{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:35:30Z","timestamp":1725755730666},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1016\/j.cmpb.2023.107334","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T20:30:36Z","timestamp":1672777836000},"page":"107334","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis"],"prefix":"10.1016","volume":"230","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0693-6187","authenticated-orcid":false,"given":"Andrea","family":"Urru","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-4395-3878","authenticated-orcid":false,"given":"Ayako","family":"Nakaki","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3922-7643","authenticated-orcid":false,"given":"Oualid","family":"Benkarim","sequence":"additional","affiliation":[]},{"given":"Francesca","family":"Crovetto","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8583-8835","authenticated-orcid":false,"given":"Laura","family":"Segal\u00e9s","sequence":"additional","affiliation":[]},{"given":"Valentin","family":"Comte","sequence":"additional","affiliation":[]},{"given":"Nadine","family":"Hahner","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-7379-9608","authenticated-orcid":false,"given":"Elisenda","family":"Eixarch","sequence":"additional","affiliation":[]},{"given":"Eduard","family":"Gratacos","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-7422-5240","authenticated-orcid":false,"given":"F\u00e0tima","family":"Crispi","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5236-5819","authenticated-orcid":false,"given":"Gemma","family":"Piella","sequence":"additional","affiliation":[]},{"given":"Miguel A.","family":"Gonz\u00e1lez Ballester","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2023.107334_bib0001","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","first-page":"620","article-title":"Revealing regional associations of cortical folding alterations with in utero ventricular dilation using joint spectral embedding","author":"Benkarim","year":"2018"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107334_bib0002","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1176\/ajp.157.1.16","article-title":"Meta-analysis of regional brain volumes in schizophrenia","volume":"157","author":"Wright","year":"2000","journal-title":"Am. J. Psychiatry"},{"issue":"12","key":"10.1016\/j.cmpb.2023.107334_bib0003","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1111\/j.1528-1167.2011.03323.x","article-title":"Ventricular enlargement in new-onset pediatric epilepsies","volume":"52","author":"Jackson","year":"2011","journal-title":"Epilepsia"},{"key":"10.1016\/j.cmpb.2023.107334_bib0004","doi-asserted-by":"crossref","first-page":"101750","DOI":"10.1016\/j.media.2020.101750","article-title":"A novel approach to multiple anatomical shape analysis: application to fetal ventriculomegaly","volume":"64","author":"Benkarim","year":"2020","journal-title":"Med. Image Anal."},{"issue":"9","key":"10.1016\/j.cmpb.2023.107334_bib0005","first-page":"1567","article-title":"Global and regional changes in cortical development assessed by MRI in fetuses with isolated nonsevere ventriculomegaly correlate with neonatal neurobehavior","volume":"40","author":"Hahner","year":"2019","journal-title":"Am. J. Neuroradiol."},{"issue":"5","key":"10.1016\/j.cmpb.2023.107334_bib0006","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1002\/hbm.23536","article-title":"Toward the automatic quantification of in utero brain development in 3D structural MRI: a review","volume":"38","author":"Benkarim","year":"2017","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.cmpb.2023.107334_bib0007","article-title":"A review on automatic fetal and neonatal brain MRI segmentation","author":"Makropoulos","year":"2017","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2023.107334_bib0008","doi-asserted-by":"crossref","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"},{"key":"10.1016\/j.cmpb.2023.107334_bib0009","doi-asserted-by":"crossref","unstructured":"K. Payette, R. Kottke, A. Jakab, Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels, 2020. 2009.06275","DOI":"10.1007\/978-3-030-60334-2_29"},{"key":"10.1016\/j.cmpb.2023.107334_bib0010","doi-asserted-by":"crossref","first-page":"116324","DOI":"10.1016\/j.neuroimage.2019.116324","article-title":"An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI","volume":"206","author":"Ebner","year":"2019","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107334_bib0011","doi-asserted-by":"crossref","first-page":"476","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":"365","key":"10.1016\/j.cmpb.2023.107334_bib0012","first-page":"1","article-title":"Advanced normalization tools (ANTS)","volume":"2","author":"Avants","year":"2009","journal-title":"Insight J."