BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations

Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with...

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Detalles Bibliográficos
Autores principales: Zhang, Ji, Wang, Yibo, Donarski, Eric D., Toma, Tanjin T., Miles, Madeline T., Acton, Scott T., Gahlmann, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760640/
https://www.ncbi.nlm.nih.gov/pubmed/36529755
http://dx.doi.org/10.1038/s41522-022-00362-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9760640/
https://www.ncbi.nlm.nih.gov/pubmed/36529755
http://dx.doi.org/10.1038/s41522-022-00362-4

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