Enhanced Liver Segmentation Using Hybrid U-Net-Transformer Architecture

Enhanced Liver Segmentation Using Hybrid U-Net-Transformer Architecture | 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 Enhanced Liver Segmentation Using Hybrid U-Net-Transformer Architecture Shivaleela Betageri, G N Girish, Rohini A. Bhusnurmath This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8677562/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 Segmentation of the liver plays a crucial role in the detection and diagnosis of liver disorders. Conventional manual segmentation methods are time-intensive, susceptible to operator-dependent inconsistencies, and ineffective for extensive clinical applications. To overcome these obstacles, this study presents a hybrid U-Net model with a Transformer bottleneck, which enhances accuracy in segmentation by capturing both local and global contextual information. The model extracts spatial characteristics by utilizing the U-Net encoder, which are then handled by the Transformer encoder to refine global representations before passing through the U-Net decoder. Through the combination of transformer architecture and convolutional neural networks (CNNs), this integration ensures reliable liver segmentation. The proposed model obtained Dice Similarity Coefficients (DSC) of 98.09% and 98.12%, respectively, when evaluated using two publicly available benchmark datasets, CHAOS and 3D-IRCADb. These findings show that the hybrid U-Net-Transformer model enhances segmentation accuracy while maintaining computing efficiency. Liver Segmentation U-Net Transformer Computed Tomography 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. 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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-8677562","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":582401172,"identity":"a770591d-8a79-4392-851d-e76378af2df5","order_by":0,"name":"Shivaleela Betageri","email":"","orcid":"","institution":"Akkamahadevi Women’s University: Karnataka State Akkamahadevi Women's University","correspondingAuthor":false,"prefix":"","firstName":"Shivaleela","middleName":"","lastName":"Betageri","suffix":""},{"id":582401173,"identity":"2665a14c-0865-4827-aa4e-236c37ec472d","order_by":1,"name":"G N Girish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3RsQqCQBjA8e84OBfN9aDgXkFp1HLsOUJoa3UWgnN09TEaG08OmsLmcMmlvVFoSC2EljvHhvtPd8f34w4OwGT6xzCAQGlok25BgfRHKJ1Cdh3BU8lnRg74SzSxjdOU7em6mFluWdsJMDfF/K4ivrQ86Vzq/mE4sCvwC4EyT0kOBCTiAyHzPQd0BMSpjpQtr0YSaQnDBITDxUi2WuJ1RDo87skyeFU0LqTulvyMny1fRywvm1uRhKs8yx7qW8Tvng6fq4ylmgGTyWQywRuLizscSVtyxgAAAABJRU5ErkJggg==","orcid":"","institution":"Indian Institute of Information Technology Dharwad","correspondingAuthor":true,"prefix":"","firstName":"G","middleName":"N","lastName":"Girish","suffix":""},{"id":582401174,"identity":"3cf60f5a-62a3-4cdf-bda3-848950ce6e13","order_by":2,"name":"Rohini A. 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