CSBDeep, a toolbox for Content-aware Image Restoration (CARE)

Fluorescence microscopy is a key driver of discoveries in the life-sciences, with observable phenomena being limited by the optics of the microscope, the chemistry of the fluorophores, and the maximum photon exposure tolerated by the sample. These limits necessitate trade-offs between imaging speed, spatial resolution, light exposure, and imaging depth. In this work we show how deep learning enables biological observations beyond the physical limitations of microscopes. On seven concrete examples we illustrate how microscopy images can be restored even if 60-fold fewer photons are used during acquisition, how isotropic resolution can be achieved even with a 10-fold under-sampling along the axial direction, and how diffraction-limited structures can be resolved at 20-times higher frame-rates compared to state-of-the-art methods. All described restoration networks are freely available as open source software in Fiji and KNIME.

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Related Publications

Please see the paper in Nature Methods. (Preprint on bioRxiv) Supplementary material can be downloaded here.

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Authors and Contributors

Martin Weigert1,2,*, Uwe Schmidt1,2, Tobias Boothe2, Andreas Müller8,9,10, Alexandr Dibrov1,2, Akanksha Jain2, Benjamin Wilhelm1,6, Deborah Schmidt1, Coleman Broaddus1,2, Siân Culley4,5, Mauricio Rocha-Martins1,2, Fabián Segovia-Miranda2, Caren Norden2, Ricardo Henriques4,5, Marino Zerial1,2, Michele Solimena2,8,9,10, Jochen Rink2, Pavel Tomancak2, Loic Royer1,2,7,*, Florian Jug1,2,* & Eugene W. Myers1,2,3

1 Center for Systems Biology Dresden (CSBD), Dresden, Germany
2 Max-Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
3 Department of Computer Science, Technical University Dresden
4 MRC Laboratory for Molecular Cell Biology, University College London, London, UK
5 The Francis Crick Institute, London, UK
6 University of Konstanz, Konstanz, Germany
7 CZ Biohub, San Francisco, USA
8 Molecular Diabetology, University Hospital and Faculty of Medicine Carl Gustav Carus, TU Dresden, Dresden, Germany
9 Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Faculty of Medicine of the TU Dresden, Dresden, Germany
10 German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany
* Co-corresponding authors.

Acknowledgements

The authors want to thank Philipp Keller (Janelia) who provided Drosophila data. We thank Suzanne Eaton (MPI-CBG), Franz Gruber and Romina Piscitello for sharing the expertise in fly imaging and providing fly lines. We thank Anke Sönmez for cell culture work. We thank Marija Matejcic (MPI-CBG) for generating and sharing the LAP2B transgenic line Tg(bactin:eGFP-LAP2B). We thank Benoit Lombardot from the Scientific Computing Facility (MPI-CBG). We thank the following Services and Facilities of the MPI-CBG for their support: Computer Department, Light Microscopy Facility (LMF) and Fish Facility. This work was supported by the German Federal Ministry of Research and Education (BMBF) under the codes 031L0102 (de.NBI) and 031L0044 (Sysbio II). M.S. was supported by the German Center for Diabetes Research (DZD e.V.). R.H. and S.C. was supported grants from the UK BBSRC (BB/M022374/1; BB/P027431/1; BB/R000697/1), UK MRC (MR/K015826/1) and Wellcome Trust (203276/Z/16/Z).