Digital video microscopy enhanced by deep learning

TL;DR

DeepTrack, a convolutional neural network-based method, significantly improves single particle tracking in digital video microscopy, especially under noisy and poor illumination conditions, outperforming traditional algorithmic approaches.

Contribution

Introduces DeepTrack, a data-driven deep learning approach for particle tracking that surpasses existing methods in noisy and challenging imaging environments.

Findings

DeepTrack outperforms traditional algorithms in noisy conditions.

It successfully tracks multiple particles and non-spherical objects.

Provides an accessible Python software package for users.

Abstract

Single particle tracking is essential in many branches of science and technology, from the measurement of biomolecular forces to the study of colloidal crystals. Standard current methods rely on algorithmic approaches: by fine-tuning several user-defined parameters, these methods can be highly successful at tracking a well-defined kind of particle under low-noise conditions with constant and homogenous illumination. Here, we introduce an alternative data-driven approach based on a convolutional neural network, which we name DeepTrack. We show that DeepTrack outperforms algorithmic approaches, especially in the presence of noise and under poor illumination conditions. We use DeepTrack to track an optically trapped particle under very noisy and unsteady illumination conditions, where standard algorithmic approaches fail. We then demonstrate how DeepTrack can also be used to track multiple particles and non-spherical objects such as bacteria, also at very low signal-to-noise ratios. In order to make DeepTrack readily available for other users, we provide a Python software package, which can be easily personalized and optimized for specific applications.