ERBNet: an automatic polyp image segmentation method to enhance receptive field and boundary

Glands and polyp automatic segmentation is the basis of artificial intelligence auxiliary diagnosis of colorectal cancer. Considering that polyps have different sizes, shapes, colors, textures and blurred boundaries with surrounding mucous membranes, an enhanced receptive field and boundary network (ERBNet) was proposed for polyp segmentation. Firstly, res2net50 is used as the backbone network to extract image features. Secondly, a novel receptive field enhancement module (REM) is designed to obtain both low-level features with boundary details and high-level features with global semantics. Finally, the boundary enhancement module (BEM) is used to enhance the edge contour information and improve the segmentation performance of the network for small targets and polyps with fuzzy boundary organization. ERBNet was tested on five polyp datasets and achieved good results in mean Intersection over Union (mIoU) and mean dice coefficient (mdice).