  Particle tracking is a powerful biophysical tool that requires conversion of
large video files into position time series, i.e. traces of the species of
interest for data analysis. Current tracking methods, based on a limited set of
input parameters to identify bright objects, are ill-equipped to handle the
spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios
typically presented by submicron species in complex biological environments.
Extensive user involvement is frequently necessary to optimize and execute
tracking methods, which is not only inefficient but introduces user bias. To
develop a fully automated tracking method, we developed a convolutional neural
network for particle localization from image data, comprised of over 6,000
parameters, and employed machine learning techniques to train the network on a
diverse portfolio of video conditions. The neural network tracker provides
unprecedented automation and accuracy, with exceptionally low false positive
and false negative rates on both 2D and 3D simulated videos and 2D experimental
videos of difficult-to-track species.
