Dual Coordinate Attention (DCA) Network for Accurate Cerebral Vascular Endothelium Segmentation in OCT Images

Dual Coordinate Attention (DCA) Network for Accurate Cerebral Vascular Endothelium Segmentation in OCT 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 Article Dual Coordinate Attention (DCA) Network for Accurate Cerebral Vascular Endothelium Segmentation in OCT Images zhaoye wu, Yue Shen, Eddie Yin Kwee Ng, Chenxi Huang, Quan Lan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7940745/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted 16 You are reading this latest preprint version Abstract Accurate cerebral vascular endothelium segmentation in Optical Coherence Tomography (OCT) images is crucial for cerebrovascular disease assessment, yet faces challenges including laborious manual annotation, high-quality dataset needs, and limitations in existing attention mechanisms for unified feature modeling. This paper proposes a novel segmentation framework with a Dual Coordinate Attention (DCA) mechanism, validated on a self-constructed, meticulously annotated cerebrovascular OCT dataset. DCA facilitates robust feature interaction between Cartesian and polar coordinate representations, effectively capturing complementary structural cues from both domains to enhance endothelial features and suppress noise. Extensive experiments demonstrate the framework's superior performance (Dice and HD95) over traditional baseline models. Ablation studies confirm the DCA module's benefits and pinpoint optimal deployment. Leveraging dedicated data curation and novel DCA, this work provides an an accurate, robust automated segmentation tool for cerebral vascular endothelium in OCT images, promising to aid cerebrovascular condition assessment and monitoring. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Physical sciences/Engineering Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Mar, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 02 Jan, 2026 Reviews received at journal 01 Jan, 2026 Reviews received at journal 24 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviews received at journal 14 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 07 Dec, 2025 Reviewers agreed at journal 06 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 03 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers invited by journal 25 Nov, 2025 Editor assigned by journal 25 Nov, 2025 Editor invited by journal 12 Nov, 2025 Submission checks completed at journal 03 Nov, 2025 First submitted to journal 03 Nov, 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. 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