{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T06:03:25Z","timestamp":1775023405056,"version":"3.50.1"},"reference-count":78,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2017,8,1]],"date-time":"2017-08-01T00:00:00Z","timestamp":1501545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2017,8,1]],"date-time":"2017-08-01T00:00:00Z","timestamp":1501545600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2018,7,3]],"date-time":"2018-07-03T00:00:00Z","timestamp":1530576000000},"content-version":"am","delay-in-days":336,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["MOP-111169"],"award-info":[{"award-number":["MOP-111169"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"name":"les Fonds de Research Sant\u00e9 Qu\u00e9bec Pfizer Innovation","award":["4140438"],"award-info":[{"award-number":["4140438"]}]},{"name":"Levesque Foundation"},{"name":"Douglas Hospital Research Centre and Foundation"},{"DOI":"10.13039\/501100000023","name":"Government of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000023","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Canada Fund for Innovation"},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["P30AG010129"],"award-info":[{"award-number":["P30AG010129"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["K01 AG030514"],"award-info":[{"award-number":["K01 AG030514"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01 AG024904"],"award-info":[{"award-number":["U01 AG024904"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000005","name":"Department of Defense","doi-asserted-by":"publisher","award":["W81XWH-12-2-0012"],"award-info":[{"award-number":["W81XWH-12-2-0012"]}],"id":[{"id":"10.13039\/100000005","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000070","name":"National Institute of Biomedical Imaging and Bioengineering","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000070","id-type":"DOI","asserted-by":"publisher"}]},{"name":"AbbVie, Alzheimer's Association"},{"DOI":"10.13039\/100002565","name":"Alzheimer's Drug Discovery Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100002565","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Araclon Biotech"},{"DOI":"10.13039\/100007742","name":"BioClinica, Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007742","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100005614","name":"Biogen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005614","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100002491","name":"Bristol-Myers Squibb Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100002491","id-type":"DOI","asserted-by":"publisher"}]},{"name":"CereSpir, Inc."},{"name":"Cogstate"},{"name":"Eisai Inc."},{"name":"Elan Pharmaceuticals, Inc."},{"DOI":"10.13039\/100004312","name":"Eli Lilly and Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004312","id-type":"DOI","asserted-by":"publisher"}]},{"name":"EuroImmun"},{"DOI":"10.13039\/100004337","name":"F. Hoffmann-La Roche Ltd","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100004337","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004328","name":"Genentech, Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004328","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005062","name":"Fujirebio","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005062","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006775","name":"GE Healthcare","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015725","name":"IXICO Ltd.","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100015725","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Janssen Alzheimer Immunotherapy Research & Development, LLC"},{"name":"Johnson & Johnson Pharmaceutical Research & Development LLC"},{"name":"Lumosity"},{"DOI":"10.13039\/501100013327","name":"Lundbeck","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100013327","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Merck & Co., Inc"},{"name":"Meso Scale Diagnostics, LLC"},{"name":"NeuroRx Research"},{"name":"Neurotrack Technologies"},{"DOI":"10.13039\/100008272","name":"Novartis Pharmaceuticals Corporation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008272","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100004319","name":"Pfizer Inc.","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004319","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Piramal Imaging"},{"DOI":"10.13039\/501100011725","name":"Servier","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100011725","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100008373","name":"Takeda Pharmaceutical Company","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008373","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Transition Therapeutics"},{"name":"NIA\/NIH","award":["U01 AG016976"],"award-info":[{"award-number":["U01 AG016976"]}]},{"name":"NIA\/NIH","award":["P30 AG019610"],"award-info":[{"award-number":["P30 AG019610"]}]},{"name":"NIA\/NIH","award":["P30 AG013846"],"award-info":[{"award-number":["P30 AG013846"]}]},{"name":"NIA\/NIH","award":["P50 AG008702"],"award-info":[{"award-number":["P50 AG008702"]}]},{"name":"NIA\/NIH","award":["P50 AG025688"],"award-info":[{"award-number":["P50 AG025688"]}]},{"name":"NIA\/NIH","award":["P50 