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.

BACKGROUND: Lung cancer was the second most commonly diagnosed cancer and the leading cause of cancer death in 2020. Although artificial intelligence (AI)-assisted diagnostic technologies have shown promise and has been used in clinical practice in recent years, no products related to AI-assisted CT diagnostic technologies for the classification of pulmonary nodules have been approved by the National Medical Products Administration in China. The objective of this article was to systematically review the diagnostic performance of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant and to analyze physicians\' perceptions of this technology in China.
METHODS: All relevant studies from 6 literature databases were searched and screened according to the inclusion and exclusion criteria. Data were extracted and the study quality was assessed by two reviewers. The study heterogeneity and publication bias were estimated. A questionnaire survey on the perceptions of physicians was conducted in 9 public tertiary hospitals in China. A meta-analysis, meta-regression and univariate logistic model were used in the systematic review and to explore the association of physicians\' perceptions with their rate of support for the clinical application of the technology.
RESULTS: Twenty-seven studies with 5,727 pulmonary nodules were finally included in the meta-analysis. We found that the quality of the included studies was generally acceptable and that the pooled sensitivity and specificity of AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant were 0.90 and 0.89, respectively. The pooled diagnostic odds ratio (DOR) was 70.33. The majority of the surveyed physicians in China perceived \"reduced workload for radiologists\" and \"improved diagnostic efficiency\" as the important benefits of this technology. In addition, diagnostic accuracy (including misdiagnosis) and practical experience were significantly associated with whether physicians supported its clinical application.
CONCLUSIONS: In the context of lung cancer diagnosis, AI-assisted CT diagnostic technology for the classification of pulmonary nodules as benign or malignant has good diagnostic performance, but its specificity needs to be improved.