Περίληψη:
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.
Συγγραφείς:
Suri, Jasjit S.
Agarwal, Sushant
Elavarthi, Pranav
Pathak,
Rajesh
Ketireddy, Vedmanvitha
Columbu, Marta
Saba, Luca and
Gupta, Suneet K.
Faa, Gavino
Singh, Inder M.
Turk, Monika
and Chadha, Paramjit S.
Johri, Amer M.
Khanna, Narendra N. and
Viskovic, Klaudija
Mavrogeni, Sophie
Laird, John R.
Pareek,
Gyan
Miner, Martin
Sobel, David W.
Balestrieri, Antonella
and Sfikakis, Petros P.
Tsoulfas, George
Protogerou, Athanasios
and Misra, Durga Prasanna
Agarwal, Vikas
Kitas, George D. and
Teji, Jagjit S.
Al-Maini, Mustafa
Dhanjil, Surinder K. and
Nicolaides, Andrew
Sharma, Aditya
Rathore, Vijay
Fatemi,
Mostafa
Alizad, Azra
Krishnan, Pudukode R.
Ferenc, Nagy and
Ruzsa, Zoltan
Gupta, Archna
Naidu, Subbaram
Kalra, Mannudeep
K.
