{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T15:57:58Z","timestamp":1780761478223,"version":"3.54.1"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,4,1]],"date-time":"2018-04-01T00:00:00Z","timestamp":1522540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007601","name":"European Union","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["medicalimageanalysisjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2018,4]]},"DOI":"10.1016\/j.media.2018.02.001","type":"journal-article","created":{"date-parts":[[2018,2,2]],"date-time":"2018-02-02T07:32:45Z","timestamp":1517556765000},"page":"108-120","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":37,"special_numbering":"C","title":["A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography"],"prefix":"10.1016","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1616-2234","authenticated-orcid":false,"given":"Bruno","family":"Oliveira","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5259-1891","authenticated-orcid":false,"given":"Sandro","family":"Queir\u00f3s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1995-7879","authenticated-orcid":false,"given":"Pedro","family":"Morais","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helena R.","family":"Torres","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o","family":"Gomes-Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaime C.","family":"Fonseca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jo\u00e3o L.","family":"Vila\u00e7a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2018.02.001_bib0001","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1007\/s10851-008-0135-9","article-title":"A fast and log-Euclidean polyaffine framework for locally linear registration","volume":"33","author":"Arsigny","year":"2009","journal-title":"J. Math. Imaging Vis."},{"key":"10.1016\/j.media.2018.02.001_bib0002","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1109\/TMI.2009.2014372","article-title":"Combination strategies in multi-atlas image segmentation: application to brain MR data","volume":"28","author":"Artaechevarria","year":"2009","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0003","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.media.2012.10.002","article-title":"Non-local statistical label fusion for multi-atlas segmentation","volume":"17","author":"Asman","year":"2013","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0004","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.media.2015.04.003","article-title":"Automatic multi-resolution shape modeling of multi-organ structures","volume":"25","author":"Cerrolaza","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0005","doi-asserted-by":"crossref","unstructured":"Cha, K.H., Hadjiiski, L., Samala, R.K., Chan, H., Caoili, E.M., Cohan, R.H., 2016. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets 43, 1882\u20131896. doi:10.1118\/1.4944498","DOI":"10.1118\/1.4944498"},{"key":"10.1016\/j.media.2018.02.001_bib0006","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1016\/j.media.2013.01.001","article-title":"Regression forests for efficient anatomy detection and localization in computed tomography scans","volume":"17","author":"Criminisi","year":"2013","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0007","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2012","first-page":"66","article-title":"Automatic detection and segmentation of kidneys in 3D CT images using random forests","author":"Cuingnet","year":"2012"},{"key":"10.1016\/j.media.2018.02.001_bib0008","series-title":"International MICCAI Workshop on Medical Computer Vision","article-title":"Multi-atlas segmentation and landmark localization in images with large field of view","author":"Gass","year":"2014"},{"key":"10.1016\/j.media.2018.02.001_bib0009","doi-asserted-by":"crossref","unstructured":"Hadjiiski, L., Chan, H., Cohan, R.H., Caoili, E.M., Law, Y., Cha, K., Zhou, C., Hadjiiski, L., Chan, H., Cohan, R.H., Caoili, E.M., Law, Y., 2015. Urinary bladder segmentation in CT urography (CTU) using CLASS Urinary bladder segmentation in CT urography (CTU) using CLASS 111906. doi:10.1118\/1.4823792","DOI":"10.1118\/1.4823792"},{"key":"10.1016\/j.media.2018.02.001_bib0010","series-title":"VISCERAL Challenge@ ISBI","first-page":"18","article-title":"Fully automatic multi-organ segmentation based on multi-boost learning and statistical shape model search","author":"He","year":"2015"},{"key":"10.1016\/j.media.2018.02.001_bib0011","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1016\/j.media.2009.05.004","article-title":"Statistical shape models for 3D medical image segmentation: a review","volume":"13","author":"Heimann","year":"2009","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0012","series-title":"VISCERAL Challenge@ ISBI","first-page":"27","article-title":"Multi-modal multi-atlas segmentation using discrete optimisation and self-similarities","author":"Heinrich","year":"2015"},{"key":"10.1016\/j.media.2018.02.001_bib0013","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.media.2015.06.012","article-title":"Multi-atlas segmentation of biomedical images: a survey","volume":"24","author":"Iglesias","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0014","series-title":"VISCERAL Challenge@ ISBI","first-page":"22","article-title":"Hierarchic anatomical structure segmentation guided by spatial correlations (AnatSeg-Gspac): VISCERAL anatomy3","author":"Jimenez-Del-Toro","year":"2015"},{"key":"10.1016\/j.media.2018.02.001_bib0015","series-title":"Proceedings of the VISCERAL Challenge at ISBI, CEUR Workshop Proceedings","article-title":"Hierarchical multi \u2013 structure segmentation guided by anatomical correlations","author":"Jimenez-Del-Toro","year":"2014"},{"key":"10.1016\/j.media.2018.02.001_bib0016","doi-asserted-by":"crossref","first-page":"2459","DOI":"10.1109\/TMI.2016.2578680","article-title":"Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: VISCERAL anatomy benchmarks","volume":"35","author":"Jimenez-Del-Toro","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0017","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.media.2013.02.006","article-title":"STEPS: similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation","volume":"17","author":"Jorge Cardoso","year":"2013","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0018","series-title":"International MICCAI Workshop on Medical Computer Vision","first-page":"201","article-title":"Automatic 3D multiorgan segmentation via clustering and graph cut using spatial relations and hierarchically-registered atlases","author":"K\u00e9chichian","year":"2014"},{"key":"10.1016\/j.media.2018.02.001_bib0019","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s11263-008-0168-y","article-title":"Adaptive stochastic gradient descent optimisation for image registration","volume":"81","author":"Klein","year":"2009","journal-title":"Int. J. Comput. Vis."