{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T19:33:14Z","timestamp":1742931194863,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030326944"},{"type":"electronic","value":"9783030326951"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-32695-1_9","type":"book-chapter","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T17:10:36Z","timestamp":1570727436000},"page":"77-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Relevance Vector Machines for Harmonization of MRI Brain Volumes Using Image Descriptors"],"prefix":"10.1007","author":[{"given":"Maria Ines","family":"Meyer","sequence":"first","affiliation":[]},{"given":"Ezequiel","family":"de la Rosa","sequence":"additional","affiliation":[]},{"given":"Koen","family":"Van Leemput","sequence":"additional","affiliation":[]},{"given":"Diana M.","family":"Sima","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,10,7]]},"reference":[{"issue":"2","key":"9_CR1","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/S1474-4422(06)70349-0","volume":"5","author":"RA Bermel","year":"2006","unstructured":"Bermel, R.A., Bakshi, R.: The measurement and clinical relevance of brain atrophy in multiple sclerosis. Lancet Neurol. 5(2), 158\u2013170 (2006)","journal-title":"Lancet Neurol."},{"key":"9_CR2","doi-asserted-by":"publisher","first-page":"188","DOI":"10.1016\/j.neuroimage.2016.07.035","volume":"142","author":"V Biberacher","year":"2016","unstructured":"Biberacher, V., et al.: Intra- and interscanner variability of magnetic resonance imaging based volumetry in multiple sclerosis. NeuroImage 142, 188\u2013197 (2016). https:\/\/doi.org\/10.1016\/j.neuroimage.2016.07.035","journal-title":"NeuroImage"},{"key":"9_CR3","unstructured":"Biomedical Image Analysis Group, Imperial College London: Ixi dataset. https:\/\/brain-development.org\/ixi-dataset\/. Accessed on 18 Mar 2019"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/j.jneumeth.2014.04.023","volume":"230","author":"J Chen","year":"2014","unstructured":"Chen, J., et al.: Exploration of scanning effects in multi-site structural MRI studies. J. Neurosci. Methods 230, 37\u201350 (2014). https:\/\/doi.org\/10.1016\/j.jneumeth.2014.04.023","journal-title":"J. Neurosci. Methods"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"606","DOI":"10.1016\/j.nicl.2015.06.009","volume":"8","author":"AS Chua","year":"2015","unstructured":"Chua, A.S., et al.: Handling changes in MRI acquisition parameters in modeling whole brain lesion volume and atrophy data in multiple sclerosis subjects: comparison of linear mixed-effect models. NeuroImage: Clin. 8, 606\u2013610 (2015)","journal-title":"NeuroImage: Clin."},{"key":"9_CR6","doi-asserted-by":"publisher","first-page":"104","DOI":"10.1016\/j.neuroimage.2017.11.024","volume":"167","author":"JP Fortin","year":"2018","unstructured":"Fortin, J.P., et al.: Harmonization of cortical thickness measurements across scanners and sites. NeuroImage 167, 104\u2013120 (2018). https:\/\/doi.org\/10.1016\/j.neuroimage.2017.11.024","journal-title":"NeuroImage"},{"key":"9_CR7","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.nicl.2015.05.003","volume":"8","author":"S Jain","year":"2015","unstructured":"Jain, S., et al.: Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images. NeuroImage: Clin. 8, 367\u2013375 (2015). https:\/\/doi.org\/10.1016\/j.nicl.2015.05.003","journal-title":"NeuroImage: Clin."},{"key":"9_CR8","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/j.nicl.2013.08.001","volume":"3","author":"BC Jones","year":"2013","unstructured":"Jones, B.C., et al.: Quantification of multiple-sclerosis-related brain atrophy in two heterogeneous MRI datasets using mixed-effects modeling. NeuroImage: Clin. 3, 171\u2013179 (2013). https:\/\/doi.org\/10.1016\/j.nicl.2013.08.001","journal-title":"NeuroImage: Clin."},{"issue":"3","key":"9_CR9","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1162\/neco.1992.4.3.415","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415\u2013447 (1992). https:\/\/doi.org\/10.1162\/neco.1992.4.3.415","journal-title":"Neural Comput."},{"issue":"9","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1498","DOI":"10.1162\/jocn.2007.19.9.1498","volume":"19","author":"DS Marcus","year":"2007","unstructured":"Marcus, D.S., et al.: Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J. Cogn. Neurosci. 19(9), 1498\u20131507 (2007)","journal-title":"J. Cogn. Neurosci."},{"issue":"6","key":"9_CR11","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1002\/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M","volume":"42","author":"LG Ny\u00fal","year":"1999","unstructured":"Ny\u00fal, L.G., Udupa, J.K.: On standardizing the MR image intensity scale. Magn. Reson. Med. 42(6), 1072\u20131081 (1999)","journal-title":"Magn. Reson. Med."},{"key":"9_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2012\/347120","volume":"2012","author":"N Robitaille","year":"2012","unstructured":"Robitaille, N., Mouiha, A., Cr\u00e9peault, B., Valdivia, F., Duchesne, S.: Tissue-based MRI intensity standardization: application to multicentric datasets. Int. J. Biomed. Imaging 2012, 1\u201311 (2012)","journal-title":"Int. J. Biomed. Imaging"},{"issue":"1","key":"9_CR13","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.neuroimage.2009.06.074","volume":"48","author":"D Salat","year":"2009","unstructured":"Salat, D., et al.: Age-associated alterations in cortical gray and white matter signal intensity and gray to white matter contrast. NeuroImage 48(1), 21\u201328 (2009)","journal-title":"NeuroImage"},{"issue":"2","key":"9_CR14","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1002\/jmri.22636","volume":"34","author":"H Takao","year":"2011","unstructured":"Takao, H., Hayashi, N., Ohtomo, K.: Effect of scanner in longitudinal studies of brain volume changes. J. Magn. Reson. Imaging 34(2), 438\u2013444 (2011). https:\/\/doi.org\/10.1002\/jmri.22636","journal-title":"J. Magn. Reson. Imaging"},{"issue":"3","key":"9_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1162\/15324430152748236","volume":"1","author":"ME Tipping","year":"2001","unstructured":"Tipping, M.E.: Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1(3), 211\u2013244 (2001). https:\/\/doi.org\/10.1162\/15324430152748236","journal-title":"J. Mach. Learn. Res."},{"issue":"10","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1095","DOI":"10.1016\/J.CVIU.2009.06.003","volume":"113","author":"Y Zhuge","year":"2009","unstructured":"Zhuge, Y., Udupa, J.K.: Intensity standardization simplifies brain MR image segmentation. Comput. Vis. Image Understand. 113(10), 1095\u20131103 (2009). https:\/\/doi.org\/10.1016\/J.CVIU.2009.06.003","journal-title":"Comput. Vis. Image Understand."}],"container-title":["Lecture Notes in Computer Science","OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32695-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:12:10Z","timestamp":1728519130000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32695-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030326944","9783030326951"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32695-1_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"7 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLCN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Clinical Neuroimaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 October 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlcn2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/mlcnws.com","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}