PT  - JOURNAL ARTICLE
AU  - Nils Wagner
AU  - Fynn Beuttenmueller
AU  - Nils Norlin
AU  - Jakob Gierten
AU  - Joachim Wittbrodt
AU  - Martin Weigert
AU  - Lars Hufnagel
AU  - Robert Prevedel
AU  - Anna Kreshuk
TI  - Deep learning-enhanced light-field imaging with continuous validation
AID  - 10.1101/2020.07.30.228924
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.07.30.228924
4099  - http://biorxiv.org/content/early/2020/07/31/2020.07.30.228924.short
4100  - http://biorxiv.org/content/early/2020/07/31/2020.07.30.228924.full
AB  - 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.Competing Interest StatementThe authors declare competing financial interests. L.H. is scientific co-founder and employee of Luxendo GmbH (part of Bruker), which makes light sheet-based microscopes commercially available.