SAM2-RFANet: A multi-receptive-field aware attention network for enhanced polyp segmentation in colonoscopy imagery

Accurate polyp segmentation in colonoscopy images is crucial for early colorectal cancer diagnosis but remains challenging due to irregular shapes, weak boundaries, and low contrast. Although foundation segmentation models like SAM2 offer powerful representations, their generic decoders often lack the multiscale modelling and cross-layer semantic constraints needed for precise medical image analysis, particularly under weak-boundary conditions. To address this, we propose SAM2-RFANet, a novel network that synergistically couples multireceptive-field feature extraction with collaborative attention mechanisms. Our framework introduces three core modules: an Adaptive Receptive Field Modulation (ARFM) module for dynamic context modelling via learnable hybrid pooling and multi-scale dilated convolutions; a Multi-scale Feature Decoding (MSFD) module employing direction-aware large-kernel decomposition to better capture anisotropic structures like polyp boundaries; and a Semantic Feature Alignment Module (SFAM) for gated, semantically consistent fusion of multi-level features. Extensive experiments on five public datasets (Kvasir-SEG, CVC-ClinicDB, CVC-ColonDB, ETIS-LaribPolypDB, and Endoscene) demonstrate that SAM2-RFANet achieves state-of-the-art performance. Notably, it attains mean Dice scores of 0.923 on Kvasir-SEG and 0.942 on CVC-ClinicDB, while showing strong generalisation on more challenging datasets (e.g., 0.813 on CVC-ColonDB).These results confirm the effectiveness of integrating multi-receptive-field modelling with cross-layer semantic alignment for robust polyp segmentation in complex endoscopic scenarios. Our work illustrates how task-specific architectural innovations can effectively adapt powerful foundation models to the nuanced demands of medical image analysis.The code will be available at https://github.com/253458lgc/SAM2-RFANet.