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Deep learning-enhanced light-field imaging with continuous validation

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 b...

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Main Authors: Wagner, Nils (Author) , Beuttenmueller, Fynn (Author) , Norlin, Nils (Author) , Gierten, Jakob (Author) , Boffi, Juan Carlos (Author) , Wittbrodt, Joachim (Author) , Weigert, Martin (Author) , Hufnagel, Lars (Author) , Prevedel, Robert (Author) , Kreshuk, Anna (Author)
Format: Article (Journal)
Language:English
Published: 7 May 2021
In: Nature methods
Year: 2021, Volume: 18, Issue: 5, Pages: 557-563
ISSN:1548-7105
DOI:10.1038/s41592-021-01136-0
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41592-021-01136-0
Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41592-021-01136-0
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Author Notes:Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Juan Carlos Boffi, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel and Anna Kreshuk
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Summary: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.
Item Description:13 Seiten Anhang
Gesehen am 29.06.2021
Physical Description:Online Resource
ISSN:1548-7105
DOI:10.1038/s41592-021-01136-0

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