{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:37:42Z","timestamp":1769024262285,"version":"3.49.0"},"publisher-location":"Cham","reference-count":12,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030322472","type":"print"},{"value":"9783030322489","type":"electronic"}],"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\/978-3-030-32248-9_72","type":"book-chapter","created":{"date-parts":[[2019,10,9]],"date-time":"2019-10-09T23:08:49Z","timestamp":1570662529000},"page":"645-653","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["A Hybrid Deep Learning Framework for Integrated Segmentation and Registration: Evaluation on Longitudinal White Matter Tract Changes"],"prefix":"10.1007","author":[{"given":"Bo","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wiro J.","family":"Niessen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Klein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marius","family":"de Groot","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. Arfan","family":"Ikram","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meike W.","family":"Vernooij","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Esther E.","family":"Bron","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"issue":"8","key":"72_CR1","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","unstructured":"Balakrishnan, G., et al.: Voxelmorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"72_CR2","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.jalz.2014.06.011","volume":"11","author":"M de Groot","year":"2015","unstructured":"de Groot, M., et al.: Tract-specific white matter degeneration in aging: the Rotterdam Study. Alzheimer\u2019s Dement. 11(3), 321\u2013330 (2015)","journal-title":"Alzheimer\u2019s Dement."},{"issue":"8","key":"72_CR3","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1007\/s10654-015-0082-x","volume":"30","author":"A Hofman","year":"2015","unstructured":"Hofman, A., et al.: The Rotterdam Study: 2016 objectives and design update. Eur. J. Epidemiol. 30(8), 661\u2013708 (2015)","journal-title":"Eur. J. Epidemiol."},{"key":"72_CR4","doi-asserted-by":"crossref","unstructured":"Hu, Y., et al.: Label-driven weakly-supervised learning for multimodal deformable image registration. In: 15th ISBI, pp. 1070\u20131074. IEEE (2018)","DOI":"10.1109\/ISBI.2018.8363756"},{"issue":"1","key":"72_CR5","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1109\/TMI.2009.2035616","volume":"29","author":"S Klein","year":"2010","unstructured":"Klein, S., et al.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imag. 29(1), 196\u2013205 (2010)","journal-title":"IEEE Trans. Med. Imag."},{"key":"72_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/978-3-030-00919-9_24","volume-title":"Machine Learning in Medical Imaging","author":"B Li","year":"2018","unstructured":"Li, B., de Groot, M., Vernooij, M.W., Ikram, M.A., Niessen, W.J., Bron, E.E.: Reproducible white matter tract segmentation using 3D U-Net on a large-scale DTI dataset. In: Shi, Y., Suk, H.-I., Liu, M. (eds.) MLMI 2018. LNCS, vol. 11046, pp. 205\u2013213. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00919-9_24"},{"issue":"4","key":"72_CR7","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1016\/j.media.2014.02.006","volume":"18","author":"S Parisot","year":"2014","unstructured":"Parisot, S., et al.: Concurrent tumor segmentation and registration with uncertainty-based sparse non-uniform graphs. Med. Image Anal. 18(4), 647\u2013659 (2014)","journal-title":"Med. Image Anal."},{"key":"72_CR8","doi-asserted-by":"crossref","unstructured":"Pohl, K.M., et al.: An expectation maximization approach for integrated registration, segmentation, and intensity correction (2005)","DOI":"10.1007\/11566465_39"},{"key":"72_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"72_CR10","unstructured":"Vlontzos, A., Mikolajczyk, K.: Deep segmentation and registration in x-ray angiography video. arXiv preprint arXiv:1805.06406 (2018)"},{"key":"72_CR11","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.neuroimage.2015.12.003","volume":"127","author":"A Yendiki","year":"2016","unstructured":"Yendiki, A., et al.: Joint reconstruction of white-matter pathways from longitudinal diffusion MRI data with anatomical priors. Neuroimage 127, 277\u2013286 (2016)","journal-title":"Neuroimage"},{"issue":"2","key":"72_CR12","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1016\/S1361-8415(03)00004-5","volume":"7","author":"A Yezzi","year":"2003","unstructured":"Yezzi, A., et al.: A variational framework for integrating segmentation and registration through active contours. Med. Image Anal. 7(2), 171\u2013185 (2003)","journal-title":"Med. Image Anal."}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-32248-9_72","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,10]],"date-time":"2024-10-10T00:25:28Z","timestamp":1728519928000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-32248-9_72"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030322472","9783030322489"],"references-count":12,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-32248-9_72","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"10 October 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MICCAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Medical Image Computing and Computer-Assisted Intervention","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":"13 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":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2019.org\/","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":"1730","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":"539","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":"31% - 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":"3.07","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":"6.31","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":"Yes","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"}]}}