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

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dc.contributor.authorFung, Daryl L.X.
dc.contributor.authorLiu, Qian
dc.contributor.authorZammit, Judah
dc.contributor.authorLeung, Carson
dc.contributor.authorHu, Pingzhao
dc.date.accessioned2022-01-25T17:57:58Z
dc.date.available2022-01-25T17:57:58Z
dc.date.issued2021-07-26
dc.date.submitted2022-01-22T04:00:41Zen_US
dc.description.abstractBACKGROUND: 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).en_US
dc.description.sponsorshipNatural Sciences and Engineering Research Council of Canada (NSERC); University of Manitoba, Canadaen_US
dc.identifier.citationFung, D.L.X., Liu, Q., Zammit, J., Leung, C.K., Hu, P. Self-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19. J Transl Med, 2021; 19: 318.en_US
dc.identifier.doi10.1186/s12967-021-02992-2
dc.identifier.urihttp://hdl.handle.net/1993/36216
dc.language.isoengen_US
dc.publisherBMCen_US
dc.rightsopen accessen_US
dc.subjectCOVID-19en_US
dc.subjectself-supervised learningen_US
dc.subjectlung CT imagesen_US
dc.subjectimage segmentationen_US
dc.subjectmediation analysisen_US
dc.titleSelf-supervised deep learning model for COVID-19 lung CT image segmentation highlighting putative causal relationship among age, underlying disease and COVID-19en_US
dc.typeArticleen_US
local.author.affiliationRady Faculty of Health Sciencesen_US
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