{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T06:00:26Z","timestamp":1774677626586,"version":"3.50.1"},"reference-count":109,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"publisher","award":["2023YFC2705700"],"award-info":[{"award-number":["2023YFC2705700"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62,306,217"],"award-info":[{"award-number":["62,306,217"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62225113"],"award-info":[{"award-number":["62225113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012556","name":"Foundation for Innovative Research Groups of Hubei Province of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012556","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/T018119\/1"],"award-info":[{"award-number":["MR\/T018119\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006390","name":"Universit\u00e9 de Lausanne","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006390","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004837","name":"Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006389","name":"Universit\u00e9 de Gen\u00e8ve","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006389","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008722","name":"University of Girona","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008722","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M752471"],"award-info":[{"award-number":["2024M752471"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024T170686"],"award-info":[{"award-number":["2024T170686"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS114087"],"award-info":[{"award-number":["R01NS114087"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000780","name":"European Commission","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006447","name":"Universit\u00e4t Z\u00fcrich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006447","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031359","name":"Anna Mueller Grocholski-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031359","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001703","name":"\u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001703","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010008","name":"Postdoctoral Science Foundation of Guangxi Province of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100010008","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Gobierno de Espa\u00f1a Ministerio de Ciencia e Innovaci\u00f3n","doi-asserted-by":"publisher","award":["PID2023-146187OB-I00"],"award-info":[{"award-number":["PID2023-146187OB-I00"]}],"id":[{"id":"10.13039\/501100003329","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"}]},{"DOI":"10.13039\/501100013915","name":"EPSRC Centre for Doctoral Training in Smart Medical Imaging","doi-asserted-by":"publisher","award":["EP\/S022104\/1"],"award-info":[{"award-number":["EP\/S022104\/1"]}],"id":[{"id":"10.13039\/501100013915","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006391","name":"Centre d'Imagerie BioM\u00e9dicale","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006391","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100008494","name":"Vontobel-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008494","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["SNSF 31NE30 203977"],"award-info":[{"award-number":["SNSF 31NE30 203977"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["IZKSZ3_218590"],"award-info":[{"award-number":["IZKSZ3_218590"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["215641"],"award-info":[{"award-number":["215641"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Swiss National Science Foundation","doi-asserted-by":"publisher","award":["182602"],"award-info":[{"award-number":["182602"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","award":["R01EB031170"],"award-info":[{"award-number":["R01EB031170"]}],"id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006392","name":"Centre Hospitalier Universitaire Vaudois","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100006392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.media.2026.103941","type":"journal-article","created":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T16:28:36Z","timestamp":1768580916000},"page":"103941","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":3,"special_numbering":"C","title":["Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge"],"prefix":"10.1016","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-0191-299X","authenticated-orcid":false,"given":"Vladyslav","family":"Zalevskyi","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Sanchez","sequence":"additional","affiliation":[]},{"given":"Misha","family":"Kaandorp","sequence":"additional","affiliation":[]},{"given":"Margaux","family":"Roulet","sequence":"additional","affiliation":[]},{"given":"Diego","family":"Fajardo-Rojas","sequence":"additional","affiliation":[]},{"given":"Liu","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jana","family":"Hutter","sequence":"additional","affiliation":[]},{"given":"Hongwei