{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T04:28:52Z","timestamp":1744777732762,"version":"3.40.3"},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030875886"},{"type":"electronic","value":"9783030875893"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-87589-3_60","type":"book-chapter","created":{"date-parts":[[2021,9,25]],"date-time":"2021-09-25T07:02:35Z","timestamp":1632553355000},"page":"586-595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Hybrid Deep Registration of MR Scans to Interventional Ultrasound for Neurosurgical Guidance"],"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":[[2021,9,21]]},"reference":[{"key":"60_CR1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.jmir.2017.06.005","volume":"48","author":"RC Miner","year":"2017","unstructured":"Miner, R.C.: Image-guided neurosurgery. J. Med. Imaging Radiat. Sci. 48, 328\u2013335 (2017)","journal-title":"J. Med. Imaging Radiat. Sci."},{"issue":"3","key":"60_CR2","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1007\/s11060-018-03052-4","volume":"141","author":"J Coburger","year":"2018","unstructured":"Coburger, J., Wirtz, C.R.: Fluorescence guided surgery by 5-ALA and intraoperative MRI in high grade glioma: a systematic review. J. Neurooncol. 141(3), 533\u2013546 (2018). https:\/\/doi.org\/10.1007\/s11060-018-03052-4","journal-title":"J. Neurooncol."},{"issue":"3","key":"60_CR3","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1007\/s11548-015-1259-1","volume":"11","author":"E De Momi","year":"2016","unstructured":"De Momi, E., et al.: A method for the assessment of time-varying brain shift during navigated epilepsy surgery. Int. J. Comput. Assist. Radiol. Surg. 11(3), 473\u2013481 (2016). https:\/\/doi.org\/10.1007\/s11548-015-1259-1","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"60_CR4","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/TMI.2009.2027993","volume":"29","author":"C Delorenzo","year":"2010","unstructured":"Delorenzo, C., Papademetris, X., Staib, L.H., Vives, K.P., Spencer, D.D., Duncan, J.S.: Image-guided intraoperative cortical deformation recovery using game theory: application to neocortical epilepsy surgery. IEEE Trans. Med. Imaging 29, 322\u2013338 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"60_CR5","doi-asserted-by":"crossref","unstructured":"Liu, J., et al.: Image registration in medical robotics and intelligent systems: fundamentals and applications. Adv. Intell. Syst. 1, (2019)","DOI":"10.1002\/aisy.201900048"},{"issue":"1-2","key":"60_CR6","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. Vis. Appl. 31(1\u20132), 1\u201318 (2020). https:\/\/doi.org\/10.1007\/s00138-020-01060-x","journal-title":"Mach. Vis. Appl."},{"key":"60_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-642-40811-3_5","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"W Wein","year":"2013","unstructured":"Wein, W., Ladikos, A., Fuerst, B., Shah, A., Sharma, K., Navab, N.: Global registration of ultrasound to mri using the LC2 metric for enabling neurosurgical guidance. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 34\u201341. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40811-3_5"},{"key":"60_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1007\/978-3-642-40811-3_24","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2013","author":"MP Heinrich","year":"2013","unstructured":"Heinrich, M.P., Jenkinson, M., Papie\u017c, B.W., Brady, S.M., Schnabel, J.A.: Towards realtime multimodal fusion for image-guided interventions using self-similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8149, pp. 187\u2013194. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-40811-3_24"},{"key":"60_CR9","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1016\/j.media.2013.12.003","volume":"18","author":"H Rivaz","year":"2014","unstructured":"Rivaz, H., Karimaghaloo, Z., Collins, D.L.: Self-similarity weighted mutual information: a new nonrigid image registration metric. Med. Image Anal. 18, 343\u2013358 (2014)","journal-title":"Med. Image Anal."},{"key":"60_CR10","doi-asserted-by":"crossref","unstructured":"Machado, I., et al.: Deformable MRI-Ultrasound registration using correlation-based attribute matching for brain shift correction: accuracy and generality in multi-site data. Neuroimage 202, 116094 (2019)","DOI":"10.1016\/j.neuroimage.2019.116094"},{"key":"60_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/978-3-030-01045-4_15","volume-title":"Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation","author":"X Zhong","year":"2018","unstructured":"Zhong, X., et al.: Resolve intraoperative brain shift as imitation game. In: Stoyanov, D., et al. (eds.) POCUS\/BIVPCS\/CuRIOUS\/CPM -2018. LNCS, vol. 11042, pp. 129\u2013137. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01045-4_15"},{"key":"60_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1007\/978-3-030-01045-4_18","volume-title":"Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation","author":"L Sun","year":"2018","unstructured":"Sun, L., Zhang, S.: Deformable MRI-ultrasound registration using 3D convolutional neural network. In: Stoyanov, D., et al. (eds.) POCUS\/BIVPCS\/CuRIOUS\/CPM -2018. LNCS, vol. 11042, pp. 152\u2013158. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01045-4_18"},{"key":"60_CR13","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 (2019)","DOI":"10.1109\/CVPR.2018.00964"},{"key":"60_CR14","doi-asserted-by":"publisher","first-page":"3253","DOI":"10.1118\/1.4709600","volume":"39","author":"L Mercier","year":"2012","unstructured":"Mercier, L., Del Maestro, R.F., Petrecca, K., Araujo, D., Haegelen, C., Collins, D.L.: Online database of clinical MR and ultrasound images of brain tumors. Med. Phys. 39, 3253\u20133261 (2012)","journal-title":"Med. Phys."},{"key":"60_CR15","doi-asserted-by":"publisher","first-page":"3875","DOI":"10.1002\/mp.12268","volume":"44","author":"Y Xiao","year":"2017","unstructured":"Xiao, Y., Fortin, M., Unsgard, G., Rivaz, H., Reinertsen, I.: REtroSpective evaluation of cerebral tumors (RESECT): a clinical database of pre-operative MRI and intra-operative ultrasound in low-grade glioma surgeries. Med. Phys. 44, 3875\u20133882 (2017)","journal-title":"Med. Phys."},{"key":"60_CR16","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 \u2014 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"},{"issue":"6","key":"60_CR17","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(6), 909\u2013920 (2020). https:\/\/doi.org\/10.1007\/s11548-020-02186-z","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"60_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1007\/978-3-030-01045-4_19","volume-title":"Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation","author":"MP Heinrich","year":"2018","unstructured":"Heinrich, M.P.: Intra-operative ultrasound to MRI fusion with a public multimodal discrete registration tool. In: Stoyanov, D., et al. (eds.) POCUS\/BIVPCS\/CuRIOUS\/CPM -2018. LNCS, vol. 11042, pp. 159\u2013164. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01045-4_19"},{"issue":"6","key":"60_CR19","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1007\/s11548-016-1407-2","volume":"11","author":"D Jiang","year":"2016","unstructured":"Jiang, D., Shi, Y., Yao, D., Wang, M., Song, Z.: miLBP: a robust and fast modality-independent 3D LBP for multimodal deformable registration. Int. J. Comput. Assist. Radiol. Surg. 11(6), 997\u20131005 (2016). https:\/\/doi.org\/10.1007\/s11548-016-1407-2","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"60_CR20","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.inffus.2018.09.009","volume":"49","author":"VA Zimmer","year":"2019","unstructured":"Zimmer, V.A., Gonz\u00e1lez Ballester, M.\u00c1., Piella, G.: Multimodal image registration using Laplacian commutators. Inf. Fus. 49, 130\u2013145 (2019)","journal-title":"Inf. Fus."},{"issue":"3","key":"60_CR21","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s11548-018-1897-1","volume":"14","author":"N Masoumi","year":"2018","unstructured":"Masoumi, N., Xiao, Y., Rivaz, H.: ARENA: Inter-modality affine registration using evolutionary strategy. Int. J. Comput. Assist. Radiol. Surg. 14(3), 441\u2013450 (2018). https:\/\/doi.org\/10.1007\/s11548-018-1897-1","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"60_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-030-01045-4_17","volume-title":"Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation","author":"W Wein","year":"2018","unstructured":"Wein, W.: Brain-shift correction with image-based registration and landmark accuracy evaluation. In: Stoyanov, D., et al. (eds.) POCUS\/BIVPCS\/CuRIOUS\/CPM -2018. LNCS, vol. 11042, pp. 146\u2013151. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01045-4_17"},{"key":"60_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-33642-4_15","volume-title":"Large-Scale Annotation of Biomedical Data and Expert Label Synthesis and Hardware Aware Learning for Medical Imaging and Computer Assisted Intervention","author":"D Drobny","year":"2019","unstructured":"Drobny, D., Ranzini, M., Ourselin, S., Vercauteren, T., Modat, M.: Landmark-based evaluation of a block-matching registration framework on the RESECT pre- and intra-operative brain image data set. In: Zhou, L., et al. (eds.) LABELS\/HAL-MICCAI\/CuRIOUS -2019. LNCS, vol. 11851, pp. 136\u2013144. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-33642-4_15"}],"container-title":["Lecture Notes in Computer Science","Machine Learning in Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87589-3_60","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,10]],"date-time":"2022-04-10T15:13:55Z","timestamp":1649603635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87589-3_60"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030875886","9783030875893"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87589-3_60","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"21 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MLMI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Machine Learning in Medical Imaging","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Strasbourg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mlmi-med2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/mlmi2021\/","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":"92","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":"71","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":"77% - 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":"2","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":"3","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":"The workshop was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}