},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0013","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neuroimage.2007.07.030","article-title":"Automatic segmentation and reconstruction of the cortex from neonatal MRI","volume":"38","author":"Xue","year":"2007","journal-title":"Neuroimage"},{"issue":"10","key":"10.1016\/j.cmpb.2023.107334_bib0014","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/42.811270","article-title":"Automated model-based tissue classification of MR images of the brain","volume":"18","author":"Van Leemput","year":"1999","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0015","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/S1361-8415(00)00013-X","article-title":"Segmentation and measurement of brain structures in MRI including confidence bounds","volume":"4","author":"Gonz\u00e1lez Ballester","year":"2000","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2023.107334_bib0016","series-title":"MICCAI Grand Challenge on Neonatal Brain Segmentation","first-page":"9","article-title":"Automatic tissue and structural segmentation of neonatal brain MRI using expectation-maximization","volume":"Vol.\u00a02012","author":"Makropoulos","year":"2012"},{"key":"10.1016\/j.cmpb.2023.107334_bib0017","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.neuroimage.2014.07.023","article-title":"Automated fetal brain segmentation from 2D MRI slices for motion correction","volume":"101","author":"Keraudren","year":"2014","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2023.107334_bib0018","series-title":"2014\u00a0IEEE 11th International Symposium on Biomedical Imaging (ISBI)","first-page":"1230","article-title":"Fast fully automatic brain detection in fetal MRI using dense rotation invariant image descriptors","author":"Kainz","year":"2014"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107334_bib0019","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/TMI.2016.2621185","article-title":"DeepCut: object segmentation from bounding box annotations using convolutional neural networks","volume":"36","author":"Rajchl","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107334_bib0020","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/srep23470","article-title":"Accurate learning with few atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods","volume":"6","author":"Serag","year":"2016","journal-title":"Sci. Rep."},{"issue":"5","key":"10.1016\/j.cmpb.2023.107334_bib0021","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1109\/TMI.2016.2548501","article-title":"Automatic segmentation of MR brain images with a convolutional neural network","volume":"35","author":"Moeskops","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2023.107334_bib0022","series-title":"International Workshop on Machine Learning in Medical Imaging","first-page":"27","article-title":"Building an ensemble of complementary segmentation methods by exploiting probabilistic estimates","author":"Sanroma","year":"2016"},{"key":"10.1016\/j.cmpb.2023.107334_bib0023","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"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0024","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1002\/hbm.10062","article-title":"Fast robust automated brain extraction","volume":"17","author":"Smith","year":"2002","journal-title":"Hum. Brain Mapp."},{"issue":"1","key":"10.1016\/j.cmpb.2023.107334_bib0025","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.neuroimage.2011.01.051","article-title":"Multi-contrast human neonatal brain atlas: application to normal neonate development analysis","volume":"56","author":"Oishi","year":"2011","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0026","first-page":"1","article-title":"A multi-channel 4D probabilistic atlas of the developing brain: application to fetuses and neonates","volume":"2012","author":"Serag","year":"2012","journal-title":"Ann. BMVA"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107334_bib0027","doi-asserted-by":"crossref","first-page":"2750","DOI":"10.1016\/j.neuroimage.2010.10.019","article-title":"A dynamic 4D probabilistic atlas of the developing brain","volume":"54","author":"Kuklisova-Murgasova","year":"2011","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0028","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1016\/j.neuroimage.2011.09.062","article-title":"Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression","volume":"59","author":"Serag","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2023.107334_bib0029","series-title":"International Workshop on Spatio-Temporal Image Analysis for Longitudinal and Time-Series Image Data","first-page":"27","article-title":"Construction of a 4D brain atlas and growth model using diffeomorphic registration","author":"Schuh","year":"2014"},{"issue":"2","key":"10.1016\/j.cmpb.2023.107334_bib0030","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.neuroimage.2010.06.054","article-title":"A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation","volume":"53","author":"Habas","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2023.107334_bib0031","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":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.cmpb.2023.107334_bib0032","series-title":"Medical Imaging 2015: Image Processing","first-page":"94130Y","article-title":"Automatic brain extraction in fetal MRI using multi-atlas-based segmentation","volume":"Vol.