Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN

BACKGROUND: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated se...

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Detalles Bibliográficos
Autores principales: Connell, Marc, Xin, Yi, Gerard, Sarah E., Herrmann, Jacob, Shah, Parth K., Martin, Kevin T., Rezoagli, Emanuele, Ippolito, Davide, Rajaei, Jennia, Baron, Ryan, Delvecchio, Paolo, Humayun, Shiraz, Rizi, Rahim R., Bellani, Giacomo, Cereda, Maurizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Published by Elsevier Inc. 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288584/
https://www.ncbi.nlm.nih.gov/pubmed/35817338
http://dx.doi.org/10.1016/j.ymeth.2022.07.007

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9288584/
https://www.ncbi.nlm.nih.gov/pubmed/35817338
http://dx.doi.org/10.1016/j.ymeth.2022.07.007

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