Advanced Machine Learning Techniques for Retinal Lesion Segmentation: A Comprehensive Review Across Ophthalmic Pathologies

Advanced Machine Learning Techniques for Retinal Lesion Segmentation: A Comprehensive Review Across Ophthalmic Pathologies | 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 Research Article Advanced Machine Learning Techniques for Retinal Lesion Segmentation: A Comprehensive Review Across Ophthalmic Pathologies Marie Chadrabova, Jan Kubicek, Martin Augustynek, Juraj Timkovic This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6614538/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Automated segmentation of retinal lesions using machine learning methods represents a cru- cial tool for diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy (DR), retinopathy of prematurity (ROP), pathological myopia, and age-related macular degeneration (AMD). This review systematically evaluates the current state of machine learning- and deep learning-based segmentation methods for pathological retinal lesions in fundus imaging. A total of 86 publications meeting PRISMA criteria were analyzed, covering various architectures of convolutional neural networks (CNN), transformers, hybrid models, and generative adversarial networks (GAN). Variants of the U-Net architecture were found to be the most frequently used models, with hybrid approaches integrating CNNs and transformers demonstrating increasing potential. While lesion segmentation in DR dominates the available literature, the area of ROP remains underexplored, largely due to the limited availability of annotated datasets. The review identifies key challenges such as model generalization across different imaging platforms and patient populations, emphasizing the need for further research aimed at dynamic lesion quantification, especially in clinically underserved domains like ROP. The findings indicate that advanced deep learning-based models achieve high segmentation accuracy, offering substantial potential for enhancing diagnostic and therapeutic procedures in ophthalmology. Machine learning retinal lesions segmentation methods diabetic retinopathy retinopathy of prematurity pathological myopia age-realted macualar degenereation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-6614538","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":457869382,"identity":"a8795ff3-b785-4613-94c9-fb26c600bcdf","order_by":0,"name":"Marie Chadrabova","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYJCCA0CcAELMIB4/AwMbiVokG4jQwoCixeAAAS267WcfHvjAYJfH35778HNBxWF54xvpzx4w1NTh1GJ2Jt3g4AyG5GKJM8+NpWecOWy47UaOuQHDMdw2mR1IYzjMw3AgseFGGoM0b1sa47YzZ9gkGBt4cGs5/4zh8B+glvk30ph/A7XYb+45/gyoRQK3FqDhh4GBlrjhRhob0BabxA3sDWZALQZ4tDxjONhjkJy48cwzNusZZ2ySZxzvMTdIOJaAx2FpzB9+VNglzjuexny7oELCtr+Z/dmDD3hCDAIwXIHbjlEwCkbBKBgFxAAAdmdXKx58u1EAAAAASUVORK5CYII=","orcid":"","institution":"Technical University of Ostrava","correspondingAuthor":true,"prefix":"","firstName":"Marie","middleName":"","lastName":"Chadrabova","suffix":""},{"id":457869383,"identity":"a6caa644-113a-41d7-96ef-28bf74cf7350","order_by":1,"name":"Jan Kubicek","email":"","orcid":"","institution":"Technical University of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Kubicek","suffix":""},{"id":457869384,"identity":"0815ba50-fde0-45c5-ae5a-7480914ef52d","order_by":2,"name":"Martin Augustynek","email":"","orcid":"","institution":"Technical University of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Augustynek","suffix":""},{"id":457869385,"identity":"327c10c9-f963-4fcb-a833-49cdd221ccf7","order_by":3,"name":"Juraj Timkovic","email":"","orcid":"","institution":"Faculty Hospital of Ostrava","correspondingAuthor":false,"prefix":"","firstName":"Juraj","middleName":"","lastName":"Timkovic","suffix":""}],"badges":[],"createdAt":"2025-05-07 18:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6614538/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6614538/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85857085,"identity":"75f2e425-ef66-47c1-aee8-4bfd893abb5e","added_by":"auto","created_at":"2025-07-02 11:38:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1247934,"visible":true,"origin":"","legend":"","description":"","filename":"Article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6614538/v1_covered_35a8ee35-1bff-43d2-ba41-0bd7684cc046.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Advanced Machine Learning Techniques for Retinal Lesion Segmentation: A Comprehensive Review Across Ophthalmic Pathologies","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Machine learning, retinal lesions, segmentation methods, diabetic retinopathy, retinopathy of prematurity, pathological myopia, age-realted macualar degenereation","lastPublishedDoi":"10.21203/rs.3.rs-6614538/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6614538/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Automated segmentation of retinal lesions using machine learning methods represents a cru- cial tool for diagnosing and monitoring ophthalmic diseases such as diabetic retinopathy (DR), retinopathy of prematurity (ROP), pathological myopia, and age-related macular degeneration (AMD). 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