Deep learning enables fast, gentle STED microscopy.

Journal: Communications biology
Published Date: Jun 27, 2023

Abstract

STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.

Authors

  • Vahid Ebrahimi
    CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL, USA.
  • Till Stephan
    Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • Jiah Kim
    Department of Cell and Developmental Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
  • Pablo Carravilla
    Leibniz Institute of Photonic Technology e.V., Jena, Germany, member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany.
  • Christian Eggeling
    Leibniz Institute of Photonic Technology e.V., Jena, Germany, member of the Leibniz Centre for Photonics in Infection Research (LPI), Jena, Germany.
  • Stefan Jakobs
    Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany.
  • Kyu Young Han
    CREOL, The College of Optics and Photonics, University of Central Florida, Orlando, FL, USA. kyhan@creol.ucf.edu.

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