The Improvement of Segmentation of Lung Pathologies and Pleural Effusion on CT-scans of Patients with Covid-19

In 2020 the outbreak of Covid-19 influenced lives of billions of people all around the globe and motivated governments of different countries to revisit the current situation with regards to public healthcare systems and to methods used in modern medicine. As the workload on radiologists and physicians increased, so did the demand on systems that automatically analyse medical images and detect pathologies. Many current computer vision papers assume that the solution would be integrated into a healthcare system. However improvement according to "classic" metrics like mAP or IoU does not necessarily mean improvement from the radiologist's point of view. In this paper we suggest that while calculating metrics, averaging should be performed not by all studies, but by different groups of studies, in order to be close to human perception of a quality of a segmentation. And that we should count the number of false positive components, found outside lungs, because the presence of such components is negatively perceived by radiologists. Also we propose a method that improves the segmentation of lung pathologies and pleural effusion according to the points given above.