Fig. 2.

Segment visualization and data split. A Examples of raw lung CT images in both Med-seg dataset and ICTCF dataset. Images are all in the axial view which looks down through the body. B The overall lesion segment. This is the label for the proposed single self-supervised COVID-19 network (SSInfNet) model for lung infection segmentation, and it exists only in Med-seg dataset. C The ground-glass opacity segment (red) and consolidation segment (green). This is the label for multi SSInfNet and it is also only available for the Med-seg dataset. D The table shows the data utilization in the development of the proposed SSInfNet models. As ICTCF does not contain segment labels, it was used only for the self-supervised image inpainting in the training stage. The Med-seg image data was split into training, validation, and testing sets, approximately under the ratio of 6:1:1. After the model was well developed, it was applied to the ICTCF dataset for further statistic mediation analysis because only ICTCF contains COVID-19 clinical severity information, which means Med-seg data was not used in the mediation analysis