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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...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
eLife Sciences Publications, Ltd
2021
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| Materias: | |
| 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 |