GLSNet: A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation

Abstract

Accurate polyp segmentation is of great significance for the prevention and diagnosis of early colon cancer. Transformer-based image segmentation models have been proposed for polyp segmentation with good results, however, these methods do not sufficiently consider the negative impact of background noise in different levels of features, resulting in the loss of local details. To alleviate this problem, we propose a GLSNet (A Global Guided Local Feature Stepwise Aggregation Network for polyp segmentation) network, including a spatial feature enhancement (SFE) module, a globally guided local feature enhancement (GLFE) module, and a feature stepwise aggregation (FSA) module. SFE can enhance the spatial feature representation of polyps to better capture the polyp information in the features. GLFE uses high-level features to filter noise in low-level features to capture polyp information hidden in shallow features; FSA fuses the semantic and positional information of polyps across scales to obtain the final segmentation results. Qualitative and quantitative experiments were conducted on 7 benchmark datasets, and the experimental results demonstrated that GLSNet outperforms other existing methods and has stronger generalization performance. In particular, we achieved a mean Dice of 92.9% on the large-scale Kvasir dataset, and mean Dice of 81.3% and 81.6% on CVC-ColonDB and ETIS, respectively, which are significantly higher than those of the competing methods.