{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,10,6]],"date-time":"2024-10-06T01:02:06Z","timestamp":1728176526618},"reference-count":61,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.jp","clinicalkey.com","clinicalkey.es","clinicalkey.com.au","clinicalkey.fr","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computerized Medical Imaging and Graphics"],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1016\/j.compmedimag.2019.101660","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T16:37:46Z","timestamp":1573835866000},"page":"101660","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":78,"special_numbering":"C","title":["Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation"],"prefix":"10.1016","volume":"79","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-2436-7750","authenticated-orcid":false,"given":"Jose","family":"Dolz","sequence":"first","affiliation":[]},{"given":"Christian","family":"Desrosiers","sequence":"additional","affiliation":[]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Dinggang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Ismail","family":"Ben Ayed","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.compmedimag.2019.101660_bib0005","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1203\/PDR.0b013e31815ed071","article-title":"Probabilistic brain tissue segmentation in neonatal magnetic resonance imaging","volume":"63","author":"Anbeek","year":"2008","journal-title":"Pediatric research"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0010","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"253","article-title":"Semi-supervised learning for network-based cardiac mr image segmentation","author":"Bai","year":"2017"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0015","first-page":"1","article-title":"Theano: A CPU and GPU math compiler in Python","author":"Bergstra","year":"2010","journal-title":"Proc. 9th Python in Science Conf."},{"key":"10.1016\/j.compmedimag.2019.101660_bib0020","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.neuroimage.2018.08.003","article-title":"Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images","volume":"183","author":"Carass","year":"2018","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0025","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.neuroimage.2012.08.009","article-title":"AdaPT: an adaptive preterm segmentation algorithm for neonatal brain MRI","volume":"65","author":"Cardoso","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0030","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"424","article-title":"3d u-net: learning dense volumetric segmentation from sparse annotation","author":"\u00c7i\u00e7ek","year":"2016"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0035","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0040","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"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0045","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1109\/TMI.2018.2878669","article-title":"Hyperdense-net: A hyper-densely connected CNN for multi-modal image segmentation","volume":"38","author":"Dolz","year":"2018","journal-title":"IEEE transactions on medical imaging"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0050","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.irbm.2015.06.001","article-title":"Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: a survey","volume":"36","author":"Dolz","year":"2015","journal-title":"IRBM"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0055","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"149","article-title":"3D deeply supervised network for automatic liver segmentation from CT volumes","author":"Dou","year":"2016"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0060","doi-asserted-by":"crossref","DOI":"10.1002\/mp.12593","article-title":"Esophagus segmentation in CT via 3D fully convolutional neural network and random walk","author":"Fechter","year":"2017","journal-title":"Medical Physics"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0065","series-title":"International conferences computer graphics, visualization, computer vision and image processing","first-page":"305","article-title":"Deep learning in medical image analysis: recent advances and future trends","author":"Goceri","year":"2017"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0070","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1016\/j.media.2012.07.006","article-title":"Morphology-driven automatic segmentation of MR images of the neonatal brain","volume":"16","author":"Gui","year":"2012","journal-title":"Medical image analysis"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0075","first-page":"1026","article-title":"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification","author":"He","year":"2015","journal-title":"Proceedings of the IEEE International Conference on Computer Vision"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0080","series-title":"European Conference on Computer Vision","first-page":"630","article-title":"Identity mappings in deep residual networks","author":"He","year":"2016"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0085","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.neuroimage.2012.08.081","article-title":"Automated detection of white matter signal abnormality using T2 relaxometry: application to brain segmentation on term MRI in very preterm infants","volume":"64","author":"He","year":"2013","journal-title":"Neuroimage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0090","doi-asserted-by":"crossref","unstructured":"Kamnitsas, K., Bai, W., Ferrante, E., McDonagh, S., Sinclair, M., Pawlowski, N., Rajchl, M., Lee, M., Kainz, B., Daniel Rueckert, B. G. (2017a). Ensembles of multiple models and architectures for robust brain tumour segmentation. arXiv preprint arXiv:1711.01468.","DOI":"10.1007\/978-3-319-75238-9_38"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0095","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.media.2016.10.004","article-title":"Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation","volume":"36","author":"Kamnitsas","year":"2017","journal-title":"Medical image analysis"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0100","first-page":"285","article-title":"Boundary loss for highly unbalanced segmentation","author":"Kervadec","year":"2019","journal-title":"International Conference on Medical Imaging with Deep Learning"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0105","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","author":"Krizhevsky","year":"2012","journal-title":"Advances in neural information processing systems."