Use of the speckle imaging sub-pixel correlation analysis in revealing a mechanism of microbial colony growth

Balmages, Ilya; Liepiņš, Jānis; Auzins, Ernests Tomass; Bliznuks, Dmitrijs; Baranovics, Edgars; Lihacova, Ilze; Lihachev, Alexey

doi:10.1038/s41598-023-29809-0

Cited by 12 publications

(12 citation statements)

References 22 publications

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“…An increase of signal values represents spatiotemporal activity patterns during colony growth ( Balmages et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

“…In previous studies using a non-contact optical technique called laser speckle contrast imaging, we determined bacterial growth after 3 h from the beginning of activity ( Balmages et al, 2021b ). In our microbiology experiments performed on solid media the term “activity” refers to a wide range of environmental changes including bacteria doubling, surface roughness changes caused by colony growth, as well as fluctuations of optical properties of the media caused by bacteria growing process ( Balmages et al, 2023 ). A laser speckle is an interference pattern produced by coherent light reflected from an illuminated rough surface.…”

Section: Introductionmentioning

confidence: 99%

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Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

et al. 2023

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Rapid identification of effective antibiotic treatment is crucial for increasing patient survival and preventing the formation of new antibiotic-resistant bacteria due to preventative antibiotic use. Currently utilized “gold standard” methods require 16–24 h to determine the most appropriate antibiotic for the patient’s treatment. The proposed technique of laser speckle imaging with subpixel correlation analysis allows for identifying dynamics and changes in the zone of inhibition, which are impossible to observe with classical methods. Furthermore, it obtains the resulting zone of inhibition diameter earlier than the disk diffusion method which is recommended by the European Committee on Antimicrobial Susceptibility Testing (EUCAST). These results could improve mathematical models of changes in the diameter of the zone of inhibition around the disc containing the antimicrobial agent, thereby speeding up and facilitating epidemiological analysis.

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“…An increase of signal values represents spatiotemporal activity patterns during colony growth ( Balmages et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

et al. 2023

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“…In comparison, sub-pixel correlation analysis in speckle imaging is a new frontier for this task. 12 At the same time, the detection of bacterial colonies using neural networks is a promising approach 13,14 . However, when the aim is only to detect and count bacterial colonies, it is not necessary for the colony identifying feature (e.g.…”

Section: Introductionmentioning

confidence: 99%

Bacterial colony size growth estimation by deep learning

Nagy

Makrai

Csabai

et al. 2023

Preprint

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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 μm/h 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 μm/h, rifampicin pretreated: 36.5 μm/h). 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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“…A limitation of these approaches is that objects in the image that are not colonies (e.g., pieces of the wall of a Petri dish, air bubbles) may also appear in the result as colonies [ 12 ]. More recently, the potential of laser speckle imaging (LSI) for the study of bacterial colony growth has been demonstrated by Balmages et al [ 13 ]. This approach allows the identification of pixels in images of cultures that do not change over time and those that do, using the laser as a coherent light source.…”

Section: Introductionmentioning

confidence: 99%

Bacterial colony size growth estimation by deep learning

Nagy,

Makrai,

Csabai

et al. 2023

BMC Microbiol

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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 $$\mu m/h$$ μ m / h 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 $$\mu m/h$$ μ m / h , rifampicin pretreated: 36.5$$\mu m/h$$ μ m / h ). 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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Use of the speckle imaging sub-pixel correlation analysis in revealing a mechanism of microbial colony growth

Cited by 12 publications

References 22 publications

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

Laser speckle imaging for visualization of hidden effects for early detection of antibacterial susceptibility in disc diffusion tests

Bacterial colony size growth estimation by deep learning

Bacterial colony size growth estimation by deep learning

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