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Bacterial colony size growth estimation by deep learning

The bacterial growth rate is important for pathogenicity and food safety. Therefore, the study of bacterial growth rate over time can provide important data from a medical and veterinary point of view. We trained convolutional neural networks (CNNs) on manually annotated solid medium cultures to det...

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Autores principales: Nagy, Sára Ágnes, Makrai, László, Csabai, István, Tőzsér, Dóra, Szita, Géza, Solymosi, Norbert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601293/
https://www.ncbi.nlm.nih.gov/pubmed/37880630
http://dx.doi.org/10.1186/s12866-023-03053-y
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author Nagy, Sára Ágnes
Makrai, László
Csabai, István
Tőzsér, Dóra
Szita, Géza
Solymosi, Norbert
author_facet Nagy, Sára Ágnes
Makrai, László
Csabai, István
Tőzsér, Dóra
Szita, Géza
Solymosi, Norbert
author_sort Nagy, Sára Ágnes
collection PubMed
description The bacterial growth rate is important for pathogenicity and food safety. Therefore, the study of bacterial growth rate over time can provide important data from a medical and veterinary point of view. We trained convolutional neural networks (CNNs) on manually annotated solid medium cultures to detect bacterial colonies as accurately as possible. Predictions of bacterial colony size and growth rate were estimated from image sequences of independent Staphylococcus aureus cultures using trained CNNs. A simple linear model for control cultures with less than 150 colonies estimated that the mean growth rate was 60.3 [Formula: see text] for the first 24 h. Analyzing with a mixed effect model that also takes into account the effect of culture, smaller values of change in colony size were obtained (control: 51.0 [Formula: see text] , rifampicin pretreated: 36.5[Formula: see text] ). An increase in the number of neighboring colonies clearly reduces the colony growth rate in the control group but less typically in the rifampicin-pretreated group. Based on our results, CNN-based bacterial colony detection and the subsequent analysis of bacterial colony growth dynamics might become an accurate and efficient tool for bacteriological work and research.
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spelling pubmed-106012932023-10-27 Bacterial colony size growth estimation by deep learning Nagy, Sára Ágnes Makrai, László Csabai, István Tőzsér, Dóra Szita, Géza Solymosi, Norbert BMC Microbiol Research The bacterial growth rate is important for pathogenicity and food safety. Therefore, the study of bacterial growth rate over time can provide important data from a medical and veterinary point of view. We trained convolutional neural networks (CNNs) on manually annotated solid medium cultures to detect bacterial colonies as accurately as possible. Predictions of bacterial colony size and growth rate were estimated from image sequences of independent Staphylococcus aureus cultures using trained CNNs. A simple linear model for control cultures with less than 150 colonies estimated that the mean growth rate was 60.3 [Formula: see text] for the first 24 h. Analyzing with a mixed effect model that also takes into account the effect of culture, smaller values of change in colony size were obtained (control: 51.0 [Formula: see text] , rifampicin pretreated: 36.5[Formula: see text] ). An increase in the number of neighboring colonies clearly reduces the colony growth rate in the control group but less typically in the rifampicin-pretreated group. Based on our results, CNN-based bacterial colony detection and the subsequent analysis of bacterial colony growth dynamics might become an accurate and efficient tool for bacteriological work and research. BioMed Central 2023-10-26 /pmc/articles/PMC10601293/ /pubmed/37880630 http://dx.doi.org/10.1186/s12866-023-03053-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Nagy, Sára Ágnes
Makrai, László
Csabai, István
Tőzsér, Dóra
Szita, Géza
Solymosi, Norbert
Bacterial colony size growth estimation by deep learning
title Bacterial colony size growth estimation by deep learning
title_full Bacterial colony size growth estimation by deep learning
title_fullStr Bacterial colony size growth estimation by deep learning
title_full_unstemmed Bacterial colony size growth estimation by deep learning
title_short Bacterial colony size growth estimation by deep learning
title_sort bacterial colony size growth estimation by deep learning
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601293/
https://www.ncbi.nlm.nih.gov/pubmed/37880630
http://dx.doi.org/10.1186/s12866-023-03053-y
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