Diagnostics (Nov 2021)
Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography
- Jasjit S. Suri,
- Sushant Agarwal,
- Pranav Elavarthi,
- Rajesh Pathak,
- Vedmanvitha Ketireddy,
- Marta Columbu,
- Luca Saba,
- Suneet K. Gupta,
- Gavino Faa,
- Inder M. Singh,
- Monika Turk,
- Paramjit S. Chadha,
- Amer M. Johri,
- Narendra N. Khanna,
- Klaudija Viskovic,
- Sophie Mavrogeni,
- John R. Laird,
- Gyan Pareek,
- Martin Miner,
- David W. Sobel,
- Antonella Balestrieri,
- Petros P. Sfikakis,
- George Tsoulfas,
- Athanasios Protogerou,
- Durga Prasanna Misra,
- Vikas Agarwal,
- George D. Kitas,
- Jagjit S. Teji,
- Mustafa Al-Maini,
- Surinder K. Dhanjil,
- Andrew Nicolaides,
- Aditya Sharma,
- Vijay Rathore,
- Mostafa Fatemi,
- Azra Alizad,
- Pudukode R. Krishnan,
- Ferenc Nagy,
- Zoltan Ruzsa,
- Archna Gupta,
- Subbaram Naidu,
- Mannudeep K. Kalra
Affiliations
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Sushant Agarwal
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
- Pranav Elavarthi
- Advanced Knowledge Engineering Centre, GBTI, Roseville, CA 95661, USA
- Rajesh Pathak
- Department of Computer Science Engineering, Rawatpura Sarkar University, Raipur 492001, India
- Vedmanvitha Ketireddy
- Mira Loma High School, Sacramento, CA 95821, USA
- Marta Columbu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy
- Suneet K. Gupta
- Department of Computer Science, Bennett University, Noida 201310, India
- Gavino Faa
- Department of Pathology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy
- Inder M. Singh
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, 27753 Delmenhorst, Germany
- Paramjit S. Chadha
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
- Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India
- Klaudija Viskovic
- University Hospital for Infectious Diseases, 10000 Zagreb, Croatia
- Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, 10558 Athens, Greece
- John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA
- Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
- Martin Miner
- Men’s Health Center, Miriam Hospital, Providence, RI 02906, USA
- David W. Sobel
- Minimally Invasive Urology Institute, Brown University, Providence, RI 02912, USA
- Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy
- Petros P. Sfikakis
- Rheumatology Unit, National & Kapodistrian University of Athens, 10679 Athens, Greece
- George Tsoulfas
- Aristoteleion University of Thessaloniki, 54636 Thessaloniki, Greece
- Athanasios Protogerou
- National & Kapodistrian University of Athens, 10679 Athens, Greece
- Durga Prasanna Misra
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
- Vikas Agarwal
- Department of Immunology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
- George D. Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, UK
- Jagjit S. Teji
- Ann and Robert H. Lurie Children’s Hospital of Chicago, Chicago, IL 60611, USA
- Mustafa Al-Maini
- Allergy, Clinical Immunology and Rheumatology Institute, Toronto, ON L4Z 4C4, Canada
- Surinder K. Dhanjil
- AtheroPoint LLC, Roseville, CA 95611, USA
- Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia 2368, Cyprus
- Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22904, USA
- Vijay Rathore
- AtheroPoint LLC, Roseville, CA 95611, USA
- Mostafa Fatemi
- Department of Physiology & Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Azra Alizad
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA
- Pudukode R. Krishnan
- Neurology Department, Fortis Hospital, Bangalore 560076, India
- Ferenc Nagy
- Internal Medicine Department, University of Szeged, 6725 Szeged, Hungary
- Zoltan Ruzsa
- Zoltan Invasive Cardiology Division, University of Szeged, 6725 Szeged, Hungary
- Archna Gupta
- Radiology Department, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, India
- Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, USA
- Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- DOI
- https://doi.org/10.3390/diagnostics11112025
- Journal volume & issue
-
Vol. 11,
no. 11
p. 2025
Abstract
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 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.
Keywords