},{"key":"10.1016\/j.compmedimag.2019.101660_bib0110","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proceedings of the IEEE"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0115","first-page":"33","article-title":"Neonatal brain segmentation using second order neighborhood information","author":"Ledig","year":"2012","journal-title":"Workshop on Perinatal and Paediatric Imaging: PaPI, MICCAI"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0120","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1093\/cercor\/bhs413","article-title":"Mapping longitudinal hemispheric structural asymmetries of the human cerebral cortex from birth to 2 years of age","volume":"24","author":"Li","year":"2013","journal-title":"Cerebral cortex"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0125","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Medical image analysis"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0130","first-page":"3431","article-title":"Fully convolutional networks for semantic segmentation","author":"Long","year":"2015","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0135","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11548-016-1467-3","article-title":"Automatic 3D liver location and segmentation via convolutional neural network and graph cut","volume":"12","author":"Lu","year":"2017","journal-title":"International journal of computer assisted radiology and surgery"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0140","article-title":"A review on automatic fetal and neonatal brain MRI segmentation","author":"Makropoulos","year":"2017","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0145","first-page":"9","article-title":"Automatic tissue and structural segmentation of neonatal brain MRI using Expectation-Maximization","volume":"2012","author":"Makropoulos","year":"2012","journal-title":"MICCAI Grand Chall. Neonatal Brain Segmentation"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0150","first-page":"16","article-title":"Neobrains12 challenge: adaptive neonatal MRI brain segmentation with myelinated white matter class and automated extraction of ventricles i-iv.","author":"Melbourne","year":"2012","journal-title":"MICCAI Grand Challenge: Neonatal Brain Segmentation (NeoBrainSI2),."},{"key":"10.1016\/j.compmedimag.2019.101660_bib0155","series-title":"3D Vision (3DV), 2016 Fourth International Conference on","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0160","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.neuroimage.2015.06.007","article-title":"Automatic segmentation of MR brain images of preterm infants using supervised classification","volume":"118","author":"Moeskops","year":"2015","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0165","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 transactions on medical imaging"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0170","series-title":"Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on","first-page":"1342","article-title":"Fully convolutional networks for multi-modality isointense infant brain image segmentation","author":"Nie","year":"2016"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0175","doi-asserted-by":"crossref","first-page":"1041","DOI":"10.1016\/j.neuroimage.2006.05.020","article-title":"Detailed semiautomated MRI based morphometry of the neonatal brain: preliminary results","volume":"32","author":"Nishida","year":"2006","journal-title":"Neuroimage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0180","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/S0361-9230(00)00434-2","article-title":"Maturation of white matter in the human brain: a review of magnetic resonance studies","volume":"54","author":"Paus","year":"2001","journal-title":"Brain research bulletin"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0185","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1016\/j.media.2005.05.007","article-title":"Automatic segmentation of MR images of the developing newborn brain","volume":"9","author":"Prastawa","year":"2005","journal-title":"Medical image analysis"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0190","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":"2017","journal-title":"IEEE transactions on medical imaging"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0195","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.neuroimage.2009.07.066","article-title":"Neonatal brain image segmentation in longitudinal MRI studies","volume":"49","author":"Shi","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0200","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.neuroimage.2010.02.025","article-title":"Construction of multi-region-multi-reference atlases for neonatal brain MRI segmentation","volume":"51","author":"Shi","year":"2010","journal-title":"Neuroimage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0205","doi-asserted-by":"crossref","first-page":"e18746","DOI":"10.1371\/journal.pone.0018746","article-title":"Infant brain atlases from neonates to 1-and 2-year-olds","volume":"6","author":"Shi","year":"2011","journal-title":"PloS one"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0210","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0215","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"883","article-title":"Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based markov