Sponsor
The authors would like to acknowledge the Gordon and Betty Moore Foundation Grant Numbers 4037/4038 as the source of funding for this work
Published In
AIMS Biophysics
Document Type
Article
Publication Date
2-2018
Subjects
Machine learning, Interferometry -- Applications to microscopy, Digital holographic microscopy, Bacteria -- Motility
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.
DOI
10.3934/biophy.2018.1.36
Persistent Identifier
https://archives.pdx.edu/ds/psu/26157
Citation Details
Bedrossian, M., El-Kholy, M., Neamati, D., & Nadeau, J. (2018). A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy. AIMS Biophysics, 5(1), 36-49.
Description
Originally appeared in AIMS Biophysics, 5(1): 36–49, published by AIMS Press. Accessible at https://doi.org/10.3934/biophy.2018.1.36.
© 2018 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)