Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria

PURPOSE: This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multip...

Descripción completa

Detalles Bibliográficos
Autores principales: Lizzi, Francesca, Agosti, Abramo, Brero, Francesca, Cabini, Raffaella Fiamma, Fantacci, Maria Evelina, Figini, Silvia, Lascialfari, Alessandro, Laruina, Francesco, Oliva, Piernicola, Piffer, Stefano, Postuma, Ian, Rinaldi, Lisa, Talamonti, Cinzia, Retico, Alessandra
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547130/
https://www.ncbi.nlm.nih.gov/pubmed/34698988
http://dx.doi.org/10.1007/s11548-021-02501-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8547130/
https://www.ncbi.nlm.nih.gov/pubmed/34698988
http://dx.doi.org/10.1007/s11548-021-02501-2

Ejemplares similares