFFM employs multi-level attention gating to achieve cross-scale feature integration while effectively combining low-level edge cues with high-level semantic representations. Experimental results on five public datasets demonstrate that SGNet outperforms 16 state-of-the-art methods across six evaluation metrics, highlighting its superior segmentation performance and robustness.