AG047266"],"award-info":[{"award-number":["P50 AG047266"]}]},{"name":"NIA\/NIH","award":["P30 AG010133"],"award-info":[{"award-number":["P30 AG010133"]}]},{"name":"NIA\/NIH","award":["P50 AG005146"],"award-info":[{"award-number":["P50 AG005146"]}]},{"name":"NIA\/NIH","award":["P50 AG005134"],"award-info":[{"award-number":["P50 AG005134"]}]},{"name":"NIA\/NIH","award":["P50 AG016574"],"award-info":[{"award-number":["P50 AG016574"]}]},{"name":"NIA\/NIH","award":["P50 AG005138"],"award-info":[{"award-number":["P50 AG005138"]}]},{"name":"NIA\/NIH","award":["P30 AG008051"],"award-info":[{"award-number":["P30 AG008051"]}]},{"name":"NIA\/NIH","award":["P30 AG013854"],"award-info":[{"award-number":["P30 AG013854"]}]},{"name":"NIA\/NIH","award":["P30 AG008017"],"award-info":[{"award-number":["P30 AG008017"]}]},{"name":"NIA\/NIH","award":["P30 AG010161"],"award-info":[{"award-number":["P30 AG010161"]}]},{"name":"NIA\/NIH","award":["P50 AG047366"],"award-info":[{"award-number":["P50 AG047366"]}]},{"name":"NIA\/NIH","award":["P30 AG010129"],"award-info":[{"award-number":["P30 AG010129"]}]},{"name":"NIA\/NIH","award":["P50 AG016573"],"award-info":[{"award-number":["P50 AG016573"]}]},{"name":"NIA\/NIH","award":["P50 AG016570"],"award-info":[{"award-number":["P50 AG016570"]}]},{"name":"NIA\/NIH","award":["P50 AG005131"],"award-info":[{"award-number":["P50 AG005131"]}]},{"name":"NIA\/NIH","award":["P50 AG023501"],"award-info":[{"award-number":["P50 AG023501"]}]},{"name":"NIA\/NIH","award":["P30 AG035982"],"award-info":[{"award-number":["P30 AG035982"]}]},{"name":"NIA\/NIH","award":["P30 AG028383"],"award-info":[{"award-number":["P30 AG028383"]}]},{"name":"NIA\/NIH","award":["P30 AG010124"],"award-info":[{"award-number":["P30 AG010124"]}]},{"name":"NIA\/NIH","award":["P50 AG005133"],"award-info":[{"award-number":["P50 AG005133"]}]},{"name":"NIA\/NIH","award":["P50 AG005142"],"award-info":[{"award-number":["P50 AG005142"]}]},{"name":"NIA\/NIH","award":["P30 AG012300"],"award-info":[{"award-number":["P30 AG012300"]}]},{"name":"NIA\/NIH","award":["P50 AG005136"],"award-info":[{"award-number":["P50 AG005136"]}]},{"name":"NIA\/NIH","award":["P50 AG033514"],"award-info":[{"award-number":["P50 AG033514"]}]},{"name":"NIA\/NIH","award":["P50 AG005681"],"award-info":[{"award-number":["P50 AG005681"]}]},{"name":"NIA\/NIH","award":["P50 AG047270"],"award-info":[{"award-number":["P50 AG047270"]}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["NeuroImage"],"published-print":{"date-parts":[[2017,8]]},"DOI":"10.1016\/j.neuroimage.2017.06.009","type":"journal-article","created":{"date-parts":[[2017,7,3]],"date-time":"2017-07-03T06:32:21Z","timestamp":1499063541000},"page":"233-249","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":110,"special_numbering":"C","title":["Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging"],"prefix":"10.1016","volume":"157","author":[{"given":"Mahsa","family":"Dadar","sequence":"first","affiliation":[]},{"given":"Josefina","family":"Maranzano","sequence":"additional","affiliation":[]},{"given":"Karen","family":"Misquitta","sequence":"additional","affiliation":[]},{"given":"Cassandra J.","family":"Anor","sequence":"additional","affiliation":[]},{"given":"Vladimir S.","family":"Fonov","sequence":"additional","affiliation":[]},{"given":"M. Carmela","family":"Tartaglia","sequence":"additional","affiliation":[]},{"given":"Owen T.","family":"Carmichael","sequence":"additional","affiliation":[]},{"given":"Charles","family":"Decarli","sequence":"additional","affiliation":[]},{"given":"D. Louis","family":"Collins","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neuroimage.2017.06.009_bib1","doi-asserted-by":"crossref","first-page":"26","DOI":"10.4236\/ojmi.2011.12005","article-title":"Textural based SVM for MS lesion segmentation in FLAIR MRIs","volume":"01","author":"Abdullah","year":"2011","journal-title":"Open J. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib2","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.neuroimage.2005.06.061","article-title":"Fully automatic segmentation of white matter hyperintensities in MR images of the elderly","volume":"28","author":"Admiraal-Behloul","year":"2005","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib3","doi-asserted-by":"crossref","first-page":"2461","DOI":"10.1109\/TBME.2008.926671","article-title":"Automatic segmentation and classification of multiple sclerosis in multichannel MRI","volume":"56","author":"Akselrod-Ballin","year":"2009","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib4","first-page":"1507","article-title":"Adult cerebrovascular disease: role of modified rapid fluid-attenuated inversion-recovery sequences","volume":"17","author":"Alexander","year":"1996","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib5","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/00031305.1992.10475879","article-title":"An introduction to kernel and nearest-neighbor nonparametric regression","volume":"46","author":"Altman","year":"1992","journal-title":"Am. Stat."