No-Reference Hallucination Assessment for AI-Reconstructed Fluorescence Microscopy Image

No-Reference Hallucination Assessment for AI-Reconstructed Fluorescence Microscopy Image | 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 No-Reference Hallucination Assessment for AI-Reconstructed Fluorescence Microscopy Image Bo Yan, Chenxi Ma, Weimin Tan, Yuqi Sun, Hongju Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7026761/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 Artificial intelligence (AI) has revolutionized fluorescence microscopy image restoration, enabling high-resolution imaging with cost-effective computations. However, the inherent biases of AI models in training sets may lead to hallucinations, such as hallucinating artificial structures that do not exist in the original sample or removing real biological features, compromising the scientific authenticity and reliability of imaging-based discoveries. To address this critical challenge, we present the first systematic investigation and formal definition of AI-reconstructed hallucinations in fluorescence microscopy. We introduce HallAssess, a no-reference assessment method explicitly tailored for the reliable identification and quantification of AI-reconstructed hallucinations. By shifting the assessment from the high-quality (HQ) image domain to the low-quality (LQ) image domain, HallAssess effectively transforms a no-reference problem into a full-reference one. Our approach enables accurate hallucination quantification without requiring ground-truth HQ images by re-degrading AI-reconstructed images using an imaging model that simulates real-world image degradation processes, and then comparing them with the original LQ inputs. We validate HallAssess across multiple imaging modalities (SIM, confocal), diverse AI models, and common fluorescence microscopy image restoration tasks such as denoising and super-resolution. The results demonstrate its effectiveness in detecting AI-reconstructed hallucinations. Furthermore, we provide an open-access platform featuring an interactive web demo and a dynamic leaderboard, allowing researchers to evaluate hallucinations in fluorescence microscopy image restoration results and benchmark state-of-the-art methods under a standardized framework. This work provides a foundational tool for ensuring the reliability of AI-assisted imaging in life sciences, particularly in cell biology, where accurate interpretation of subcellular structures is essential for understanding cellular function and disease mechanisms. Biological sciences/Biological techniques/Microscopy/Super-resolution microscopy Biological sciences/Biological techniques/Microscopy/Confocal microscopy Biological sciences/Biological techniques/Bioinformatics Biological sciences/Biological techniques/Imaging/Fluorescence imaging Biological sciences/Cell biology/Cellular imaging/Super-resolution microscopy Full Text Additional Declarations There is NO Competing Interest. 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. 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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-7026761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496027547,"identity":"8f0e2ec7-108a-47dd-bd3f-207babc22ebe","order_by":0,"name":"Bo Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACAxDxwYaBsQHE4CFWC+OMNFK1MPOQpMVcIvnZY5uEw7L9sxsYH7xtY5A3J6TFckaauXFOwmHjGXcOMBvObWMw3NlAyGE3Esykc38cTmy4kcAmzdvGkGBwgKCW9G/SFgmHE+ffSGD/TaSWHDNpBqCWDUBbmInSYtnzpkyyJyHdeOONxGbJOeckDDcQ0mLOnr5N4keCtey8G8kHP7wps5EnaAuDQAKMBY4aCULqgYCfoKGjYBSMglEw4gEAcQlCLzxjFu4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0001-5692-3486","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Bo","middleName":"","lastName":"Yan","suffix":""},{"id":496027548,"identity":"0e286e7e-0795-4369-94af-8452607f1099","order_by":1,"name":"Chenxi Ma","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Chenxi","middleName":"","lastName":"Ma","suffix":""},{"id":496027549,"identity":"27b4d3e4-cad6-423d-bb3c-729e260afa10","order_by":2,"name":"Weimin Tan","email":"","orcid":"https://orcid.org/0000-0001-7677-4772","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Tan","suffix":""},{"id":496027550,"identity":"afdcd1db-7e0b-46df-8714-ba55acea7665","order_by":3,"name":"Yuqi Sun","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuqi","middleName":"","lastName":"Sun","suffix":""},{"id":496027551,"identity":"7c81699b-1942-4c8c-be76-77c6a413bc68","order_by":4,"name":"Hongju Fu","email":"","orcid":"","institution":"Fudan university","correspondingAuthor":false,"prefix":"","firstName":"Hongju","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-07-02 08:05:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7026761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7026761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91351960,"identity":"b6bcefc9-17a2-425b-ad29-e7d382306811","added_by":"auto","created_at":"2025-09-15 14:45:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4769226,"visible":true,"origin":"","legend":"Article File","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7026761/v1_covered_0d11340a-8f1e-4b8c-97d3-4ab645f9e973.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"No-Reference Hallucination Assessment for AI-Reconstructed Fluorescence Microscopy Image","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7026761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7026761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Artificial intelligence (AI) has revolutionized fluorescence microscopy image restoration, enabling high-resolution imaging with cost-effective computations. However, the inherent biases of AI models in training sets may lead to hallucinations, such as hallucinating artificial structures that do not exist in the original sample or removing real biological features, compromising the scientific authenticity and reliability of imaging-based discoveries. To address this critical challenge, we present the first systematic investigation and formal definition of AI-reconstructed hallucinations in fluorescence microscopy. We introduce HallAssess, a no-reference assessment method explicitly tailored for the reliable identification and quantification of AI-reconstructed hallucinations. By shifting the assessment from the high-quality (HQ) image domain to the low-quality (LQ) image domain, HallAssess effectively transforms a no-reference problem into a full-reference one. 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