TY - JOUR
T1 - COVLIAS 1.0Lesion vs. MedSeg
T2 - An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans
AU - Suri, Jasjit S.
AU - Agarwal, Sushant
AU - Chabert, Gian Luca
AU - Carriero, Alessandro
AU - Paschè, Alessio
AU - Danna, Pietro S.C.
AU - Saba, Luca
AU - Mehmedović, Armin
AU - Faa, Gavino
AU - Singh, Inder M.
AU - Turk, Monika
AU - Chadha, Paramjit S.
AU - Johri, Amer M.
AU - Khanna, Narendra N.
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 D.
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 - Nagy, Ferenc
AU - Ruzsa, Zoltan
AU - Fouda, Mostafa M.
AU - Naidu, Subbaram
AU - Viskovic, Klaudija
AU - Kalra, Manudeep K.
N1 - Publisher Copyright:
© 2022 by the au-thors. Licensee MDPI, Basel, Switzerland.
PY - 2022/5
Y1 - 2022/5
N2 - Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whit-ney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was < 1 s. Conclusions: The AI models reliably located and seg-mented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervaria-bility test.
AB - Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann–Whit-ney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was < 1 s. Conclusions: The AI models reliably located and seg-mented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervaria-bility test.
KW - COVID lesions
KW - COVID-19
KW - computed tomography
KW - ground-glass opacities
KW - hybrid deep learning
KW - segmentation
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UR - http://www.scopus.com/inward/citedby.url?scp=85131208633&partnerID=8YFLogxK
U2 - 10.3390/diagnostics12051283
DO - 10.3390/diagnostics12051283
M3 - Article
AN - SCOPUS:85131208633
SN - 2075-4418
VL - 12
JO - Diagnostics
JF - Diagnostics
IS - 5
M1 - 1283
ER -