Polyp Image Segmentation Based on Multi-Scale Edge Perception and Enhancement
In order to solve the problems of large polyp scale differences,unclear boundaries and reflection of endoscopic images in colorectal images,this paper proposes a network model based on edge perception and boundary enhancement.Firstly,the pyramid structure is used to extract multi-layer features,and the first active contour mask is obtained by using the centrally guided edge-aware aggregation strategy and the complementary information of the middle and low layers and the upper layers.Secondly,the hierarchical multi-scale module is used to extract the features of the last three layers of the backbone network.Finally,the forward and reverse integrated attention unit is proposed,and more edge information of the contour mask is mined through local feature preservation and contour mask merging.Experiments are carried out on five popular polyp segmentation datasets,namely Kvasir,CVC-ClinicalDB,ETIS,CVC-ColonDB and CVC-300,and the three indexes are compared with several mainstream polyp segmentation methods,among which the average Dice coefficient and average intersection union ratio are improved,the average absolute error is reduced,and the performance effect is significantly better than that of other methods.In particular,the average Dice coefficient on the ETIS dataset reaches 0.729 7,an improvement of 0.042 9 over the previous state-of-the-art method.
万方数据
维普
