TY - JOUR
T1 - Inter-variability study of covlias 1.0
T2 - Hybrid deep learning models for covid-19 lung segmentation in computed tomography
AU - Suri, Jasjit S.
AU - Agarwal, Sushant
AU - Elavarthi, Pranav
AU - Pathak, Rajesh
AU - Ketireddy, Vedmanvitha
AU - Columbu, Marta
AU - Saba, Luca
AU - Gupta, Suneet K.
AU - Faa, Gavino
AU - Singh, Inder M.
AU - Turk, Monika
AU - Chadha, Paramjit S.
AU - Johri, Amer M.
AU - Khanna, Narendra N.
AU - Viskovic, Klaudija
AU - Mavrogeni, Sophie
AU - Laird, John R.
AU - Pareek, Gyan
AU - Miner, Martin
AU - Sobel, David W.
AU - Balestrieri, Antonella
AU - Sfikakis, Petros P.
AU - Tsoulfas, George
AU - Protogerou, Athanasios
AU - Misra, Durga Prasanna
AU - Agarwal, Vikas
AU - Kitas, George D.
AU - Teji, Jagjit S.
AU - Al-Maini, Mustafa
AU - Dhanjil, Surinder K.
AU - Nicolaides, Andrew
AU - Sharma, Aditya
AU - Rathore, Vijay
AU - Fatemi, Mostafa
AU - Alizad, Azra
AU - Krishnan, Pudukode R.
AU - Ferenc, Nagy
AU - Ruzsa, Zoltan
AU - Gupta, Archna
AU - Naidu, Subbaram
AU - Kalra, Mannudeep K.
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/11
Y1 - 2021/11
N2 - Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
AB - Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.
KW - COVID-19
KW - Computed tomography
KW - Hybrid deep learning
KW - Lungs
KW - Segmentation
KW - Variability
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U2 - 10.3390/diagnostics11112025
DO - 10.3390/diagnostics11112025
M3 - Article
C2 - 34829372
AN - SCOPUS:85118859046
SN - 2075-4418
VL - 11
JO - Diagnostics
JF - Diagnostics
IS - 11
M1 - 2025
ER -