priors","author":"Song","year":"2007"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0220","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.neuroimage.2014.12.042","article-title":"Links: Learning-based multi-source integration framework for segmentation of infant brain images","volume":"108","author":"Wang","year":"2015","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0225","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2019.2901712","article-title":"Benchmark on automatic 6-month-old infant brain segmentation algorithms: The iseg-2017 challenge","author":"Wang","year":"2019","journal-title":"IEEE transactions on medical imaging"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0230","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.neuroimage.2013.11.040","article-title":"Integration of sparse multi-modality representation and anatomical constraint for isointense infant brain MR image segmentation","volume":"89","author":"Wang","year":"2014","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0235","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.neuroimage.2013.08.008","article-title":"Segmentation of neonatal brain MR images using patch-driven level sets","volume":"84","author":"Wang","year":"2014","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0240","doi-asserted-by":"crossref","first-page":"805","DOI":"10.1016\/j.neuroimage.2011.06.064","article-title":"Automatic segmentation of neonatal images using convex optimization and coupled level sets","volume":"58","author":"Wang","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0245","doi-asserted-by":"crossref","first-page":"e44596","DOI":"10.1371\/journal.pone.0044596","article-title":"4D multi-modality tissue segmentation of serial infant images","volume":"7","author":"Wang","year":"2012","journal-title":"PloS one"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0250","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1002\/hbm.21486","article-title":"Longitudinally guided level sets for consistent tissue segmentation of neonates","volume":"34","author":"Wang","year":"2013","journal-title":"Human brain mapping"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0255","first-page":"28","article-title":"An atlas-based method for neonatal MR brain tissue segmentation","author":"Wang","year":"2012","journal-title":"Proceedings of the MICCAI Grand Challenge: Neonatal Brain Segmentation"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0260","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TMI.2004.828354","article-title":"Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation","volume":"23","author":"Warfield","year":"2004","journal-title":"IEEE transactions on medical imaging"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0265","series-title":"Biomedical Imaging: Nano to Macro, 2006. 3rd IEEE International Symposium on","first-page":"766","article-title":"Segmentation of newborn brain MRI","author":"Weisenfeld","year":"2006"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0270","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.neuroimage.2009.04.068","article-title":"Automatic segmentation of newborn brain MRI","volume":"47","author":"Weisenfeld","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0275","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1109\/TBME.2015.2496253","article-title":"Scalable high-performance image registration framework by unsupervised deep feature representations learning","volume":"63","author":"Wu","year":"2015","journal-title":"IEEE Transactions on Biomedical Engineering"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0280","first-page":"36","article-title":"Automatic registration-based segmentation for neonatal brains using ANTs and Atropos","author":"Wu","year":"2012","journal-title":"MICCAI Grand Challenge: Neonatal Brain Segmentation (NeoBrainS12)"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0285","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"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0290","first-page":"399","article-title":"Suggestive annotation: A deep active learning framework for biomedical image segmentation.","author":"Yang","year":"2017","journal-title":"International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2017)"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0295","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"287","article-title":"Automatic 3d cardiovascular mr segmentation with densely-connected volumetric convnets","author":"Yu","year":"2017"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0300","series-title":"European conference on computer vision","first-page":"818","article-title":"Visualizing and understanding convolutional networks","author":"Zeiler","year":"2014"},{"key":"10.1016\/j.compmedimag.2019.101660_bib0305","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neuroimage.2014.12.061","article-title":"Deep convolutional neural networks for multi-modality isointense infant brain image segmentation","volume":"108","author":"Zhang","year":"2015","journal-title":"NeuroImage"}],"container-title":["Computerized Medical Imaging and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611119300771?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0895611119300771?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2021,2,3]],"date-time":"2021-02-03T15:11:53Z","timestamp":1612365113000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0895611119300771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1]]},"references-count":61,"alternative-id":["S0895611119300771"],"URL":"https:\/\/doi.org\/10.1016\/j.compmedimag.2019.101660","relation":{},"ISSN":["0895-6111"],"issn-type":[{"value":"0895-6111","type":"print"}],"subject":[],"published":{"date-parts":[[2020,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","name":"articletitle","label":"Article Title"},{"value":"Computerized Medical Imaging and Graphics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compmedimag.2019.101660","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"101660"}}