  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.
