Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning

TY - JOUR

T1 - Real-time 3D tracking of swimming microbes using digital holographic microscopy and deep learning

AU - Matthews, Samuel A.

AU - Coelho, Carlos

AU - Rodriguez Salas, Erick E.

AU - Brock, Emma E.

AU - Hodge, Victoria J.

AU - Walker, James A.

AU - Wilson, Laurence G.

N1 - Funding Information: LW: EPSRC EP/N014731/1 https://www.ukri.org/councils/epsrc/ SM, JW: Digital Creativity Labs: EPSRC/AHRC/Innovate UK (EP/M023265/1) https://www.ukri.org/councils/epsrc/ VH: The Assuring Autonomy International Programme (http://www.york.ac.uk/assuring-autonomy) The funders played no part in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Publisher Copyright: © 2024 Matthews et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

PY - 2024/4

Y1 - 2024/4

N2 - The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells’ responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

AB - The three-dimensional swimming tracks of motile microorganisms can be used to identify their species, which holds promise for the rapid identification of bacterial pathogens. The tracks also provide detailed information on the cells’ responses to external stimuli such as chemical gradients and physical objects. Digital holographic microscopy (DHM) is a well-established, but computationally intensive method for obtaining three-dimensional cell tracks from video microscopy data. We demonstrate that a common neural network (NN) accelerates the analysis of holographic data by an order of magnitude, enabling its use on single-board computers and in real time. We establish a heuristic relationship between the distance of a cell from the focal plane and the size of the bounding box assigned to it by the NN, allowing us to rapidly localise cells in three dimensions as they swim. This technique opens the possibility of providing real-time feedback in experiments, for example by monitoring and adapting the supply of nutrients to a microbial bioreactor in response to changes in the swimming phenotype of microbes, or for rapid identification of bacterial pathogens in drinking water or clinical samples.

UR - http://www.scopus.com/inward/record.url?scp=85191626954&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0301182

DO - 10.1371/journal.pone.0301182

M3 - Article

C2 - 38669245

AN - SCOPUS:85191626954

SN - 1932-6203

VL - 19

JO - PLoS ONE

JF - PLoS ONE

IS - 4 April

M1 - e0301182

ER -