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Digital video microscopy enhanced by deep learning
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DOI：10.1364/OPTICA.6.000506.png)
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Single particle tracking is essential in many branches of science and technology, from the measurement of biomo-lecular forces to the study of colloidal crystals. Standard 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 datadriven 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. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
Keyword:
PARTICLE TRACKING
NEURAL-NETWORKS
LOCALIZATION
期刊
IF:
8.5
论文数:
2.4K
被引数:
2.1W
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NATURE METHODS
NATURE METHODSIF32.1
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