  Background: Triage of patients is important to control the pandemic of
coronavirus disease 2019 (COVID-19), especially during the peak of the pandemic
when clinical resources become extremely limited.
  Purpose: To develop a method that automatically segments and quantifies lung
and pneumonia lesions with synthetic chest CT and assess disease severity in
COVID-19 patients.
  Materials and Methods: In this study, we incorporated data augmentation to
generate synthetic chest CT images using public available datasets (285
datasets from "Lung Nodule Analysis 2016"). The synthetic images and masks were
used to train a 2D U-net neural network and tested on 203 COVID-19 datasets to
generate lung and lesion segmentations. Disease severity scores (DL: damage
load; DS: damage score) were calculated based on the segmentations.
Correlations between DL/DS and clinical lab tests were evaluated using
Pearson's method. A p-value < 0.05 was considered as statistical significant.
  Results: Automatic lung and lesion segmentations were compared with manual
annotations. For lung segmentation, the median values of dice similarity
coefficient, Jaccard index and average surface distance, were 98.56%, 97.15%
and 0.49 mm, respectively. The same metrics for lesion segmentation were
76.95%, 62.54% and 2.36 mm, respectively. Significant (p << 0.05) correlations
were found between DL/DS and percentage lymphocytes tests, with r-values of
-0.561 and -0.501, respectively.
  Conclusion: An AI system that based on thoracic radiographic and data
augmentation was proposed to segment lung and lesions in COVID-19 patients.
Correlations between imaging findings and clinical lab tests suggested the
value of this system as a potential tool to assess disease severity of
COVID-19.
