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Deep learning-enhan...
Deep learning-enhanced light-field imaging with continuous validation
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- Wagner, Nils (författare)
- European Molecular Biology Laboratory Heidelberg
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Beuttenmueller, Fynn (författare)
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- Norlin, Nils (författare)
- Lund University,Lunds universitet,Molekylär neuromodulering,Forskargrupper vid Lunds universitet,Molecular Neuromodulation,Lund University Research Groups,European Molecular Biology Laboratory Heidelberg
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Gierten, Jakob (författare)
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Wittbrodt, Joachim (författare)
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Weigert, Martin (författare)
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Hufnagel, Lars (författare)
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Prevedel, Robert (författare)
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- Kreshuk, Anna (författare)
- European Molecular Biology Laboratory Heidelberg
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(creator_code:org_t)
- Cold Spring Harbor Laboratory, 2020
- Engelska 24 s.
- Relaterad länk:
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http://dx.doi.org/10... (free)
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https://www.biorxiv....
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https://lup.lub.lu.s...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Cell- och molekylärbiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Cell and Molecular Biology (hsv//eng)
Publikations- och innehållstyp
- ovr (ämneskategori)
- vet (ämneskategori)