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bioRxiv · 10.1101/2020.07.30.228924

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.
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Abstract

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.

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Wagner, N., Beuttenmueller, F., Norlin, N., Gierten, J., Wittbrodt, J., Weigert, M., Hufnagel, L., Prevedel, R., Kreshuk, A.. 2020-07-31. Deep learning-enhanced light-field imaging with continuous validation. https://doi.org/10.1101/2020.07.30.228924

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