Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI

Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI | 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 Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI Surajit Das, Pavel Zun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7791521/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 Brightfield microscopy of unstained live cells is challenging due to low contrast, dynamic morphology, uneven illumination, and lack of labels. Deep learning achieved SOTA performance on stained, high-contrast images but needs large labeled datasets, expensive hardware, and fails under uneven illumination. This study presents a low-cost, lightweight, annotation-free segmentation method by introducing one-time calibration-assisted unsupervised framework adaptable across imaging modalities and image type. The framework determines background via spatial standard deviation from the local mean. Uncertain pixels are resolved using fuzzy logic, cumulative squared shift of nodal intensity, statistical features, followed by post-segmentation denoising calibration which is saved as a profile for reuse until noise pattern or object type substantially change. The program runs as a script or graphical interface for non-programmers. The method was rigorously evaluated using IoU, F1-score, and other metrics, with statistical significance confirmed via Wilcoxon signed-rank tests. On unstained brightfield myoblast (C2C12) images, it outperformed Cellpose 3.0 and StarDist, improving IoU by up to 48% (average IoU = 0.43, F1 = 0.60). In phase-contrast microscopy, it achieved a mean IoU of 0.69 and an F1-score of 0.81 on the LIVECell dataset (n = 3178), with substantial expert agreement (κ > 0.75) confirming cross-modality robustness. Successful segmentation of laser-affected polymer surfaces further confirmed cross-domain robustness. By introducing the Homogeneous Image Plane concept, this work provides a new theoretical foundation for training-free, annotation-free segmentation. The framework operates efficiently on CPU, avoids cell staining, and is practical for live-cell imaging and biomedical applications. Code and data are available for reproducibility.* Computer vision image analysis live cell imaging quantitative microscopy unsupervised segmentation 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7791521","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533129149,"identity":"9f3a96ca-d4ab-4f44-b96c-5dc037fb36e1","order_by":0,"name":"Surajit Das","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBADZjYGBgMGhgoQk7mBSC1sIC1nQExG4rQwMIC0MLaBWAS06M4+wPyZd48dO59888aPP+fVRvO3A7X8qNiGU4vZuQQ2aZ5nyUCHsRVL8247njvjMGMDY8+Z27i1nGFgY+Y5wAzUwmMgzbjtWG4DUAszYxteLcyfeQ7Ug7QY//w551jufCK0MEjzHDgM0mImwdtQk7uBsBbGNsk5B44DtaSVWfMcO5C7EajlIH6/MB/+8OZAdbJ88+HNN3/U1OXOO3/44IMfFbi1wGIhGco7DCYP4FEPB3ZQuo4YxaNgFIyCUTDCAADsYFKIt81ZFAAAAABJRU5ErkJggg==","orcid":"","institution":"ITMO University","correspondingAuthor":true,"prefix":"","firstName":"Surajit","middleName":"","lastName":"Das","suffix":""},{"id":533129150,"identity":"580f2d16-9b0e-4807-8aad-691f3d2a3749","order_by":1,"name":"Pavel Zun","email":"","orcid":"","institution":"ITMO University","correspondingAuthor":false,"prefix":"","firstName":"Pavel","middleName":"","lastName":"Zun","suffix":""}],"badges":[],"createdAt":"2025-10-06 12:53:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7791521/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7791521/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94378079,"identity":"db1dde0b-cad3-4fde-b46b-072bfc70827e","added_by":"auto","created_at":"2025-10-27 13:38:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8449394,"visible":true,"origin":"","legend":"","description":"","filename":"HIPCV1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7791521/v1/5b79d92eb2e851776ede3f14.pdf"},{"id":94377782,"identity":"f92ec4f5-ad9c-495e-92ab-f539dd7ace4e","added_by":"auto","created_at":"2025-10-27 13:37:47","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4720,"visible":true,"origin":"","legend":"","description":"","filename":"cbba19150aa14cd2bf29b6713d60ea18.json","url":"https://assets-eu.researchsquare.com/files/rs-7791521/v1/8baaa6f3a06db4fa9bdbdfc1.json"},{"id":98861200,"identity":"b78bcdea-aa58-4283-a00d-0b9a22457c91","added_by":"auto","created_at":"2025-12-23 09:24:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5103073,"visible":true,"origin":"","legend":"","description":"","filename":"HIPCV1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7791521/v1_covered_e24244b0-dc56-4293-8955-cc856560162a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Semi-Unsupervised Microscopy Segmentation with Fuzzy Logic and Spatial Statistics for Cross-Domain Analysis Using a GUI","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Computer vision, image analysis, live cell imaging, quantitative microscopy, unsupervised segmentation","lastPublishedDoi":"10.21203/rs.3.rs-7791521/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7791521/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Brightfield microscopy of unstained live cells is challenging due to low contrast, dynamic morphology, uneven illumination, and lack of labels. 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