Abstract
The covid-19 pandemic has quickly spread all over the world, overwhelming public healthcare systems in many countries. In this situation demand for automatic assistance systems, to facilitate and accelerate a doctor’s job has rapidly increased. Antibody tests were introduced for diagnosing covid-19, but physicians still need tools for quantification of disease severity, since treatment choice strongly depends on it. To estimate the severity of the disease physicians use computer tomography scans. It provides physicians with information about lung lesions and their types and they use this information to determine proper treatment. In this paper we made an attempt to build a system that uses patients’ computer tomography scans for lung and lesion segmentation and for segmentation of specific types of lesions (i.e. pulmonary consolidation and “crazypaving”). Models for lung, lesions, consolidation, and “crazy-paving” segmentation performed with 0.96, 0.65, 0.48, 0.45 Dice coefficients respectively. Also it was shown that removing images with inaccurate ground-truth from the training subset can improve the quality of models trained on it.
Highlights
Covid-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has a considerable mortality rate
The aim of this study was to create a solution for segmentation of lungs and abnormal regions of lungs and for detecting regions with pulmonary consolidation and “crazy-paving” pattern
Linda Wang [9] introduced COVIDNet, a neural network architecture that could classify if a person was ill and distinguish covid-19 from non-covid-19 pneumonia
Summary
Covid-19 is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has a considerable mortality rate. CT provides physicians with information about the lesions in lungs They can calculate the percentage of lung opacity and identify the type of the lesion so they can select appropriate treatment for the patient and monitor the course of the disease. CT provides radiologists with information about disease features, or radiographic findings These features are examined in order to identify their type and volume. This information is used for selection of appropriate treatment for the patient and monitoring the course of the disease. The primary CT findings of covid-19 have been reported in [10] They include pulmonary consolidation, “ground-glass” opacity and “crazy-paving” pattern. The aim of this study was to create a solution for segmentation of lungs and abnormal regions of lungs and for detecting regions with pulmonary consolidation and “crazy-paving” pattern
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