Research article Topical Sections

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

  • 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.

    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

    Related Papers:

  • 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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