Dual-branch adaptive fusion network with edge supervision for breast ultrasound image segmentation

Dual-branch adaptive fusion network with edge supervision for breast ultrasound image segmentation | 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 Dual-branch adaptive fusion network with edge supervision for breast ultrasound image segmentation Jing Xu, Hongkun Sun, Baipan Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7297694/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Breast ultrasound image segmentation is a crucial task in computer-aided diagnosis , enabling rapid localization of the studied lesion regions. However, for breast ultrasound image with small, blurry, and irregularly shaped lesions, many current methods solely rely on supervised segmentation based on masks, neglecting the relevant edge information. In this article, we propose a simple and direct edge supervision (ES) block to pay additional attention to the edge information of breast lesions to improve the edge quality in the segmentation results. The proposed block can be applied not only at the final layer of the network but also at intermediate layers to enhance supervision of the edge in the segmented regions, thereby improving the segmentation quality. We also propose an adap-tive feature fusion (AFF) block to efficiently combine the dual-branch features of local and global information. Specifically, we construct a dual-branch adaptive fusion network based on CNNs and Transformers to learn the feature representation of the breast ultrasound image. Additionally, we introduce the proposed edge supervision (ES) block, which employs a hierarchical supervision approach to learn edge features. Our method achieves remarkable segmentation results on two widely adopted breast ultrasound image datasets, BUSI and UDIAT. Extensive ablation experiments confirm the effectiveness of our method in the fusion of CNNs and Transformers, as well as the edge supervision. Comparison with several existing methods demonstrates that the proposed approach achieves competitive performance in breast ultrasound image segmentation. And robustness experiment demonstrates the high generalization capability of our method. (a) benign (b) malignant Fig. 1 Breast ultrasound images in the area of the lesion, edge irregularities, and similarities in the edge and surrounding areas. CNNs Transformers Adaptive feature fusion Edge supervision Breast ultrasound image Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 17 Aug, 2025 Submission checks completed at journal 15 Aug, 2025 First submitted to journal 15 Aug, 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-7297694","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502748443,"identity":"03440d05-7de8-4ae1-aedb-ba7327eec2b8","order_by":0,"name":"Jing Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYBACxmYwZUO6ljTSLTtMglrmdt7Dr3nbzsvr9h9/wPCjhkHenLDD+NIsZ7bdNtx2I8eAsecYg+HOBoJaeMwMPrbdZtx2g4eBgbeBIcHgADFaEtvO2W87f/wB418itRg/+Nh2IHHbgQQDZqJtYZxxLjkZ5JfDMsckDDcQ0mLYf8b4M0+ZnS3QYQ8fvqmxkSdoi2EDA5sEjANULIFHLRTIA6PmA2Flo2AUjIJRMKIBAP7EQA02sl1iAAAAAElFTkSuQmCC","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":true,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""},{"id":502748444,"identity":"32ebcf4e-07ff-4813-9a28-4270d6b7e95e","order_by":1,"name":"Hongkun Sun","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Hongkun","middleName":"","lastName":"Sun","suffix":""},{"id":502748445,"identity":"973de1e7-128b-480c-aac5-07bbeaad6b22","order_by":2,"name":"Baipan Hou","email":"","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":false,"prefix":"","firstName":"Baipan","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2025-08-05 07:38:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7297694/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7297694/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89885852,"identity":"6af143f6-e828-456d-8c0e-7640d5db3b50","added_by":"auto","created_at":"2025-08-26 06:34:52","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3871066,"visible":true,"origin":"","legend":"","description":"","filename":"DAFnetsubmissionxu3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7297694/v1_covered_d2159e84-1f57-4823-b9f5-5e6603b950a5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-branch adaptive fusion network with edge supervision for breast ultrasound image segmentation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-imaging","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmim","sideBox":"Learn more about [BMC Medical Imaging](http://bmcmedimaging.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bmim/default.aspx","title":"BMC Medical Imaging","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"CNNs, Transformers, Adaptive feature fusion, Edge supervision, Breast ultrasound image","lastPublishedDoi":"10.21203/rs.3.rs-7297694/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7297694/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Breast ultrasound image segmentation is a crucial task in computer-aided diagnosis , enabling rapid localization of the studied lesion regions. 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