Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19

摘要

The general flow of Unet neural network to segment lung and lesions. Our neural network model was trained in the training dataset, and tested on test dataset. 550 CT images were split into primary dataset and 100 were primary dataset, respectively. First, CT images were inputted into this neural network to extract image features, segment lung, and lesion, and further classify whether the lesion was consolidation or GGO. The outputted results were the volumes of the lesions in underlying disease group and no underlying disease group