HIGF-Net: Hierarchical information-guided fusion network for polyp segmentation based on transformer and convolution feature learning

Abstract

Polyp segmentation plays a role in image analysis during colonoscopy screening, thus improving the diagnostic efficiency of early colorectal cancer. However, due to the variable shape and size characteristics of polyps, small difference between lesion area and background, and interference of image acquisition conditions, existing segmentation methods have the phenomenon of missing polyp and rough boundary division. To overcome the above challenges, we propose a multi-level fusion network called HIGF-Net, which uses hierarchical guidance strategy to aggregate rich information to produce reliable segmentation results. Specifically, our HIGF-Net excavates deep global semantic information and shallow local spatial features of images together with Transformer encoder and CNN encoder. Then, Double-stream structure is used to transmit polyp shape properties between feature layers at different depths. The module calibrates the position and shape of polyps in different sizes to improve the model's efficient use of the rich polyp features. In addition, Separate Refinement module refines the polyp profile in the uncertain region to highlight the difference between the polyp and the background. Finally, in order to adapt to diverse collection environments, Hierarchical Pyramid Fusion module merges the features of multiple layers with different representational capabilities. We evaluate the learning and generalization abilities of HIGF-Net on five datasets using six evaluation metrics, including Kvasir-SEG, CVC-ClinicDB, ETIS, CVC-300, and CVC-ColonDB. Experimental results show that the proposed model is effective in polyp feature mining and lesion identification, and its segmentation performance is better than ten excellent models.