Deep Learning-Based Segmentation and Density Estimation of Corneal Nerves and Dendritic Cells from In Vivo Confocal Microscopy Images

Deep Learning-Based Segmentation and Density Estimation of Corneal Nerves and Dendritic Cells from In Vivo Confocal Microscopy Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Deep Learning-Based Segmentation and Density Estimation of Corneal Nerves and Dendritic Cells from In Vivo Confocal Microscopy Images Meichen Ji, Yan Song, Jenny Roth, Ava Dashti, Jorge Lazo, Alisa Lincke, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748380/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract The purpose of this study was to compare manual assessment of corneal nerve fiber length (CNFL) and dendritic cell (DC) density with an automated assessment method utilizing deep learning segmentation to perform rule-based density estimation. Corneal images were acquired using in vivo confocal microscopy (IVCM) from 100 participants with persistent ocular symptoms after mild COVID-19 (Group 1) and 30 controls without symptoms (Group 2). A total of 1,300 IVCM images were selected and manually annotated for CNFL and 1,300 for DCs (mature and immature) using ImageJ-based tools. The difference in mean CNFL between methods was 0.2 mm/mm2 (95% CI: [-0.49, 0.09]) for Group 1 and 0.3 mm/mm2 (95% CI: [-0.01, 0.61]) for Group 2, while the difference in mean DC densities for Group 1 was 1.4 [0.35, 2.45] mature cells/mm2 and 3.6 [-0.43, 7.63] immature cells/mm2, and for Group 2, 0.8 [0.33, 1.27] mature cells/mm2 and 1.3 [-4.73, 2.13] immature cells/mm2. Both manual and automated methods showed significant differences between groups for CNFL (p=0.012 and p=0.034, respectively) and DC densities (p=0.005 and p=0.010). The automated approach performed comparably to manual assessment, supporting its potential for reliable, scalable analysis of CNFL and DC in IVCM images. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialsrestructured.docx Cite Share Download PDF Status: Published Journal Publication published 13 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Nov, 2025 Reviews received at journal 10 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 13 Oct, 2025 Submission checks completed at journal 08 Oct, 2025 First submitted to journal 08 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7748380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534713301,"identity":"01beba1b-92a5-4bfd-8dcb-260657d90a1f","order_by":0,"name":"Meichen Ji","email":"","orcid":"","institution":"Linnaeus University","correspondingAuthor":false,"prefix":"","firstName":"Meichen","middleName":"","lastName":"Ji","suffix":""},{"id":534713302,"identity":"9679f305-682f-4dfb-99f6-403abdb2fde1","order_by":1,"name":"Yan Song","email":"","orcid":"","institution":"Linnaeus 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Images","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7748380/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7748380/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The purpose of this study was to compare manual assessment of corneal nerve fiber length (CNFL) and dendritic cell (DC)\ndensity with an automated assessment method utilizing deep learning segmentation to perform rule-based density estimation.\nCorneal images were acquired using in vivo confocal microscopy (IVCM) from 100 participants with persistent ocular symptoms\nafter mild COVID-19 (Group 1) and 30 controls without symptoms (Group 2). A total of 1,300 IVCM images were selected and\nmanually annotated for CNFL and 1,300 for DCs (mature and immature) using ImageJ-based tools. The difference in mean\nCNFL between methods was 0.2 mm/mm2 (95% CI: [-0.49, 0.09]) for Group 1 and 0.3 mm/mm2 (95% CI: [-0.01, 0.61]) for\nGroup 2, while the difference in mean DC densities for Group 1 was 1.4 [0.35, 2.45] mature cells/mm2 and 3.6 [-0.43, 7.63]\nimmature cells/mm2, and for Group 2, 0.8 [0.33, 1.27] mature cells/mm2 and 1.3 [-4.73, 2.13] immature cells/mm2. Both manual\nand automated methods showed significant differences between groups for CNFL (p=0.012 and p=0.034, respectively) and\nDC densities (p=0.005 and p=0.010). The automated approach performed comparably to manual assessment, supporting its\npotential for reliable, scalable analysis of CNFL and DC in IVCM images.","manuscriptTitle":"Deep Learning-Based Segmentation and Density Estimation of Corneal Nerves and Dendritic Cells from In Vivo Confocal Microscopy Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:42:48","doi":"10.21203/rs.3.rs-7748380/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-12T03:07:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-10T11:31:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-29T06:24:51+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T07:36:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"176738001577641172203159893001063192062","date":"2025-10-17T19:47:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327043666767689786839546873782932086192","date":"2025-10-16T07:59:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251085496526070731270832747483692096795","date":"2025-10-15T17:06:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43255762008718781743585029091763474678","date":"2025-10-15T04:35:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T03:47:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-13T04:48:10+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-08T08:19:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-08T07:42:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcf9b5c7-ccdc-49ab-ae6f-649bf0cde94e","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56853054,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56853055,"name":"Health sciences/Diseases"},{"id":56853056,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-19T17:07:35+00:00","versionOfRecord":{"articleIdentity":"rs-7748380","link":"https://doi.org/10.1038/s41598-025-34412-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-13 16:30:07","publishedOnDateReadable":"January 13th, 2026"},"versionCreatedAt":"2025-10-28 16:42:48","video":"","vorDoi":"10.1038/s41598-025-34412-6","vorDoiUrl":"https://doi.org/10.1038/s41598-025-34412-6","workflowStages":[]},"version":"v1","identity":"rs-7748380","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7748380","identity":"rs-7748380","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}