Colorectal Polyp Segmentation in the Deep Learning Era: A Comprehensive
  Survey
Zhenyu Wu Fengmao Lv Chenglizhao Chen Aimin Hao Shuo Li
Colorectal polyp segmentation (CPS), an essential problem in medical image
analysis, has garnered growing research attention. Recently, the deep
learning-based model completely overwhelmed traditional methods in the field of
CPS, and more and more deep CPS methods have emerged, bringing the CPS into the
deep learning era. To help the researchers quickly grasp the main techniques,
datasets, evaluation metrics, challenges, and trending of deep CPS, this paper
presents a systematic and comprehensive review of deep-learning-based CPS
methods from 2014 to 2023, a total of 115 technical papers. In particular, we
first provide a comprehensive review of the current deep CPS with a novel
taxonomy, including network architectures, level of supervision, and learning
paradigm. More specifically, network architectures include eight subcategories,
the level of supervision comprises six subcategories, and the learning paradigm
encompasses 12 subcategories, totaling 26 subcategories. Then, we provided a
comprehensive analysis the characteristics of each dataset, including the
number of datasets, annotation types, image resolution, polyp size, contrast
values, and polyp location. Following that, we summarized CPS's commonly used
evaluation metrics and conducted a detailed analysis of 40 deep SOTA models,
including out-of-distribution generalization and attribute-based performance
analysis. Finally, we discussed deep learning-based CPS methods' main
challenges and opportunities.