These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
140 related articles for article (PubMed ID: 23384318)
61. Automated determination of brain parenchymal fraction in multiple sclerosis. Vågberg M; Lindqvist T; Ambarki K; Warntjes JB; Sundström P; Birgander R; Svenningsson A AJNR Am J Neuroradiol; 2013 Mar; 34(3):498-504. PubMed ID: 22976234 [TBL] [Abstract][Full Text] [Related]
62. Normalization of white matter intensity on T1-weighted images of patients with acquired central nervous system demyelination. Ghassemi R; Brown R; Narayanan S; Banwell B; Nakamura K; Arnold DL J Neuroimaging; 2015; 25(2):184-190. PubMed ID: 24942347 [TBL] [Abstract][Full Text] [Related]
63. Exploring individual multiple sclerosis lesion volume change over time: Development of an algorithm for the analyses of longitudinal quantitative MRI measures. Köhler C; Wahl H; Ziemssen T; Linn J; Kitzler HH Neuroimage Clin; 2019; 21():101623. PubMed ID: 30545687 [TBL] [Abstract][Full Text] [Related]
68. Spatio-temporal analysis of brain MRI images using hidden Markov models. Wang Y; Resnick SM; Davatzikos C Med Image Comput Comput Assist Interv; 2010; 13(Pt 2):160-8. PubMed ID: 20879311 [TBL] [Abstract][Full Text] [Related]
69. A pyramidal approach for automatic segmentation of multiple sclerosis lesions in brain MRI. Pachai C; Zhu YM; Grimaud J; Hermier M; Dromigny-Badin A; Boudraa A; Gimenez G; Confavreux C; Froment JC Comput Med Imaging Graph; 1998; 22(5):399-408. PubMed ID: 9890184 [TBL] [Abstract][Full Text] [Related]
70. Segmentation of subtraction images for the measurement of lesion change in multiple sclerosis. Duan Y; Hildenbrand PG; Sampat MP; Tate DF; Csapo I; Moraal B; Bakshi R; Barkhof F; Meier DS; Guttmann CR AJNR Am J Neuroradiol; 2008 Feb; 29(2):340-6. PubMed ID: 18272569 [TBL] [Abstract][Full Text] [Related]
71. Modeling MR imaging enhancing-lesion volumes in multiple sclerosis: application in clinical trials. van den Elskamp IJ; Knol DL; Uitdehaag BM; Barkhof F AJNR Am J Neuroradiol; 2011 Dec; 32(11):2093-7. PubMed ID: 22051810 [TBL] [Abstract][Full Text] [Related]
72. Rotation-invariant multi-contrast non-local means for MS lesion segmentation. Guizard N; Coupé P; Fonov VS; Manjón JV; Arnold DL; Collins DL Neuroimage Clin; 2015; 8():376-89. PubMed ID: 26106563 [TBL] [Abstract][Full Text] [Related]
73. T2 hyperintensity of medial lemniscus: higher threshold application to ROI measurements is more accurate in predicting small vessel disease. Hakky MM; Erbay KD; Brewer E; Midle JB; French R; Erbay SH J Neuroimaging; 2013 Jul; 23(3):345-51. PubMed ID: 23343196 [TBL] [Abstract][Full Text] [Related]
74. Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. Brosch T; Tang LY; Youngjin Yoo ; Li DK; Traboulsee A; Tam R IEEE Trans Med Imaging; 2016 May; 35(5):1229-1239. PubMed ID: 26886978 [TBL] [Abstract][Full Text] [Related]
75. A Model of Population and Subject (MOPS) Intensities With Application to Multiple Sclerosis Lesion Segmentation. Tomas-Fernandez X; Warfield SK IEEE Trans Med Imaging; 2015 Jun; 34(6):1349-61. PubMed ID: 25616008 [TBL] [Abstract][Full Text] [Related]
76. An effective method for computerized prediction and segmentation of multiple sclerosis lesions in brain MRI. Roy S; Bhattacharyya D; Bandyopadhyay SK; Kim TH Comput Methods Programs Biomed; 2017 Mar; 140():307-320. PubMed ID: 28254088 [TBL] [Abstract][Full Text] [Related]
78. BOOST: a supervised approach for multiple sclerosis lesion segmentation. Cabezas M; Oliver A; Valverde S; Beltran B; Freixenet J; Vilanova JC; Ramió-Torrentà L; Rovira A; Lladó X J Neurosci Methods; 2014 Nov; 237():108-17. PubMed ID: 25194638 [TBL] [Abstract][Full Text] [Related]
79. Health effects of lesion localization in multiple sclerosis: spatial registration and confounding adjustment. Eloyan A; Shou H; Shinohara RT; Sweeney EM; Nebel MB; Cuzzocreo JL; Calabresi PA; Reich DS; Lindquist MA; Crainiceanu CM PLoS One; 2014; 9(9):e107263. PubMed ID: 25233361 [TBL] [Abstract][Full Text] [Related]
80. Fast and Robust Unsupervised Identification of MS Lesion Change Using the Statistical Detection of Changes Algorithm. Nguyen TD; Zhang S; Gupta A; Zhao Y; Gauthier SA; Wang Y AJNR Am J Neuroradiol; 2018 May; 39(5):830-833. PubMed ID: 29519791 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]