From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysi...
From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans
About this item
Full title
Author / Creator
Li, Zhang , Zhong, Zheng , Li, Yang , Zhang, Tianyu , Gao, Liangxin , Jin, Dakai , Sun, Yue , Ye, Xianghua , Yu, Li , Hu, Zheyu , Xiao, Jing , Huang, Lingyun and Tang, Yuling
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
Formats
Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Subjects
More information
Scope and Contents
Contents
Objective
To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images.
Methods
In this retrospective study, an AI system was developed to automatically segment and quantify the COVID-19-infected lung regions on thick-section chest CT images. Fi...
Alternative Titles
Full title
From community-acquired pneumonia to COVID-19: a deep learning–based method for quantitative analysis of COVID-19 on thick-section CT scans
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_proquest_journals_2471891297
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2471891297
Other Identifiers
ISSN
0938-7994
E-ISSN
1432-1084
DOI
10.1007/s00330-020-07042-x
