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Deep learning-enhanced light-field imaging with continuous validation

Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel, Anna Kreshuk
doi: https://doi.org/10.1101/2020.07.30.228924
Nils Wagner
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
10Computational Molecular Medicine, Technical University of Munich, Munich, Germany
11Munich school for data science (MUDS), Munich, Germany
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Fynn Beuttenmueller
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
12Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences
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Nils Norlin
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
2Department of Experimental Medical Science, Lund University, Sweden
3Lund Bioimaging Center, Lund University, Sweden
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Jakob Gierten
4Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
5Department of Pediatric Cardiology, University Hospital Heidelberg, Heidelberg, Germany
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Joachim Wittbrodt
4Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
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Martin Weigert
6Institute of Bioengineering, School of Life Sciences, EPFL, Lausanne, Switzerland
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Lars Hufnagel
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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Robert Prevedel
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
7Developmental Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
8Epigenetics and Neurobiology Unit, European Molecular Biology Laboratory, Monterotondo, Italy
9Molecular Medicine Partnership Unit (MMPU), European Molecular Biology Laboratory, Heidelberg, Germany
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  • For correspondence: prevedel@embl.de kreshuk@embl.de
Anna Kreshuk
1Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
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  • For correspondence: prevedel@embl.de kreshuk@embl.de
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Abstract

Light field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.

Competing Interest Statement

The authors declare competing financial interests. L.H. is scientific co-founder and employee of Luxendo GmbH (part of Bruker), which makes light sheet-based microscopes commercially available.

Copyright 
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 July 31, 2020.
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Deep learning-enhanced light-field imaging with continuous validation
Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel, Anna Kreshuk
bioRxiv 2020.07.30.228924; doi: https://doi.org/10.1101/2020.07.30.228924
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Deep learning-enhanced light-field imaging with continuous validation
Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel, Anna Kreshuk
bioRxiv 2020.07.30.228924; doi: https://doi.org/10.1101/2020.07.30.228924

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