\u00a09413","author":"Tourbier","year":"2015"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107334_bib0033","doi-asserted-by":"crossref","first-page":"e59990","DOI":"10.1371\/journal.pone.0059990","article-title":"Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions","volume":"8","author":"Gousias","year":"2013","journal-title":"PLoS ONE"},{"key":"10.1016\/j.cmpb.2023.107334_bib0034","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.neuroimage.2015.10.047","article-title":"Regional growth and atlasing of the developing human brain","volume":"125","author":"Makropoulos","year":"2016","journal-title":"Neuroimage"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0035","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1016\/j.neuroimage.2012.05.083","article-title":"Magnetic resonance imaging of the newborn brain: manual segmentation of labelled atlases in term-born and preterm infants","volume":"62","author":"Gousias","year":"2012","journal-title":"Neuroimage"},{"key":"10.1016\/j.cmpb.2023.107334_bib0036","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.neuroimage.2016.09.068","article-title":"A new neonatal cortical and subcortical brain atlas: the melbourne children\u2019s regional infant brain (M-CRIB) atlas","volume":"147","author":"Alexander","year":"2017","journal-title":"Neuroimage"},{"issue":"1","key":"10.1016\/j.cmpb.2023.107334_bib0037","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.cmpb.2023.107334_bib0038","doi-asserted-by":"crossref","unstructured":"P. de Dumast, H. Kebiri, C. Atat, V. Dunet, M. Koob, M.B. Cuadra, Segmentation of the cortical plate in fetal brain MRI with a topological loss, 2020. 2010.12391","DOI":"10.1007\/978-3-030-87735-4_19"},{"issue":"3","key":"10.1016\/j.cmpb.2023.107334_bib0039","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1016\/j.neuroimage.2011.09.062","article-title":"Construction of a consistent high-definition spatio-temporal atlas of the developing brain using adaptive kernel regression","volume":"59","author":"Serag","year":"2012","journal-title":"Neuroimage"},{"issue":"21","key":"10.1016\/j.cmpb.2023.107334_bib0040","doi-asserted-by":"crossref","first-page":"2150","DOI":"10.1001\/jama.2021.20178","article-title":"Effects of mediterranean diet or mindfulness-based stress reduction on prevention of small-for-gestational age birth weights in newborns born to at-risk pregnant individuals: the IMPACT BCN randomized clinical trial","volume":"326","author":"Crovetto","year":"2021","journal-title":"JAMA"},{"issue":"4","key":"10.1016\/j.cmpb.2023.107334_bib0041","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0059990","article-title":"Magnetic resonance imaging of the newborn brain: automatic segmentation of brain images into 50 anatomical regions","volume":"8","author":"Gousias","year":"2013","journal-title":"PLoS ONE"},{"key":"10.1016\/j.cmpb.2023.107334_bib0042","doi-asserted-by":"crossref","first-page":"1310","DOI":"10.1109\/TMI.2010.2046908","article-title":"N4ITK: improved N3 bias correction","volume":"29","author":"Tustison","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.cmpb.2023.107334_bib0043","series-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2018","first-page":"313","article-title":"An automated localization, segmentation and reconstruction framework for fetal brain MRI","author":"Ebner","year":"2018"},{"key":"10.1016\/j.cmpb.2023.107334_bib0044","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.media.2018.06.009","article-title":"A cortical shape-adaptive approach to local gyrification index","volume":"48","author":"Lyu","year":"2018","journal-title":"Med. Image Anal."}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723000019?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260723000019?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T03:24:29Z","timestamp":1697081069000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260723000019"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":44,"alternative-id":["S0169260723000019"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107334","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2023.107334","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"107334"}}