Automatic Extraction Algorithm for Complex Vascular Skeletons Based on TMOBB

Automatic Extraction Algorithm for Complex Vascular Skeletons Based on TMOBB | 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 Automatic Extraction Algorithm for Complex Vascular Skeletons Based on TMOBB Pengjin Liu, Xiaofang Yin, Huimin Chang, Zixuan Wang, Keyuan Qiu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6594188/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 Vascular diseases pose significant challenges in the realm of medical sciences, often giving rise to critical health issues and potential life-threatening conditions. Accurate extraction of vascular skeletons plays a pivotal role in furnishing vital information and support for the diagnosis, treatment, and prognostic management of vascular ailments. Computational image analysis assumes a crucial role in the diagnosis and quantification of vascular anomalies. Given the scale and complexity of data from modern vascular imaging acquisition, achieving rapid, automated, and precise vascular segmentation constitutes a challenging task.In this paper, we introduce an automatic propagation-based approach utilizing directed Oriented Bounding Boxes (OBB). This technique efficiently extracts the skeletal lines of individual vessels without necessitating prior segmentation. Leveraging raw 3D Computed Tomography Angiography (CTA) images as input, the method involves successive stages encompassing Directed Oriented Bounding Box computation, threshold segmentation, centroid coordinate determination, ultimately yielding a fitted skeletal representation of the vessels. Moreover, we propose a complex vascular skeleton extraction method based on Two Moves Oriention Bounding Box (TMOBB), which encompasses skeleton curve sampling, bifurcation point discrimination, and erroneous skeleton line rectification. Experimental outcomes underscore that the approach presented in this paper exhibits notable precision and elevated extraction velocity in the domain of vascular skeleton extraction, thereby furnishing robust support for hepatic vascular structure analysis. Biological sciences/Computational biology and bioinformatics/Image processing Biological sciences/Biological techniques/Imaging Vascular disease Directed bounding box Skeleton extraction Marching algorithm Computer image analysis 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-6594188","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508031118,"identity":"98b5e16b-356f-4ba3-b293-7f240c726280","order_by":0,"name":"Pengjin Liu","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Pengjin","middleName":"","lastName":"Liu","suffix":""},{"id":508031119,"identity":"2566f668-af70-4c5b-ab83-d9e23e73c4ec","order_by":1,"name":"Xiaofang Yin","email":"","orcid":"","institution":"Shenzhen Sontu medical imaging equipment","correspondingAuthor":false,"prefix":"","firstName":"Xiaofang","middleName":"","lastName":"Yin","suffix":""},{"id":508031120,"identity":"9e4d1a21-d422-41be-ac96-fc7aa18eb157","order_by":2,"name":"Huimin Chang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Chang","suffix":""},{"id":508031121,"identity":"85f37684-c58f-4212-9626-5b3af64699a1","order_by":3,"name":"Zixuan Wang","email":"","orcid":"","institution":"Qingdao Municipal Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Wang","suffix":""},{"id":508031122,"identity":"e4a06216-761c-4bae-bd84-fde3caf55f87","order_by":4,"name":"Keyuan Qiu","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Keyuan","middleName":"","lastName":"Qiu","suffix":""},{"id":508031123,"identity":"52c0e302-a43e-4a4f-9d51-05d608e5f153","order_by":5,"name":"Quan Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACfgbmAwc+GJCiRbKBLeHgDJK0GBzgMWDmIclhQC2Gh20KbOSALjz28QuxthzOMUgzBrowebYMCVoOJ244wGPMLEGMFrMDvBsOWxj8r99PtBbjAzwfDgPtSjBg4DFm/ECMFslmYCD3GCQbzjjMlsxMjA4Gfvfmyx9+/LGT529vPsz4gyg9zEgMEiMIBIi0ZRSMglEwCkYaAADjcC/A4sj9qAAAAABJRU5ErkJggg==","orcid":"","institution":"Shihezi University","correspondingAuthor":true,"prefix":"","firstName":"Quan","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2025-05-05 11:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6594188/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6594188/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96927265,"identity":"4865e747-62ff-4e35-985c-079592f6c3e1","added_by":"auto","created_at":"2025-11-27 14:27:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1319632,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplateyxf2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6594188/v1_covered_c22e19b6-5f76-43e9-b8be-32cd918bc58f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Automatic Extraction Algorithm for Complex Vascular Skeletons Based on TMOBB","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":"Vascular disease, Directed bounding box, Skeleton extraction, Marching algorithm, Computer image analysis","lastPublishedDoi":"10.21203/rs.3.rs-6594188/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6594188/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVascular diseases pose significant challenges in the realm of medical sciences, often giving rise to critical health issues and potential life-threatening conditions. Accurate extraction of vascular skeletons plays a pivotal role in furnishing vital information and support for the diagnosis, treatment, and prognostic management of vascular ailments. Computational image analysis assumes a crucial role in the diagnosis and quantification of vascular anomalies. Given the scale and complexity of data from modern vascular imaging acquisition, achieving rapid, automated, and precise vascular segmentation constitutes a challenging task.In this paper, we introduce an automatic propagation-based approach utilizing directed Oriented Bounding Boxes (OBB). This technique efficiently extracts the skeletal lines of individual vessels without necessitating prior segmentation. 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