The authors would like to acknowledge the Gordon and Betty Moore Foundation Grant Numbers 4037/4038 as the source of funding for this work
Machine learning, Interferometry -- Applications to microscopy, Digital holographic microscopy, Bacteria -- Motility
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.
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.