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.

翻译：结直肠息肉分割（CPS）作为医学图像分析中的关键问题，正受到日益增长的研究关注。近年来，基于深度学习的方法已全面超越传统方法在CPS领域的表现，越来越多的深度CPS方法不断涌现，推动该领域进入深度学习时代。为帮助研究者快速掌握深度CPS的主要技术、数据集、评估指标、挑战及发展趋势，本文对2014年至2023年间共115篇技术论文中基于深度学习的CPS方法进行了系统全面的综述。具体而言，我们首先提出了一种新颖的分类框架对当前深度CPS方法进行综合评述，该框架涵盖网络架构、监督级别和学习范式三个维度。其中网络架构包含8个子类别，监督级别包含6个子类别，学习范式包含12个子类别，共计26个子类别。随后，我们对每个数据集的特征进行了全面分析，包括数据集数量、标注类型、图像分辨率、息肉尺寸、对比度值及息肉位置。接着，我们总结了CPS常用的评估指标，并对40个深度SOTA模型进行了详细分析，涵盖分布外泛化能力和基于属性的性能分析。最后，我们探讨了基于深度学习的CPS方法面临的主要挑战与发展机遇。