Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning.

Journal: Nature methods
Published Date: Nov 4, 2019

Abstract

We demonstrate that a deep neural network can be trained to virtually refocus a two-dimensional fluorescence image onto user-defined three-dimensional (3D) surfaces within the sample. Using this method, termed Deep-Z, we imaged the neuronal activity of a Caenorhabditis elegans worm in 3D using a time sequence of fluorescence images acquired at a single focal plane, digitally increasing the depth-of-field by 20-fold without any axial scanning, additional hardware or a trade-off of imaging resolution and speed. Furthermore, we demonstrate that this approach can correct for sample drift, tilt and other aberrations, all digitally performed after the acquisition of a single fluorescence image. This framework also cross-connects different imaging modalities to each other, enabling 3D refocusing of a single wide-field fluorescence image to match confocal microscopy images acquired at different sample planes. Deep-Z has the potential to improve volumetric imaging speed while reducing challenges relating to sample drift, aberration and defocusing that are associated with standard 3D fluorescence microscopy.

Authors

  • Yichen Wu
    Department of Electrical Engineering, University of California Los Angeles (UCLA), USA. ozcan@ucla.edu.
  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Hongda Wang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Yilin Luo
    Department of Medical Imaging, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
  • Eyal Ben-David
    Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Laurent A Bentolila
    California NanoSystems Institute, University of California, Los Angeles, CA, USA.
  • Christian Pritz
    Department of Genetics, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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