Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet

Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy to identify, the process of manually annotating cel...

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Detalles Bibliográficos
Autores principales: Morelli, Roberto, Clissa, Luca, Amici, Roberto, Cerri, Matteo, Hitrec, Timna, Luppi, Marco, Rinaldi, Lorenzo, Squarcio, Fabio, Zoccoli, Antonio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617067/
https://www.ncbi.nlm.nih.gov/pubmed/34824294
http://dx.doi.org/10.1038/s41598-021-01929-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8617067/
https://www.ncbi.nlm.nih.gov/pubmed/34824294
http://dx.doi.org/10.1038/s41598-021-01929-5

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