Bran","family":"Li","sequence":"additional","affiliation":[]},{"given":"Matthew J.","family":"Barkovich","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Ji","sequence":"additional","affiliation":[]},{"given":"Luca","family":"Wilhelmi","sequence":"additional","affiliation":[]},{"given":"Aline","family":"D\u00e4ndliker","sequence":"additional","affiliation":[]},{"given":"C\u00e9line","family":"Steger","sequence":"additional","affiliation":[]},{"given":"M\u00e9riam","family":"Koob","sequence":"additional","affiliation":[]},{"given":"Yvan","family":"Gomez","sequence":"additional","affiliation":[]},{"given":"Anton","family":"Jakov\u010di\u0107","sequence":"additional","affiliation":[]},{"given":"Melita","family":"Klai\u0107","sequence":"additional","affiliation":[]},{"given":"Ana","family":"Ad\u017ei\u0107","sequence":"additional","affiliation":[]},{"given":"Pavel","family":"Markovi\u0107","sequence":"additional","affiliation":[]},{"given":"Gracia","family":"Grabari\u0107","sequence":"additional","affiliation":[]},{"given":"Milan","family":"Rados","sequence":"additional","affiliation":[]},{"given":"Jordina","family":"Aviles Verdera","sequence":"additional","affiliation":[]},{"given":"Gregor","family":"Kasprian","sequence":"additional","affiliation":[]},{"given":"Gregor","family":"Dovjak","sequence":"additional","affiliation":[]},{"given":"Raphael","family":"Gaubert-Rachm\u00fchl","sequence":"additional","affiliation":[]},{"given":"Maurice","family":"Aschwanden","sequence":"additional","affiliation":[]},{"given":"Qi","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Davood","family":"Karimi","sequence":"additional","affiliation":[]},{"given":"Denis","family":"Peruzzo","sequence":"additional","affiliation":[]},{"given":"Tommaso","family":"Ciceri","sequence":"additional","affiliation":[]},{"given":"Giorgio","family":"Longari","sequence":"additional","affiliation":[]},{"given":"Rachika E.","family":"Hamadache","sequence":"additional","affiliation":[]},{"given":"Amina","family":"Bouzid","sequence":"additional","affiliation":[]},{"given":"Xavier","family":"Llad\u00f3","sequence":"additional","affiliation":[]},{"given":"Simone","family":"Chiarella","sequence":"additional","affiliation":[]},{"given":"Gerard","family":"Mart\u00ed-Juan","sequence":"additional","affiliation":[]},{"given":"Miguel \u00c1ngel","family":"Gonz\u00e1lez Ballester","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Castellaro","sequence":"additional","affiliation":[]},{"given":"Marco","family":"Pinamonti","sequence":"additional","affiliation":[]},{"given":"Valentina","family":"Visani","sequence":"additional","affiliation":[]},{"given":"Robin","family":"Cremese","sequence":"additional","affiliation":[]},{"given":"Ke\u00efn","family":"Sam","sequence":"additional","affiliation":[]},{"given":"Fleur","family":"Gaudfernau","sequence":"additional","affiliation":[]},{"given":"Param","family":"Ahir","sequence":"additional","affiliation":[]},{"given":"Mehul","family":"Parikh","sequence":"additional","affiliation":[]},{"given":"Maximilian","family":"Zenk","sequence":"additional","affiliation":[]},{"given":"Michael","family":"Baumgartner","sequence":"additional","affiliation":[]},{"given":"Klaus","family":"Maier-Hein","sequence":"additional","affiliation":[]},{"given":"Li","family":"Tianhong","sequence":"additional","affiliation":[]},{"given":"Yang","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Zhao","family":"Longfei","sequence":"additional","affiliation":[]},{"given":"Domen","family":"Preloznik","sequence":"additional","affiliation":[]},{"given":"\u017diga","family":"\u0160piclin","sequence":"additional","affiliation":[]},{"given":"Jae","family":"Won Choi","sequence":"additional","affiliation":[]},{"given":"Muyang","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jia","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jingwen","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Lyuyang","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Bo","family":"Du","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Gondova","sequence":"additional","affiliation":[]},{"given":"Sungmin","family":"You","sequence":"additional","affiliation":[]},{"given":"Kiho","family":"Im","sequence":"additional","affiliation":[]},{"given":"Abdul","family":"Qayyum","sequence":"additional","affiliation":[]},{"given":"Moona","family":"Mazher","sequence":"additional","affiliation":[]},{"given":"Steven A.","family":"Niederer","sequence":"additional","affiliation":[]},{"given":"Andras","family":"Jakab","sequence":"additional","affiliation":[]},{"given":"Roxane","family":"Licandro","sequence":"additional","affiliation":[]},{"given":"Kelly","family":"Payette","sequence":"additional","affiliation":[]},{"given":"Meritxell","family":"Bach Cuadra","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0001","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.ejrad.2009.06.030","article-title":"Fetal MRI as complement to US in the diagnosis and characterization of anomalies of the genito-urinary tract","volume":"76","author":"Alamo","year":"2010","journal-title":"Eur. J. Radiol."