Deep learning-enhanced light-field imaging with continuous validation

Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Juan Carlos Boffi, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel, Anna Kreshuk

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4 Citations (SciVal)


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.

Original languageEnglish
Pages (from-to)557-563
JournalNature Methods
Issue number5
Publication statusPublished - 2021 May

Subject classification (UKÄ)

  • Radiology, Nuclear Medicine and Medical Imaging


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  • Deep learning-enhanced light-field imaging with continuous validation

    Wagner, N., Beuttenmueller, F., Norlin, N., Gierten, J., Wittbrodt, J., Weigert, M., Hufnagel, L., Prevedel, R. & Kreshuk, A., 2020 Jul 31, 24 p. bioRxiv.

    Research output: Other contributionMiscellaneousResearch

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