Skip to main content

Lund University Publications

LUND UNIVERSITY LIBRARIES

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.

(Less)
Please use this url to cite or link to this publication:
author
; ; ; ; ; ; ; ; and
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
2024-11-18 03:37:47
@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}},
}