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Deep learning-enhanced light-field imaging with continuous validation

Wagner, Nils ; Beuttenmueller, Fynn ; Norlin, Nils LU ; Gierten, Jakob ; Wittbrodt, Joachim ; Weigert, Martin ; Hufnagel, Lars ; Prevedel, Robert and Kreshuk, Anna (2020) In bioRxiv
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.
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
bioRxiv
article number
2020.07.30.228924
pages
24 pages
publisher
Cold Spring Harbor Laboratory Press (CSHL)
DOI
10.1101/2020.07.30.228924
language
English
LU publication?
yes
id
08f256f9-fe0a-4f92-ad39-8af163e441bf
date added to LUP
2020-09-09 18:23:46
date last changed
2020-09-10 14:05:33
@article{08f256f9-fe0a-4f92-ad39-8af163e441bf,
  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.},
  author       = {Wagner, Nils and Beuttenmueller, Fynn and Norlin, Nils and Gierten, Jakob and Wittbrodt, Joachim and Weigert, Martin and Hufnagel, Lars and Prevedel, Robert and Kreshuk, Anna},
  language     = {eng},
  month        = {07},
  publisher    = {Cold Spring Harbor Laboratory Press (CSHL)},
  series       = {bioRxiv},
  title        = {Deep learning-enhanced light-field imaging with continuous validation},
  url          = {http://dx.doi.org/10.1101/2020.07.30.228924},
  doi          = {10.1101/2020.07.30.228924},
  year         = {2020},
}