{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:56:11Z","timestamp":1792806971737,"version":"4.5.0"},"reference-count":53,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,7,1]],"date-time":"2022-07-01T00:00:00Z","timestamp":1656633600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000287","name":"Royal Academy of Engineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000287","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000691","name":"Academy of Medical Sciences","doi-asserted-by":"publisher","award":["SBF005\\1136"],"award-info":[{"award-number":["SBF005\\1136"]}],"id":[{"id":"10.13039\/501100000691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["203139\/Z\/16\/Z"],"award-info":[{"award-number":["203139\/Z\/16\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["215573\/Z\/19\/Z"],"award-info":[{"award-number":["215573\/Z\/19\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000734","name":"University College, Oxford","doi-asserted-by":"publisher","award":["EP\/M013774\/1"],"award-info":[{"award-number":["EP\/M013774\/1"]}],"id":[{"id":"10.13039\/501100000734","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000734","name":"University College, Oxford","doi-asserted-by":"publisher","award":["EP\/T028572\/1"],"award-info":[{"award-number":["EP\/T028572\/1"]}],"id":[{"id":"10.13039\/501100000734","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["EP\/L016052\/1"],"award-info":[{"award-number":["EP\/L016052\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000272","name":"National Institute for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2022,7]]},"DOI":"10.1016\/j.neuroimage.2022.119117","type":"journal-article","created":{"date-parts":[[2022,3,21]],"date-time":"2022-03-21T13:38:10Z","timestamp":1647869890000},"page":"119117","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":46,"special_numbering":"C","title":["Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning"],"prefix":"10.1016","volume":"254","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7594-5445","authenticated-orcid":false,"given":"Linde S.","family":"Hesse","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6728-0068","authenticated-orcid":false,"given":"Moska","family":"Aliasi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7633-1047","authenticated-orcid":false,"given":"Felipe","family":"Moser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Monique C.","family":"Haak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weidi","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mark","family":"Jenkinson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana I.L.","family":"Namburete","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"4","key":"10.1016\/j.neuroimage.2022.119117_bib0001","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s10278-017-9983-4","article-title":"Deep learning for brain MRI segmentation: state of the art and future directions","volume":"30","author":"Akkus","year":"2017","journal-title":"J. Digit. Imag."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0002","article-title":"Ifss-net: interactive few-shot siamese network for faster muscle segmentation and propagation in volumetric ultrasound","author":"Al Chanti","year":"2021","journal-title":"IEEE Trans. Med. Imag."},{"issue":"11","key":"10.1016\/j.neuroimage.2022.119117_bib0003","first-page":"5274","article-title":"Complex trajectories of brain development in the healthy human fetus","volume":"27","author":"Andescavage","year":"2017","journal-title":"Cerebral Cortex"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0004","first-page":"1","article-title":"3D evaluation of fetal brain structures: reference values and growth curves","author":"Babucci","year":"2019","journal-title":"J. Maternal-Fetal Neonat. Med."},{"issue":"5","key":"10.1016\/j.neuroimage.2022.119117_bib0005","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1002\/uog.6343","article-title":"Three-dimensional sonographic calculation of the volume of intracranial structures in growth-restricted and appropriate-for-gestational age fetuses","volume":"33","author":"Benavides-Serralde","year":"2009","journal-title":"Ultrasound Obstetric. Gynecol.: Off. J. Int. Soc. Ultrasound Obstetric. Gynecol."},{"issue":"6","key":"10.1016\/j.neuroimage.2022.119117_bib0006","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1016\/S0301-5629(00)00225-8","article-title":"Assessment of fetal cerebellar volume using three-dimensional ultrasound","volume":"26","author":"Chang","year":"2000","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0007","series-title":"2013\u00a0IEEE 10th International Symposium on Biomedical Imaging","first-page":"768","article-title":"Where is my baby? a fast fetal head auto-alignment in 3d-ultrasound","author":"Cuingnet","year":"2013"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0008","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.neuroimage.2017.04.039","article-title":"3D fully convolutional networks for subcortical segmentation in MRI: a large-scale study","volume":"170","author":"Dolz","year":"2018","journal-title":"Neuroimage"},{"issue":"2","key":"10.1016\/j.neuroimage.2022.119117_bib0009","doi-asserted-by":"crossref","first-page":"340","DOI":"10.3174\/ajnr.A5939","article-title":"Cavum septum pellucidum in the general pediatric population and its relation to surrounding brain structure volumes, cognitive function, and emotional or behavioral problems","volume":"40","author":"Dremmen","year":"2019","journal-title":"Am. J. Neuroradiol."