},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0002","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1002\/jmri.28408","article-title":"Low-field MRI: clinical promise and challenges","volume":"57","author":"Arnold","year":"2023","journal-title":"J. Magn. Reson. Imaging"},{"issue":"9","key":"10.1016\/j.media.2026.103941_bib0003","doi-asserted-by":"crossref","first-page":"231","DOI":"10.3390\/brainsci9090231","article-title":"Fetal brain abnormality classification from MRI images of different gestational age","volume":"9","author":"Attallah","year":"2019","journal-title":"Brain Sci."},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0004","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.223050","article-title":"Reliability and feasibility of low-field-strength fetal MRI at 0.55 t during pregnancy","volume":"309","author":"Aviles Verdera","year":"2023","journal-title":"Radiology"},{"key":"10.1016\/j.media.2026.103941_bib0005","series-title":"BiometryNet: Landmark-Based Fetal Biometry Estimation from Standard Ultrasound Planes","first-page":"279-289","author":"Avisdris","year":"2022"},{"issue":"9","key":"10.1016\/j.media.2026.103941_bib0006","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1007\/s11548-021-02436-8","article-title":"Automatic linear measurements of the fetal brain on MRI with deep neural networks","volume":"16","author":"Avisdris","year":"2021","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.media.2026.103941_bib0007","unstructured":"Bach Cuadra, M., Sanchez, T., Mart\u00ed Juan, G., Mihailov, A., Auzias, G., 2025. Protocol for the quality rating of 3D super- resolution reconstruction in fetal brain MRI. 10.5281\/zenodo.15696638."},{"key":"10.1016\/j.media.2026.103941_bib0008","series-title":"An MRI atlas of the human fetal brain: reference and segmentation tools for fetal brain MRI analysis","author":"Bagheri","year":"2025"},{"key":"10.1016\/j.media.2026.103941_bib0009","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"228","article-title":"AutoFB: automating fetal biometry estimation from standard ultrasound planes","author":"Bano","year":"2021"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0010","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/07038992.1998.10874685","article-title":"On the hausdorff distance used for the evaluation of segmentation results","volume":"24","author":"Beauchemin","year":"1998","journal-title":"Can. J. Remote Sens."},{"key":"10.1016\/j.media.2026.103941_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102789","article-title":"SynthSeg: segmentation of brain MRI scans of any contrast and resolution without retraining","volume":"86","author":"Billot","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103941_bib0012","series-title":"Archives of Disease in Childhood - Fetal and Neonatal Edition, fetalneonatal\u20132024\u2013328310","article-title":"Normative range of MRI-derived fetal brain volume throughout gestation: a prospective study","author":"Bouachba","year":"2025"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0013","doi-asserted-by":"crossref","first-page":"3673","DOI":"10.1038\/s41467-020-17478-w","article-title":"Causality matters in medical imaging","volume":"11","author":"Castro","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.media.2026.103941_bib0014","first-page":"1","article-title":"Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly","volume":"34,11","author":"Chen","year":"2024","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.media.2026.103941_bib0015","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece, October 17\u201321, 2016, Proceedings, Part II 19","first-page":"424","article-title":"3D U-Net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.media.2026.103941_bib0016","doi-asserted-by":"crossref","DOI":"10.1016\/j.neuroimage.2024.120603","article-title":"Fetal brain MRI atlases and datasets: a review","author":"Ciceri","year":"2024","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.media.2026.103941_bib0017","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1007\/s12021-023-09635-5","article-title":"Geometric reliability of super-resolution reconstructed images from clinical fetal MRI in the second trimester","volume":"21","author":"Ciceri","year":"2023","journal-title":"Neuroinformatics"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0018","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s00429-011-0325-x","article-title":"Quantitative in vivo MRI measurement of cortical development in the fetus","volume":"217","author":"Clouchoux","year":"2011","journal-title":"Brain Struct. Funct."