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Fig. 3 | Journal of Translational Medicine

Fig. 3

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

Fig. 3

Visual comparison and quantitative comparison of segmentation results among different networks. A Four examples of the original lung CT images, their overall segments predicted by three different networks and the ground truth overall lesion annotation. The two baseline models are the single U-net and the single SInfNet (supervised COVID-19 lung infection segmentation) model. The proposed model is the single SSInfNet (self-supervised COVID-19 lung infection segmentation) model. B The mean and error of five quantitative model performance metrices calculated from the 35 test samples. C Three examples of the original lung CT images, their GGO and consolidation segments predicted by three different networks and the ground truth lesion annotations. The two baseline models are the multi U-net and the multi SInfNet models. The proposed model is the multi SSInfNet model. D The mean and error of five model performance metrics calculated from the 35 test samples. The Overall showed the averaged performance for GGO, consolidation, and background

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