Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks

Quantitative lung measures derived from computed tomography (CT) have been demonstrated to improve prognostication in Coronavirus disease 2019 (COVID-19) patients, but are not part of the clinical routine since required manual segmentation of lung lesions is prohibitively time-consuming. We propose...

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Detalles Bibliográficos
Autores principales: Grodecki, Kajetan, Killekar, Aditya, Lin, Andrew, Cadet, Sebastien, McElhinney, Priscilla, Razipour, Aryabod, Chan, Cato, Pressman, Barry D., Julien, Peter, Simon, Judit, Maurovich-Horvat, Pal, Gaibazzi, Nicola, Thakur, Udit, Mancini, Elisabetta, Agalbato, Cecilia, Munechika, Jiro, Matsumoto, Hidenari, Menè, Roberto, Parati, Gianfranco, Cernigliaro, Franco, Nerlekar, Nitesh, Torlasco, Camilla, Pontone, Gianluca, Dey, Damini, Slomka, Piotr J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cornell University 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020980/
https://www.ncbi.nlm.nih.gov/pubmed/33821209

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8020980/
https://www.ncbi.nlm.nih.gov/pubmed/33821209

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