乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,14]],"date-time":"2024-09-14T06:30:32Z","timestamp":1726295432280},"reference-count":82,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2015,9,1]],"date-time":"2015-09-01T00:00:00Z","timestamp":1441065600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"CIHR","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010564","name":"AMOSO","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100010564","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2015,9]]},"DOI":"10.1016\/j.neuroimage.2015.05.099","type":"journal-article","created":{"date-parts":[[2015,6,13]],"date-time":"2015-06-13T08:34:52Z","timestamp":1434184492000},"page":"13-25","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets"],"prefix":"10.1016","volume":"118","author":[{"given":"Wu","family":"Qiu","sequence":"first","affiliation":[]},{"given":"Jing","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Martin","family":"Rajchl","sequence":"additional","affiliation":[]},{"given":"Jessica","family":"Kishimoto","sequence":"additional","affiliation":[]},{"given":"Yimin","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Sandrine","family":"de Ribaupierre","sequence":"additional","affiliation":[]},{"given":"Bernard","family":"Chiu","sequence":"additional","affiliation":[]},{"given":"Aaron","family":"Fenster","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neuroimage.2015.05.099_bb0005","doi-asserted-by":"publisher","first-page":"e1167","DOI":"10.1542\/peds.2007-0423","article-title":"Neurodevelopmental outcome of extremely low birth weight infants with posthemorrhagic hydrocephalus requiring shunt insertion","volume":"121","author":"Adams-Chapman","year":"2008","journal-title":"Pediatrics"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0010","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1016\/j.neuroimage.2009.02.018","article-title":"Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy","volume":"46","author":"Aljabar","year":"2009","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0015","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1137\/0331020","article-title":"Curvature-driven flows: a variational approach","volume":"31","author":"Almgren","year":"1993","journal-title":"SIAM J. Control. Optim."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0020","doi-asserted-by":"publisher","first-page":"795","DOI":"10.1016\/j.neuroimage.2005.05.046","article-title":"Probabilistic segmentation of brain tissue in MR imaging","volume":"27","author":"Anbeek","year":"2005","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0025","doi-asserted-by":"publisher","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":"Pediatr. Res."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0030","series-title":"The Isometric Log-Ratio Transform for Probabilistic Multi-Label Anatomical Shape Representation","author":"Andrews","year":"2014"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0035","doi-asserted-by":"publisher","first-page":"839","DOI":"10.1016\/j.neuroimage.2005.02.018","article-title":"Unified segmentation","volume":"26","author":"Ashburner","year":"2005","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0040","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1002\/uog.7554","article-title":"Neurodevelopmental outcome of fetuses referred for ventriculomegaly","volume":"35","author":"Beeghly","year":"2010","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0045","first-page":"359","article-title":"An experimental comparison of min-cut\/max-flow algorithms for energy minimization in vision","volume":"26","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0050","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0055","series-title":"ECCV","first-page":"409","article-title":"An integral solution to surface evolution pdes via geo-cuts","author":"Boykov","year":"2006"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0060","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/S0022-3476(05)80701-2","article-title":"Measurement of progressive cerebral ventriculomegaly in infants after grades III and IV intraventricular hemorrhages","volume":"117","author":"Brann","year":"1990","journal-title":"J. Pediatr."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0065","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1002\/pd.1624","article-title":"Obstetric and neonatal outcomes in severe fetal ventriculomegaly","volume":"27","author":"Breeze","year":"2007","journal-title":"Prenat. Diagn."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0070","series-title":"IEEE International Conference on Image Processing (ICIP)","first-page":"33","article-title":"Active contours based on chambolle's mean curvature motion","author":"Bresson","year":"2007"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0075","doi-asserted-by":"publisher","first-page":"e158","DOI":"10.1016\/j.cmpb.2011.07.015","article-title":"A review of atlas-based segmentation for magnetic resonance brain images","volume":"104","author":"Cabezas","year":"2011","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0080","doi-asserted-by":"publisher","first-page":"195","DOI":"10.4171\/IFB\/97","article-title":"An algorithm for mean curvature motion","volume":"6","author":"Chambolle","year":"2004","journal-title":"Interfaces Free Bound."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0085","series-title":"Proc. Conf Signals, Systems and Computers Record of the Thirty-Fourth Asilomar Conf","first-page":"490","article-title":"An efficient variational multiphase motion for the Mumford\u2013Shah segmentation model","author":"Chan","year":"2000"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0090","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0095","doi-asserted-by":"publisher","first-page":"1632","DOI":"10.1137\/040615286","article-title":"Algorithms for finding global minimizers of image segmentation and denoising models","volume":"66","author":"Chan","year":"2006","journal-title":"SIAM J. Appl. Math."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0100","doi-asserted-by":"publisher","first-page":"940","DOI":"10.1016\/j.neuroimage.2010.09.018","article-title":"Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation","volume":"54","author":"Coup\u00e9","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0105","series-title":"Computer Vision\u2013ECCV 2008","first-page":"99","article-title":"Geos: geodesic image segmentation","author":"Criminisi","year":"2008"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0110","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.neuroimage.2012.01.024","article-title":"Brain templates and atlases","volume":"62","author":"Evans","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0115","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/S0896-6273(02)00569-X","article-title":"Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain","volume":"33","author":"Fischl","year":"2002","journal-title":"Neuron"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0120","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1002\/uog.1857","article-title":"Fetal cerebral ventriculomegaly: outcome in 176 cases","volume":"25","author":"Gaglioti","year":"2005","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0125","doi-asserted-by":"publisher","first-page":"663","DOI":"10.1016\/j.laa.2007.03.009","article-title":"Distribution metrics and image segmentation","volume":"425","author":"Georgiou","year":"2007","journal-title":"Linear Algebra Appl."