},{"issue":"2","key":"10.1016\/j.neuroimage.2022.119117_bib0010","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.neuroimage.2012.01.021","article-title":"Freesurfer","volume":"62","author":"Fischl","year":"2012","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-017-00525-w","article-title":"A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth","volume":"7","author":"Gholipour","year":"2017","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.neuroimage.2022.119117_bib0012","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1007\/s11517-013-1082-1","article-title":"Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3d statistical shape model","volume":"51","author":"Guti\u00e9rrez-Becker","year":"2013","journal-title":"Med. Biol. Eng. Comput."},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0013","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s10396-006-0122-y","article-title":"Three-dimensional sonographic volume measurement of the fetal cerebellum","volume":"34","author":"Hata","year":"2007","journal-title":"J. Med. Ultrasonic."},{"issue":"4","key":"10.1016\/j.neuroimage.2022.119117_bib0014","doi-asserted-by":"crossref","first-page":"840","DOI":"10.1002\/jmri.21290","article-title":"Mr volume of the fetal cerebellum in relation to growth","volume":"27","author":"Hatab","year":"2008","journal-title":"J. Magnet. Resonance Imag."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0015","unstructured":"Health, National Institute for and Excellence, C., 2008. Antenatal care for uncomplicated pregnancies (NICE Guideline CG62). Updated February 2019."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0016","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"69","article-title":"Improving u-net segmentation with active contour based label correction","author":"Hesse","year":"2020"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0017","doi-asserted-by":"crossref","first-page":"014007","DOI":"10.1117\/1.JMI.5.1.014007","article-title":"Learning to segment key clinical anatomical structures in fetal neurosonography informed by a region-based descriptor","volume":"5","author":"Huang","year":"2018","journal-title":"J. Med. Imag."},{"issue":"9","key":"10.1016\/j.neuroimage.2022.119117_bib0018","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1109\/34.232073","article-title":"Comparing images using the hausdorff distance","volume":"15","author":"Huttenlocher","year":"1993","journal-title":"IEEE Trans. Pattern. Anal. Mach. Intell."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0019","series-title":"International conference on machine learning","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","author":"Ioffe","year":"2015"},{"issue":"4","key":"10.1016\/j.neuroimage.2022.119117_bib0020","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1515\/jpm-2019-0401","article-title":"Fetal brain development in small-for-gestational age (SGA) fetuses and normal controls","volume":"48","author":"Jacob","year":"2020","journal-title":"J. Perinat Med."},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0021","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s00404-006-0192-5","article-title":"Validation of fetal cerebellar volume by three-dimensional ultrasonography in brazilian population","volume":"275","author":"J\u00fanior","year":"2007","journal-title":"Arch. Gynecol. Obstet."},{"issue":"3","key":"10.1016\/j.neuroimage.2022.119117_bib0022","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1002\/uog.17409","article-title":"Cavum septi pellucidi (CSP) ratio: a marker for partial agenesis of the fetal corpus callosum","volume":"50","author":"Karl","year":"2017","journal-title":"Ultrasound Obstetric. Gynecol."},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0023","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1002\/uog.12526","article-title":"Brain abnormalities and neurodevelopmental delay in congenital heart disease: systematic review and meta-analysis","volume":"43","author":"Khalil","year":"2014","journal-title":"Ultrasound Obstetric. Gynecology"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0024","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.mri.2019.05.020","article-title":"Automatic brain tissue segmentation in fetal MRI using convolutional neural networks","volume":"64","author":"Khalili","year":"2019","journal-title":"Magn. Reson. Imag."