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, 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.

References in corpus (6)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Fully Convolutional Networks for Semantic Segmentation
• ParseNet: Looking Wider to See Better
• DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
• Deep-STORM: super-resolution single-molecule microscopy by deep learning
• Light Microscopy at Maximal Precision

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