},{"key":"10.1016\/j.media.2026.103941_bib0019","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"181","article-title":"Cina: conditional implicit neural atlas for spatio-temporal representation of fetal brains","author":"Dannecker","year":"2024"},{"issue":"9","key":"10.1016\/j.media.2026.103941_bib0020","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giab055","article-title":"Preventing dataset shift from breaking machine-learning biomarkers","volume":"10","author":"Dock\u00e8s","year":"2021","journal-title":"Gigascience"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0021","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1002\/uog.21890","article-title":"Quantitative fetal magnetic resonance imaging assessment of cystic posterior fossa malformations","volume":"56","author":"Dovjak","year":"2020","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.media.2026.103941_bib0022","series-title":"Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI","author":"de Dumast","year":"2022"},{"key":"10.1016\/j.media.2026.103941_bib0023","doi-asserted-by":"crossref","DOI":"10.3389\/fnins.2022.886772","article-title":"The developing human connectome project neonatal data release","volume":"16","author":"Edwards","year":"2022","journal-title":"Front. Neurosci."},{"issue":"4","key":"10.1016\/j.media.2026.103941_bib0024","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1159\/000366160","article-title":"Neurosonographic assessment of the corpus callosum as imaging biomarker of abnormal neurodevelopment in late-onset fetal growth restriction","volume":"37","author":"Ega\u00f1a-Ugrinovic","year":"2015","journal-title":"Fetal. Diagn. Ther."},{"key":"10.1016\/j.media.2026.103941_bib0025","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"19955","article-title":"Why is the winner the best?","author":"Eisenmann","year":"2023"},{"key":"10.1016\/j.media.2026.103941_bib0026","unstructured":"EU, EU., 2024. Regulation (EU) 2024\/1689 of the European parliament and of the council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending regulations. https:\/\/eur-lex.europa.eu\/eli\/reg\/2024\/1689\/oj. Official Journal of the European Union, OJ L, 2024\/1689, 12.7.2024."},{"issue":"5","key":"10.1016\/j.media.2026.103941_bib0027","doi-asserted-by":"crossref","first-page":"3784","DOI":"10.1109\/TPAMI.2023.3346330","article-title":"A dempster-shafer approach to trustworthy AI with application to fetal brain MRI segmentation","volume":"46","author":"Fidon","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2026.103941_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2020.109369","article-title":"Fetal brain biometry: is there an agreement among ultrasound, MRI and the measurements at birth?","volume":"133","author":"Gafner","year":"2020","journal-title":"Eur. J. Radiol."},{"issue":"3","key":"10.1016\/j.media.2026.103941_bib0029","doi-asserted-by":"crossref","first-page":"1257","DOI":"10.1016\/j.neuroimage.2012.07.053","article-title":"Larger is twistier: spectral analysis of gyrification (SPANGY) applied to adult brain size polymorphism","volume":"63","author":"Germanaud","year":"2012","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0030","doi-asserted-by":"crossref","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."},{"key":"10.1016\/j.media.2026.103941_bib0031","series-title":"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)","first-page":"241-246","article-title":"Medical image denoising using convolutional denoising autoencoders","author":"Gondara","year":"2016"},{"issue":"10068","key":"10.1016\/j.media.2026.103941_bib0032","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/S0140-6736(16)31723-8","article-title":"Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): a multicentre, prospective cohort study","volume":"389","author":"Griffiths","year":"2017","journal-title":"Lancet"},{"issue":"4","key":"10.1016\/j.media.2026.103941_bib0033","doi-asserted-by":"crossref","first-page":"836","DOI":"10.1161\/HYPERTENSIONAHA.123.22442","article-title":"Preeclampsia associated differences in the placenta, fetal brain, and maternal heart can be demonstrated antenatally: an observational cohort study using MRI","volume":"81","author":"Hall","year":"2024","journal-title":"Hypertension"},{"key":"10.1016\/j.media.2026.103941_bib0034","series-title":"Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images","first-page":"272-284","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.media.2026.103941_bib0035","series-title":"Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part IV 14","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"He","year":"2016"},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0036","doi-asserted-by":"crossref","DOI":"10.3390\/s23020655","article-title":"Deep learning-based multiclass brain tissue segmentation in fetal MRIs","volume":"23","author":"Huang","year":"2023","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0037","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1002\/mrm.26462","article-title":"A dedicated neonatal brain imaging system: a dedicated neonatal brain imaging system","volume":"78","author":"Hughes","year":"2016","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2026.103941_bib0038","series-title":"nnU-Net: self-adapting framework for U-Net-based medical image segmentation","author":"Isensee","year":"2018"},{"key":"10.1016\/j.media.2026.103941_bib0039","series-title":"CVPR Workshops","first-page":"10","article-title":"Style augmentation: data augmentation via style randomization","volume":"Vol. 6","author":"Jackson","year":"2019"},{"key":"10.1016\/j.media.2026.103941_bib0040","series-title":"Pathological MRI segmentation by synthetic pathological data generation in fetuses and neonates","author":"Kaandorp","year":"2025"},{"key":"10.1016\/j.media.2026.103941_bib0041","series-title":"Pathological MRI segmentation by synthetic pathological data generation in fetuses and neonates","author":"Kaandorp","year":"2025"},{"key":"10.1016\/j.media.2026.103941_bib0042","doi-asserted-by":"crossref","DOI":"10.3389\/fped.2021.639746","article-title":"Fetal brain biometric measurements on 3D super-resolution reconstructed t2-weighted MRI: an intra- and inter-observer agreement study","volume":"9","author":"Khawam","year":"2021","journal-title":"Front. Pediatr."},{"issue":"5","key":"10.1016\/j.media.2026.103941_bib0043","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":"2016","journal-title":"Brain Struct. Funct."},{"key":"10.1016\/j.media.2026.103941_bib0044","doi-asserted-by":"crossref","DOI":"10.3389\/fneur.2024.1358741","article-title":"Assessment of fetal corpus callosum biometry by 3d super-resolution reconstructed t2-weighted magnetic resonance imaging","volume":"15","author":"Lamon","year":"2024","journal-title":"Front. Neurol."},{"key":"10.1016\/j.media.2026.103941_bib0045","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2024-081554","article-title":"Future-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare","author":"Lekadir","year":"2025","journal-title":"BMJ"},{"key":"10.1016\/j.media.2026.103941_bib0046","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"67","article-title":"Robust segmentation via topology violation detection and feature synthesis","author":"Li","year":"2023"},{"key":"10.1016\/j.media.2026.103941_bib0047","article-title":"Topology optimization in medical image segmentation with fast euler characteristic","author":"Li","year":"2025","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2026.103941_bib0048","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"676","article-title":"Pepsi: pathology-enhanced pulse-sequence-invariant representations for brain MRI","author":"Liu","year":"2024"},{"key":"10.1016\/j.media.2026.103941_bib0049","series-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","first-page":"10012","article-title":"Swin transformer: hierarchical vision transformer using shifted windows","author":"Liu","year":"2021"},{"key":"10.1016\/j.media.2026.103941_bib0050","article-title":"Towards automated fetal brain biometry reporting for 3-dimensional t2-weighted 0.55-3t magnetic resonance imaging at 20\u201340 weeks gestational age range","author":"Luis","year":"2025","journal-title":"medRxiv"},{"key":"10.1016\/j.media.2026.103941_bib0051","series-title":"Advances in Neural Information Processing Systems 30","first-page":"4765","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017"},{"key":"10.1016\/j.media.2026.103941_bib0052","series-title":"Topograph: an efficient graph-based framework for strictly topology preserving image segmentation","author":"Lux","year":"2024"},{"key":"10.1016\/j.media.2026.103941_bib0053","doi-asserted-by":"crossref","first-page":"183","DOI":"10.5114\/pjr.2021.105014","article-title":"Imaging spectrum of posterior fossa anomalies on foetal magnetic resonance imaging with an algorithmic approach to diagnosis","volume":"86","author":"Mahalingam","year":"2021","journal-title":"Polish J. Radiol."},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0054","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41592-023-02151-z","article-title":"Metrics reloaded: recommendations for image analysis validation","volume":"21","author":"Maier-Hein","year":"2024","journal-title":"Nat. Methods"},{"key":"10.1016\/j.media.2026.103941_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2020.101796","article-title":"Bias: transparent reporting of biomedical image analysis challenges","volume":"66","author":"Maier-Hein","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103941_bib0056","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neuroimage.2018.01.054","article-title":"The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction","volume":"173","author":"Makropoulos","year":"2018","journal-title":"Neuroimage"},{"issue":"4","key":"10.1016\/j.media.2026.103941_bib0057","doi-asserted-by":"crossref","first-page":"430","DOI":"10.3390\/diagnostics14040430","article-title":"Fetal MRI analysis of corpus callosal abnormalities: classification, and associated anomalies","volume":"14","author":"Marathu","year":"2024","journal-title":"Diagnostics"},{"issue":"6","key":"10.1016\/j.media.2026.103941_bib0058","doi-asserted-by":"crossref","first-page":"1528","DOI":"10.1002\/jmri.26637","article-title":"Low-field MRI: an MR physics perspective","volume":"49","author":"Marques","year":"2019","journal-title":"J. Magn. Reson. Imaging"},{"issue":"12","key":"10.1016\/j.media.2026.103941_bib0059","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pdig.0000663","article-title":"Automated craniofacial biometry with 3d t2w fetal MRI","volume":"3","author":"Matthew","year":"2024","journal-title":"PLOS Digit. Health"},{"issue":"S1","key":"10.1016\/j.media.2026.103941_bib0060","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1002\/uog.26875","article-title":"Ep15.06: fetal brain development in fetal growth restriction using MRI: a systematic review","volume":"62","author":"Meijerink","year":"2023","journal-title":"Ultrasound Obstet. Gynecol."},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0061","doi-asserted-by":"crossref","DOI":"10.1038\/s42003-025-08155-z","article-title":"Burst of gyrification in the human brain after birth","volume":"8","author":"Mihailov","year":"2025","journal-title":"Commun. Biol."},{"key":"10.1016\/j.media.2026.103941_bib0062","doi-asserted-by":"crossref","DOI":"10.1016\/j.compbiomed.2024.109336","article-title":"Structural-based uncertainty in deep learning across anatomical scales: analysis in white matter lesion segmentation","volume":"184","author":"Molchanova","year":"2025","journal-title":"Comput. Biol. Med."},{"issue":"7","key":"10.1016\/j.media.2026.103941_bib0063","doi-asserted-by":"crossref","DOI":"10.1002\/nbm.4992","article-title":"Bringing MRI to low- and middle-income countries: directions, challenges and potential solutions","volume":"37","author":"Murali","year":"2023","journal-title":"NMR Biomed."},{"issue":"3","key":"10.1016\/j.media.2026.103941_bib0064","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1002\/mrm.30122","article-title":"Fully automated planning for anatomical fetal brain MRI on 0.55 t","volume":"92","author":"Neves Silva","year":"2024","journal-title":"Magn. Reson. Med."},{"key":"10.1016\/j.media.2026.103941_bib0065","series-title":"Attention U-Net: learning where to look for the pancreas","author":"Oktay","year":"2018"},{"issue":"4","key":"10.1016\/j.media.2026.103941_bib0066","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/TMI.2022.3224067","article-title":"Causality-inspired single-source domain generalization for medical image segmentation","volume":"42","author":"Ouyang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0067","doi-asserted-by":"crossref","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"},{"key":"10.1016\/j.media.2026.103941_bib0068","series-title":"Fetal tissue annotation dataset","author":"Payette","year":"2021"},{"key":"10.1016\/j.media.2026.103941_bib0069","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2023.102833","article-title":"Fetal brain tissue annotation and segmentation challenge results","volume":"88","author":"Payette","year":"2023","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103941_bib0070","first-page":"1","article-title":"Multi-center fetal brain tissue annotation (feTA) challenge 2022 results","author":"Payette","year":"2024","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0071","doi-asserted-by":"crossref","DOI":"10.1007\/s13755-023-00220-3","article-title":"The nnU-Net based method for automatic segmenting fetal brain tissues","volume":"11","author":"Peng","year":"2023","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.media.2026.103941_bib0072","series-title":"Enhancing corpus callosum segmentation in fetal MRI via pathology-informed domain randomization","author":"Plana","year":"2025"},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0073","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ejrad.2005.11.020","article-title":"MRI of normal fetal brain development","volume":"57","author":"Prayer","year":"2006","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.media.2026.103941_bib0074","series-title":"Proceedings of the Annual Meeting of the International Society of Magnetic Resonance in Medicine (ISMRM)","article-title":"The developing human connectome project (dHCP): fetal acquisition protocol","volume":"Vol. 244","author":"Price","year":"2019"},{"key":"10.1016\/j.media.2026.103941_bib0075","series-title":"2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)","first-page":"1","article-title":"Tackling bias in the dice similarity coefficient: introducing NDSC for white matter lesion segmentation","author":"Raina","year":"2023"},{"key":"10.1016\/j.media.2026.103941_bib0076","series-title":"Trustworthy AI in Medical Imaging","first-page":"127","article-title":"Domain shift, domain adaptation, and generalization: a focus on MRI","author":"Richiardi","year":"2025"},{"key":"10.1016\/j.media.2026.103941_bib0077","series-title":"Medical