GCA-Net: Geometrical Constraints-based Advanced Network for Polyp Segmentation

Abstract

Colonoscopy is a common procedure for detecting and screening colorectal polyps, however, it poses a number of challenges because of the properties of images and the features of polyps inside, which can vary in shape, color, condition, and clear non-distinction with the surrounding context. Deep learning-based approaches have recently emerged, which considerably boost polyp segmentation procedures and gradually convert and replace out-of-date methods. Nevertheless, determining an effective measure remains a significant difficulty, which motivates us to propose GCA-Net for polyp segmentation. We first employ EfficientNetV2 as the backbone to extract improved salient features, then pass them via two modules, SE-PASPP and SE-RFB, to capture more contexture before feeding them into a dual-asymmetrical partial decoder to build a final resolution map. Finally, we develop a novel loss function based on the region-based strength of Dice loss and the geometrical constraints-based advantages of Active Contour with Elastica (ACE) loss. The suggested method's preeminence is demonstrated by comparing results to over state-of-the-art approaches in both learning and generalization assessment.