},{"key":"10.1016\/j.media.2018.02.001_bib0020","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","article-title":"Elastix: a toolbox for intensity-based medical image registration","volume":"29","author":"Klein","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0021","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2011","first-page":"338","article-title":"Automatic multi-organ segmentation using learning-based segmentation and level set optimization","author":"Kohlberger","year":"2011"},{"key":"10.1016\/j.media.2018.02.001_bib0022","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1109\/TMI.2010.2057442","article-title":"Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE)","volume":"29","author":"Langerak","year":"2010","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0023","series-title":"MICCAI 2012 Workshop on Medical Contentbased Retrieval for Clinical Decision Support (MCBRCDS)","first-page":"92","article-title":"VISCERAL: towards large data in medical imaging challenges and directions","author":"Langs","year":"2012"},{"key":"10.1016\/j.media.2018.02.001_bib0024","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1016\/j.media.2012.02.001","article-title":"Statistical 4D graphs for multi-organ abdominal segmentation from multiphase CT","volume":"16","author":"Linguraru","year":"2012","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0025","doi-asserted-by":"crossref","first-page":"e02758","DOI":"10.1002\/cnm.2758","article-title":"Dense motion field estimation from myocardial boundary displacements","volume":"32","author":"Morais","year":"2016","journal-title":"Int. J. Numer. Method. Biomed. Eng."},{"key":"10.1016\/j.media.2018.02.001_bib0026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2015.06.009","article-title":"Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors","volume":"26","author":"Okada","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0027","series-title":"Academic Radiolog","first-page":"86","article-title":"Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model","author":"Okada","year":"2007"},{"key":"10.1016\/j.media.2018.02.001_bib0028","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"556","article-title":"Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation","author":"Roth","year":"2015"},{"key":"10.1016\/j.media.2018.02.001_bib0029","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/42.796284","article-title":"Nonrigid registration using free-form deformations: application to breast MR images","volume":"18","author":"Rueckert","year":"1999","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0030","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.media.2009.07.009","article-title":"Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images","volume":"13","author":"Rusk\u00f3","year":"2009","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0031","doi-asserted-by":"crossref","first-page":"830","DOI":"10.1016\/j.cmpb.2013.12.008","article-title":"Segmentation of abdominal organs from CT using a multi-level, hierarchical neural network strategy","volume":"113","author":"Selver","year":"2014","journal-title":"Comput. Methods Programs Biomed."},{"key":"10.1016\/j.media.2018.02.001_bib0032","series-title":"International Conference on Information Processing in Medical Imaging","first-page":"668","article-title":"Using region trajectories to construct an accurate and efficient polyaffine transform model","author":"Song","year":"2013"},{"key":"10.1016\/j.media.2018.02.001_bib0033","series-title":"International MICCAI Workshop on Medical Computer Vision","first-page":"16","article-title":"Rule-based ventral cavity multi-organ automatic segmentation in CT scans","author":"Spanier","year":"2014"},{"key":"10.1016\/j.media.2018.02.001_bib0034","doi-asserted-by":"crossref","first-page":"29","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"},{"key":"10.1016\/j.media.2018.02.001_bib0035","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.media.2015.04.015","article-title":"Discriminative dictionary learning for abdominal multi-organ segmentation","volume":"23","author":"Tong","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0036","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"157","article-title":"Collaborative multi organ segmentation by integrating deformable and graphical models","author":"Uzunbas","year":"2013"},{"key":"10.1016\/j.media.2018.02.001_bib0037","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"149","article-title":"Multi-organ segmentation using shape model guided local phase analysis","author":"Wang","year":"2015"},{"key":"10.1016\/j.media.2018.02.001_bib0038","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2012.143","article-title":"Multi-atlas segmentation with joint label fusion","volume":"35","author":"Wang","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2018.02.001_bib0039","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 Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0040","doi-asserted-by":"crossref","first-page":"1723","DOI":"10.1109\/TMI.2013.2265805","article-title":"Automated abdominal multi-organ segmentation with subject-specific atlas generation","volume":"32","author":"Wolz","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2018.02.001_bib0041","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.media.2015.05.009","article-title":"Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning","volume":"24","author":"Xu","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2018.02.001_bib0042","series-title":"SPIE Medical Imaging","first-page":"223","article-title":"Shape-constrained multi-atlas segmentation of spleen in CT","author":"Xu","year":"2014"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841518300227?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841518300227?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,8,9]],"date-time":"2019-08-09T04:40:46Z","timestamp":1565325646000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841518300227"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4]]},"references-count":42,"alternative-id":["S1361841518300227"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2018.02.001","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2018,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel multi-atlas strategy with dense deformation field reconstruction for abdominal and thoracic multi-organ segmentation from computed tomography","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2018.02.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2018 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}