Vascular segment bifurcation detection and key point calculation based on CT radiomics

Vascular segment bifurcation detection and key point calculation based on CT radiomics | 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 Vascular segment bifurcation detection and key point calculation based on CT radiomics Huimin Chang, Zhendu Diao, Zixuan Wang, Quan Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3934399/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 Hepatic vascular neoplasms often occur at the bifurcation of hepatic blood vessels, making them vulnerable to ruptures and bleeding, which can be life-threatening. Therefore, accurately detecting and localizing hepatic vascular bifurcation is crucial for diagnosing hepatic vascular tumors and planning surgeries. However, existing research methods have limited sensitivity to noise and vascular deformations. To address this issue, we propose a classification algorithm based on radiomic feature extraction and machine learning (ML) techniques. We begin by segmenting and processing the original hepatic vascular CT images using Gaussian filtering to remove small connected regions. This process generates vessel mask images through alignment. Next, these images are annotated, and 1042 features are extracted through radiomic feature extraction. To simplify the model, we employ Lasso regression for feature selection, resulting in the identification of 82 optimal features. We then establish and experimentally validate a machine learning classification model. Our experimental results demonstrate that the SVM classification model performs the best, achieving a validation accuracy of 0.932, precision of 0.900, recall of 0.936, F1-score of 0.915, and AUC value of 0.987. Additionally, we compute the key points of vessel segments using both the projection method and the direct thinning method. Experimental comparisons reveal that the direct thinning method yields more accurate results.In conclusion, our proposed classification algorithm, utilizing radiomic feature extraction and machine learning techniques, is crucial for rapidly and accurately detecting hepatic vascular bifurcation. These findings have significant implications for improving the diagnosis and treatment of hepatic vascular tumors. Radiomics Vascular segment bifurcation detection Machine learning Vascular keypoints 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-3934399","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271550994,"identity":"3a96dc7e-675f-498a-9eb2-b252b68cbb95","order_by":0,"name":"Huimin Chang","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Huimin","middleName":"","lastName":"Chang","suffix":""},{"id":271550995,"identity":"174a1fae-f693-49a9-9fa3-a86972f75142","order_by":1,"name":"Zhendu Diao","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Zhendu","middleName":"","lastName":"Diao","suffix":""},{"id":271550996,"identity":"84b85ae1-8601-49c8-830c-e895bd2cb251","order_by":2,"name":"Zixuan Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zixuan","middleName":"","lastName":"Wang","suffix":""},{"id":271550997,"identity":"fad1a0ef-2c82-4b39-9d15-39fed2727053","order_by":3,"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":"2024-02-06 15:59:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3934399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3934399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51003828,"identity":"fb3052a7-3163-4af8-a744-e46c12f065cf","added_by":"auto","created_at":"2024-02-12 14:23:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":959398,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3934399/v1_covered_6879924d-c01b-4430-acf9-85519a8bad3e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vascular segment bifurcation detection and key point calculation based on CT radiomics","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":"Radiomics, Vascular segment bifurcation detection, Machine learning, Vascular keypoints","lastPublishedDoi":"10.21203/rs.3.rs-3934399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3934399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Hepatic vascular neoplasms often occur at the bifurcation of hepatic blood vessels, making them vulnerable to ruptures and bleeding, which can be life-threatening. Therefore, accurately detecting and localizing hepatic vascular bifurcation is crucial for diagnosing hepatic vascular tumors and planning surgeries. However, existing research methods have limited sensitivity to noise and vascular deformations. To address this issue, we propose a classification algorithm based on radiomic feature extraction and machine learning (ML) techniques. We begin by segmenting and processing the original hepatic vascular CT images using Gaussian filtering to remove small connected regions. This process generates vessel mask images through alignment. Next, these images are annotated, and 1042 features are extracted through radiomic feature extraction. To simplify the model, we employ Lasso regression for feature selection, resulting in the identification of 82 optimal features. We then establish and experimentally validate a machine learning classification model. Our experimental results demonstrate that the SVM classification model performs the best, achieving a validation accuracy of 0.932, precision of 0.900, recall of 0.936, F1-score of 0.915, and AUC value of 0.987. Additionally, we compute the key points of vessel segments using both the projection method and the direct thinning method. Experimental comparisons reveal that the direct thinning method yields more accurate results.In conclusion, our proposed classification algorithm, utilizing radiomic feature extraction and machine learning techniques, is crucial for rapidly and accurately detecting hepatic vascular bifurcation. 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