{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T13:44:39Z","timestamp":1742996679337,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441523"},{"type":"electronic","value":"9783031441530"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-44153-0_3","type":"book-chapter","created":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:21Z","timestamp":1707066141000},"page":"25-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients"],"prefix":"10.1007","author":[{"given":"Ramy A.","family":"Zeineldin","sequence":"first","affiliation":[]},{"given":"Mohamed E.","family":"Karar","sequence":"additional","affiliation":[]},{"given":"Franziska","family":"Mathis-Ullrich","sequence":"additional","affiliation":[]},{"given":"Oliver","family":"Burgert","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,5]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1111\/bpa.12832","volume":"30","author":"DN Louis","year":"2020","unstructured":"Louis, D.N., et al.: CIMPACT-NOW update 6: new entity and diagnostic principle recommendations of the cIMPACT-Utrecht meeting on future CNS tumor classification and grading. Brain Pathol. 30, 844\u2013856 (2020)","journal-title":"Brain Pathol."},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Han, X., et al.: Patient-specific registration of pre-operative and post-recurrence brain tumor mri scans. brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries, pp. 105\u2013114 (2019)","DOI":"10.1007\/978-3-030-11723-8_10"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Image registration in medical robotics and intelligent systems: fundamentals and applications. Adv. Intell. Syst. 1(6), 100098 (2019)","DOI":"10.1002\/aisy.201900048"},{"issue":"1-2","key":"3_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00138-020-01060-x","volume":"31","author":"G Haskins","year":"2020","unstructured":"Haskins, G., Kruger, U., Yan, P.: Deep learning in medical image registration: a survey. Mach. Vision Appl. 31(1\u20132), 1\u201318 (2020). https:\/\/doi.org\/10.1007\/s00138-020-01060-x","journal-title":"Mach. Vision Appl."},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/j.cmpb.2009.09.002","volume":"98","author":"M Modat","year":"2010","unstructured":"Modat, M., et al.: Fast free-form deformation using graphics processing units. Comput. Methods Progr. Biomed. 98, 278\u2013284 (2010)","journal-title":"Comput. Methods Progr. Biomed."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S., Ourselin, S.: Global image registration using a symmetric block-matching approach. J. Med. Imag. 1(2), 024003 (2014)","DOI":"10.1117\/1.JMI.1.2.024003"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"622","DOI":"10.1016\/j.media.2010.07.002","volume":"15","author":"Y Ou","year":"2011","unstructured":"Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15, 622\u2013639 (2011)","journal-title":"Med. Image Anal."},{"key":"3_CR8","first-page":"1","volume":"2","author":"BB Avants","year":"2009","unstructured":"Avants, B.B., Tustison, N., Song, G.: Advanced normalization tools (ANTS). Insight j 2, 1\u201335 (2009)","journal-title":"Insight j"},{"key":"3_CR9","doi-asserted-by":"crossref","unstructured":"Johnson, H., Harris, G., Williams, K.: BRAINSFit: mutual information registrations of whole-brain 3D Images, using the insight toolkit. Insight J. 57(1), 1\u20130 (2007)","DOI":"10.54294\/hmb052"},{"key":"3_CR10","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1007\/s11548-020-02186-z","volume":"15","author":"RA Zeineldin","year":"2020","unstructured":"Zeineldin, R.A., Karar, M.E., Coburger, J., Wirtz, C.R., Burgert, O.: DeepSeg: deep neural network framework for automatic brain tumor segmentation using magnetic resonance FLAIR images. Int. J. Comput. Assist. Radiol. Surg. 15, 909\u2013920 (2020)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"3_CR11","first-page":"248","volume":"6","author":"X Cheng","year":"2018","unstructured":"Cheng, X., Zhang, L., Zheng, Y.: Deep similarity learning for multimodal medical images. Comput. Methods Biomech. Biomed. Eng.: Imag. Visual. 6, 248\u2013252 (2018)","journal-title":"Comput. Methods Biomech. Biomed. Eng.: Imag. Visual."},{"key":"3_CR12","doi-asserted-by":"publisher","first-page":"586","DOI":"10.1007\/978-3-030-87589-3_60","volume-title":"Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings","author":"RA Zeineldin","year":"2021","unstructured":"Zeineldin, R.A., Karar, M.E., Mathis-Ullrich, F., Burgert, O.: A hybrid deep registration of MR scans to interventional ultrasound for neurosurgical guidance. In: Lian, C., Cao, X., Rekik, I., Xuanang, Xu., Yan, P. (eds.) Machine Learning in Medical Imaging: 12th International Workshop, MLMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings, pp. 586\u2013595. Springer International Publishing, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87589-3_60"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Zeineldin, R.A., Karar, M.E., Coburger, J., Wirtz, C.R., Mathis-Ullrich, F., Burgert, O.: Towards automated correction of brain shift using deep deformable magnetic resonance imaging-intraoperative ultrasound (MRI-iUS) registration. Curr. Direct. Biomed. Eng. 6(1), 20200039 (2020)","DOI":"10.1515\/cdbme-2020-0039"},{"key":"3_CR14","doi-asserted-by":"crossref","unstructured":"Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: A learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788\u20131800 (2019)","DOI":"10.1109\/TMI.2019.2897538"},{"key":"3_CR15","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.media.2018.11.010","volume":"52","author":"BD de Vos","year":"2019","unstructured":"de Vos, B.D., Berendsen, F.F., Viergever, M.A., Sokooti, H., Staring, M., Isgum, I.: A deep learning framework for unsupervised affine and deformable image registration. Med. Image Anal. 52, 128\u2013143 (2019)","journal-title":"Med. Image Anal."},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Chen, J., Frey, E.C., He, Y., Segars, W.P., Li, Y., Du, Y.: TransMorph: transformer for unsupervised medical image registration. Med. Image Anal. 82, 102615 (2022)","DOI":"10.1016\/j.media.2022.102615"},{"key":"3_CR17","doi-asserted-by":"crossref","unstructured":"Abbasi, S., et al.: Medical image registration using unsupervised deep neural network: a scoping literature review. Biomed. Signal Process. Contr. 73, 103444 (2022)","DOI":"10.1016\/j.bspc.2021.103444"},{"key":"3_CR18","doi-asserted-by":"publisher","first-page":"147579","DOI":"10.1109\/ACCESS.2021.3120306","volume":"9","author":"RA Zeineldin","year":"2021","unstructured":"Zeineldin, R.A., et al.: IRegNet: non-rigid registration of MRI to interventional us for brain-shift compensation using convolutional neural networks. IEEE Access 9, 147579\u2013147590 (2021)","journal-title":"IEEE Access"},{"key":"3_CR19","unstructured":"Baheti, B., et al.: The brain tumor sequence registration challenge: Establishing correspondence between pre-operative and follow-up MRI scans of diffuse glioma patients. arXiv preprint arXiv:2112.06979 (2021)"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted Intervention \u2013 MICCAI 2015, pp. 234\u2013241 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"3_CR21","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Medical Image Computing and Computer-assisted Intervention \u2013 MICCAI 2016, pp. 424\u2013432 (2016)","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"3_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"}],"container-title":["Lecture Notes in Computer Science","Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44153-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,4]],"date-time":"2024-02-04T17:02:48Z","timestamp":1707066168000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44153-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441523","9783031441530"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44153-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 February 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BrainLes","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International MICCAI Brainlesion Workshop","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwb2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.brainlesion-workshop.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":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"65","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":"46","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":"71% - 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","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":"1-2","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}