Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams
Date
02/11/2022Author
Grant ID
EP/P030017/1
EP/R004854/1
871212
863203
Metadata
Show full item recordAbstract
Deconvolution is a challenging inverse problem, particularly in techniques that employ complex engineered point-spread functions, such as microscopy with propagation-invariant beams. Here, we present a deep-learning method for deconvolution that, in lieu of end-to-end training with ground truths, is trained using known physics of the imaging system. Specifically, we train a generative adversarial network with images generated with the known point-spread function of the system, and combine this with unpaired experimental data that preserve perceptual content. Our method rapidly and robustly deconvolves and super-resolves microscopy images, demonstrating a two-fold improvement in image contrast to conventional deconvolution methods. In contrast to common end-to-end networks that often require 1000–10,000s paired images, our method is experimentally unsupervised and can be trained solely on a few hundred regions of interest. We demonstrate its performance on light-sheet microscopy with propagation-invariant Airy beams in oocytes, preimplantation embryos and excised brain tissue, as well as illustrate its utility for Bessel-beam LSM. This method aims to democratise learned methods for deconvolution, as it does not require data acquisition outwith the conventional imaging protocol.
Citation
Wijesinghe , P , Corsetti , S , Chow , D J X , Sakata , S , Dunning , K R & Dholakia , K 2022 , ' Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams ' , Light: Science & Applications , vol. 11 , 319 . https://doi.org/10.1038/s41377-022-00975-6
Publication
Light: Science & Applications
Status
Peer reviewed
ISSN
2047-7538Type
Journal article
Description
Funding: This project was funded by the UK Engineering and Physical Sciences Research Council (grants EP/P030017/1 and EP/R004854/1), and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement (EC-GA 871212) and H2020 FETOPEN project "Dynamic” (EC-GA 863203). P.W. was supported by the 1851 Research Fellowship from the Royal Commission. KRD was supported by a Mid-Career Fellowship from the Hospital Research Foundation (C-MCF-58-2019). K.D. acknowledges support from the Australian Research Council through a Laureate Fellowship. S.S. was funded by BBSRC (BB/M00905X/1).Collections
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