Region-Adaptive Attention and Edge-Guided Alignment for Medical Image Registration

Region-Adaptive Attention and Edge-Guided Alignment for Medical Image Registration | 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 Region-Adaptive Attention and Edge-Guided Alignment for Medical Image Registration Linghui Liu, Yue Zeng, Weiqiang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7059275/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 Medical image registration is essential for tasks such as surgical navigation and disease diagnosis, aiming to spatially align medical images for consistent anatomical interpretation. In brain MR images, structural complexity and intensity heterogeneity lead to significant variation in registration difficulty across regions. However, most existing methods adopt uniform processing strategies, which often result in inaccurate alignment in regions with complex anatomy or blurred boundaries. To address these challenges, we propose a novel registration method that integrates a Region-Adaptive Attention (RAA) module and an Edge-Guided Alignment (EGA) module. Specifically, the RAA module extracts multi-scale features and employs a structure-aware gating mechanism to dynamically modulate the network's focus according to regional registration difficulty. This enables the network to emphasize difficult areas such as low-contrast or highly deformed regions. The EGA module incorporates edge-aware information and leverages an attention-based fusion strategy to enhance the registration precision near anatomical boundaries. The outputs of both modules are fused and decoded to generate a dense deformation field. Our method is evaluated on three public datasets: OASIS, IXI, and LPBA40. Compared to ten state-of-the-art registration techniques, our approach demonstrates distinct advantage, achieving DSC coefficients of 0.902, 0.792, and 0.705, respectively. These results validate the effectiveness of the proposed framework in addressing regional registration difficulties and improving boundary accuracy, offering a robust solution for high-precision MR image alignment. Medical image registration Region-Adaptive Attention Edge-Guided Alignment Deformable registration 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. 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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-7059275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":484753041,"identity":"54e3d443-a938-4c61-ab01-fd490945aab0","order_by":0,"name":"Linghui Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwUlEQVRIiWNgGAWjYBACPmYGxsMMFTAuGxFa2JgZGA4znAExmYnVAsSHGdtI0sLOY3C4cJ6d7Ibb/QcYPpQdZuCf3UDIYUAtM7clG2+4c5iBcca5wwwSdw4QoYV324HEDTeSGZh52w4zGEgkEKNlDlTLX+K1NEC1MBKnha3gMM+xZOOZdw4bHOw5l84jcYOAFn7+wxsf89TYyfbdbnz44EeZtRz/DAJaYICxQYKB4QCQwUOcepiWUTAKRsEoGAVYAQChLT/m0PPBVwAAAABJRU5ErkJggg==","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":true,"prefix":"","firstName":"Linghui","middleName":"","lastName":"Liu","suffix":""},{"id":484753042,"identity":"81c5cf2f-241b-4f4d-9320-3edf9fb13b95","order_by":1,"name":"Yue Zeng","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zeng","suffix":""},{"id":484753043,"identity":"58307b4b-6970-4978-be8f-239612776d99","order_by":2,"name":"Weiqiang Wang","email":"","orcid":"","institution":"Chongqing University of Posts and Telecommunications","correspondingAuthor":false,"prefix":"","firstName":"Weiqiang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-07-06 17:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7059275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7059275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107300709,"identity":"f5407b5e-948c-41db-9102-85e501615dce","added_by":"auto","created_at":"2026-04-20 07:28:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4234908,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7059275/v1_covered_bab728d0-033d-4227-a337-08c07ff6eaa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Region-Adaptive Attention and Edge-Guided Alignment for Medical Image Registration","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Medical image registration, Region-Adaptive Attention, Edge-Guided Alignment, Deformable registration","lastPublishedDoi":"10.21203/rs.3.rs-7059275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7059275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMedical image registration is essential for tasks such as surgical navigation and disease diagnosis, aiming to spatially align medical images for consistent anatomical interpretation. In brain MR images, structural complexity and intensity heterogeneity lead to significant variation in registration difficulty across regions. However, most existing methods adopt uniform processing strategies, which often result in inaccurate alignment in regions with complex anatomy or blurred boundaries. To address these challenges, we propose a novel registration method that integrates a Region-Adaptive Attention (RAA) module and an Edge-Guided Alignment (EGA) module. Specifically, the RAA module extracts multi-scale features and employs a structure-aware gating mechanism to dynamically modulate the network's focus according to regional registration difficulty. This enables the network to emphasize difficult areas such as low-contrast or highly deformed regions. The EGA module incorporates edge-aware information and leverages an attention-based fusion strategy to enhance the registration precision near anatomical boundaries. The outputs of both modules are fused and decoded to generate a dense deformation field. Our method is evaluated on three public datasets: OASIS, IXI, and LPBA40. Compared to ten state-of-the-art registration techniques, our approach demonstrates distinct advantage, achieving \u003cem\u003eDSC\u003c/em\u003e coefficients of 0.902, 0.792, and 0.705, respectively. 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