Robust Myocardium Detection and Scar Severity Classification in LGE-CMR Using ScarYOLO and Contrastive Learning

Robust Myocardium Detection and Scar Severity Classification in LGE-CMR Using ScarYOLO and Contrastive Learning | 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 Robust Myocardium Detection and Scar Severity Classification in LGE-CMR Using ScarYOLO and Contrastive Learning Abinaya B, Malleswaran M, Muthupriya V This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6763973/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in European Journal of Medical Research → Version 1 posted 11 You are reading this latest preprint version Abstract Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR) imaging plays a crucial role in assessing myocardial scar tissues, aiding in the diagnosis and prognosis of cardiovascular diseases. However, accurately classifying scar tissue severity into mild and severe remains a challenge due to low contrast, noise interference, and inter-patient variability in LGE-CMR images. Existing methods often rely on manual assessment or traditional deep learning models that struggle with precise myocardium localization and discriminative feature extraction from scarred regions. To overcome these challenges, we propose a novel framework incorporating ScarYOLO, an optimized YOLOv8-based myocardium detection model, followed by Contrastive Myocardial Scar Learning (CMSL) for severity classification. ScarYOLO enhances myocardium localization accuracy, ensuring precise detection of scarred tissue. The detected myocardium is then processed using CMSL, which employs a fine-tuned Xception-based encoder trained on a labeled LGE-CMR dataset. CMSL leverages contrastive self-supervised learning to enhance feature representation and improve class separability between mild and severe scar regions. Additional dense layers and a classification head are appended to the encoder for final severity prediction. The proposed approach enhances myocardial scar detection accuracy while improving robustness in low-contrast LGE-CMR images. By leveraging ScarYOLO for precise segmentation and CMSL for effective classification, our model outperforms conventional deep learning methods in classifying scar tissue severity. Experimental evaluations demonstrate significant improvements in detection precision, classification accuracy, and model generalization, making it a reliable tool for automated myocardial scar assessment in clinical settings. Myocardium Detection Scar Severity Classification LGE-CMR Images YOLOv8 Contrastive Learning ScarYOLO Contrastive Myocardial Scar Learning Manhattan Distance Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in European Journal of Medical Research → Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 29 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviews received at journal 20 Jun, 2025 Reviewers agreed at journal 18 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor assigned by journal 09 Jun, 2025 Submission checks completed at journal 07 Jun, 2025 First submitted to journal 28 May, 2025 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. 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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-6763973","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470479129,"identity":"f8480b0e-8e8b-4407-8258-a3c81542874d","order_by":0,"name":"Abinaya B","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCcYGIJnAwMDMfMDgA5DJxk68FraEwhkgLcwEtYBJoBYGHoPPPCA2IS38s5ubP/zckSZv3s6WuNnm1zZ5PmYGxg8fc/BYcudgm2TvmRzDOYeZDxvn9t02bGNmYJacuQ2PNTcS2xh42yoYZzCzpRnn9txmBGphY+bFo0X+RmLzx79tFfYzmHnMf1v23LYnqMXgRmKDNG9bTiJQi4Exw4/biQS1GAL9Ii3blpYMdFiCYW/D7eQ2ZsZmvH6Ru93++OPbtmTbGfyHDxj8+HPbdn5788EPH/F5HwUwtoHJBmLVg8AfUhSPglEwCkbBSAEA6aBRpP7RX40AAAAASUVORK5CYII=","orcid":"","institution":"Easwari Engineering College Ramapuram","correspondingAuthor":true,"prefix":"","firstName":"Abinaya","middleName":"","lastName":"B","suffix":""},{"id":470479130,"identity":"3e38c4d5-6963-414b-9596-62bfff183c6e","order_by":1,"name":"Malleswaran M","email":"","orcid":"","institution":"University College of Engineering Kancheepuram Ponnerikkarai","correspondingAuthor":false,"prefix":"","firstName":"Malleswaran","middleName":"","lastName":"M","suffix":""},{"id":470479131,"identity":"82254dd4-ba06-4c1a-b672-c20b0a510d15","order_by":2,"name":"Muthupriya V","email":"","orcid":"","institution":"B.S. 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However, accurately classifying scar tissue severity into mild and severe remains a challenge due to low contrast, noise interference, and inter-patient variability in LGE-CMR images. Existing methods often rely on manual assessment or traditional deep learning models that struggle with precise myocardium localization and discriminative feature extraction from scarred regions. To overcome these challenges, we propose a novel framework incorporating ScarYOLO, an optimized YOLOv8-based myocardium detection model, followed by Contrastive Myocardial Scar Learning (CMSL) for severity classification. ScarYOLO enhances myocardium localization accuracy, ensuring precise detection of scarred tissue. The detected myocardium is then processed using CMSL, which employs a fine-tuned Xception-based encoder trained on a labeled LGE-CMR dataset. CMSL leverages contrastive self-supervised learning to enhance feature representation and improve class separability between mild and severe scar regions. 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