Experimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams.

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dc.contributor.authorWijesinghe, P.
dc.contributor.authorCorsetti, S.
dc.contributor.authorChow, D.J.X.
dc.contributor.authorSakata, S.
dc.contributor.authorDunning, K.R.
dc.contributor.authorDholakia, K.
dc.date.issued2022
dc.description.abstractDeconvolution 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.description.statementofresponsibilityPhilip Wijesinghe, Stella Corsetti, Darren J. X. Chow, Shuzo Sakata, Kylie R. Dunning, and Kishan Dholakia
dc.identifier.citationLight: Science & Applications, 2022; 11(1):319-319
dc.identifier.doi10.1038/s41377-022-00975-6
dc.identifier.issn2095-5545
dc.identifier.issn2047-7538
dc.identifier.orcidChow, D.J.X. [0000-0002-2648-4600]
dc.identifier.orcidDunning, K.R. [0000-0002-0462-6479]
dc.identifier.orcidDholakia, K. [0000-0001-6534-9009]
dc.identifier.urihttps://hdl.handle.net/2440/137133
dc.language.isoen
dc.publisherSpringer Science and Business Media LLC
dc.relation.grantARC
dc.rights© The Author(s) 2022 This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any mediumor format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changesweremade. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.source.urihttps://doi.org/10.1038/s41377-022-00975-6
dc.titleExperimentally unsupervised deconvolution for light-sheet microscopy with propagation-invariant beams.
dc.typeJournal article
pubs.publication-statusPublished

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