A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy

Manuel Bedrossian; Marwan El-Kholy; Daniel Neamati; Jay Nadeau; Manuel Bedrossian; Marwan El-Kholy; Daniel Neamati; Jay Nadeau

doi:10.3934/biophy.2018.1.36

AIMS Biophysics

2018, Volume 5, Issue 1: 36-49. doi: 10.3934/biophy.2018.1.36

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Research article Topical Sections

A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy

1.
Department of Medical Engineering, California Institute of Technology, Pasadena, CA 91125, USA
2.
Department of Biomedical Engineering, McGill University, Montreal, QC H3A 2B4, Canada
3.
Department of Engineering and Applied Sciences, California Institute of Technology, Pasadena, CA 91125, USA
4.
Department of Physics, Portland State University, Portland, OR 97201, USA

Received: 06 October 2017 Accepted: 26 January 2018 Published: 24 February 2018

Digital Holographic Microscopy (DHM) is an emerging technique for three-dimensional imaging of microorganisms due to its high throughput and large depth of field relative to traditional microscopy techniques. While it has shown substantial success for use with eukaryotes, it has proven challenging for bacterial imaging because of low contrast and sources of noise intrinsic to the method (e.g. laser speckle). This paper describes a custom written MATLAB routine using machine-learning algorithms to obtain three-dimensional trajectories of live, lab-grown bacteria as they move within an essentially unrestrained environment with more than 90% precision. A fully annotated version of the software used in this work is available for public use.
- interferometric microscopy,
- digital holographic microscopy,
- machine learning,
- particle tracking
Citation: Manuel Bedrossian, Marwan El-Kholy, Daniel Neamati, Jay Nadeau. A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy[J]. AIMS Biophysics, 2018, 5(1): 36-49. doi: 10.3934/biophy.2018.1.36

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Abstract

Digital Holographic Microscopy (DHM) is an emerging technique for three-dimensional imaging of microorganisms due to its high throughput and large depth of field relative to traditional microscopy techniques. While it has shown substantial success for use with eukaryotes, it has proven challenging for bacterial imaging because of low contrast and sources of noise intrinsic to the method (e.g. laser speckle). This paper describes a custom written MATLAB routine using machine-learning algorithms to obtain three-dimensional trajectories of live, lab-grown bacteria as they move within an essentially unrestrained environment with more than 90% precision. A fully annotated version of the software used in this work is available for public use.

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