Coronary Arteries Segmentation in Invasive X-ray Angiography: A Comprehensive Review and Benchmarking

Coronary Arteries Segmentation in Invasive X-ray Angiography: A Comprehensive Review and Benchmarking | 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 Coronary Arteries Segmentation in Invasive X-ray Angiography: A Comprehensive Review and Benchmarking Mojtaba Lashgari, Mohammad Atwany, Robin P. Choudhury, Abhirup Banerjee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9309347/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 Coronary artery disease remains the leading cause of morbidity and mortality worldwide, and X-ray coronary angiography (XCA) is the gold standard for its diagnosis and management during real-time cardiac interventions. Accurate segmentation of coronary arteries in XCA is a critical step in quantitative analysis, supporting stenosis detection, treatment planning, and 3D reconstruction. However, segmentation is highly challenging due to overlapping vessels, bone shadows, low contrast, and complex vascular geometry. In this review, we provide the first comprehensive synthesis that systematically categorises and critically evaluates segmentation methods for XCA, covering both classical image processing techniques and emerging machine learning and deep learning approaches. We summarise their evolution, strengths, and limitations, and present benchmarking of advanced deep learning models on two public datasets using Dice score, sensitivity, and precision. The observed performance variability highlights the need for robust algorithms capable of addressing label scarcity, cross-domain generalisability, and interpretability. By consolidating methodological advances and benchmarking evidence, this review offers a foundation to guide future developments in reliable coronary artery segmentation for clinical translation. Artificial Intelligence and Machine Learning X-ray angiography Coronary artery disease Segmentation Machine learning Deep learning Full Text Additional Declarations The authors declare no competing interests. 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-9309347","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616965127,"identity":"6c12878c-7d1f-43d5-abee-653ad4666322","order_by":0,"name":"Mojtaba Lashgari","email":"","orcid":"https://orcid.org/0000-0002-5307-3495","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Mojtaba","middleName":"","lastName":"Lashgari","suffix":""},{"id":616965128,"identity":"516e3cf1-5d1f-4815-94f1-c46d891602b7","order_by":1,"name":"Mohammad Atwany","email":"","orcid":"https://orcid.org/0000-0002-3607-8649","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Atwany","suffix":""},{"id":616965129,"identity":"15d04c1e-b97e-4b82-adb7-c83f3cfe5c4b","order_by":2,"name":"Robin P. 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