  This paper proposes a semi-automatic system based on quantitative
characterization of the specific image patterns in lung ultrasound (LUS)
images, in order to assess the lung conditions of patients with COVID-19
pneumonia, as well as to differentiate between the severe / and no-severe
cases. Specifically, four parameters are extracted from each LUS image, namely
the thickness (TPL) and roughness (RPL) of the pleural line, and the
accumulated with (AWBL) and acoustic coefficient (ACBL) of B lines. 27 patients
are enrolled in this study, which are grouped into 13 moderate patients, 7
severe patients and 7 critical patients. Furthermore, the severe and critical
patients are regarded as the severe cases, and the moderate patients are
regarded as the non-severe cases. Biomarkers among different groups are
compared. Each single biomarker and a classifier with all the biomarkers as
input are utilized for the binary diagnosis of severe case and non-severe case,
respectively. The classifier achieves the best classification performance among
all the compared methods (area under the receiver operating characteristics
curve = 0.93, sensitivity = 0.93, specificity = 0.85). The proposed image
analysis system could be potentially applied to the grading and prognosis
evaluation of patients with COVID-19 pneumonia.

Medical Physics;Computer Vision and Pattern Recognition;Image and Video Processing