DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.
Journal
Communications biology
ISSN: 2399-3642
Titre abrégé: Commun Biol
Pays: England
ID NLM: 101719179
Informations de publication
Date de publication:
09 07 2022
09 07 2022
Historique:
received:
09
12
2021
accepted:
23
06
2022
entrez:
9
7
2022
pubmed:
10
7
2022
medline:
14
7
2022
Statut:
epublish
Résumé
This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.
Identifiants
pubmed: 35810255
doi: 10.1038/s42003-022-03634-z
pii: 10.1038/s42003-022-03634-z
pmc: PMC9271087
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
688Subventions
Organisme : Wellcome Trust
ID : 203276/Z/16/Z
Pays : United Kingdom
Informations de copyright
© 2022. The Author(s).
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