Distribution Atlas of COVID-19 Pneumonia on Computed Tomography: A Deep Learning Based Description.

ABSTRACT:

Objectives

To construct a distribution atlas of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) and further explore the difference in distribution by location and disease severity through a retrospective study of 484 cases in Jiangsu, China.

Methods

All patients diagnosed with COVID-19 from January 10 to February 18 in Jiangsu Province, China, were enrolled in our study. The patients were further divided into asymptomatic/mild, moderate, and severe/critically ill groups. A deep learning algorithm was applied to the anatomic pulmonary segmentation and pneumonia lesion extraction. The frequency of opacity on CT was calculated, and a color-coded distribution atlas was built. A further comparison was made between the upper and lower lungs, between bilat

SUBMITTER: Huang S 

PROVIDER: S-EPMC8111058 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature