| Summary: | Colorectal polyp is an abnormal tissue growing in the gastrointestinal tract with the potential to develop into colorectal cancer. Therefore, early detection and removal of colorectal polyps are crucial for preventing colorectal cancer. In recent years, deep learning technology has made significant strides in the field of colonic polyp image segmentation, substantially enhancing both the accuracy and automation levels of segmentation. This paper focuses on research related to deep learning in colorectal polyp image segmentation. Firstly, it introduces various imaging techniques for colonic polyps and commonly used datasets, including both image and video datasets, and elaborates on the characteristics of these datasets. Subsequently, the deep learning-based segmentation methods are summarized, covering fully convolutional networks, Mask R-CNN, generative adversarial networks, U-Net, Transformer, and multi-network fusion models. Particular emphasis is placed on the application of U-Net and its variants in colonic polyp image segmentation, analyzing their structural improvements, performance enhancements, and practical application outcomes. Furthermore, the review comprehensively compares the main improvements, advantages, disadvantages, and segmentation results of each network model. Finally, it points out the main challenges currently faced by deep learning in this field and provides an outlook on future research directions.
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