Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.
肺癌和新冠肺炎等肺部疾病严重危害着人类的健康与生命安全，其早期筛查与诊断尤为重要。电子计算机断层扫描（CT）技术是肺部疾病筛查的重要途径之一。其中，基于 CT 图像的肺实质分割是肺部疾病筛查的关键步骤，高质量的肺实质分割能有效提高肺部疾病早期诊断和治疗水平。基于 CT 图像的肺实质自动、快速、准确分割能有效弥补手动分割效率低、主观性强等不足，已成为该领域研究的热点之一。本文结合近年国内外发表的相关文献，对肺实质分割的研究进展进行综述，对比分析了传统机器学习方法和深度学习方法，重点介绍了深度学习模型网络结构的改进等研究进展。讨论了肺实质分割中待解决的一些问题，对发展前景进行了展望，为相关领域的科研工作者提供参考。.
Keywords: computed tomography; deep learning; lung parenchyma segmentation.