A machine learning algorithm for identifying and tracking bacteria in three dimensions using Digital Holographic Microscopy
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.
Additional Information
© 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) Received: 06 October 2017 , Accepted: 26 January 2018 , Published: 24 February 2018. The authors would like to acknowledge the Gordon and Betty Moore Foundation Grant Numbers 4037/4038 as the source of funding for this work, as well as the Keck Center at Caltech for hosting our collaborations. The authors declare no conflicts of interest in this paper.Attached Files
Published - biophys-05-00036.pdf
Files
Name | Size | Download all |
---|---|---|
md5:2d900e4983a2fd7c4817f02603acdbcd
|
689.4 kB | Preview Download |
Additional details
- Eprint ID
- 86117
- Resolver ID
- CaltechAUTHORS:20180430-101112906
- 4037
- Gordon and Betty Moore Foundation
- 4038
- Gordon and Betty Moore Foundation
- Created
-
2018-04-30Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field