Xiao Hanguang, Ran Zhiqiang, Huang Jinfeng, Ren Huijiao, Liu Chang, Zhang Banglin, Zhang Bolong, Dang Jun
Department of Intelligent Science, School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P.R.China.
Department of Radiotherapy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R.China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2021 Apr 25;38(2):379-386. doi: 10.7507/1001-5515.202008032.
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威胁人类健康和生命安全，因此早期筛查和诊断尤为重要。计算机断层扫描（CT）技术是筛查肺部疾病的重要手段之一，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够有效提高肺部疾病的早期诊断和治疗水平。基于CT图像自动、快速、准确地分割肺实质能够有效弥补手工分割效率低、主观性强的缺点，已成为该领域的研究热点之一。本文基于近年来国内外发表的相关文献，综述了肺实质分割的研究进展。对传统机器学习方法和深度学习方法进行了比较分析，重点介绍了改进深度学习模型网络结构的研究进展。讨论了肺实质分割中一些尚未解决的问题，并对发展前景进行了展望，为相关领域的研究人员提供参考。
