Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D.

Abstract:

: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, comprising over 6,000 parameters, and used 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.

authors

Newby JM,Schaefer AM,Lee PT,Forest MG,Lai SK

doi

10.1073/pnas.1804420115

subject

Has Abstract

pub_date

2018-09-04 00:00:00

pages

9026-9031

issue

36

eissn

0027-8424

issn

1091-6490

pii

1804420115

journal_volume

115

pub_type

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