{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T04:30:27Z","timestamp":1783830627194,"version":"3.55.0"},"reference-count":45,"publisher":"Wiley","issue":"17","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":3,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["024.001.003"],"award-info":[{"award-number":["024.001.003"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Human Brain Mapping"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>The human brain undergoes major developmental changes during pregnancy. Three\u2010dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Transabdominal ultrasound can be challenging due to the small fetal brain and its movement, as well as multiple sweeps that may not yield high\u2010quality images, especially when brain structures are unclear. By applying the latest developments in artificial intelligence for automated image processing allowing automated training of brain anatomy in these images retrieving reliable quantitative brain measurements becomes possible at a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound. We applied the trained model in a large longitudinal population sample from the YOUth Baby and Child cohort measured at 20\u2010 and 30\u2010week of gestational age to investigate biological sex differences in fetal ICV as a proof\u2010of\u2010principle and validation for our automated method (<jats:italic>N<\/jats:italic>\u2009=\u20092235 individuals with 43492 ultrasounds). A total of 168 annotated, randomly selected, good quality 3D ultrasound whole\u2010brain images were included to train a 3D CNN for automated fetal ICV segmentation. A data augmentation strategy provided physical variation to train the network. K\u2010fold cross\u2010validation and Bayesian optimization were used for network selection and the ensemble\u2010based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high\u2010quality segmentations of ICV (Dice Similarity Coefficient (DSC)\u2009&gt;\u20090.93, Hausdorff Distance (HD): HD<jats:sub>voxel<\/jats:sub>\u2009&lt;\u20094.6 voxels, and HD<jats:sub>physical<\/jats:sub>\u2009&lt;\u20091.4\u2009mm). In addition, we developed an automated quality control procedure to include the ultrasound scans that successfully predicted ICV from all 43492 3D ultrasounds available in all individuals, no longer requiring manual selection of the best scan for analysis. Our trained model automatically retrieved ultrasounds with brain data and estimated ICV and ICV growth in 7672 (18%) of ultrasounds in 1762 participants that passed the automatic quality control procedure. Boys had significantly larger ICV at 20\u2010weeks (81.7\u2009\u00b1\u20090.4\u2009mL vs. 80.8\u2009\u00b1\u20090.5\u2009mL; B\u2009=\u20092.86; <jats:italic>p<\/jats:italic>\u2009=\u20095.7e\u201014) and 30\u2010weeks (257.0\u2009\u00b1\u20090.9\u2009mL vs. 245.1\u2009\u00b1\u20090.9\u2009mL; B\u2009=\u200912.35; <jats:italic>p<\/jats:italic>\u2009=\u20098.2e\u201027) of pregnancy, and more pronounced ICV growth than girls (delta growth 0.12\u2009mL\/day; <jats:italic>p<\/jats:italic>\u2009=\u20091.8e\u20105). Our automated artificial intelligence approach provides an opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.<\/jats:p>","DOI":"10.1002\/hbm.70058","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T12:55:51Z","timestamp":1733316951000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automated Segmentation of Fetal Intracranial Volume in Three\u2010Dimensional Ultrasound Using Deep Learning: Identifying Sex Differences in Prenatal Brain Development"],"prefix":"10.1002","volume":"45","author":[{"given":"Sonja M. C.","family":"de Zwarte","sequence":"first","affiliation":[{"name":"Department of Psychiatry, UMC Brain Center University Medical Center Utrecht, Utrecht University  Utrecht The Netherlands"},{"name":"Department of Developmental Psychology Utrecht University  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jalmar","family":"Teeuw","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, UMC Brain Center University Medical Center Utrecht, Utrecht University  Utrecht The Netherlands"},{"name":"Department of Experimental Psychology, Helmholtz Institute Utrecht University  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaojiao","family":"He","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, UMC Brain Center University Medical Center Utrecht, Utrecht University  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mireille N.","family":"Bekker","sequence":"additional","affiliation":[{"name":"Department of Obstetrics University Medical Center Utrecht  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruud J. G.","family":"van Sloun","sequence":"additional","affiliation":[{"name":"Lab of Biomedical