Deep learning enables cross-modality super-resolution in fluorescence microscopy.

Journal: Nature methods
Published Date: Jan 1, 2019

Abstract

We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to transform diffraction-limited input images into super-resolved ones. Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super-resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope. We further demonstrate that total internal reflection fluorescence (TIRF) microscopy images of subcellular structures within cells and tissues can be transformed to match the results obtained with a TIRF-based structured illumination microscope. The deep network rapidly outputs these super-resolved images, without any iterations or parameter search, and could serve to democratize super-resolution imaging.

Authors

  • Hongda Wang
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • 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.
  • Yiyin Jin
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Zhensong Wei
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
  • Ronald Gao
    Computer Science Department, University of California, Los Angeles, CA, USA.
  • Harun Günaydın
    Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA.
  • Laurent A Bentolila
    California NanoSystems Institute, University of California, Los Angeles, CA, USA.
  • Comert Kural
    Department of Physics, Ohio State University, Columbus, OH, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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