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Yang Y, Li Q, Guo Y, Liu Y, Li X, Guo J, Li W, Cheng L, Chen H, Kang Y. Lung parenchyma parameters measure of rats from pulmonary window computed tomography images based on ResU-Net model for medical respiratory researches. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2021; 18:4193-4211. [PMID: 34198432 DOI: 10.3934/mbe.2021210] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Our paper proposes a method to measure lung parenchyma parameters from pulmonary window computed tomography images based on ResU-Net model including the CT value, the density, the lung volume, and the surface area of the lungs of healthy rats, to help promote the quantitative analysis of lung parenchyma parameters of rats in medical respiratory researches. Through the analysis of the lung parenchyma parameters of the control group and the treatment group, the law of change among the lung parenchyma parameters is given in our paper. After comparing and analyzing the lung parenchyma parameter CT value and the density of the two groups, it is discovered that the lung parenchyma parameter CT value and the density significantly increase in the treatment group which is after continuously inhaling the nebulization of contrast agents. The change of the lung volume with the surface area in both two groups conforms to the law of lung changes during breathing. The relationship between the lung volume and the CT value or the density is analyzed and it is concluded that the lung volume is negatively correlated with the CT value or the density.
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Affiliation(s)
- Yingjian Yang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Qiang Li
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Yingwei Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Yang Liu
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Xian Li
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Jiaqi Guo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
| | - Wei Li
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Lei Cheng
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
| | - Huai Chen
- Department of Radiology, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou 510120, China
| | - Yan Kang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China
- Medical Health and Intelligent Simulation Laboratory, Medical Device Innovation Center, Shenzhen Technology University, Shenzhen 518118, China
- Engineering Research Centre of Medical Imaging and Intelligent Analysis, Ministry of Education, Shenyang 110169, China
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