PUBLICATION

Deep learning-enhanced light-field imaging with continuous validation

Authors
Wagner, N., Beuttenmueller, F., Norlin, N., Gierten, J., Boffi, J.C., Wittbrodt, J., Weigert, M., Hufnagel, L., Prevedel, R., Kreshuk, A.
ID
ZDB-PUB-210509-2
Date
2021
Source
Nature Methods   18: 557-563 (Journal)
Registered Authors
Wittbrodt, Jochen
Keywords
none
MeSH Terms
  • Animals
  • Biomechanical Phenomena
  • Calcium/chemistry
  • Deep Learning*
  • Heart/physiology*
  • Image Processing, Computer-Assisted/methods*
  • Larva/physiology
  • Microscopy/methods*
  • Oryzias/physiology
  • Reproducibility of Results
  • Zebrafish/physiology
PubMed
33963344 Full text @ Nat. Methods
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 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.
Genes / Markers
Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping