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Deep learning-enhanced light-field imaging with continuous validation

Wagner, Nils ; Beuttenmueller, Fynn ; Norlin, Nils LU ; Gierten, Jakob ; Boffi, Juan Carlos ; Wittbrodt, Joachim ; Weigert, Martin ; Hufnagel, Lars ; Prevedel, Robert and Kreshuk, Anna (2021) In Nature Methods 18(5). p.557-563
Abstract

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural... (More)

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.

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author
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Methods
volume
18
issue
5
pages
557 - 563
publisher
Nature Publishing Group
external identifiers
  • pmid:33963344
  • scopus:85105450059
ISSN
1548-7105
DOI
10.1038/s41592-021-01136-0
language
English
LU publication?
yes
id
24dfdeda-5006-482b-ba55-0d120dd89f7c
date added to LUP
2021-05-14 20:02:22
date last changed
2024-07-14 15:14:40
@article{24dfdeda-5006-482b-ba55-0d120dd89f7c,
  abstract     = {{<p>Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence-enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz.</p>}},
  author       = {{Wagner, Nils and Beuttenmueller, Fynn and Norlin, Nils and Gierten, Jakob and Boffi, Juan Carlos and Wittbrodt, Joachim and Weigert, Martin and Hufnagel, Lars and Prevedel, Robert and Kreshuk, Anna}},
  issn         = {{1548-7105}},
  language     = {{eng}},
  number       = {{5}},
  pages        = {{557--563}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Methods}},
  title        = {{Deep learning-enhanced light-field imaging with continuous validation}},
  url          = {{http://dx.doi.org/10.1038/s41592-021-01136-0}},
  doi          = {{10.1038/s41592-021-01136-0}},
  volume       = {{18}},
  year         = {{2021}},
}