ABC-Net: Area-Boundary Constraint Network With Dynamical Feature Selection for Colorectal Polyp Segmentation

Untreated colorectal polyps can develop into colorectal cancer, which is a leading cause of cancer-related deaths. Colonoscopy is a commonly-used method for colorectal polyp scanning, but limited to the experience and subjectivity of clinicians, one out of four polyps cannot be correctly recognized. In this article, we propose an automatic colorectal polyp segmentation system based on the deep convolutional neural network, aiming to improve the accuracy of colorectal polyp scanning. The proposed ABC-Net is comprised of a shared encoder and two novel mutually-constrained decoders for simultaneous polyp area and boundary segmentation. To sufficiently exploit multi-scale image information, the selective feature modules are embedded into the network and used for dynamically learning and fusing multi-scale feature representations. Furthermore, a new boundary-sensitive loss is proposed to model the interdependencies between the area and boundary branches, the information of the two branches are reciprocally propagated and constrained, yielding a significant improvement in segmentation accuracy. Extensive experiments are conducted on three public colorectal polyp datasets, and the results, e.g., F1 scores are 0.866, 0.915, 0.874 in EndoScene, Kvasir-SEG, and ETIS-Larib datasets, demonstrate the advantages of the proposed method.

Automated summary

The study introduces an automatic colorectal polyp segmentation system based on deep convolutional neural network to improve accuracy of colorectal polyp scanning. By incorporating feature modules and a new boundary-sensitive loss, the segmentation accuracy is significantly enhanced.