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. 2020 Oct 1;2(5):e200389. doi: 10.1148/ryct.2020200389

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2020 by the Radiological Society of North America, Inc.

This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.

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(a) Workflow for the quantification of COVID-19 pneumonia on chest CT in a patient with typical ground-glass opacities. (b-c) First, both lungs and (d-e) their respective lobes were automatically segmented by a deep-learning algorithm. Second, semi-automated segmentation of lesions was performed using (f) axial slices and shown with (g) three-dimensional rendering. — (a) Workflow for the quantification of COVID-19 pneumonia on chest CT in a patient with typical ground-glass opacities. (b-c) First, both lungs and (d-e) their respective lobes were automatically segmented by a deep-learning algorithm. Second, semi-automated segmentation of lesions was performed using (f) axial slices and shown with (g) three-dimensional rendering.