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Single-shot autofocus microscopy using deep learning

View ORCID ProfileHenry Pinkard, Zachary Phillips, Arman Babakhani, Daniel A. Fletcher, Laura Waller
doi: https://doi.org/10.1101/587485
Henry Pinkard
1Computational Biology Graduate Group, University of California, Berkeley, CA 94720, USA
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
3Berkeley Institute for Data Science
4University of California San Francisco Bakar Computational Health Sciences Institute
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  • ORCID record for Henry Pinkard
  • For correspondence: henry.pinkard@gmail.com
Zachary Phillips
5Graduate Group in Applied Science and Technology, University of California, Berkeley, CA 94720, USA
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Arman Babakhani
6Department of Physics, University of California, Berkeley, CA 94720, USA
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Daniel A. Fletcher
7Department of Bioengineering and Biophysics Program, University of California, Berkeley, CA 94720, USA
8Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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Laura Waller
2Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
3Berkeley Institute for Data Science
5Graduate Group in Applied Science and Technology, University of California, Berkeley, CA 94720, USA
8Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
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Abstract

Maintaining an in-focus image over long time scales is an essential and non-trivial task for a variety of microscopic imaging applications. Here, we present an autofocusing method that is inexpensive, fast, and robust. It requires only the addition of one or a few off-axis LEDs to a conventional transmitted light microscope. Defocus distance can be estimated and corrected based on a single image under this LED illumination using a neural network that is small enough to be trained on a desktop CPU in a few hours. In this work, we detail the procedure for generating data and training such a network, explore practical limits, and describe relevant design principles governing the illumination source and network architecture.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 23, 2019.
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Single-shot autofocus microscopy using deep learning
Henry Pinkard, Zachary Phillips, Arman Babakhani, Daniel A. Fletcher, Laura Waller
bioRxiv 587485; doi: https://doi.org/10.1101/587485
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Single-shot autofocus microscopy using deep learning
Henry Pinkard, Zachary Phillips, Arman Babakhani, Daniel A. Fletcher, Laura Waller
bioRxiv 587485; doi: https://doi.org/10.1101/587485

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