Image Computing and Computer-assisted Intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5\u20139, 2015, Proceedings, Part III 18","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.media.2026.103941_bib0078","series-title":"Mednext: transformer-driven scaling of convnets for medical image segmentation","author":"Roy","year":"2023"},{"issue":"15","key":"10.1016\/j.media.2026.103941_bib0079","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1161\/CIRCULATIONAHA.121.056305","article-title":"Fetal brain volume predicts neurodevelopment in congenital heart disease","volume":"145","author":"Sadhwani","year":"2022","journal-title":"Circulation"},{"key":"10.1016\/j.media.2026.103941_bib0080","series-title":"Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems","first-page":"1","article-title":"\u201cEveryone wants to do the model work, not the data work\u201d: data cascades in high-stakes AI","author":"Sambasivan","year":"2021"},{"key":"10.1016\/j.media.2026.103941_bib0081","series-title":"International Workshop on Preterm, Perinatal and Paediatric Image Analysis","first-page":"46","article-title":"Assessing data quality on fetal brain MRI reconstruction: a multi-site and multi-rater study","author":"Sanchez","year":"2024"},{"key":"10.1016\/j.media.2026.103941_bib0082","article-title":"Biometry and volumetry in multi-centric fetal brain MRI: assessing the bias of super-resolution reconstruction","author":"Sanchez","year":"2024","journal-title":"medRxiv"},{"issue":"10","key":"10.1016\/j.media.2026.103941_bib0083","doi-asserted-by":"crossref","first-page":"2064","DOI":"10.1007\/s00247-025-06347-7","article-title":"Biometry and volumetry in multi-centric fetal brain magnetic resonance imaging: assessing the bias of super-resolution reconstruction","volume":"55","author":"Sanchez","year":"2025","journal-title":"Pediatr. Radiol."},{"key":"10.1016\/j.media.2026.103941_bib0084","series-title":"Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction","author":"Sanchez","year":"2025"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0085","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-43867-4","article-title":"Automatic biometry of fetal brain MRIs using deep and machine learning techniques","volume":"13","author":"She","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0086","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-43867-4","article-title":"Automatic biometry of fetal brain MRIs using deep and machine learning techniques","volume":"13","author":"She","year":"2023","journal-title":"Sci. Rep."},{"key":"10.1016\/j.media.2026.103941_bib0087","article-title":"Brain volumetry in fetuses that deliver very preterm: an MRI pilot study","volume":"30","author":"Story","year":"2021","journal-title":"NeuroImage: Clin."},{"key":"10.1016\/j.media.2026.103941_bib0088","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","article-title":"Metrics for evaluating 3d medical image segmentation: analysis, selection, and tool","volume":"15","author":"Taha","year":"2015","journal-title":"BMC Med. Imaging"},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0089","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1002\/uog.6276","article-title":"Cerebral biometry in fetal magnetic resonance imaging: new reference data","volume":"33","author":"Tilea","year":"2009","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.media.2026.103941_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107334","article-title":"An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis","volume":"230","author":"Urru","year":"2023","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2026.103941_bib0091","unstructured":"Uus, A., Kyriakopoulou, V., Cordero Grande, L., Christiaens, D., Pietsch, M., Price, A., Wilson, S., Patkee, P., Karolis, S., Schuh, A., Gartner, A., Williams, L., Hughes, E., Arichi, T., O\u2019Muircheartaigh, J., Hutter, J., Robinson, E., Tournier, J. D., Rueckert, D., Counsell, S., Rutherford, M., Deprez, M., Hajnal, J. V., Edwards, A. D., 2023a. Multi-channel spatio-temporal MRI atlas of the normal fetal brain development from the developing human connectome project. 10.12751\/G-NODE.YSGSY1."},{"issue":"9","key":"10.1016\/j.media.2026.103941_bib0092","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1109\/TMI.2020.2974844","article-title":"Deformable slice-to-volume registration for motion correction of fetal body and placenta MRI","volume":"39","author":"Uus","year":"2020","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1147","key":"10.1016\/j.media.2026.103941_bib0093","article-title":"Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice","volume":"96","author":"Uus","year":"2023","journal-title":"Br. J. Radiol."