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib6","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/S0165-0270(03)00237-1","article-title":"Segmentation of magnetic resonance brain images through discriminant analysis","volume":"131","author":"Amato","year":"2003","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib7","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1016\/j.neuroimage.2003.10.012","article-title":"Probabilistic segmentation of white matter lesions in MR imaging","volume":"21","author":"Anbeek","year":"2004","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib8","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.neuroimage.2013.05.065","article-title":"A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood","volume":"82","author":"Aubert-Broche","year":"2013","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib9","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1001\/archneur.58.5.742","article-title":"Fluid-attenuated inversion recovery magnetic resonance imaging detects cortical and juxtacortical multiple sclerosis lesions","volume":"58","author":"Bakshi","year":"2001","journal-title":"Arch. Neurol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib10","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1159\/000049146","article-title":"Imaging of white matter lesions","volume":"13","author":"Barkhof","year":"2002","journal-title":"Cerebrovasc. Dis."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib11","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neuroimage.2009.03.055","article-title":"Development and validation of morphological segmentation of age-related cerebral white matter hyperintensities","volume":"47","author":"Beare","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib12","first-page":"270","article-title":"The National Alzheimer's Coordinating center (NACC) database: an Alzheimer disease database","volume":"18","author":"Beekly","year":"2004","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib13","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I.M., Vapnik, V.N., 1992. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, (ACM), pp. 144\u2013152.","DOI":"10.1145\/130385.130401"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib14","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib15","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib16","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s12021-015-9260-y","article-title":"Automatic detection of white matter hyperintensities in healthy aging and pathology using magnetic resonance imaging: a review","volume":"13","author":"Caligiuri","year":"2015","journal-title":"Neuroinformatics"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.compmedimag.2008.10.008","article-title":"Automatic segmentation of magnetic resonance images using a decision tree with spatial information","volume":"33","author":"Chao","year":"2009","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib18","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/0730-725X(94)00124-L","article-title":"MRI segmentation: methods and applications","volume":"13","author":"Clarke","year":"1995","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib19","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1142\/S0218001497000597","article-title":"Animal: validation and applications of nonlinear registration-based segmentation","volume":"11","author":"Collins","year":"1997","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib20","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1097\/00004728-199403000-00005","article-title":"Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space","volume":"18","author":"Collins","year":"1994","journal-title":"J. Comput. Assist. Tomogr."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib21","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1002\/ana.24285","article-title":"Are acute infarcts the cause of leukoaraiosis? Brain mapping for 16 consecutive weeks","volume":"76","author":"Conklin","year":"2014","journal-title":"Ann. Neurol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib22","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/TMI.2007.906087","article-title":"An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images","volume":"27","author":"Coupe","year":"2008","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib23","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1111\/j.2517-6161.1958.tb00292.x","article-title":"The regression analysis of binary sequences","author":"Cox","year":"1958","journal-title":"J. R. Stat. Soc. Ser. B Methodol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib24","doi-asserted-by":"crossref","DOI":"10.1109\/TMI.2017.2693978","article-title":"Validation of a regression technique for segmentation of white matter hyperintensities in Alzheimer's disease","author":"Dadar","year":"2017","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib25","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1016\/j.neuroimage.2009.01.011","article-title":"White matter lesion extension to automatic brain tissue segmentation on MRI","volume":"45","author":"De Boer","year":"2009","journal-title":"Neuroimage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib26","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.neuroimage.2017.06.009_bib27","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1016\/S1474-4422(14)70090-0","article-title":"Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria","volume":"13","author":"Dubois","year":"2014","journal-title":"Lancet