Review on Precise Segmentation Technology of colon polyp Integrating Convolutional Neural Networks and Pathological Features

Abstract

Colon polyps are one of the common intestinal lesions in clinical practice and also the most typical type of precancerous lesions for colorectal cancer. Accurate segmentation is crucial for computer-aided diagnosis systems. Deep learning methods based on convolutional neural networks (CNNs) have been applied to the automatic segmentation of colorectal polyps, but they still face challenges such as inter-polyp differences, intra-polyp variations, and changes in imaging environments. These issues make it difficult to meet the clinical requirements for segmentation accuracy. Therefore, integrating pathological prior knowledge into deep learning models to extract colorectal polyp-related features has become a core approach to address the aforementioned problems. This review summarizes the current research status of colorectal polyp segmentation technologies that combine convolutional networks and pathological features, introduces the research background and significance of this study, and compares representative domestic and international research works. On this basis, the existing problems are summarized and the future development trends are analyzed, aiming to provide certain references for subsequent research.