LMSGNet: Semantic prior and edge guided network for robust polyp segmentation

Abstract Automated polyp segmentation, which aims to accurately delineate polyp regions from colonoscopy images, is a critical task for computer-aided diagnosis in colorectal cancer prevention. Although many deep learning-based models have been proposed for this task, there are still some challenges. First, existing models still suffer performance degradation when confronted with small polyps, blurry boundaries, and cross-dataset testing, indicating limited robustness and generalization. Second, existing approaches predominantly focus on visual features, leaving the potential guidance of textual semantic information largely unexplored. To address these problems, we propose a novel Large-Model Semantics-Guided Network (LMSGNet) that leverages semantic guidance to achieve high-precision polyp segmentation. Specifically, we introduce semantic prompts encoded by Contrastive Language-Image Pre-Training (CLIP), employ a Multi-level Memory Router (MMR) to dynamically select relevant semantics, and incorporate a cross-modal attention (CMA) mechanism to enable bidirectional interactions between visual and semantic features, thereby enhancing global semantic consistency. In addition, we design a Semantic-Edge Guided Block (SEGB) combined with Multi-scale Edge Features (MSEF) to refine ambiguous boundaries and small targets, yielding synergistic global–local enhancement. Extensive experiments on five public datasets demonstrate that our model consistently outperforms existing state-of-the-art models. Two ablation studies respectively demonstrate the effectiveness of different modules and the contribution of semantic guidance.