Research progress in lung parenchyma segmentation based on computed tomography.
10.7507/1001-5515.202008032
- Author:
Hanguang XIAO
1
;
Zhiqiang RAN
1
;
Jinfeng HUANG
1
;
Huijiao REN
1
;
Chang LIU
1
;
Banglin ZHANG
1
;
Bolong ZHANG
1
;
Jun DANG
2
Author Information
1. Department of Intelligent Science, School of Artificial Intelligence, Chongqing University of Technology, Chongqing 401135, P.R.China.
2. Department of Radiotherapy, The First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, P.R.China.
- Publication Type:Review
- Keywords:
computed tomography;
deep learning;
lung parenchyma segmentation
- MeSH:
COVID-19;
Humans;
Lung/diagnostic imaging*;
Machine Learning;
SARS-CoV-2;
Tomography, X-Ray Computed
- From:
Journal of Biomedical Engineering
2021;38(2):379-386
- CountryChina
- Language:Chinese
-
Abstract:
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.
