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Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams
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dc.contributor.author | Wijesinghe, Philip | |
dc.contributor.author | Corsetti, Stella | |
dc.contributor.author | Chow, Darren J. X. | |
dc.contributor.author | Sakata, Shuzo | |
dc.contributor.author | Dunning, Kylie R. | |
dc.contributor.author | Dholakia, Kishan | |
dc.date.accessioned | 2022-11-09T17:30:29Z | |
dc.date.available | 2022-11-09T17:30:29Z | |
dc.date.issued | 2022-11-02 | |
dc.identifier | 281276818 | |
dc.identifier | 3c495d47-d070-4562-aa58-d5d176d5bc69 | |
dc.identifier | 85141058450 | |
dc.identifier | 000877710800001 | |
dc.identifier.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 | en |
dc.identifier.issn | 2047-7538 | |
dc.identifier.other | ORCID: /0000-0002-8378-7261/work/122719886 | |
dc.identifier.uri | https://hdl.handle.net/10023/26350 | |
dc.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). | en |
dc.description.abstract | 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. | |
dc.format.extent | 15 | |
dc.format.extent | 4608806 | |
dc.language.iso | eng | |
dc.relation.ispartof | Light: Science & Applications | en |
dc.subject | QA75 Electronic computers. Computer science | en |
dc.subject | QC Physics | en |
dc.subject | DAS | en |
dc.subject | MCC | en |
dc.subject.lcc | QA75 | en |
dc.subject.lcc | QC | en |
dc.title | Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams | en |
dc.type | Journal article | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.sponsor | EPSRC | en |
dc.contributor.sponsor | European Commission | en |
dc.contributor.sponsor | European Commission | en |
dc.contributor.institution | University of St Andrews.School of Physics and Astronomy | en |
dc.contributor.institution | University of St Andrews.Sir James Mackenzie Institute for Early Diagnosis | en |
dc.contributor.institution | University of St Andrews.Centre for Biophotonics | en |
dc.contributor.institution | University of St Andrews.Institute of Behavioural and Neural Sciences | en |
dc.contributor.institution | University of St Andrews.Biomedical Sciences Research Complex | en |
dc.identifier.doi | 10.1038/s41377-022-00975-6 | |
dc.description.status | Peer reviewed | en |
dc.identifier.grantnumber | EP/P030017/1 | en |
dc.identifier.grantnumber | EP/R004854/1 | en |
dc.identifier.grantnumber | 871212 | en |
dc.identifier.grantnumber | 863203 | en |
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