  This paper proposes an automated segmentation method of infection and normal
regions in the lung from CT volumes of COVID-19 patients. From December 2019,
novel coronavirus disease 2019 (COVID-19) spreads over the world and giving
significant impacts to our economic activities and daily lives. To diagnose the
large number of infected patients, diagnosis assistance by computers is needed.
Chest CT is effective for diagnosis of viral pneumonia including COVID-19. A
quantitative analysis method of condition of the lung from CT volumes by
computers is required for diagnosis assistance of COVID-19. This paper proposes
an automated segmentation method of infection and normal regions in the lung
from CT volumes using a COVID-19 segmentation fully convolutional network
(FCN). In diagnosis of lung diseases including COVID-19, analysis of conditions
of normal and infection regions in the lung is important. Our method recognizes
and segments lung normal and infection regions in CT volumes. To segment
infection regions that have various shapes and sizes, we introduced dense
pooling connections and dilated convolutions in our FCN. We applied the
proposed method to CT volumes of COVID-19 cases. From mild to severe cases of
COVID-19, the proposed method correctly segmented normal and infection regions
in the lung. Dice scores of normal and infection regions were 0.911 and 0.753,
respectively.
