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Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
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,...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774807/ https://www.ncbi.nlm.nih.gov/pubmed/35054267 http://dx.doi.org/10.3390/diagnostics12010101 |
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author | Kim, Young-Gon Kim, Kyungsang Wu, Dufan Ren, Hui Tak, Won Young Park, Soo Young Lee, Yu Rim Kang, Min Kyu Park, Jung Gil Kim, Byung Seok Chung, Woo Jin Kalra, Mannudeep K. Li, Quanzheng |
author_facet | Kim, Young-Gon Kim, Kyungsang Wu, Dufan Ren, Hui Tak, Won Young Park, Soo Young Lee, Yu Rim Kang, Min Kyu Park, Jung Gil Kim, Byung Seok Chung, Woo Jin Kalra, Mannudeep K. Li, Quanzheng |
author_sort | Kim, Young-Gon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8774807 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87748072022-01-21 Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis Kim, Young-Gon Kim, Kyungsang Wu, Dufan Ren, Hui Tak, Won Young Park, Soo Young Lee, Yu Rim Kang, Min Kyu Park, Jung Gil Kim, Byung Seok Chung, Woo Jin Kalra, Mannudeep K. Li, Quanzheng Diagnostics (Basel) Article 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. MDPI 2022-01-03 /pmc/articles/PMC8774807/ /pubmed/35054267 http://dx.doi.org/10.3390/diagnostics12010101 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kim, Young-Gon Kim, Kyungsang Wu, Dufan Ren, Hui Tak, Won Young Park, Soo Young Lee, Yu Rim Kang, Min Kyu Park, Jung Gil Kim, Byung Seok Chung, Woo Jin Kalra, Mannudeep K. Li, Quanzheng Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title | Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title_full | Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title_fullStr | Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title_full_unstemmed | Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title_short | Deep Learning-Based Four-Region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis |
title_sort | deep learning-based four-region lung segmentation in chest radiography for covid-19 diagnosis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8774807/ https://www.ncbi.nlm.nih.gov/pubmed/35054267 http://dx.doi.org/10.3390/diagnostics12010101 |
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