Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19

Abstract

Background Coronavirus disease 2019 (COVID-19) is very contagious. Cases appear faster than the available Polymerase Chain Reaction test kits in many countries. Recently, lung computerized tomography (CT) has been used as an auxiliary COVID-19 testing approach. Automatic analysis of the lung CT images is needed to increase the diagnostic efficiency and release the human participant. Deep learning is successful in automatically solving computer vision problems. Thus, it can be introduced to the automatic and rapid COVID-19 CT diagnosis. Many advanced deep learning-based computer vison techniques were developed to increase the model performance but have not been introduced to medical image analysis. Methods In this study, we propose a self-supervised two-stage deep learning model to segment COVID-19 lesions (ground-glass opacity and consolidation) from chest CT images to support rapid COVID-19 diagnosis. The proposed deep learning model integrates several advanced computer vision techniques such as generative adversarial image inpainting, focal loss, and lookahead optimizer. Two real-life datasets were used to evaluate the model’s performance compared to the previous related works. To explore the clinical and biological mechanism of the predicted lesion segments, we extract some engineered features from the predicted lung lesions. We evaluate their mediation effects on the relationship of age with COVID-19 severity, as well as the relationship of underlying diseases with COVID-19 severity using statistic mediation analysis. Results The best overall F1 score is observed in the proposed self-supervised two-stage segmentation model (0.63) compared to the two related baseline models (0.55, 0.49). We also identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases with COVID-19 severity as well as the potential causal relationship between age with COVID-19 severity. Conclusions This work contributes a promising COVID-19 lung CT image segmentation model and provides predicted lesion segments with potential clinical interpretability. The model could automatically segment the COVID-19 lesions from the raw CT images with higher accuracy than related works. The features of these lesions are associated with COVID-19 severity through mediating the known causal of the COVID-19 severity (age and underlying diseases).

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

“…To facilitate fast COVID-19 diagnosis, Fung, Liu [ 42 ] presented a self-supervised two-stage DL algorithm to segment COVID-19 lesions (Ground-Glass Opacity (GGO) and consolidation) from chest CT images. The suggested DL model incorporated generative adversarial picture in painting, focus loss, and a look ahead optimizer, among other sophisticated computer vision techniques.…”

“… - Attained average accuracies of 99.4% and 92.9%, respectively, and sensitivity scores of 99.8% and 93.7%. -High complexity -Security is not considered Yes No Python Fung, Liu [ 42 ] Proposing a model incorporating generative adversarial image inpainting, focus loss, and a lookahead optimizer. -High accuracy -High robustness -High energy consumption -High delay -Security is not considered No No Python Radiomic package PyRadiomics Kundu, Basak [ 43 ] Creating a COVID-19 detection system that categorizes CT scan images of the lungs into binary instances.…”

Study of Different Deep Learning Methods for Coronavirus (COVID-19) Pandemic: Taxonomy, Survey and Insights

“…In [46], it was suggested that a CNN model could be used for COVID-19 lung CT segmentation (SSInfNet). The self-supervised InfNet incorporated various techniques, such as generative adversarial image inpainting, lookahead optimizer, and focal loss.…”

A Promising and Challenging Approach: Radiologists’ Perspective on Deep Learning and Artificial Intelligence for Fighting COVID-19

“…Therefore, their findings indicated that the use and necessity of CT for pediatric patients should be reconsidered, as the model can effectively predict CT outcomes. Fung et al [25] proposed a self-supervised two-stage deep-learning model named SSInfNet to segment COVID-19 lesions (including ground-glass opacity and consolidation) from chest CT images. The authors identified several CT image phenotypes that mediate the potential causal relationship between underlying diseases and age with COVID-19 severity.…”