CT图像中肺结节的自动和准确分割可以帮助医生进行更准确的定量分析、诊断疾病并改善患者的生存率。近年来，随着深度学习技术的发展，基于深度神经网络的肺结节分割方法逐渐取代了传统的分割方法。本文回顾了基于深度神经网络的最新肺结节分割算法。首先，讨论了肺结节的异质性、分割结果的可解释性和外部环境因素，然后将近年来医学分割任务中的开源2D和3D模型应用于肺图像数据库联盟和影像数据库资源倡议（LIDC）和肺结节分析16（Luna16）数据集进行比较，并逐一评估放射科医生标记的视觉诊断特征。根据实验数据的分析，得出以下结论：（1）在肺结节分割任务中，2D分割模型的DSC性能通常优于3D分割模型。（2）“细微性”、“球状度”、“边缘”、“纹理”和“大小”对肺结节分割影响较大，而“成釉状”、“伸带状”和“良恶性”特征对肺结节分割影响较小。（3）基于质量更好的CT图像可以达到更高的肺结节分割准确性。（4）良好的背景信息获取和注意力机制设计对肺结节分割起积极作用。版权所有 © 2023作者。由Elsevier Ltd.出版。保留所有权利。

Automatic and accurate segmentation of pulmonary nodules in CT images can help physicians perform more accurate quantitative analysis, diagnose diseases, and improve patient survival. In recent years, with the development of deep learning technology, pulmonary nodule segmentation methods based on deep neural networks have gradually replaced traditional segmentation methods. This paper reviews the recent pulmonary nodule segmentation algorithms based on deep neural networks. First, the heterogeneity of pulmonary nodules, the interpretability of segmentation results, and external environmental factors are discussed, and then the open-source 2D and 3D models in medical segmentation tasks in recent years are applied to the Lung Image Database Consortium and Image Database Resource Initiative (LIDC) and Lung Nodule Analysis 16 (Luna16) datasets for comparison, and the visual diagnostic features marked by radiologists are evaluated one by one. According to the analysis of the experimental data, the following conclusions are drawn: (1) In the pulmonary nodule segmentation task, the performance of the 2D segmentation models DSC is generally better than that of the 3D segmentation models. (2) 'Subtlety', 'Sphericity', 'Margin', 'Texture', and 'Size' have more influence on pulmonary nodule segmentation, while 'Lobulation', 'Spiculation', and 'Benign and Malignant' features have less influence on pulmonary nodule segmentation. (3) Higher accuracy in pulmonary nodule segmentation can be achieved based on better-quality CT images. (4) Good contextual information acquisition and attention mechanism design positively affect pulmonary nodule segmentation.Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.