{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T19:14:14Z","timestamp":1791832454024,"version":"4.3.4"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:00:00Z","timestamp":1776124800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:00:00Z","timestamp":1780617600000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100020194","name":"Wellcome \/ EPSRC Centre for Interventional and Surgical Sciences","doi-asserted-by":"publisher","award":["203145\/Z\/16\/Z"],"award-info":[{"award-number":["203145\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/501100020194","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P027938\/1, EP\/R004080\/1, EP\/P012841\/1, NS\/A000027\/1"],"award-info":[{"award-number":["EP\/P027938\/1, EP\/R004080\/1, EP\/P012841\/1, NS\/A000027\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000287","name":"Royal Academy of Engineering","doi-asserted-by":"publisher","award":["Chair in Emerging Technologies programme CiET1819\/2\/36"],"award-info":[{"award-number":["Chair in Emerging Technologies programme CiET1819\/2\/36"]}],"id":[{"id":"10.13039\/501100000287","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["863146"],"award-info":[{"award-number":["863146"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["WT101957"],"award-info":[{"award-number":["WT101957"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024250","name":"Israel Innovation Authority","doi-asserted-by":"publisher","award":["Kamin Grants 63418, 72126"],"award-info":[{"award-number":["Kamin Grants 63418, 72126"]}],"id":[{"id":"10.13039\/501100024250","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Accurate fetal growth assessment from ultrasound (US) relies on precise biometry measured by manually identifying anatomical landmarks in standard planes. Manual annotation of landmarks 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-access, multicentre benchmark dataset of fetal US images with expert anatomical landmark annotations for clinically used fetal biometric measurements. These measurements include head biparietal and occipitofrontal diameters, abdominal transverse and anteroposterior diameters, and femoral length. The dataset contains 4,513 de-identified US images from 1,904 subjects acquired at four clinical sites using seven different US devices. We provide subject-disjoint train\/test splits, evaluation code, and baseline results to enable fair and reproducible comparisons of methods. Using an automated landmark-based fetal biometry model on pre-selected standard planes, we quantify domain shift and show that training and evaluation confined to a single centre can overestimate performance relative to multicentre testing. To the best of our knowledge, this is the first publicly available multicentre, multi-device, landmark-annotated dataset that covers all primary fetal biometry measures, providing a robust benchmark for studying domain shift and multicentre generalisation and enabling more reliable AI-assisted fetal biometry across centres. All data and annotations are available on the\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.5522\/04\/30819911\" ext-link-type=\"uri\">UCL Research Data Repository<\/jats:ext-link>\n                    . Training code and evaluation pipelines are available at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/surgical-vision\/Multicentre-Fetal-Biometry.git\" ext-link-type=\"uri\">https:\/\/github.com\/surgical-vision\/Multicentre-Fetal-Biometry.git<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-47854-3","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:49:03Z","timestamp":1776160143000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A multicentre benchmark dataset for comprehensive landmark-based fetal ultrasound biometry"],"prefix":"10.1038","volume":"16","author":[{"given":"Chiara","family":"Di Vece","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhehua","family":"Mao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Netanell","family":"Avisdris","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian","family":"Dromey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaele","family":"Napolitano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dafna","family":"Ben Bashat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francisco","family":"Vasconcelos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danail","family":"Stoyanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leo","family":"Joskowicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophia","family":"Bano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,14]]},"reference":[{"key":"47854_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-67076-5","volume":"10","author":"XP Burgos-Artizzu","year":"2020","unstructured":"Burgos-Artizzu, X. P. et al. Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci. Rep. 10, 1\u201312 (2020).","journal-title":"Sci. Rep."},{"key":"47854_CR2","doi-asserted-by":"crossref","unstructured":"Dromey, B.