Early detection and assessment of polyps play a crucial role in the prevention and treatment of colorectal cancer (CRC). Polyp segmentation provides an effective solution to assist clinicians in accurately locating and segmenting polyp regions. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the advent of deep learning, more and more outstanding medical image segmentation algorithms based on deep learning networks have emerged, making significant progress in this field. This paper provides a comprehensive review of polyp segmentation algorithms. We first review some traditional algorithms based on manually extracted features and deep segmentation algorithms, then detail benchmark datasets related to the topic. Specifically, we carry out a comprehensive evaluation of recent deep learning models and results based on polyp sizes, considering the pain points of research topics and differences in network structures. Finally, we discuss the challenges of polyp segmentation and future trends in this field. The models, benchmark datasets, and source code links we collected are all published at https://github.com/taozh2017/Awesome-Polyp-Segmentation.

翻译：息肉的早期检测与评估在结直肠癌（CRC）的预防与治疗中起着关键作用。息肉分割为辅助临床医生精准定位并分割息肉区域提供了有效解决方案。过去，人们常依赖人工提取的底层特征（如颜色、纹理和形状），但这些方法往往难以捕捉全局上下文信息，且对复杂场景缺乏鲁棒性。随着深度学习的兴起，越来越多基于深度学习网络的优秀医学图像分割算法不断涌现，在该领域取得了显著进展。本文对息肉分割算法进行了全面综述。我们首先回顾了部分基于人工提取特征的传统算法与深度分割算法，随后详细阐述了与该主题相关的基准数据集。具体而言，我们针对息肉尺寸差异，系统评估了近期深度学习模型及其分割结果，并探讨了研究痛点的差异与网络结构的区别。最后，我们讨论了息肉分割面临的挑战及该领域的未来发展趋势。我们收集的模型、基准数据集及源代码链接均发布于 https://github.com/taozh2017/Awesome-Polyp-Segmentation。