ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms

ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms | 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 Technical Report ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms Jianlin Cheng, Joel Selvaraj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8841204/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Cryogenic Electron Tomography (cryo-ET) is an emerging experimental technique to visualize cell structures and macromolecules in their native cellular environment. Accurate segmentation of cell structures in cryo-ET tomograms, such as cell membranes, is crucial to advance our understanding of cellular organization and function. However, several inherent limitations in cryo-ET tomograms, including the very low signal-to-noise ratio, missing wedge artifacts from limited tilt angles, and other noise artifacts, collectively hinder the reliable identification and delineation of these structures. In this study, we introduce ETSAM - a two-stage Segment Anything Model 2 (SAM2)-based fine-tuned AI method that effectively segments cell membranes in cryo-ET tomograms. It is trained on a diverse dataset comprising 83 experimental tomograms from the CryoET Data Portal (CDP) database and 28 simulated tomograms generated using PolNet. ETSAM achieves state-of-the-art performance on an independent test set comprising 10 experimental tomograms for which ground-truth annotations are available. It robustly segments cell membranes with high sensitivity and precision, significantly outperforming existing deep learning methods. Biological sciences/Biological techniques/Structure determination/Electron microscopy/Cryoelectron tomography Biological sciences/Structural biology/Electron microscopy/Cryoelectron tomography Cryo-Electron Tomography Cell Membrane Segmentation Artificial Intelligence (AI) Deep Learning Full Text Additional Declarations There is NO Competing Interest. Supplementary Files ETSAMSupplementaryTables.pdf Supplementary tables ETSAMSupplementaryFigures.pdf Supplementary figures Cite Share Download PDF Status: Under Review 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-8841204","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Technical Report","associatedPublications":[],"authors":[{"id":591090041,"identity":"87f5ff1c-40e8-4068-b2d4-b2f072481deb","order_by":0,"name":"Jianlin Cheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCRDBIyHHwAzh8wCxAVFajEnVwsCQ2IAkhl8L/+zmYw+/yFikr21nf/zhY9s9GQb25m0SeC25cyzdWIZHInfbYR4zyZltxTwMPMfK8GoxkMgxk5aAaGFj5m1L4GEAihDQkv8NpCXd7DD7489gLfJvCGnJYZP8wCORYHaYwUAaYgsPfi0SN9LMpIGBbAj2y4xzCTxsPGnFFvi08M9Ifib5s6dO3uz88ccfPpQl2POzH954A58WEGDm7UHisRFSDgKMP34Qo2wUjIJRMApGLAAAWOY8ybNhuQUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-0305-2853","institution":"University of Missouri","correspondingAuthor":true,"prefix":"","firstName":"Jianlin","middleName":"","lastName":"Cheng","suffix":""},{"id":591090042,"identity":"d308bd52-c885-4fd0-8e5e-1635c35db6bc","order_by":1,"name":"Joel Selvaraj","email":"","orcid":"","institution":"University of Missouri","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"","lastName":"Selvaraj","suffix":""}],"badges":[],"createdAt":"2026-02-10 12:52:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8841204/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8841204/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104779129,"identity":"bf86b822-5428-4199-8882-6054f3964b7b","added_by":"auto","created_at":"2026-03-17 07:35:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6885030,"visible":true,"origin":"","legend":"Article File","description":"","filename":"ETSAMmainmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8841204/v1_covered_93d01440-f5c5-4d8d-8ee6-0fdf652fa3d4.pdf"},{"id":103060011,"identity":"fc99caee-c4ec-4f8c-8419-3cadeb9b79c2","added_by":"auto","created_at":"2026-02-20 09:49:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":134160,"visible":true,"origin":"","legend":"Supplementary tables","description":"","filename":"ETSAMSupplementaryTables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8841204/v1/64ec7ae9418375a48d1c1de2.pdf"},{"id":103060012,"identity":"aabfa455-c84f-471c-a7c7-a3d0d447dc3a","added_by":"auto","created_at":"2026-02-20 09:49:12","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16442463,"visible":true,"origin":"","legend":"Supplementary figures","description":"","filename":"ETSAMSupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8841204/v1/86b1d4357378aa6519231142.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"ETSAM: Effectively Segmenting Cell Membranes in cryo-Electron Tomograms","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cryo-Electron Tomography, Cell Membrane, Segmentation, Artificial Intelligence (AI), Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8841204/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8841204/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Cryogenic Electron Tomography (cryo-ET) is an emerging experimental technique to visualize cell structures and macromolecules in their native cellular environment. 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