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DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches
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Spahn, Christoph, Gómez-de-Mariscal, Estibaliz, Laine, Romain F., Pereira, Pedro M., von Chamier, Lucas, Conduit, Mia, Pinho, Mariana G., Jacquemet, Guillaume, Holden, Séamus, Heilemann, Mike and Henriques, Ricardo (2022) DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches. Communications Biology, 5 . 688. doi:10.1038/s42003-022-03634-z ISSN 2399-3642.
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Official URL: https://doi.org/10.1038/s42003-022-03634-z
Abstract
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.
Item Type: | Journal Article | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software Q Science > QH Natural history T Technology > TA Engineering (General). Civil engineering (General) |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Library of Congress Subject Headings (LCSH): | Image processing -- Digital techniques, Image analysis, Microscopy -- Technique, Neural networks (Computer science) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Journal or Publication Title: | Communications Biology | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Publisher: | Nature Publishing Group | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
ISSN: | 2399-3642 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Official Date: | 9 July 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Dates: |
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Volume: | 5 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Article Number: | 688 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
DOI: | 10.1038/s42003-022-03634-z | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Access rights to Published version: | Open Access (Creative Commons) | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant deposit: | 21 October 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Date of first compliant Open Access: | 21 October 2022 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
RIOXX Funder/Project Grant: |
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