A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry

A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry | 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 A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry Chiara Di Vece, Zhehua Mao, Netanell Avisdris, Brian Dromey, Raffaele Napolitano, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8405225/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Accurate fetal growth assessment from ultrasound (US) relies on precise biometry measured by manually identifying anatomical landmarks in standard planes. Manual landmarking is time-consuming, operator-dependent, and sensitive to variability across scanners and sites, limiting the reproducibility of automated approaches. There is a need for multi-source annotated datasets to develop artificial intelligence-assisted fetal growth assessment methods. To address this bottleneck, we present an open, multi-centre, multi-device benchmark dataset of fetal US images with expert anatomical landmark annotations for clinically used fetal biometric measurements. These measurements include head bi-parietal and occipito-frontal diameters, abdominal transverse and antero-posterior diameters, and femoral length. The dataset contains 4,513 de-identified US images from 1,904 subjects acquired at three clinical sites using seven different US devices. We provide standardised, subject-disjoint train/test splits, evaluation code, and baseline results to enable fair and reproducible comparison of methods. Using an automatic biometry model, we quantify domain shift and demonstrate that training and evaluation confined to a single centre substantially overestimate performance relative to multi-centre testing. To the best of our knowledge, this is the first publicly available multi-centre, multi-device, landmark-annotated dataset that covers all primary fetal biometry measures, providing a robust benchmark for domain adaptation and multi-centre generalisation in fetal biometry and enabling more reliable AI-assisted fetal growth assessment across centres. All data, annotations, training code, and evaluation pipelines are made publicly available at https://github.com/surgical-vision/Multicentre-Fetal-Biometry.git. Health sciences/Anatomy Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 11 Mar, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers agreed at journal 15 Feb, 2026 Reviews received at journal 28 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 15 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers invited by journal 08 Jan, 2026 Editor assigned by journal 08 Jan, 2026 Editor invited by journal 30 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 29 Dec, 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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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-8405225","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":572846428,"identity":"5e3cdb92-ac40-490e-b4a2-875399ce7d18","order_by":0,"name":"Chiara Di 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Manual landmarking is time-consuming, operator-dependent, and sensitive to variability across scanners and sites, limiting the reproducibility of automated approaches. There is a need for multi-source annotated datasets to develop artificial intelligence-assisted fetal growth assessment methods. To address this bottleneck, we present an open, multi-centre, multi-device benchmark dataset of fetal US images with expert anatomical landmark annotations for clinically used fetal biometric measurements. These measurements include head bi-parietal and occipito-frontal diameters, abdominal transverse and antero-posterior diameters, and femoral length. The dataset contains 4,513 de-identified US images from 1,904 subjects acquired at three clinical sites using seven different US devices. We provide standardised, subject-disjoint train/test splits, evaluation code, and baseline results to enable fair and reproducible comparison of methods. Using an automatic biometry model, we quantify domain shift and demonstrate that training and evaluation confined to a single centre substantially overestimate performance relative to multi-centre testing. To the best of our knowledge, this is the first publicly available multi-centre, multi-device, landmark-annotated dataset that covers all primary fetal biometry measures, providing a robust benchmark for domain adaptation and multi-centre generalisation in fetal biometry and enabling more reliable AI-assisted fetal growth assessment across centres. All data, annotations, training code, and evaluation pipelines are made publicly available at https://github.com/surgical-vision/Multicentre-Fetal-Biometry.git.\u003c/p\u003e","manuscriptTitle":"A multi-centre, multi-device benchmark dataset for landmark-based comprehensive fetal biometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 22:34:23","doi":"10.21203/rs.3.rs-8405225/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-11T07:20:48+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-26T04:12:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36946163754152308201585665003060045420","date":"2026-02-16T14:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310015789441592148480563519760817605637","date":"2026-02-16T01:34:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-29T02:58:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"255648230347152032626252735525265829230","date":"2026-01-15T19:50:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229909066496842735226449420218573016647","date":"2026-01-15T18:24:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244678923229322076580957392671185417910","date":"2026-01-13T19:38:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"155921124295119376519037207318311088123","date":"2026-01-08T20:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T16:26:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T16:19:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T17:21:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T09:29:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-29T09:22:52+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"e296650d-5126-49f3-aaca-ab034b9748f5","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60950532,"name":"Health sciences/Anatomy"},{"id":60950533,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":60950534,"name":"Physical sciences/Engineering"},{"id":60950535,"name":"Health sciences/Health care"},{"id":60950536,"name":"Physical sciences/Mathematics and computing"},{"id":60950537,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-20T16:08:36+00:00","versionOfRecord":{"articleIdentity":"rs-8405225","link":"https://doi.org/10.1038/s41598-026-47854-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-14 15:59:15","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2026-01-12 22:34:23","video":"","vorDoi":"10.1038/s41598-026-47854-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47854-3","workflowStages":[]},"version":"v1","identity":"rs-8405225","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8405225","identity":"rs-8405225","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}