Tracking Janus microswimmers in 3D with Machine Learning

TL;DR

This paper introduces a machine learning method to track Janus microswimmers in three dimensions using standard microscopy, enabling more accessible analysis of active matter in complex environments.

Contribution

The study develops and demonstrates a machine learning approach, specifically ensemble decision trees, for 3D tracking of microswimmers from wide-field microscopy data, bypassing specialized equipment.

Findings

Ensemble decision trees outperform other ML algorithms in tracking accuracy.

The method accurately localizes particles within a 40 μm volume.

It enables 3D tracking without specialized microscopy tools.

Abstract

Advancements in artificial active matter heavily rely on our ability to characterise their motion. Yet, the most widely used tool to analyse the latter is standard wide-field microscopy, which is largely limited to the study of two-dimensional motion. In contrast, real-world applications often require the navigation of complex three-dimensional environments. Here, we present a Machine Learning (ML) approach to track Janus microswimmers in three dimensions, using Z-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 a depth of more than 40 $\mu$m. 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.