A Dual-branch Framework Based on Implicit Continuous Representation for Tumor Image Segmentation

A Dual-branch Framework Based on Implicit Continuous Representation for Tumor 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 A Dual-branch Framework Based on Implicit Continuous Representation for Tumor Image Segmentation Jing Wang, Yuanjie Zheng, Junxia Wang, Xiao Xiao, Jing Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3548540/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 Breast tumor segmentation has important significance for early detection and determination of treatment plans. However, segmenting early-stage small tumors in breast images is challenging due to small and low-resolution tumor regions, variation of tumor shapes, and blurred tumor boundaries. More importantly, breast scans are usually noisy and include metal artifacts. Most of the existing tumor segmentation methods have difficulty in extracting lesion discriminative information, leading to the problem that small tumors are ignored or predictions contain a lot of noise. In addition, common reconstruction and segmentation algorithms are based on discrete images and ignore the continuity of feature space. Therefore, in this paper, we investigate a novel and flexible dual-branch framework, named High-Resolution and Information Bottleneck-based Segmentation Network (HR-IBS), for breast tumor segmentation. For the first time, this method introduces the high-resolution tumor region reconstruction (HR-TR) branch via implicit neural representations to learning functions that map the discrete input signal to continuous density. The branch enables reconstruction from lesion regions for another segmentation branch. Furthermore, we design an Information bottleneck-based segmentation (IBS) branch, which adopts information bottleneck and U-Net to retain the features most relevant while removing noisy regions, and discovering more informative lesion regions. The reconstruction and segmentation branches interact with each other to facilitate performance. Comprehensive experiments are conducted on segmentation benchmarks of two modalities of breast images. The results show that the proposed method outperforms other segmentation models and contributes to optimizing hand-crafted ground-truths. Image segmentation Medical imaging process High-Resolution reconstruction Information bottleneck Computer vision Medical imaging segmentation 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. 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-3548540","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245788354,"identity":"38a6aa79-3a6e-4504-b77e-cf80a2265416","order_by":0,"name":"Jing Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACxmYog03+/IMDHwxs7IjXwifBw3hwRkFaMvHWyUnwMB/m+XCIsYGQSuZ23sOveWru2LVJ9x44bGNwgJmB/fDRDfgdxpdmzXPsWXKbzLmEwzkGd/gYeNLSbuDXwmNmnMN2OJmNIcEAqOUZM4MEjxkRWv5BtVgYHGZsIEKL8ePctsN2bBI5BocZiNRixvy373ACG8+xhIM9BmnJbIT8Yth/xvjjjG+H7eXbmw9/+PHHxo6f/fAx/FoaGNgkgHRiA0yEDZ9yEJAHRs0HIG1PSOEoGAWjYBSMYAAA0QZNManIAaoAAAAASUVORK5CYII=","orcid":"","institution":"Shandong Normal University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":245788355,"identity":"608715b6-b841-43e1-93da-6748ac63ce61","order_by":1,"name":"Yuanjie Zheng","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuanjie","middleName":"","lastName":"Zheng","suffix":""},{"id":245788356,"identity":"f835ff4e-2f83-4863-ac4e-cf34090a4129","order_by":2,"name":"Junxia Wang","email":"","orcid":"","institution":"Shandong Normal University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junxia","middleName":"","lastName":"Wang","suffix":""},{"id":245788357,"identity":"a9cc77a4-b6e9-4a9c-b25d-014928f9a864","order_by":3,"name":"Xiao Xiao","email":"","orcid":"","institution":"Shouguang Hospital of Traditional Chinese Medicine","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Xiao","suffix":""},{"id":245788358,"identity":"fc8f48b2-0de5-4a03-9a7a-b46ccf73c4c0","order_by":4,"name":"Jing Sun","email":"","orcid":"","institution":"Weifang University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2023-11-03 03:59:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3548540/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3548540/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72429440,"identity":"9d4fd0ad-c12c-4fcf-9031-926fdca8b826","added_by":"auto","created_at":"2024-12-27 03:31:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1434186,"visible":true,"origin":"","legend":"","description":"","filename":"medicalsignalimage.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3548540/v1_covered_7539aba4-4ef0-4929-94a4-003c06bf9fbb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Dual-branch Framework Based on Implicit Continuous Representation for Tumor Image Segmentation","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":"Image segmentation, Medical imaging process, High-Resolution reconstruction, Information bottleneck, Computer vision, Medical imaging segmentation","lastPublishedDoi":"10.21203/rs.3.rs-3548540/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3548540/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreast tumor segmentation has important significance for early detection and determination of treatment plans. However, segmenting early-stage small tumors in breast images is challenging due to small and low-resolution tumor regions, variation of tumor shapes, and blurred tumor boundaries. More importantly, breast scans are usually noisy and include metal artifacts. Most of the existing tumor segmentation methods have difficulty in extracting lesion discriminative information, leading to the problem that small tumors are ignored or predictions contain a lot of noise. In addition, common reconstruction and segmentation algorithms are based on discrete images and ignore the continuity of feature space. Therefore, in this paper, we investigate a novel and flexible dual-branch framework, named High-Resolution and Information Bottleneck-based Segmentation Network (HR-IBS), for breast tumor segmentation. 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