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In: Proceedings of the 17th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2014), Springer Berlin Heidelberg. pp. 520\u2013527.","DOI":"10.1007\/978-3-319-10404-1_65"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib92","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1007\/s00234-015-1552-2","article-title":"A toolbox for multiple sclerosis lesion segmentation","volume":"57","author":"Roura","year":"2015","journal-title":"Neuroradiology"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib93","doi-asserted-by":"crossref","unstructured":"Roy, S., Carass, A., Prince, J.L., Pham, D.L., 2014a. Subject specific sparse dictionary learning for atlas based brain MRI segmentation. 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In: Proceedings of the 7th International Symposium on Biomedical Imaging (ISBI 2010), pp. 932\u2013935.","DOI":"10.1109\/ISBI.2010.5490140"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib96","doi-asserted-by":"crossref","unstructured":"Roy, S., He, Q., Carass, A., Jog, A., Cuzzocreo, J.L., Reich, D.S., Prince, J.L., Pham, D.L., 2014b. Example based lesion segmentation. In: Proceedings of SPIE Medical Imaging (SPIE-MI 2014), San Diego, CA, February 15\u201320, 2014, pp. 90341Y\u201390341Y\u20138.","DOI":"10.1117\/12.2043917"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib97","doi-asserted-by":"crossref","first-page":"1598","DOI":"10.1109\/JBHI.2015.2439242","article-title":"Subject-specific sparse dictionary learning for atlas-based brain MRI segmentation","volume":"19","author":"Roy","year":"2015","journal-title":"IEEE J. Biomed. 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In: Proceedings of the 11th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2008) 3D Segmentation in the Clinic: A Grand Challenge II, pp. 1\u20136.","DOI":"10.54294\/lmkqvm"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib106","doi-asserted-by":"crossref","unstructured":"Subbanna, N., Precup, D., Arnold, D.L., Arbel, T., 2015. IMaGe: Iterative Multilevel Probabilistic Graphical Model for Detection and Segmentation of Multiple Sclerosis Lesions in Brain MRI. In: 24th Inf. Proceedings in Med. Imaging (IPMI 2015), Springer Berlin Heidelberg. pp. 514\u2013526.","DOI":"10.1007\/978-3-319-19992-4_40"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib107","doi-asserted-by":"crossref","first-page":"2079","DOI":"10.1109\/TMI.2015.2419072","article-title":"Bayesian model selection for pathological neuroimaging data applied to white matter lesion segmentation","volume":"34","author":"Sudre","year":"2015","journal-title":"IEEE Trans. Med. 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Imag."},{"key":"10.1016\/j.neuroimage.2016.12.064_bib121","doi-asserted-by":"crossref","unstructured":"Weiss, N., Rueckert, D., Rao, A., 2013. Multiple sclerosis lesion segmentation using dictionary learning and sparse coding. In: Proceedings of the 16th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2013), Springer Berlin Heidelberg. pp. 735\u2013742.","DOI":"10.1007\/978-3-642-40811-3_92"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib122","doi-asserted-by":"crossref","unstructured":"Welti, D., Gerig, G., Rad\u00fc, E.W., Kappos, L., Sz\u00e9kely, G., 2001. Spatio-temporal segmentation of active multiple scleroris lesions in serial MRI data. In: 17th Inf. Proceedings in Med. Imaging (IPMI 2001), Springer Berlin Heidelberg. pp. 438\u2013445.","DOI":"10.1007\/3-540-45729-1_46"},{"key":"10.1016\/j.neuroimage.2016.12.064_bib123","doi-asserted-by":"crossref","first-page":"80","DOI":"10.2307\/3001968","article-title":"Individual comparisons by ranking methods","volume":"1","author":"Wilcoxon","year":"1945","journal-title":"Biom. Bull."},{"key":"10.1016\/j.neuroimage.2016.12.064_bib124","unstructured":"World Health Organization, 2008. Atlas: Multiple Sclerosis Resources in the World 2008. Springer, Geneva, Switzerland."},{"key":"10.1016\/j.neuroimage.2016.12.064_bib125","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.2016.12.064_bib126","doi-asserted-by":"crossref","unstructured":"Xie, Y., Tao, X., 2011. 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