Shape Features Guided Network for Polyp Segmentation
Polyp segmentation is a challenging task due to the variable size and shape of polyps and the low contrast between polyps and surrounding mucosa.To improve the segmentation accuracy at polyps with different shapes and tiny-polyps,we propose a shape-guided polyp segmentation network namely SGNet.SGNet mainly includes two contributions:Firstly,we design a detail-sensitive encoder by in-troducing ConvNext,so that the network can extract the details that are crucial to polyp segmentation while extracting global information.Secondly,we design a shape-guided decoder,which can not only effectively adapt to polyps with different shapes,but also extract rich polyp shape features and multi-scale features,thus effectively improving the segmentation accuracy in polyps with different shapes and tiny-polyps.Extensive experiments on three public polyp segmentation datasets(ETIS,Kvasir and CVC-ClinicDB)show that SGNet can accurately segment polyps with different shapes and tiny-polyps,and is superior to the popular polyp segmentation networks in terms of segmentation accuracy in recent years.
deep learningconvolutional neural networkmedical image segmentationpolyp segmentationshape feature
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