{"paper_id":"11cca257-e0bd-4bbf-9855-974a3fcaa21f","body_text":"MEP-Net: A PIDNet-Based Model with Median-Enhanced Spatial-Channel Attention for Segmentation of Hepatocellular Carcinoma in CEUS Images | 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 MEP-Net: A PIDNet-Based Model with Median-Enhanced Spatial-Channel Attention for Segmentation of Hepatocellular Carcinoma in CEUS Images Si-Hua Yang, Jing-Bin Wen, Yi-Ran Li, Fang-Fang Zhang, Wei-Qi Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6965816/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: Hepatocellular carcinoma (HCC) remains a major global health concern due to its high incidence and mortality. Contrast-enhanced ultrasound (CEUS) offers notable advantages in HCC diagnosis, including real-time imaging and non-invasiveness. However, challenges such as blurred lesion boundaries and noise interference in CEUS images significantly hinder the accuracy and robustness of automatic segmentation. Methods: To address these issues, we propose MEP-Net, an enhanced segmentation model based on PIDNet. MEP-Net incorporates a Median-Enhanced Spatial-Channel Attention (MECS) mechanism and an Efficient Channel Attention (ECA) module to better capture blurred areas and fine-grained structural details. We evaluate the model on a self-built CEUS dataset and the publicly available BUSI breast ultrasound dataset. Results: The results indicate that MEP-Net outperforms the baseline PIDNet by 1.96%, 1.96%, and 2.38% in Dice, MIoU, and Recall, respectively, on the CEUS dataset, and by 1.37%, 1.06%, and 2.41% on the BUSI dataset. In comparisons with eight mainstream segmentation methods, MEP-Net demonstrates superior boundary detection and small-lesion recovery, achieving leading overall performance. Ablation studies further confirm the complementary benefits of the MECS and ECA modules in boosting segmentation accuracy. Conclusion: The improvements of MEP-Net provide stronger support for CEUS image segmentation, which may have a positive impact on the early diagnosis and treatment of HCC. Hepatocellular carcinoma Contrast-enhanced ultrasound Median-enhanced spatial-channel attention Efficient channel attention Semantic segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Mar, 2026 Reviews received at journal 21 Feb, 2026 Reviewers agreed at journal 05 Feb, 2026 Reviews received at journal 23 Oct, 2025 Reviewers agreed at journal 02 Aug, 2025 Reviewers invited by journal 30 Jul, 2025 Editor invited by journal 02 Jul, 2025 Editor assigned by journal 02 Jul, 2025 Submission checks completed at journal 02 Jul, 2025 First submitted to journal 24 Jun, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6965816\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":494770156,\"identity\":\"e609f7a2-5bff-44c8-9f99-731cff3e79d7\",\"order_by\":0,\"name\":\"Si-Hua Yang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"School of Biomedical Engineering, Southern Medical 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Contrast-enhanced ultrasound (CEUS) offers notable advantages in HCC diagnosis, including real-time imaging and non-invasiveness. However, challenges such as blurred lesion boundaries and noise interference in CEUS images significantly hinder the accuracy and robustness of automatic segmentation.\\u003c/p\\u003e\\u003ch2\\u003eMethods:\\u003c/h2\\u003e\\u003cp\\u003eTo address these issues, we propose MEP-Net, an enhanced segmentation model based on PIDNet. MEP-Net incorporates a Median-Enhanced Spatial-Channel Attention (MECS) mechanism and an Efficient Channel Attention (ECA) module to better capture blurred areas and fine-grained structural details. 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