Graphical-model framework for automated annotation of cell identities in dense cellular images

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers’ experiences. Here, we present a probabilistic-graphical-model framework, CR...

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Detalles Bibliográficos
Autores principales: Chaudhary, Shivesh, Lee, Sol Ah, Li, Yueyi, Patel, Dhaval S, Lu, Hang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: eLife Sciences Publications, Ltd 2021
Materias:
Computational and Systems Biology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032398/
https://www.ncbi.nlm.nih.gov/pubmed/33625357
http://dx.doi.org/10.7554/eLife.60321

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032398/
https://www.ncbi.nlm.nih.gov/pubmed/33625357
http://dx.doi.org/10.7554/eLife.60321

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