Abstract:The segmentation of polyp images has extensive research and application value in the fields of clinical treatment and computer-aided diagnostic technology, but accurate polyp segmentation is still a challenge in terms of current research and application needs. In order to solve the problems that affect the segmentation quality of endoscopic polyp images, such as the unclear boundary between polyps and mucous membranes, and the large difference in the size and shape of polyps, this paper proposed an improved U-Net polyp segmentation algorithm. Firstly, the boundary feature enhancement module was introduced on the U-Net architecture. Considering the key clues of polyp boundary and internal area, this module used the high-level features of the encoder to generate additional boundary supplementary information, which is fused at the decoder stage to improve the ability of the model to process boundary features. Secondly, the decoder of the model adopts the method of gradually fusing features from the top to the bottom. After the output features of the encoder stage are passed through local emphasis module, the boundary features are gradually fused. This multi-scale feature fusion method effectively reduces the semantic gap between the encoder and the decoder. Finally, test-time augmentation was used in the post-processing stage to further refine the segmentation results. The model has been compared and ablated on five public datasets: CVC-300, CVC-ClinicDB, Kvasir-SEG, CVC-ColonDB and ETIS-LibPolypDB. The experimental results prove the effectiveness of the modified method, and it shows better segmentation performance and stronger stability in the endoscopic polyp image, which provides a new reference for the processing and analysis of the polyp image.
