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

Light(Science & Applications) 2022年第11期11卷 2790-2804页

作者： philip Wijesinghe Stella Corsetti Darren J.X.Chow Shuzo Sakata Kylie R.Dunning Kishan Dholakia SUPA School of Physics and AstronomyUniversity of St AndrewsNorth HaughSt AndrewsFife KY169SSUK Robinson Research Institute Adelaide Medical SchoolThe University of AdelaideAdelaideSAAustralia Australian Research Council Centre of Excellence for Nanoscale BioPhotonics The University of AdelaideAdelaideSAAustralia Institute for Photonics and Advanced Sensing The University of AdelaideAdelaideSAAustralia Strathclyde Institute of Pharmacy and Biomedical Sciences University of StrathclydeGlasgow G4 OREUK School of Biological Sciences The University of AdelaideAdelaideSAAustralia Department of Physics College of ScienceYonsei UniversitySeoul 03722South Korea

Deconvolution is a challenging inverse problem,particularly in techniques that employ complex engineered pointspread functions,such as microscopy with propagation-invariant ***,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 ***,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 *** method rapidly and robustly deconvolves and super-resolves microscopy images,demonstrating a two-fold improvement in image contrast to conventional deconvolution *** 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 *** 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 *** method aims to democratise learned methods for deconvolution,as it does not require data acquisition outwith the conventional imaging protocol.

关键词： invariant propagation implantation

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