Fig. 1

Overview of the proposed self-supervised COVID-19 lung infection segmentation (SSInfNet) model and statistic causal mediation analysis of the predicted segments. The black path shows the main workflow of the proposed two-stage SSInfNet model and the follow-up statistical mediation analysis. The first stage is a single SSInfNet which takes the damaged CT image as input, and outputs the reconstructed image (blue path), the edges of overall lesion segment (orange path), and the single segment itself. The inpainting loss and edge loss are intended to increase the complexity of the single SSInfNet to improve its segmentation ability. The coach network (presented in the blue path) forms a generative adversarial mechanism with single SSInfNet to further improve the later model’s performance. Continuing to proceed along the black path, the raw CT image and the predicted overall lesion segment (as prior) are used as input for the multi SSInfNet to further divide the overall lesion segments into ground-glass opacity and consolidation segments. Image inpainting is also involved in this stage (green path). For the multi segmentation, we use the focal technique as its loss function and lookahead optimizer as its training strategy. At the end, the predicted multi segments are used to extract several images features with Python’s PyRadiomics [41] package. The image features act as mediators in the mediation analysis model between the independent variables (age, gender, and underlying diseases) and the dependent variable (COVID-19 severity)