Improving Microvascular Brain Analysis with Adversarial Learning for OCT-TPM Vascular Domain Translation

Improving Microvascular Brain Analysis with Adversarial Learning for OCT-TPM Vascular Domain Translation | 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 Improving Microvascular Brain Analysis with Adversarial Learning for OCT-TPM Vascular Domain Translation Nadia Badawi, Jaloliddin Rustamov, Zahiriddin Rustamov, Frederic Lesage, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5052423/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Modeling microscopic cerebrovascular networks is essential for understanding cerebral blood flow and oxygen transport. High-resolution imaging modalities, such as Optical Coherence Tomography (OCT) and Two-Photon Microscopy (TPM), are widely used to capture microvascular structure and topology. Although TPM angiography generally provides better localization and image quality than OCT, its use is impractical in studies involving fluorescent dye leakage. Here, we exploit generative adversarial learning to produce high-quality TPM angiographies from OCT vascular stacks. We investigate the use of 2D and 3D cycle generative adversarial networks (CycleGANs) trained on unpaired image samples. We evaluate the generated TPMvascular structures based on image similarity and signal-to-noise ratio. Additionally, we evaluated the generated vascular structures after applying vessel segmentation and extracting their 3D topological models. Our results demonstrate that the 2D adversarial learning model outperforms the 3D model in terms of image quality. However, our statistical comparisons of vascular network features show the 3D model’s consistent superiority in generating vascular structures. Our work provides a complementary approach to enhance vascular analysis when only OCT imaging is available. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Apr, 2025 Reviews received at journal 27 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 25 Mar, 2025 Editor invited by journal 21 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 08 Sep, 2024 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-5052423","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":435591253,"identity":"34a7b0b6-332f-41bf-9158-071dc182da78","order_by":0,"name":"Nadia Badawi","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"","lastName":"Badawi","suffix":""},{"id":435591254,"identity":"2cbcc18d-f544-4859-bb1e-cb5cbbf5718f","order_by":1,"name":"Jaloliddin Rustamov","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Jaloliddin","middleName":"","lastName":"Rustamov","suffix":""},{"id":435591255,"identity":"d82390d1-b614-43dd-ae90-b1c53dab9bc3","order_by":2,"name":"Zahiriddin Rustamov","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Zahiriddin","middleName":"","lastName":"Rustamov","suffix":""},{"id":435591256,"identity":"5f7932d7-4d32-4130-8d09-ae25df4eb8e3","order_by":3,"name":"Frederic Lesage","email":"","orcid":"","institution":"Ecole Polytechnique de Montreal","correspondingAuthor":false,"prefix":"","firstName":"Frederic","middleName":"","lastName":"Lesage","suffix":""},{"id":435591257,"identity":"fd8937f7-5e4f-4f08-bcfa-d70a283dd810","order_by":4,"name":"Nazar Zaki","email":"","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":false,"prefix":"","firstName":"Nazar","middleName":"","lastName":"Zaki","suffix":""},{"id":435591259,"identity":"55fe7468-42d4-497d-9546-d58aff86cf10","order_by":5,"name":"Rafat Damseh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYFCCBBBxQI50Lcaka0lsIFqDeXvy040/Ku6kbzh++JgEQ40dA387Ad0yZ56Z3eY58yx3w5m0NAmGY8kMEmcO4NciIZFgdpux7XDuhgM5ZhIMbEDlNxIIaUn/dvPnv8PpBufff5Ng+HeAQf7+A0Jacsxu8DYcTjC4kcMmwdh2gMHgBn4dDBI8b8pu8xw7bDjzxjNji8S+ZB7DM4Qcxp6+7eaPmsPyfOeTH9748M1OTu74AQLWoACg+TykqB8Fo2AUjIJRgAMAAFs6SVEZTCCZAAAAAElFTkSuQmCC","orcid":"","institution":"United Arab Emirates University","correspondingAuthor":true,"prefix":"","firstName":"Rafat","middleName":"","lastName":"Damseh","suffix":""}],"badges":[],"createdAt":"2024-09-08 11:36:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5052423/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5052423/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-07410-x","type":"published","date":"2025-07-12T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86699336,"identity":"8e787911-2986-4c42-befc-4504b22267a2","added_by":"auto","created_at":"2025-07-14 16:07:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1041783,"visible":true,"origin":"","legend":"","description":"","filename":"SubmissiontoScientificReportsv4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5052423/v1_covered_e0579799-1354-418c-8a19-e9b19c91203b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eImproving Microvascular Brain Analysis with Adversarial Learning for OCT-TPM Vascular Domain Translation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5052423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5052423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Modeling microscopic cerebrovascular networks is essential for understanding cerebral blood flow and oxygen transport. 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