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0130","doi-asserted-by":"publisher","first-page":"1819","DOI":"10.1016\/j.neuroimage.2012.01.128","article-title":"Multi-atlas multi-shape segmentation of fetal brain MRI for volumetric and morphometric analysis of ventriculomegaly","volume":"60","author":"Gholipour","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0135","doi-asserted-by":"publisher","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":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0140","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1002\/uog.6456","article-title":"Fetal cerebral ventricular measurement and ventriculomegaly: time for procedure standardization","volume":"34","author":"Guibaud","year":"2009","journal-title":"Ultrasound Obstet. Gynecol."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0145","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0150","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1016\/j.ultrasmedbio.2004.07.017","article-title":"3-d ultrasonographic imaging of the cerebral ventricular system in very low birth weight infants","volume":"31","author":"Haiden","year":"2005","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0155","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.neuroimage.2006.05.061","article-title":"Automatic anatomical brain MRI segmentation combining label propagation and decision fusion","volume":"33","author":"Heckemann","year":"2006","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0160","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1016\/j.neuroimage.2010.01.072","article-title":"Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation","volume":"51","author":"Heckemann","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0165","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.media.2014.11.001","article-title":"Evaluation of automatic neonatal brain segmentation algorithms: the NeoBrainS12 challenge","volume":"20","author":"Isgum","year":"2015","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0170","doi-asserted-by":"publisher","first-page":"F291","DOI":"10.1136\/archdischild-2012-302323","article-title":"Can neurophysiological assessment improve timing of intervention in posthaemorrhagic ventricular dilatation?","volume":"98","author":"Klebermass-Schrehof","year":"2013","journal-title":"Arch. Dis. Child. Fetal Neonatal Ed."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0175","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2012.00171","article-title":"101 labeled brain images and a consistent human cortical labeling protocol","volume":"6","author":"Klein","year":"2012","journal-title":"Front. Neurosci."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0180","doi-asserted-by":"publisher","first-page":"12176","DOI":"10.1523\/JNEUROSCI.3479-08.2008","article-title":"A structural MRI study of human brain development from birth to 2years","volume":"28","author":"Knickmeyer","year":"2008","journal-title":"J. Neurosci."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0185","series-title":"Letter to the Editor: The Kullback\u2013Leibler Distance","author":"Kullback","year":"1987"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0190","series-title":"SSVM '09","first-page":"150","article-title":"Convex multi-class image labeling by simplex-constrained total variation","author":"Lellmann","year":"2009"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0195","series-title":"Computer Vision\u2013ECCV 2010","first-page":"494","article-title":"Fast and exact primal\u2013dual iterations for variational problems in computer vision","author":"Lellmann","year":"2010"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0200","series-title":"Computer Vision and Pattern Recognition, 2000","first-page":"316","article-title":"Statistical shape influence in geodesic active contours","author":"Leventon","year":"2000"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0205","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.media.2013.12.002","article-title":"Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge","volume":"18","author":"Litjens","year":"2014","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0210","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/BF01205007","article-title":"Implicit time discretization for the mean curvature flow equation","volume":"3","author":"Luckhaus","year":"1995","journal-title":"Calc. Var."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0215","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","article-title":"Open access series of imaging studies (oasis): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults","volume":"19","author":"Marcus","year":"2007","journal-title":"J. Cogn. Neurosci."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0220","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1109\/TIP.2007.908073","article-title":"Image segmentation using active contours driven by the Bhattacharyya gradient flow","volume":"16","author":"Michailovich","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0225","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1118\/1.597685","article-title":"The variability of manual and computer assisted quantification of multiple sclerosis lesion volumes","volume":"23","author":"Mitchell","year":"1996","journal-title":"Med. Phys."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0230","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.cmpb.2009.09.002","article-title":"Fast free-form deformation using graphics processing units","volume":"98","author":"Modat","year":"2010","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0235","doi-asserted-by":"publisher","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","article-title":"Point set registration: coherent point drift","volume":"32","author":"Myronenko","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0240","doi-asserted-by":"publisher","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"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0245","series-title":"Medical Image Computing and Computer-Assisted Intervention\u00a1\u00aaMICCAI 2002","first-page":"140","article-title":"Robust registration of multi-modal images: towards real-time clinical applications","author":"Ourselin","year":"2002"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0250","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1109\/TMI.2002.801163","article-title":"Estimation of 3-d left ventricular deformation from medical images using biomechanical models","volume":"21","author":"Papademetris","year":"2002","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0255","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/S0022-3476(78)80282-0","article-title":"Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1,500gm","volume":"92","author":"Papile","year":"1978","journal-title":"J. Pediatr."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0260","doi-asserted-by":"publisher","first-page":"1065","DOI":"10.1214\/aoms\/1177704472","article-title":"On estimation of a probability density function and mode","volume":"33","author":"Parzen","year":"1962","journal-title":"Ann. Math. Stat."