CIFG-Net: Cross-level information fusion and guidance network for Polyp Segmentation

Colorectal cancer is a common malignant tumor of the digestive tract. Most colorectal cancer is caused by colorectal polyp lesions. Timely detection and removal of colorectal polyps can substantially reduce the incidence of colorectal cancer. Accurate polyp segmentation can provide important polyp information that can aid in the early diagnosis and treatment of colorectal cancer. However, polyps of the same type can vary in texture, color, and even size. Furthermore, some polyps are similar in colour to the surrounding healthy tissue, which makes the boundary between the polyp and the surrounding area unclear. In order to overcome the issues of inaccurate polyp localization and unclear boundary segmentation, we propose a polyp segmentation network based on cross-level information fusion and guidance. We use a Transformer encoder to extract a more robust feature representation. In addition, to refine the processing of feature information from encoders, we propose the edge feature processing module (EFPM) and the cross-level information processing module (CIPM). EFPM is used to focus on the boundary information in polyp features. After processing each feature, EFPM can obtain clear and accurate polyp boundary features, which can mitigate unclear boundary segmentation. CIPM is used to aggregate and process multi-scale features transmitted by various encoder layers and to solve the problem of inaccurate polyp location by using multi-level features to obtain the location information of polyps. In order to better use the processed features to optimise our segmentation effect, we also propose an information guidance module (IGM) to integrate the processed features of EFPM and CIPM to obtain accurate positioning and segmentation of polyps. Through experiments on five public polyp datasets using six metrics, it was demonstrated that the proposed network has better robustness and more accurate segmentation effect. Compared with other advanced algorithms, CIFG-Net has superior performance. Code available at: https://github.com/zspnb/CIFG-Net.