Deep learning for light-field microscopy with continuous validation

Anna Kreshuk; Robert Prevedel; Fynn Beuttenmueller; Lars Hufnagel; Nils Wagner; Nils Norlin; Martin Weigert; Jakob Gierten; Joachim Wittbrodt

doi:10.1117/12.2592777

5 March 2021 Deep learning for light-field microscopy with continuous validation

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

Author Affiliations +

Proceedings Volume 11654, High-Speed Biomedical Imaging and Spectroscopy VI; 116540Z (2021) https://doi.org/10.1117/12.2592777
Event: SPIE BiOS, 2021, Online Only

Abstract

Light field microscopy is a powerful tool for fast volumetric image acquisition in biology which requires a computationally demanding and artefact-prone image reconstruction process. I will 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 framework produces video-rate reconstructions; their fidelity can be verified on demand and the network can be fine-tuned as necessary.

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Anna Kreshuk, Robert Prevedel, Fynn Beuttenmueller, Lars Hufnagel, Nils Wagner, Nils Norlin, Martin Weigert, Jakob Gierten, and Joachim Wittbrodt "Deep learning for light-field microscopy with continuous validation", Proc. SPIE 11654, High-Speed Biomedical Imaging and Spectroscopy VI, 116540Z (5 March 2021); https://doi.org/10.1117/12.2592777

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