TY - JOUR
T1 - Automated Quantification of Pneumonia Infected Volume in Lung CT Images
T2 - A Comparison with Subjective Assessment of Radiologists
AU - Mirniaharikandehei, Seyedehnafiseh
AU - Abdihamzehkolaei, Alireza
AU - Choquehuanca, Angel
AU - Aedo, Marco
AU - Pacheco, Wilmer
AU - Estacio, Laura
AU - Cahui, Victor
AU - Huallpa, Luis
AU - Quiñonez, Kevin
AU - Calderón, Valeria
AU - Gutierrez, Ana Maria
AU - Vargas, Ana
AU - Gamero, Dery
AU - Castro-Gutierrez, Eveling
AU - Qiu, Yuchen
AU - Zheng, Bin
AU - Jo, Javier A.
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - comparison between manual and automated image segmentation
KW - COVID-19 detection
KW - COVID-19 severity assessment
KW - deep neural network
KW - infected lung segmentation
KW - quantification of lung disease severity
UR - http://www.scopus.com/inward/record.url?scp=85152694449&partnerID=8YFLogxK
U2 - 10.3390/bioengineering10030321
DO - 10.3390/bioengineering10030321
M3 - Artículo
C2 - 36978712
AN - SCOPUS:85152694449
SN - 2306-5354
VL - 10
JO - Bioengineering
JF - Bioengineering
IS - 3
M1 - 321
ER -