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0265","series-title":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, Florida","article-title":"A convex relaxation approach for computing minimal partitions","author":"Pock","year":"2009"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0270","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1016\/j.media.2007.06.003","article-title":"Using the logarithm of odds to define a vector space on probabilistic atlases","volume":"11","author":"Pohl","year":"2007","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0275","first-page":"106","article-title":"Some generalized order\u2013disorder transformations","volume":"vol. 48","author":"Potts","year":"1952"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0280","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1118\/1.4810968","article-title":"3D prostate segmentation using level set with shape constraint based on rotational slices for 3D end-firing TRUS guided biopsy","volume":"40","author":"Qiu","year":"2013","journal-title":"Med. Phys."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0285","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.media.2014.02.009","article-title":"Dual optimization based prostate zonal segmentation in 3D MR images","volume":"18","author":"Qiu","year":"2014","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0290","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1109\/TMI.2014.2300694","article-title":"Prostate segmentation: an efficient convex optimization approach with axial symmetry using 3D TRUS and MR images","volume":"33","author":"Qiu","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0295","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1016\/j.media.2012.05.005","article-title":"Mammography segmentation with maximum likelihood active contours","volume":"16","author":"Rahmati","year":"2012","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0300","series-title":"Rancor: Non-Linear Image Registration with Total Variation Regularization","author":"Rajchl","year":"2014"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0305","series-title":"Energy Minimization Methods in Computer Vision and Pattern Recognition","first-page":"278","article-title":"Variational time-implicit multiphase level-sets","author":"Rajchl","year":"2015"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0310","series-title":"Intl. Conf. Signal and Image Processing","article-title":"Seeing the unseen: segmenting with distributions","author":"Rathi","year":"2006"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0315","series-title":"Medical Imaging, International Society for Optics and Photonics","article-title":"A new distribution metric for image segmentation","author":"Sandhu","year":"2008"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0320","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0325","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0330","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1109\/42.668698","article-title":"A nonparametric method for automatic correction of intensity nonuniformity in MRI data","volume":"17","author":"Sled","year":"1998","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0335","doi-asserted-by":"publisher","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."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0340","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1067\/mpd.2001.111822","article-title":"Variations in intraventricular hemorrhage incidence rates among Canadian neonatal intensive care units","volume":"138","author":"Synnes","year":"2001","journal-title":"J. Pediatr."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0345","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/0167-8655(96)00010-4","article-title":"New geodesic distance transforms for gray-scale images","volume":"17","author":"Toivanen","year":"1996","journal-title":"Pattern Recogn. Lett."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0350","doi-asserted-by":"publisher","first-page":"770","DOI":"10.1109\/TMI.2013.2237784","article-title":"3D carotid multi-region MRI segmentation by globally optimal evolution of coupled surfaces","volume":"32","author":"Ukwatta","year":"2013","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0355","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0360","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0365","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0370","series-title":"Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2014","first-page":"666","article-title":"Geodesic patch-based segmentation","author":"Wang","year":"2014"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0375","doi-asserted-by":"publisher","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.neuroimage.2015.05.099_bb0380","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/0378-3782(92)90040-N","article-title":"Neonatal cerebral ultrasound, neonatal neurology and perinatal conditions as predictors of neurodevelopmental outcome in very low birthweight infants","volume":"31","author":"Weisglas-Kuperus","year":"1992","journal-title":"Early Hum. Dev."},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0385","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1016\/j.neuroimage.2008.12.046","article-title":"The effect of template choice on morphometric analysis of pediatric brain data","volume":"45","author":"Yoon","year":"2009","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0390","series-title":"CVPR","article-title":"A study on continuous max-flow and min-cut approaches","author":"Yuan","year":"2010"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0395","series-title":"ECCV","article-title":"A continuous max-flow approach to Potts model","author":"Yuan","year":"2010"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0400","series-title":"Technical Report CAM-12-38","article-title":"A fast global optimization-based approach to evolving contours with generic shape prior","author":"Yuan","year":"2012"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0405","series-title":"Jointly Segmenting Prostate Zones in 3D MRIs by Globally Optimized Coupled Level-Sets 8081, 12\u201325","author":"Yuan","year":"2013"},{"key":"10.1016\/j.neuroimage.2015.05.099_bb0410","doi-asserted-by":"publisher","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"}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811915005248?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811915005248?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,21]],"date-time":"2018-09-21T21:26:41Z","timestamp":1537565201000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811915005248"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,9]]},"references-count":82,"alternative-id":["S1053811915005248"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2015.05.099","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2015,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"3D MR ventricle segmentation in pre-term infants with post-hemorrhagic ventricle dilatation (PHVD) using multi-phase geodesic level-sets","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2015.05.099","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2015 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}