Neurol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib28","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/j.neuroimage.2008.02.024","article-title":"Segmentation of age-related white matter changes in a clinical multi-center study","volume":"41","author":"Dyrby","year":"2008","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib29","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.media.2014.02.003","article-title":"Individualized statistical learning from medical image databases: application to identification of brain lesions","volume":"18","author":"Erus","year":"2014","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib30","doi-asserted-by":"crossref","unstructured":"Ferrari, R.J., Wei, X., Zhang, Y., Scott, J.N., Mitchell, J.R., 2003. Segmentation of multiple sclerosis lesions using support vector machines. In Medical Imaging 2003, (International Society for Optics and Photonics), pp. 16\u201326.","DOI":"10.1117\/12.481377"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib31","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1093\/brain\/119.4.1349","article-title":"Quantitative assessment of MRI lesion load in multiple sclerosis","volume":"119","author":"Filippi","year":"1996","journal-title":"Brain"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib32","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","volume":"7","author":"Fisher","year":"1936","journal-title":"Ann. Eugen."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib33","unstructured":"Fonov, V., Coup\u00e9, P., Eskildsen, S.F., Collins, L.D., 2011a. Atrophy specific MRI brain template for Alzheimer\u2019s disease and Mild Cognitive Impairment. In Alzheimer\u2019s Association International Conference, (France), p. S58."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib34","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1016\/j.neuroimage.2010.07.033","article-title":"Unbiased average age-appropriate atlases for pediatric studies","volume":"54","author":"Fonov","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib35","first-page":"1612","article-title":"A short introduction to boosting","volume":"14","author":"Freund","year":"1999","journal-title":"J. -Jpn.\u00a0Soc. Artif. Intell."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2012.09.004","article-title":"Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging","volume":"17","author":"Garc\u00eda-Lorenzo","year":"2013","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib37","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1016\/j.neuroimage.2011.03.080","article-title":"Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images","volume":"57","author":"Geremia","year":"2011","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib38","doi-asserted-by":"crossref","first-page":"6246","DOI":"10.1118\/1.4966029","article-title":"Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease","volume":"43","author":"Ghafoorian","year":"2016","journal-title":"Med. Phys."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib39","doi-asserted-by":"crossref","unstructured":"Ghafoorian, M., Karssemeijer, N., Heskes, T., van Uden, I., Sanchez, C., Litjens, G., de Leeuw, F.-.E., van Ginneken, B., Marchiori, E., Platel, B., 2016b. Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. ArXiv Prepr. ArXiv161004834.","DOI":"10.1038\/s41598-017-05300-5"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib40","article-title":"Heterogeneity of small vessel disease: a systematic review of MRI and histopathology correlations","author":"Gouw","year":"2010","journal-title":"J. Neurol. Neurosurg. Psychiatry jnnp\u20132009"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib41","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.neuroimage.2016.07.018","article-title":"BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities","volume":"141","author":"Griffanti","year":"2016","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib42","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/0730-725X(96)00018-5","article-title":"Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques","volume":"14","author":"Grimaud","year":"1996","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib43","doi-asserted-by":"crossref","first-page":"182","DOI":"10.2478\/v10039-008-0039-3","article-title":"Automated Bayesian segmentation of microvascular white-matter lesions in the ACCORD-MIND study","volume":"53","author":"Herskovits","year":"2008","journal-title":"Adv. Med. Sci."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib44","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1148\/radiology.215.2.r00ma06470","article-title":"Limbic lobe of the human brain: evaluation with turbo fluid-attenuated inversion-recovery MR imaging","volume":"215","author":"Hirai","year":"2000","journal-title":"Radiology"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib45","unstructured":"Hunt, E.B., Marin, J., Stone, P.J., 1966. Experiments in induction."