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Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis

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Author(s)
Young-Gon KimKyungsang KimDufan WuHui RenWon Young TakSoo Young ParkYu Rim LeeMin Kyu KangJung Gil ParkByung Seok KimWoo Jin ChungMannudeep K. KalraQuanzheng Li
Keimyung Author(s)
Chung, Woo Jin
Department
Dept. of Internal Medicine (내과학)
Journal Title
Diagnostics (Basel)
Issued Date
2022
Volume
12
Issue
1
Keyword
COVID-19deep learningsegmentationdetectionlungleft hilumcarinaRALE
Abstract
Imaging plays an important role in assessing the severity of COVID-19 pneumonia. Recent COVID-19 research indicates that the disease progress propagates from the bottom of the lungs to the top. However, chest radiography (CXR) cannot directly provide a quantitative metric of radiographic opacities, and existing AI-assisted CXR analysis methods do not quantify the regional severity. In this paper, to assist the regional analysis, we developed a fully automated framework using deep learning-based four-region segmentation and detection models to assist the quantification of COVID-19 pneumonia. Specifically, a segmentation model is first applied to separate left and right lungs, and then a detection network of the carina and left hilum is used to separate upper and lower lungs. To improve the segmentation performance, an ensemble strategy with five models is exploited. We evaluated the clinical relevance of the proposed method compared with the radiographic assessment of the quality of lung edema (RALE) annotated by physicians. Mean intensities of segmented four regions indicate a positive correlation to the regional extent and density scores of pulmonary opacities based on the RALE. Therefore, the proposed method can accurately assist the quantification of regional pulmonary opacities of COVID-19 pneumonia patients.
Keimyung Author(s)(Kor)
정우진
Publisher
School of Medicine (의과대학)
Citation
Young-Gon Kim et al. (2022). Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis. Diagnostics (Basel), 12(1), 101. doi: 10.3390/diagnostics12010101
Type
Article
ISSN
2075-4418
Source
https://www.mdpi.com/2075-4418/12/1/101
DOI
10.3390/diagnostics12010101
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/44181
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Internal Medicine (내과학)
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