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 “crazy-paving”). 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. © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).