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib46","doi-asserted-by":"crossref","first-page":"4219","DOI":"10.1002\/hbm.22472","article-title":"Extracting and summarizing white matter hyperintensities using supervised segmentation methods in Alzheimer's disease risk and aging studies","volume":"35","author":"Ithapu","year":"2014","journal-title":"Hum. Brain Mapp."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib47","doi-asserted-by":"crossref","unstructured":"Kamber, M., Collins, D.L., Shinghal, R., Francis, G.S., Evans, A.C., 1992. Model-based 3-D segmentation of multiple sclerosis lesions in dual-echo MRI data. In Visualization in Biomedical Computing, (International Society for Optics and Photonics), pp. 590\u2013600.","DOI":"10.1117\/12.131112"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib48","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.compbiomed.2007.12.005","article-title":"Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and Markov random field model","volume":"38","author":"Khayati","year":"2008","journal-title":"Comput. Biol. Med."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib49","article-title":"Intraclass correlation coefficient","author":"Koch","year":"1982","journal-title":"Encycl. Stat. Sci."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib50","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.cmpb.2011.06.007","article-title":"Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images","volume":"107","author":"K\u00f6se","year":"2012","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib51","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.acra.2007.10.012","article-title":"Computer-assisted segmentation of white matter lesions in 3D MR images using support vector machine","volume":"15","author":"Lao","year":"2008","journal-title":"Acad. Radiol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib52","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1161\/01.STR.0000109226.67085.5A","article-title":"Thalamic lesions in vascular dementia low sensitivity of fluid-attenuated inversion recovery (FLAIR) imaging","volume":"35","author":"Leite","year":"2004","journal-title":"Stroke"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib53","series-title":"Naive (Bayes) at Forty: The Independence Assumption in Information Retrieval","first-page":"4","author":"Lewis","year":"1998"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib54","doi-asserted-by":"crossref","unstructured":"Li, Y., Hara, S., Ito, W., Shimura, K., 2007. A machine learning approach for interactive lesion segmentation. pp. 651246\u2013651246\u2013651248.","DOI":"10.1117\/12.708910"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib55","series-title":"Computer Vision Approaches toMedical Image Analysis","first-page":"25","article-title":"Comparing ensembles of learners: detecting prostate cancer from high resolution MRI","author":"Madabhushi","year":"2006"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib56","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jneumeth.2014.11.011","article-title":"Extra Tree forests for sub-acute ischemic stroke lesion segmentation in MR sequences","volume":"240","author":"Maier","year":"2015","journal-title":"J. Neurosci. Methods"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib57","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.3174\/ajnr.A4799","article-title":"Manual segmentation of MS cortical lesions using MRI: a comparison of 3 MRI reading protocols","volume":"37","author":"Maranzano","year":"2016","journal-title":"Am. J. Neuroradiol."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib58","series-title":"Discriminant Analysis and Statistical Pattern Recognition","author":"McLachlan","year":"2004"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib59","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.neuroimage.2014.04.056","article-title":"Lesion segmentation from multimodal MRI using random forest following ischemic stroke","volume":"98","author":"Mitra","year":"2014","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib60","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1097\/01.wad.0000213865.09806.92","article-title":"The uniform data set (UDS): clinical and cognitive variables and descriptive data from Alzheimer disease centers","volume":"20","author":"Morris","year":"2006","journal-title":"Alzheimer Dis. Assoc. Disord."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib61","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.mri.2012.01.007","article-title":"Automatic white matter lesion segmentation using an adaptive outlier detection method","volume":"30","author":"Ong","year":"2012","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib62","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib63","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1212\/WNL.0b013e3181cb3e25","article-title":"Alzheimer's Disease Neuroimaging Initiative (ADNI) clinical characterization","volume":"74","author":"Petersen","year":"2010","journal-title":"Neurology"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib64","doi-asserted-by":"crossref","unstructured":"Quddus, A., Fieguth, P., Basir, O., 2005. Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images. In Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. In: Proceedings of the 27th Annual International Conference of the, pp. 463\u2013466.","DOI":"10.1109\/IEMBS.2005.1616447"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib65","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1023\/A:1022643204877","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib66","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1007\/s10439-005-9009-0","article-title":"Unified approach for multiple sclerosis lesion segmentation on brain MRI","volume":"34","author":"Sajja","year":"2006","journal-title":"Ann. Biomed. Eng."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib67","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez, C.I., Hornero, R., Mayo, A., Garc\u00eda, M., 2009. Mixture model-based clustering and logistic regression for automatic detection of microaneurysms in retinal images. p. 72601M\u201372601M\u20138.","DOI":"10.1117\/12.812088"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib68","doi-asserted-by":"crossref","first-page":"3774","DOI":"10.1016\/j.neuroimage.2011.11.032","article-title":"An automated tool for detection of FLAIR-hyperintense white-matter lesions in multiple sclerosis","volume":"59","author":"Schmidt","year":"2012","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib69","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.1016\/j.neuroimage.2009.09.005","article-title":"A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions","volume":"49","author":"Shiee","year":"2010","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib70","doi-asserted-by":"crossref","first-page":"1182","DOI":"10.1016\/j.mri.2012.12.004","article-title":"Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images","volume":"31","author":"Sim\u00f5es","year":"2013","journal-title":"Magn. Reson. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib71","doi-asserted-by":"crossref","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.2017.06.009_bib72","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1016\/j.nicl.2013.10.003","article-title":"Accurate white matter lesion segmentation by k nearest neighbor classification with tissue type priors (kNN-TTPs)","volume":"3","author":"Steenwijk","year":"2013","journal-title":"NeuroImage Clin."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib73","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/1471-2342-12-17","article-title":"Improved assessment of multiple sclerosis lesion segmentation agreement via detection and outline error estimates","volume":"12","author":"Wack","year":"2012","journal-title":"BMC Med. Imaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib74","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1134\/S1054661808020235","article-title":"Fully automated segmentation of multiple sclerosis lesions in multispectral MRI","volume":"18","author":"Wels","year":"2008","journal-title":"Pattern Recognit. Image Anal."},{"key":"10.1016\/j.neuroimage.2017.06.009_bib75","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.pscychresns.2006.09.003","article-title":"A fully automated method for quantifying and localizing white matter hyperintensities on MR images","volume":"148","author":"Wu","year":"2006","journal-title":"Psychiatry Res. Neuroimaging"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib76","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1016\/j.neuroimage.2006.04.211","article-title":"Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI","volume":"32","author":"Wu","year":"2006","journal-title":"NeuroImage"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib77","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s00234-014-1322-6","article-title":"Application of variable threshold intensity to segmentation for white matter hyperintensities in fluid attenuated inversion recovery magnetic resonance images","volume":"56","author":"Yoo","year":"2014","journal-title":"Neuroradiology"},{"key":"10.1016\/j.neuroimage.2017.06.009_bib78","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1097\/01.rmr.0000245456.98029.a8","article-title":"Current concepts of analysis of cerebral white matter hyperintensities on magnetic resonance imaging","volume":"16","author":"Yoshita","year":"2005","journal-title":"Top. Magn. Reson. Imaging TMRI"}],"container-title":["NeuroImage"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811917304780?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1053811917304780?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,5]],"date-time":"2025-11-05T15:31:35Z","timestamp":1762356695000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1053811917304780"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,8]]},"references-count":78,"alternative-id":["S1053811917304780"],"URL":"https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.009","relation":{},"ISSN":["1053-8119"],"issn-type":[{"value":"1053-8119","type":"print"}],"subject":[],"published":{"date-parts":[[2017,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Performance comparison of 10 different classification techniques in segmenting white matter hyperintensities in aging","name":"articletitle","label":"Article Title"},{"value":"NeuroImage","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neuroimage.2017.06.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}