A Semantic Segmentation Network for Colorectal Polyp Images With Progressive Fusion of Dual-Branch Features

Abstract

Accurate segmentation of colorectal polyps is essential for early colorectal cancer screening, yet remains challenging due to weak foreground–background contrast, disrupted boundaries caused by specular reflections and intestinal folds, and pronounced scale variation among polyps. These factors make it difficult for existing methods to jointly preserve fine boundary details and robust global semantic context. To address these task-specific challenges, we propose a Dual-branch Feature Progressive Fusion Network (DFPF-Net) for colorectal polyp segmentation. DFPF-Net adopts a dual-encoder architecture that integrates a CNN-based encoder for local and boundary-sensitive representation for global semantic modelling. A boundary-aware branch equipped with stacked Inversely Perceive Information Layers (IPILs) enhances ambiguous and fragmented contours, while the semantic branch incorporates Misalignment Fusion Modules (MFMs) and a Misaligned Single-layer Reinforcement Module (MSRM) to alleviate semantic misalignment and insufficient cross-scale interaction. Furthermore, a Perceptual Information Fusion Module (PIFM) enables effective semantic–boundary collaboration, and a Multi-level Residual Decoding Module (MRDM) progressively reconstructs structurally consistent segmentation outputs. Extensive experiments on multiple public colonoscopy datasets demonstrate that DFPF-Net achieves competitive and robust segmentation performance. In particular, on the challenging ETIS dataset, DFPF-Net attains 0.785 mDice and 0.704 mIoU, indicating its capability in handling complex structures and ambiguous boundaries in colorectal polyp segmentation.