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Developing Noise-Resistant Three-Dimensional Single Particle Tracking Using Deep Neural Networks
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    Developing Noise-Resistant Three-Dimensional Single Particle Tracking Using Deep Neural Networks
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    • Yaning Zhong
      Yaning Zhong
      Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695-8204, United States
      More by Yaning Zhong
    • Chao Li
      Chao Li
      Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina 27695-8204, United States
      More by Chao Li
    • Huiyang Zhou*
      Huiyang Zhou
      Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, North Carolina 27695-8204, United States
      *E-mail: [email protected]
      More by Huiyang Zhou
    • Gufeng Wang*
      Gufeng Wang
      Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695-8204, United States
      *Tel: (919) 515-1819; e-mail: [email protected]
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    Other Access OptionsSupporting Information (12)

    Analytical Chemistry

    Cite this: Anal. Chem. 2018, 90, 18, 10748–10757
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    https://doi.org/10.1021/acs.analchem.8b01334
    Published August 24, 2018
    Copyright © 2018 American Chemical Society

    Abstract

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    Three-dimensional single particle tracking (3D SPT) is a powerful tool in various chemical and biological studies. In 3D SPT, z sensitive point spread functions (PSFs) are frequently used to generate different patterns, from which the axial position of the probe can be recovered in addition to its xy coordinates. Conventional linear classifier-based methods, for example, the correlation coefficient method, perform poorly when the signal-to-noise ratio (S/N) drops. In this work, we test deep neural networks (DNNs) in recognizing and differentiating very similar image patterns incurred in 3D SPT. The training of the deep neural networks is optimized, and a procedure is established for 3D localization. We show that for high S/N images, both DNNs and conventional correlation coefficient-based method perform well. However, when the S/N drops close to 1, conventional methods completely fail while DNNs show strong resistance to both artificial and experimental noises. This noise resistance allows us to achieve a camera integration time of 50 μs for 200 nm fluorescent particles without losing accuracy significantly. This study sheds new light on developing robust image data analysis methods and on improving the time resolution of 3D SPT.

    Copyright © 2018 American Chemical Society

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    Supporting Information

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.analchem.8b01334.

    • Typical images from 29 sets of PSFs; example images with different S/N and the impact of S/N on correlation coefficient; recovered 3D trajectory of a 100 nm particle diffusion on an oil droplet surface using DNN method; performance of DNN and CC for low S/N data with experimental noise; performance of DNN and CC methods for experimental data collected at 5.0 ms; accuracy and precision tests of localization of 40 nm polystyrene particles

      (PDF)

    • A 200 nm particle moving with 100 nm steps along the z direction for 29 steps

      (AVI)

    • The same stepping data in Movie 1 added with artificial Gaussian noise

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    • Another 200 nm particle moving with 100 nm steps along the z direction for 29 steps with a shorter integration time

      (AVI)

    • A 100 nm particle diffusing on oil droplet surface with added Gaussian noise to change the S/N to ∼1

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    • Another 100 nm particle diffusing on oil droplet surface; 500 μs integration time

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    • The same particle in Movie S5; 30 ms integration time

      (AVI)

    • A 200 nm particle diffusing in a microchannel imaged with conventional fluorescence microscopy

      (AVI)

    • The same 200 nm particle diffusing in the same microchannel as in Movie 4 with 5.0 ms integration time

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    • The same 200 nm particle as in Movie 5 imaged with 50 μs integration time

      (AVI)

    • Recovered 3D trajectory of the particle in Movie 6

      (AVI)

    • Pure noise collected with 50 μs integration time with no fluorescence signal input

      (AVI)

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    Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.

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    This article is cited by 15 publications.

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    Analytical Chemistry

    Cite this: Anal. Chem. 2018, 90, 18, 10748–10757
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.analchem.8b01334
    Published August 24, 2018
    Copyright © 2018 American Chemical Society

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