\u00a0P. et al. Dimensionless squared jerk: An objective differential to assess experienced and novice probe movement in obstetric ultrasound. Prenatal Diagnosis (2020).","DOI":"10.1002\/pd.5855"},{"key":"47854_CR3","doi-asserted-by":"publisher","first-page":"1391","DOI":"10.1007\/s00330-018-5695-5","volume":"29","author":"L Joskowicz","year":"2019","unstructured":"Joskowicz, L., Cohen, D., Caplan, N. & Sosna, J. Inter-observer variability of manual contour delineation of structures in ct. European radiology 29, 1391\u20131399 (2019).","journal-title":"European radiology"},{"key":"47854_CR4","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1002\/uog.10082","volume":"39","author":"I Sarris","year":"2012","unstructured":"Sarris, I. et al. Intra-and interobserver variability in fetal ultrasound measurements. Ultrasound Obstetr Gynecol 39, 266\u2013273 (2012).","journal-title":"Ultrasound Obstetr Gynecol"},{"key":"47854_CR5","doi-asserted-by":"crossref","unstructured":"Torres, H.\u00a0R. et al. A review of image processing methods for fetal head and brain analysis in ultrasound images. Comput. Methods Programs Biomed. 106629 (2022).","DOI":"10.1016\/j.cmpb.2022.106629"},{"key":"47854_CR6","doi-asserted-by":"crossref","unstructured":"Al-Bander, B., Alzahrani, T., Alzahrani, S., Williams, B.\u00a0M. & Zheng, Y. Improving fetal head contour detection by object localisation with deep learning. In Medical Image Understanding and Analysis Conference, 142\u2013150 (Springer, 2019).","DOI":"10.1007\/978-3-030-39343-4_12"},{"key":"47854_CR7","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dudley, N.\u00a0J., Lambrou, T., Allinson, N. & Ye, X. Automatic image quality assessment and measurement of fetal head in two-dimensional ultrasound image. J Med Imaging (Bellingham) 4, 024001 (2017).","DOI":"10.1117\/1.JMI.4.2.024001"},{"key":"47854_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0200412","volume":"13","author":"TL van den Heuvel","year":"2018","unstructured":"van den Heuvel, T. L., de Bruijn, D., de Korte, C. L. & Ginneken, B. V. Automated measurement of fetal head circumference using 2d ultrasound images. PloS One 13, e0200412 (2018).","journal-title":"PloS One"},{"key":"47854_CR9","doi-asserted-by":"publisher","first-page":"334","DOI":"10.4236\/ojog.2017.73035","volume":"7","author":"NH Khan","year":"2017","unstructured":"Khan, N. H. et al. Automatic detection and measurement of fetal biparietal diameter and femur length-feasibility on a portable ultrasound device. Open J Obstetr Gynecol 7, 334\u2013350 (2017).","journal-title":"Open J Obstetr Gynecol"},{"key":"47854_CR10","unstructured":"Ciobanu, S.\u00a0G. et al. Automatic identification of fetal abdominal planes from ultrasound images based on deep learning. Journal of Imaging Informatics in Medicine 1\u20138 (2025)."},{"key":"47854_CR11","doi-asserted-by":"crossref","unstructured":"Bano, S. et al. Autofb: Automating fetal biometry estimation from standard ultrasound planes. In Proc Int Conf on Medical Image Computing and Computer Assisted Intervention, 228\u2013238 (Springer, 2021).","DOI":"10.1007\/978-3-030-87234-2_22"},{"key":"47854_CR12","doi-asserted-by":"crossref","unstructured":"Prieto, J.\u00a0C. et al. An automated framework for image classification and segmentation of fetal ultrasound images for gestational age estimation. In Medical Imaging 2021: Image Processing, vol. 11596 (2021).","DOI":"10.1117\/12.2582243"},{"key":"47854_CR13","doi-asserted-by":"crossref","unstructured":"Avisdris, N. et al. Biometrynet: Landmark-based fetal biometry estimation from standard ultrasound planes. In International Conference on Medical Image Computing and Computer-Assisted Intervention, 279\u2013289 (Springer, 2022).","DOI":"10.1007\/978-3-031-16440-8_27"},{"key":"47854_CR14","doi-asserted-by":"crossref","unstructured":"P\u0142otka, S. et al. Fetalnet: Multi-task deep learning framework for fetal ultrasound biometric measurements. In International Conference on Neural Information Processing, 257\u2013265 (Springer, 2021).","DOI":"10.1007\/978-3-030-92310-5_30"},{"key":"47854_CR15","doi-asserted-by":"publisher","first-page":"25665","DOI":"10.1038\/s41598-025-06526-4","volume":"15","author":"U Islam","year":"2025","unstructured":"Islam, U. et al. Fetal-net: enhancing maternal-fetal ultrasound interpretation through multi-scale convolutional neural networks and transformers. Scientific Reports 15, 25665 (2025).","journal-title":"Scientific Reports"},{"key":"47854_CR16","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1002\/uog.24888","volume":"59","author":"L Salomon","year":"2022","unstructured":"Salomon, L. et al. ISUOG practice guidelines (updated): performance of the routine mid-trimester fetal ultrasound scan. Ultrasound in Obstetrics and Gynecology 59, 840\u2013856 (2022).","journal-title":"Ultrasound in Obstetrics and Gynecology"},{"key":"47854_CR17","doi-asserted-by":"publisher","first-page":"7047","DOI":"10.1038\/s41467-023-42438-5","volume":"14","author":"S Slimani","year":"2023","unstructured":"Slimani, S. et al. Fetal biometry and amniotic fluid volume assessment end-to-end automation using deep learning. Nature Communications 14, 7047 (2023).","journal-title":"Nature Communications"},{"key":"47854_CR18","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1038\/s41746-023-00774-2","volume":"6","author":"LH Lee","year":"2023","unstructured":"Lee, L. H. et al. Machine learning for accurate estimation of fetal gestational age based on ultrasound images. NPJ digital medicine 6, 36 (2023).","journal-title":"NPJ digital medicine"},{"key":"47854_CR19","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1038\/s41746-024-01406-z","volume":"8","author":"L Venturini","year":"2025","unstructured":"Venturini, L. et al. Whole examination ai estimation of fetal biometrics from 20-week ultrasound scans. NPJ Digital Medicine 8, 22 (2025).","journal-title":"NPJ Digital Medicine"},{"key":"47854_CR20","doi-asserted-by":"crossref","unstructured":"Goetz-Fu, M. et al. Development and temporal validation of a deep learning model for automatic fetal biometry from ultrasound videos. Journal of Gynecology Obstetrics and Human Reproduction 103039 (2025).","DOI":"10.1016\/j.jogoh.2025.103039"},{"key":"47854_CR21","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1038\/s41746-025-02024-z","volume":"8","author":"M Benson","year":"2025","unstructured":"Benson, M. et al. Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video. npj Digital Medicine 8, 700 (2025).","journal-title":"npj Digital Medicine"},{"key":"47854_CR22","doi-asserted-by":"crossref","unstructured":"Zhu, S., Cai, J., Xiong, R., Zheng, L. & Ma, D. Singular pooling: a spectral pooling paradigm for second-trimester prenatal level ii ultrasound standard fetal plane identification. IEEE Transactions on Circuits and Systems for Video Technology (2025).","DOI":"10.1109\/TCSVT.2025.3588395"},{"key":"47854_CR23","unstructured":"Zheng, X. et al. Openus: A fully open-source foundation model for ultrasound image analysis via self-adaptive masked contrastive learning. arXiv preprint arXiv:2511.11510 (2025)."},{"key":"47854_CR24","unstructured":"Maani, F. et al. Fetalclip: A visual-language foundation model for fetal ultrasound image analysis. arXiv preprint arXiv:2502.14807 (2025)."},{"key":"47854_CR25","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TMI.2014.2377694","volume":"34","author":"BH Menze","year":"2014","unstructured":"Menze, B. H. et al. The multimodal brain tumor image segmentation benchmark (brats). IEEE transactions on medical imaging 34, 1993\u20132024 (2014).","journal-title":"IEEE transactions on medical imaging"},{"key":"47854_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41597-021-00946-3","volume":"8","author":"K Payette","year":"2021","unstructured":"Payette, K. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset. Scientific Data 8, 1\u201314 (2021).","journal-title":"Scientific Data"},{"key":"47854_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102629","volume":"83","author":"MC Fiorentino","year":"2023","unstructured":"Fiorentino, M. C., Villani, F. P., Di Cosmo, M., Frontoni, E. & Moccia, S. A review on deep-learning algorithms for fetal ultrasound-image analysis. Medical Image Analysis 83, 102629 (2023).","journal-title":"Medical Image Analysis"},{"key":"47854_CR28","doi-asserted-by":"publisher","unstructured":"Bai, J. Iugc ultrasound dataset (miccai 2025), (2026). https:\/\/doi.org\/10.34740\/KAGGLE\/DSV\/14676210","DOI":"10.34740\/KAGGLE\/DSV\/14676210"},{"key":"47854_CR29","doi-asserted-by":"crossref","unstructured":"Sinclair, M. et al. Human-level performance on automatic head biometrics in fetal ultrasound using fully convolutional neural networks. In 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC), 714\u2013717 (IEEE, 2018).","DOI":"10.1109\/EMBC.2018.8512278"},{"key":"47854_CR30","doi-asserted-by":"publisher","first-page":"7790","DOI":"10.3934\/mbe.2021387","volume":"18","author":"F Zhu","year":"2021","unstructured":"Zhu, F. et al. Automatic measurement of fetal femur length in ultrasound images: a comparison of random forest regression model and segnet. Mathematical Biosciences and Engineering 18, 7790\u20137805 (2021).","journal-title":"Mathematical Biosciences and Engineering"},{"key":"47854_CR31","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ejmp.2021.06.020","volume":"88","author":"MG Oghli","year":"2021","unstructured":"Oghli, M. G. et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Physica Medica 88, 127\u2013137 (2021).","journal-title":"Physica Medica"},{"key":"47854_CR32","doi-asserted-by":"crossref","unstructured":"Bai, J. et al. Beyond benchmarks of iugc: Rethinking requirements of deep learning method for intrapartum ultrasound biometry from fetal ultrasound videos. Medical Image Analysis 104043 (2026).","DOI":"10.1016\/j.media.2026.104043"},{"key":"47854_CR33","unstructured":"Deng, B. et al. Baseline method of the foundation model challenge for ultrasound image analysis. arXiv preprint arXiv:2602.01055 (2026)."},{"key":"47854_CR34","doi-asserted-by":"crossref","unstructured":"Dutta, A. & Zisserman, A. The VIA annotation software for images, audio and video. In Proceedings of the 27th ACM International Conference on Multimedia, MM \u201919 (ACM, New York, NY, USA, 2019).","DOI":"10.1145\/3343031.3350535"},{"key":"47854_CR35","doi-asserted-by":"crossref","unstructured":"Fitzgibbon, A.\u00a0W., Pilu, M. & Fisher, R.\u00a0B. Direct least squares fitting of ellipses. In Proc Int Conf on Pattern Recognition, vol.\u00a01, 253\u2013257 (IEEE, 1996).","DOI":"10.1109\/ICPR.1996.546029"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47854-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47854-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47854-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:00:45Z","timestamp":1781647245000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-47854-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,14]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["47854"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-47854-3","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-8405225\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,14]]},"assertion":[{"value":"19 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"17405"}}