Multiscale attention network with structure guidance for colorectal polyp segmentation

Accurate segmentation and precise delineation of colorectal polyp structures are crucial for early clinical diagnosis and treatment planning. However, existing polyp segmentation techniques face significant challenges due to the high variability in polyp size and morphology, as well as the frequent indistinctness of polyp-tissue structures.

To address these challenges, we propose a multiscale attention network with structure guidance (MAN-SG). The core of MAN-SG is a structure extraction module (SEM) designed to capture rich structural information from fine-grained early-stage encoder features. In addition, we introduce a cross-scale structure guided attention (CSGA) module that effectively fuses multiscale features under the guidance of the structural information provided by the SEM, thereby enabling more accurate delineation of polyp structures. MAN-SG is implemented and evaluated using two high-performance backbone networks: Res2Net-50 and PVTv2-B2.

Extensive experiments were conducted on five benchmark datasets for polyp segmentation. The results demonstrate that MAN-SG consistently outperforms existing state-of-the-art methods across these datasets.

The proposed MAN-SG framework, which leverages structural guidance via SEM and CSGA modules, proves to be both highly effective and robust for the challenging task of colorectal polyp segmentation.