Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism

Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism | 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 Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism Hongyang Xu, Yuanxiu Xing, Wenbo Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1478176/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. First, we develop a BPDM network embedded with watershed and attention module and use an adaptive loss function to achieve each pixel’s robust and accurate BPDM which is defined as a two-dimensional vector, including direction and magnitude, and pointing from its nearest boundary pixel to itself. Then, we use the leaned BPDMs to obtain the refined initial segmented regions by considering the pixels near boundary have shorter magnitude and near root pixels have longer magnitude, meanwhile adjacent pixels in different regions or nearby pixels on both sides of root pixel in same region have opposite directions and nearby pixels in same region have similar directions. Last, we utilize a fast grouping method according to direction similarity to combine these initial segmented regions into final segmentation. The experimental results show that compared with the state-of-art methods in image segmentation, the IS-BPDM approach proposed in this paper achieves better segmentation accuracy and high computational efficiency, and outperforms in small regions segmentation on public datasets. Image segmentation Deep learning Boundary-to-pixel direction and magnitude Watershed Attention mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 25 Aug, 2022 Reviews received at journal 25 Aug, 2022 Reviewers agreed at journal 11 Apr, 2022 Reviewers agreed at journal 02 Apr, 2022 Reviewers invited by journal 30 Mar, 2022 Editor assigned by journal 25 Mar, 2022 Submission checks completed at journal 22 Mar, 2022 First submitted to journal 22 Mar, 2022 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-1478176","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":92771525,"identity":"0d30e696-887a-486a-b8ef-8a900f235b9a","order_by":0,"name":"Hongyang Xu","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hongyang","middleName":"","lastName":"Xu","suffix":""},{"id":92771526,"identity":"2768124b-05b0-405c-baa5-08d608f6f904","order_by":1,"name":"Yuanxiu Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYNACAwY5NvbmA6RpMebjOZZAmj2J8yRyFIg0/0bys8c8BYfT2xhyGBh+VGwjRkuaueEMg8O5bQxnDzD2nLlNjJYEM4kPIC2MfQnMjG1EaUn/JpFgcDidjZnHgFgtOWBbEtjYiNUieeZNmeQMg3TDNh62hINE+YXvePo2aZ4/1vLy8x8ffPCjgggtCgeQOAdwKEIF8g1EKRsFo2AUjIIRDQBhLzpGMwCqcgAAAABJRU5ErkJggg==","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yuanxiu","middleName":"","lastName":"Xing","suffix":""},{"id":92771527,"identity":"4ce84a8d-eb5f-48f9-bf84-9e3b33820af8","order_by":2,"name":"Wenbo Wang","email":"","orcid":"","institution":"Wuhan University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wenbo","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2022-03-22 14:29:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1478176/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1478176/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19546234,"identity":"2227a193-d39c-4fad-b86a-cab58042dd2a","added_by":"auto","created_at":"2022-03-23 19:40:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662396,"visible":true,"origin":"","legend":"","description":"","filename":"ISBPDMarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1478176/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Image Segmentation with Boundary-to-Pixel Direction and Magnitude Based on Watershed and Attention Mechanism","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1478176/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"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":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"signal-image-and-video-processing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sivp","sideBox":"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)","snPcode":"11760","submissionUrl":"https://submission.nature.com/new-submission/11760/3","title":"Signal, Image and Video Processing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Image segmentation, Deep learning, Boundary-to-pixel direction and magnitude, Watershed, Attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-1478176/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1478176/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"An improved image segmentation algorithm with boundary-to-pixel direction and magnitude (IS-BPDM) is proposed to deal with small regions segmentation while keeping the accuracy of edge segmentation. 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