Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography.
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Suri Jasjit S, Agarwal Sushant, Elavarthi Pranav, Pathak Rajesh, Ketireddy Vedmanvitha, Columbu Marta, Saba Luca, Gupta Suneet K, Faa Gavino, Singh Inder M, Turk Monika, Chadha Paramjit S, Johri Amer M, Khanna Narendra N, Viskovic Klaudija, Mavrogeni Sophie, Laird John R, Pareek Gyan, Miner Martin, Sobel David W, Balestrieri Antonella, Sfikakis Petros P, Tsoulfas George, Protogerou Athanasios, Misra Durga Prasanna, Agarwal Vikas, Kitas George D, Teji Jagjit S, Al-Maini Mustafa, Dhanjil Surinder K, Nicolaides Andrew, Sharma Aditya, Rathore Vijay, Fatemi Mostafa, Alizad Azra, Krishnan Pudukode R, Ferenc Nagy, Ruzsa Zoltan, Gupta Archna, Naidu Subbaram, Kalra Mannudeep K
Аннотация
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.