An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina

An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina | 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 An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina Changzhuan Xu, Song He, Hailin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4199298/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted 3 You are reading this latest preprint version Abstract Diabetic retinopathy, a leading cause of blindness in diabetic patients, necessitates precise lesion segmentation for effective grading. To address the challenges of low pixel occupancy and capturing multi-scale features in fundus images, we introduce a novel deep learning method: the Multi-Scale Spatial Attention Gate (MSAG) mechanism network. The model inputs images of various scales to extract diverse semantic information. Our innovative Spatial Attention Gate merges low-level spatial details with high-level semantic content, assigning hierarchical attention weights for accurate segmentation. Using the Modified Spatial Attention Gate in the inference stage enhances precision by combining prediction scales hierarchically, thus improving segmentation accuracy without increasing training costs. Testing on the IDRiD-S Dataset demonstrates that our fusion method surpasses others, with our model achieving balanced accuracy across all lesion types. Notably, our approach shows a 1%-3% higher accuracy for microaneurysms and overall accuracy compared to similar models, marking a significant advancement in diabetic retinopathy lesion segmentation. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Sep, 2024 Read the published version in Scientific Reports → Version 1 posted Editor invited by journal 09 Apr, 2024 Submission checks completed at journal 09 Apr, 2024 First submitted to journal 01 Apr, 2024 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. 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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-4199298","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":289349022,"identity":"02cfaeac-763c-4007-b764-99fbe4840256","order_by":0,"name":"Changzhuan Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACfvbG5sd/Ktjs+NkbiNQi2XP4mAHPGb5kyZ4DRGoxuJGWIMHbJse44UYCsVoO5BgYSLCZMTPcfLzxBkONTTRhhx04Y/DAgCeNj3F2WrEFw7G03AZCWvgO9hgYJEgcY2aWzjGTYGw4TFgLw2EeA4kDBv8Z2yTPEKlF4BhbgmRDAhtjjwQPkVoke5iPGTMcYEuW4AH6JYEYv/DLP2x+zPiPzc7++OGNNz7U2BDhFyRgIJFAinKIFlJ1jIJRMApGwcgAAPXiPtaL3nzLAAAAAElFTkSuQmCC","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Changzhuan","middleName":"","lastName":"Xu","suffix":""},{"id":289349023,"identity":"ae6bbe42-d27c-4adb-9e6c-1779337e0dd3","order_by":1,"name":"Song He","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"He","suffix":""},{"id":289349024,"identity":"08818e7f-5b4b-400e-a1dc-b79bf73bdf29","order_by":2,"name":"Hailin Li","email":"","orcid":"","institution":"Guizhou Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hailin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-04-01 08:33:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4199298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4199298/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-024-72481-1","type":"published","date":"2024-09-12T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64619127,"identity":"46c86dc9-7ccf-4f34-a691-64dc258e7ba4","added_by":"auto","created_at":"2024-09-16 16:11:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1489671,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsxuchangzhuan.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4199298/v1_covered_8653bae1-c2e3-41f5-b707-966b3c673956.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An attentional mechanism model for segmenting multiple lesion regions in the diabetic retina","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-4199298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4199298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Diabetic retinopathy, a leading cause of blindness in diabetic patients, necessitates precise lesion segmentation for effective grading. 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