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0025","article-title":"Adam: a method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv preprint arXiv:1412.6980"},{"issue":"5","key":"10.1016\/j.neuroimage.2022.119117_bib0026","doi-asserted-by":"crossref","first-page":"2295","DOI":"10.1007\/s00429-016-1342-6","article-title":"Normative biometry of the fetal brain using magnetic resonance imaging","volume":"222","author":"Kyriakopoulou","year":"2017","journal-title":"Brain Struct. Funct."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0027","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.neuroimage.2017.06.074","article-title":"A review on automatic fetal and neonatal brain MRI segmentation","volume":"170","author":"Makropoulos","year":"2018","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.neuroimage.2022.119117_bib0028","doi-asserted-by":"crossref","first-page":"476","DOI":"10.1002\/uog.22145","article-title":"Isuog practice guidelines (updated): sonographic examination of the fetal central nervous system. part 1: performance of screening examination and indications for targeted neurosonography","volume":"56","author":"Malinger","year":"2020","journal-title":"Ultrasound Obstetric. Gynecol."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0029","article-title":"Few-shot 3d multi-modal medical image segmentation using generative adversarial learning","author":"Mondal","year":"2018","journal-title":"arXiv preprint arXiv:1810.12241"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0030","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"151","article-title":"Automated fetal brain extraction from clinical ultrasound volumes using 3d convolutional neural networks","author":"Moser","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0031","series-title":"Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis","first-page":"76","article-title":"Multi-channel Groupwise Registration to Construct an Ultrasound-specific Fetal Brain Atlas","author":"Namburete","year":"2018"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0032","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2018.02.006","article-title":"Fully-automated alignment of 3d fetal brain ultrasound to a canonical reference space using multi-task learning","volume":"46","author":"Namburete","year":"2018","journal-title":"Med. Image. Anal."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0033","doi-asserted-by":"crossref","DOI":"10.1002\/uog.23616","article-title":"Isuog practice guidelines (updated): sonographic examination of the fetal central nervous system. part 2: performance of targeted neurosonography","author":"Paladini","year":"2021","journal-title":"Ultrasound Obstetric. Gynecol."},{"issue":"9946","key":"10.1016\/j.neuroimage.2022.119117_bib0034","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/S0140-6736(14)61490-2","article-title":"International standards for fetal growth based on serial ultrasound measurements: the fetal growth longitudinal study of the INTERGROWTH-21st project","volume":"384","author":"Papageorghiou","year":"2014","journal-title":"The Lancet"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0035","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-021-00946-3","article-title":"An automatic multi-tissue human fetal brain segmentation benchmark using the fetal tissue annotation dataset","volume":"8","author":"Payette","year":"2021","journal-title":"Sci. Data"},{"issue":"5","key":"10.1016\/j.neuroimage.2022.119117_bib0036","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1002\/uog.17412","article-title":"Isuog practice guidelines: performance of fetal magnetic resonance imaging","volume":"49","author":"Prayer","year":"2017","journal-title":"Ultrasound Obstetric. Gynecol."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0037","series-title":"International Conference on Medical image computing and computer-assisted intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0038","doi-asserted-by":"crossref","first-page":"101587","DOI":"10.1016\/j.media.2019.101587","article-title":"Squeeze & exciteguided few-shot segmentation of volumetric images","volume":"59","author":"Roy","year":"2020","journal-title":"Med. Image. Anal."},{"issue":"8","key":"10.1016\/j.neuroimage.2022.119117_bib0039","doi-asserted-by":"crossref","first-page":"1284","DOI":"10.1016\/j.ultrasmedbio.2009.03.016","article-title":"Fetal cerebellar volume and symmetry on 3-d ultrasound: volume measurement with multiplanar and vocal techniques","volume":"35","author":"Rutten","year":"2009","journal-title":"Ultrasound Med. & Biol."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0040","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.compmedimag.2018.08.007","article-title":"Learning to combine complementary segmentation methods for fetal and 6-month infant brain MRI segmentation","volume":"69","author":"Sanroma","year":"2018","journal-title":"Computer. Med. Imaging Graphic."