Deep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning

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dc.citation.firstpage284en_US
dc.citation.issueNumber1en_US
dc.citation.journalTitleBiomedical Optics Expressen_US
dc.citation.lastpage299en_US
dc.citation.volumeNumber13en_US
dc.contributor.authorLi, Bowenen_US
dc.contributor.authorTan, Shiyuen_US
dc.contributor.authorDong, Jiuyangen_US
dc.contributor.authorLian, Xiaocongen_US
dc.contributor.authorZhang, Yongbingen_US
dc.contributor.authorJi, Xiangyangen_US
dc.contributor.authorJi, Xiangyangen_US
dc.contributor.authorVeeraraghavan, Ashoken_US
dc.contributor.authorVeeraraghavan, Ashoken_US
dc.date.accessioned2022-01-21T16:24:01Zen_US
dc.date.available2022-01-21T16:24:01Zen_US
dc.date.issued2022en_US
dc.description.abstractConfocal microscopy is a standard approach for obtaining volumetric images of a sample with high axial and lateral resolution, especially when dealing with scattering samples. Unfortunately, a confocal microscope is quite expensive compared to traditional microscopes. In addition, the point scanning in confocal microscopy leads to slow imaging speed and photobleaching due to the high dose of laser energy. In this paper, we demonstrate how the advances in machine learning can be exploited to "teach" a traditional wide-field microscope, one that’s available in every lab, into producing 3D volumetric images like a confocal microscope. The key idea is to obtain multiple images with different focus settings using a wide-field microscope and use a 3D generative adversarial network (GAN) based neural network to learn the mapping between the blurry low-contrast image stacks obtained using a wide-field microscope and the sharp, high-contrast image stacks obtained using a confocal microscope. After training the network with widefield-confocal stack pairs, the network can reliably and accurately reconstruct 3D volumetric images that rival confocal images in terms of its lateral resolution, z-sectioning and image contrast. Our experimental results demonstrate generalization ability to handle unseen data, stability in the reconstruction results, high spatial resolution even when imaging thick (∼40 microns) highly-scattering samples. We believe that such learning-based microscopes have the potential to bring confocal imaging quality to every lab that has a wide-field microscope.en_US
dc.identifier.citationLi, Bowen, Tan, Shiyu, Dong, Jiuyang, et al.. "Deep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learning." <i>Biomedical Optics Express,</i> 13, no. 1 (2022) Optica Publishing Group: 284-299. https://doi.org/10.1364/BOE.444488.en_US
dc.identifier.digitalboe-13-1-284en_US
dc.identifier.doihttps://doi.org/10.1364/BOE.444488en_US
dc.identifier.urihttps://hdl.handle.net/1911/111934en_US
dc.language.isoengen_US
dc.publisherOptica Publishing Groupen_US
dc.rightsPublished under the terms of the Optica Open Access Publishing Agreementen_US
dc.rights.urihttps://www.osapublishing.org/library/license_v2.cfm#VOR-OAen_US
dc.titleDeep-3D microscope: 3D volumetric microscopy of thick scattering samples using a wide-field microscope and machine learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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