},{"key":"10.1016\/j.media.2026.103941_bib0094","doi-asserted-by":"crossref","unstructured":"Uus, A. U., Hall, M., Grigorescu, I., Zampieri, C. A., Collado, A. E., Payette, K., Matthew, J., Kyriakopoulou, V., Hajnal, J. V., Hutter, J., Rutherford, M. A., Deprez, M., Story, L., 2023c. 3D t2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas10.1101\/2023.05.31.23290751.","DOI":"10.1101\/2023.05.31.23290751"},{"key":"10.1016\/j.media.2026.103941_bib0095","doi-asserted-by":"crossref","unstructured":"Uus, A. U., Kyriakopoulou, V., Makropoulos, A., Fukami-Gartner, A., Cromb, D., Davidson, A., Cordero-Grande, L., Price, A. N., Grigorescu, I., Williams, L. Z. J., Robinson, E. C., Lloyd, D., Pushparajah, K., Story, L., Hutter, J., Counsell, S. J., Edwards, A. D., Rutherford, M. A., Hajnal, J. V., Deprez, M., 2023d. Bounti: brain volumetry and automated parcellation for 3D fetal MRI, 10.1101\/2023.04.18.537347.","DOI":"10.1101\/2023.04.18.537347"},{"key":"10.1016\/j.media.2026.103941_bib0096","article-title":"Scanner-based real-time 3D brain+ body slice-to-volume reconstruction for t2-weighted 0.55 t low field fetal MRI","author":"Uus","year":"2024","journal-title":"medRxiv"},{"issue":"3","key":"10.1016\/j.media.2026.103941_bib0097","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1017\/S0954579418000342","article-title":"Prenatal stress and the developing brain: risks for neurodevelopmental disorders","volume":"30","author":"Van den Bergh","year":"2018","journal-title":"Dev. Psychopathol."},{"issue":"6","key":"10.1016\/j.media.2026.103941_bib0098","doi-asserted-by":"crossref","DOI":"10.1002\/hbm.26674","article-title":"Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation","volume":"45","author":"Valabregue","year":"2024","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.media.2026.103941_bib0099","series-title":"MIDL 2024-Medical Imaging with Deep Learning","article-title":"Unraveling systematic biases in brain segmentation: insights from synthetic training","author":"Valabregue","year":"2024"},{"issue":"1","key":"10.1016\/j.media.2026.103941_bib0100","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41746-022-00592-y","article-title":"Machine learning for medical imaging: methodological failures and recommendations for the future","volume":"5","author":"Varoquaux","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.media.2026.103941_bib0101","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.media.2026.103941_bib0102","series-title":"A fine-grained analysis on distribution shift","author":"Wiles","year":"2021"},{"issue":"6","key":"10.1016\/j.media.2026.103941_bib0103","doi-asserted-by":"crossref","first-page":"1707","DOI":"10.1109\/TMI.2023.3236216","article-title":"NeSVor: implicit neural representation for slice-to-volume reconstruction in MRI","volume":"42","author":"Xu","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"10.1016\/j.media.2026.103941_bib0104","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.3174\/ajnr.A8046","article-title":"Automatic quantification of normal brain gyrification patterns and changes in fetuses with polymicrogyria and lissencephaly based on MRI","volume":"44","author":"Yehuda","year":"2023","journal-title":"Am. J. Neuroradiol."},{"issue":"3","key":"10.1016\/j.media.2026.103941_bib0105","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.media.2026.103941_bib0106","series-title":"Proceedings of Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2024","article-title":"Improving cross-domain brain tissue segmentation in fetal MRI with synthetic data","author":"Zalevskyi","year":"2024"},{"key":"10.1016\/j.media.2026.103941_bib0107","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103392","article-title":"Comparative benchmarking of failure detection methods in medical image segmentation: unveiling the role of confidence aggregation","volume":"101","author":"Zenk","year":"2025","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2026.103941_bib0108","series-title":"Proceedings of the 2023 SIAM International Conference on Data Mining (SDM)","first-page":"945","article-title":"Data-centric AI: perspectives and challenges","author":"Zha","year":"2023"},{"issue":"2","key":"10.1016\/j.media.2026.103941_bib0109","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/S1076-6332(03)00671-8","article-title":"Statistical validation of image segmentation quality based on a spatial overlap index1","volume":"11","author":"Zou","year":"2004","journal-title":"Acad. Radiol."}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000101?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841526000101?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T06:24:53Z","timestamp":1773987893000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841526000101"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":109,"alternative-id":["S1361841526000101"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2026.103941","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Advances in automated fetal brain MRI segmentation and biometry: Insights from the FeTA 2024 challenge","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2026.103941","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103941"}}