Tracking Janus microswimmers in 3D with machine learning
Maximilian R. Bailey, Fabio Grillo, Lucio Isa
Abstract
-stacks as labelled training data. We demonstrate several examples of ML algorithms using freely available and well-documented software, and find that an ensemble Decision Tree-based model (Extremely Randomised Decision Trees) performs the best at tracking the particles over a volume spanning more than 40 μm. With this model, we are able to localise Janus particles with a significant optical asymmetry from standard wide-field microscopy images, bypassing the need for specialised equipment and expertise such as that required for digital holographic microscopy. We expect that ML algorithms will become increasingly prevalent by necessity in the study of active matter systems, and encourage experimentalists to take advantage of this powerful tool to address the various challenges within the field.