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Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists
Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease s...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044796/ https://www.ncbi.nlm.nih.gov/pubmed/36978712 http://dx.doi.org/10.3390/bioengineering10030321 |
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author | Mirniaharikandehei, Seyedehnafiseh Abdihamzehkolaei, Alireza Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. |
author_facet | Mirniaharikandehei, Seyedehnafiseh Abdihamzehkolaei, Alireza Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. |
author_sort | Mirniaharikandehei, Seyedehnafiseh |
collection | PubMed |
description | Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice. |
format | Online Article Text |
id | pubmed-10044796 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100447962023-03-29 Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists Mirniaharikandehei, Seyedehnafiseh Abdihamzehkolaei, Alireza Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. Bioengineering (Basel) Article Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >90% in 45 testing cases. Furthermore, >73% of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice. MDPI 2023-03-02 /pmc/articles/PMC10044796/ /pubmed/36978712 http://dx.doi.org/10.3390/bioengineering10030321 Text en © 2023 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 Mirniaharikandehei, Seyedehnafiseh Abdihamzehkolaei, Alireza Choquehuanca, Angel Aedo, Marco Pacheco, Wilmer Estacio, Laura Cahui, Victor Huallpa, Luis Quiñonez, Kevin Calderón, Valeria Gutierrez, Ana Maria Vargas, Ana Gamero, Dery Castro-Gutierrez, Eveling Qiu, Yuchen Zheng, Bin Jo, Javier A. Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title | Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title_full | Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title_fullStr | Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title_full_unstemmed | Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title_short | Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists |
title_sort | automated quantification of pneumonia infected volume in lung ct images: a comparison with subjective assessment of radiologists |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044796/ https://www.ncbi.nlm.nih.gov/pubmed/36978712 http://dx.doi.org/10.3390/bioengineering10030321 |
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