Diagnostics, Department of Electrical Engineering Eindhoven University of Technology  Eindhoven The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hilleke E.","family":"Hulshoff Pol","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, UMC Brain Center University Medical Center Utrecht, Utrecht University  Utrecht The Netherlands"},{"name":"Department of Experimental Psychology, Helmholtz Institute Utrecht University  Utrecht The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.24076"},{"issue":"11","key":"e_1_2_10_3_1","first-page":"5274","article-title":"Complex Trajectories of Brain Development in the Healthy Human Fetus","volume":"27","author":"Andescavage N. N.","year":"2017","journal-title":"Cerebral Cortex"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/5.1.56"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13293-016-0119-1"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnimg.2022.996702"},{"key":"e_1_2_10_7_1","doi-asserted-by":"crossref","unstructured":"Cerrolaza J. J. M.Sinclair Y.Li et\u00a0al.2018.\u201cDeep Learning With Ultrasound Physics for Fetal Skull Segmentation.\u201d2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).564\u2013567.","DOI":"10.1109\/ISBI.2018.8363639"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/ana.410010109"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2016.01.018"},{"key":"e_1_2_10_10_1","unstructured":"Centers for Disease Control and Prevention.2017.\u201cClinical Growth Charts Internet.\u201dhttps:\/\/www.cdc.gov\/growthcharts\/clinical_charts.htm."},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2215-0366(20)30057-2"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.04.032"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMe058187"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1186\/s41073-016-0007-6"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1469-8749.2009.03513.x"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119117"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1176\/appi.ajp.157.7.1170"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.3632567"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2002.1132"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1361-8415(01)00036-6"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2019.05.020"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2018.08.030"},{"key":"e_1_2_10_23_1","volume-title":"Predictive Analytics and Data Mining: Concepts and Practice With Rapidminer","author":"Kotu V.","year":"2014"},{"issue":"3","key":"e_1_2_10_24_1","first-page":"169","article-title":"Discrepancy of Ultrasound Biometric Parameters of the Head (HC\u2010Head Circumference, BPD\u2010Biparietal Diameter) and Femur Length in Relation to Sex of the Fetus and Duration of Pregnancy","volume":"71","author":"L'ubusk\u00fd M.","year":"2006","journal-title":"Cesk\u00e1 Gynekologie"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1097\/WCO.0000000000000714"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.7863\/jum.2013.32.1.35"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-39343-4_13"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119341"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06630-3"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.02.006"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1093\/ije\/dyy157"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dcn.2020.100868"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/4271519"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-0318-1"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2006.1688199"},{"key":"e_1_2_10_36_1","first-page":"234","volume-title":"Medical Image Computing and Computer\u2010Assisted Intervention, MICCAI","author":"Ronneberger O.","year":"2015"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbi.2009.02.001"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1002\/uog.966"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0197-0"},{"key":"e_1_2_10_40_1","first-page":"1409","article-title":"Very Deep Convolutional Networks for Large\u2010Scale Image Recognition","volume":"1","author":"Simonyan K.","year":"2014","journal-title":"ArXiv"},{"key":"e_1_2_10_41_1","doi-asserted-by":"crossref","unstructured":"Sinclair M. C. F.Baumgartner J.Matthew et\u00a0al.2018a.\u201cHuman\u2010Level Performance on Automatic Head Biometrics in Fetal Ultrasound Using Fully Convolutional Neural Networks.\u201dIn 2018 40th annual international conference of the llIEEE engineering in medicine and biology society (EMBC); 714\u2013717. IEE.","DOI":"10.1109\/EMBC.2018.8512278"},{"key":"e_1_2_10_42_1","volume-title":"Medical Imaging With Deep Learning","author":"Sinclair M. 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