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      En Face and Cross-sectional Corneal Tomograms Using Sub-micron spatial resolution Optical Coherence Tomography

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          Abstract

          Accurate diagnosis of corneal pathology and morphological identification of different corneal layers require clear delineation of corneal three-dimensional structures and en face or cross-sectional imaging of palisade of Vogt (POV), neovascularization (NV) or corneal nerves. Here we report a prototype of full-field optical coherence tomography (FF-OCT) system with isotropic sub-micron spatial resolution in the en face and cross-sectional views. It can also provide three-dimensional reconstructed images and a large field of view (FOV) by stitching tomograms side by side. We validated the imaging power of this prototype in in vivo rat and rabbit eyes, and quantified anatomical characteristics such as corneal layer thickness, endothelial cell density and the intensity profile of different layers. This FF-OCT delineated the ridge-like structure of POV, corneal nerve bundles, and conjunctival vessels in rat eyes. It also clearly identified the vessel walls and red blood cells in rabbit model of corneal NV. The findings provided by this FF-OCT are expected to facilitate corneal disease diagnosis and treatment.

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          The impact of Vision 2020 on global blindness.

          Recent data suggest that there are 37 million blind people and 124 million with low vision, excluding those with uncorrected refractive errors. The main causes of global blindness are cataract, glaucoma, corneal scarring (from a variety of causes), age-related macular degeneration, and diabetic retinopathy. It would appear that the global Vision 2020 initiative is having an impact to reduce avoidable blindness particularly from ocular infections, but more needs to be done to address cataract, glaucoma, and diabetic retinopathy.
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            Limbal Stem Cells of the Corneal Epithelium

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              An Automatic Tool for Quantification of Nerve Fibers in Corneal Confocal Microscopy Images.

              We describe and evaluate an automated software tool for nerve-fiber detection and quantification in corneal confocal microscopy (CCM) images, combining sensitive nerve- fiber detection with morphological descriptors.
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                Author and article information

                Contributors
                chenweili@ntu.edu.tw
                shuang@ntu.edu.tw
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                25 September 2018
                25 September 2018
                2018
                : 8
                : 14349
                Affiliations
                [1 ]ISNI 0000 0004 0546 0241, GRID grid.19188.39, Graduate Institute of Photonics and Optoelectronics, , National Taiwan University, ; Taipei, Taiwan
                [2 ]ISNI 0000 0004 0572 7815, GRID grid.412094.a, Department of Ophthalmology, , National Taiwan University Hospital, ; Taipei, Taiwan
                [3 ]ISNI 0000 0004 1937 1063, GRID grid.256105.5, Department of Ophthalmology, , Fu-Jen Catholic University Hospital, ; New Taipei City, Taiwan
                [4 ]ISNI 0000 0004 1937 1063, GRID grid.256105.5, School of Medicine, College of Medicine, , Fu-Jen Catholic University, ; New Taipei City, Taiwan
                [5 ]ISNI 0000 0004 0546 0241, GRID grid.19188.39, Department of Ophthalmology, , College of Medicine, National Taiwan University, ; Taipei, Taiwan
                [6 ]ISNI 0000 0004 0546 0241, GRID grid.19188.39, Department of Electrical Engineering, , National Taiwan University, ; Taipei, Taiwan
                Author information
                http://orcid.org/0000-0002-1418-4221
                http://orcid.org/0000-0001-6244-1555
                Article
                32814
                10.1038/s41598-018-32814-3
                6156507
                30254253
                2301c3f9-e488-4eec-8fa7-a56d4e264ec3
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or 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 changes were made. 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/.

                History
                : 10 July 2018
                : 17 September 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100004663, Ministry of Science and Technology, Taiwan (Ministry of Science and Technology of Taiwan);
                Award ID: 106-2627-M-002-032
                Award ID: 106-2627-M-002-020
                Award Recipient :
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