融合PVTv2和多尺度边界聚合的结直肠息肉分割算法.

Considering the complex characteristics of low contrast, blurred boundaries, and irregular shapes in the lesion area and surrounding mucus in colorectal polyp images, most of the existing algorithms cannot achieve high-precision segmentation of colorectal polyps. In view of the above difficulties, this paper proposed a colorectal polyp segmentation algorithm combining PVTv2 and multi-scale boundary polymerization. Firstly, using PVTv2 to extract the lesion features in the intestinal polyp image layer by layer, to solve the problem that the traditional convolutional neural network has insufficient ability to extract the features of the lesion area. It constructed a multi-scale context space awareness module at the network skip connection. Then, it designed a multi-scale extrusion adaptation fusion module to aggregate the feature information of different scales to reduce the semantic difference of each scale feature. Finally, in order to further strengthen the edge detail features recognition ability, creatively constructed residual axial double boundary refinement module. The algorithm verified by a large number of experiments on the Kvasir-SEG dataset and the CVC-ClinicDB dataset, the similarity coefficients were 93. 29% and 94. 52%, and the average intersectional merge ratio was 88. 36% and 89. 88% respectively. The experimental results show that the segmentation accuracy of the proposed model has been greatly improved for the complex lesion area and the blurred lesion boundary. [ABSTRACT FROM AUTHOR]