},{"issue":"5","key":"10.1016\/j.neuroimage.2022.119117_bib0041","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.ijdevneu.2011.04.001","article-title":"Growth trajectories of the human fetal brain tissues estimated from 3d reconstructed in utero MRI","volume":"29","author":"Scott","year":"2011","journal-title":"Int. J. Dev. Neurosci."},{"issue":"3","key":"10.1016\/j.neuroimage.2022.119117_bib0042","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s12311-011-0338-2","article-title":"3D morphometric analysis of human fetal cerebellar development","volume":"11","author":"Scott","year":"2012","journal-title":"The Cerebellum"},{"issue":"5","key":"10.1016\/j.neuroimage.2022.119117_bib0043","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1002\/uog.14776","article-title":"Abnormal shape of the cavum septi pellucidi: an indirect sign of partial agenesis of the corpus callosum","volume":"46","author":"Shen","year":"2015","journal-title":"Ultrasound Obstetric. Gynecol."},{"issue":"2","key":"10.1016\/j.neuroimage.2022.119117_bib0044","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1080\/10641955.2020.1747487","article-title":"Predictive possibility of the transverse cerebellar diameter to abdominal circumference ratio for small-for-gestational-age fetus suspected as a cause of maternal placental syndromes: a retrospective cohort study","volume":"39","author":"Shinohara","year":"2020","journal-title":"Hypertens Pregnancy"},{"issue":"4","key":"10.1016\/j.neuroimage.2022.119117_bib0045","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1002\/jcu.21888","article-title":"Thalamic volume measurement in normal fetuses using three-dimensional sonography","volume":"40","author":"Sotiriadis","year":"2012","journal-title":"J. Clin. Ultrasound"},{"issue":"1","key":"10.1016\/j.neuroimage.2022.119117_bib0046","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0020-7292(99)00056-9","article-title":"Sonographic diagnosis of intrauterine growth restriction (IUGR) by fetal transverse cerebellar diameter (TCD)\/abdominal circumference (AC) ratio","volume":"66","author":"Tongsong","year":"1999","journal-title":"Int. J. Gynecol. Obstetric."},{"key":"10.1016\/j.neuroimage.2022.119117_bib0047","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"164","article-title":"Multi-task CNN for structural semantic segmentation in 3d fetal brain ultrasound","author":"Venturini","year":"2019"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0048","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"689","article-title":"Uncertainty estimates as data selection criteria to boost omni-supervised learning","author":"Venturini","year":"2020"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0049","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/1471-0528.12047","article-title":"The objectives, design and implementation of the INTERGROWTH-21st project","volume":"120","author":"Villar","year":"2013","journal-title":"BJOG"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0050","series-title":"2020\u00a0IEEE International Conference on Image Processing (ICIP)","first-page":"2511","article-title":"Automatic measurement of fetal cavum septum pellucidum from ultrasound images using deep attention network","author":"Wu","year":"2020"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0051","series-title":"Annual Conference on Medical Image Understanding and Analysis","first-page":"56","article-title":"Cortical plate segmentation using CNNs in 3d fetal ultrasound","author":"Wyburd","year":"2020"},{"key":"10.1016\/j.neuroimage.2022.119117_bib0052","series-title":"International Workshop on Machine Learning in Medical Imaging","first-page":"25","article-title":"Volumetric segmentation of key fetal brain structures in 3d ultrasound","author":"Yaqub","year":"2013"},{"issue":"2","key":"10.1016\/j.neuroimage.2022.119117_bib0053","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1002\/uog.14677","article-title":"Volume of intracranial structures on three-dimensional ultrasound in fetuses with congenital heart disease","volume":"46","author":"Zeng","year":"2015","journal-title":"Ultrasound Obstetric. Gynecol."}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811922002452?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811922002452?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T11:03:34Z","timestamp":1760958214000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811922002452"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7]]},"references-count":53,"alternative-id":["S1053811922002452"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119117","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.09.29.462430","asserted-by":"object"}]},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2022,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Subcortical segmentation of the fetal brain in 3D ultrasound using deep learning","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2022.119117","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Authors. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"119117"}}