{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T04:34:27Z","timestamp":1749530067900,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031338410"},{"type":"electronic","value":"9783031338427"}],"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\/978-3-031-33842-7_24","type":"book-chapter","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T06:02:26Z","timestamp":1689573746000},"page":"273-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Brain Tumor Sequence Registration with Non-iterative Coarse-To-Fine Networks and Dual Deep Supervision"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9562-1613","authenticated-orcid":false,"given":"Mingyuan","family":"Meng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9759-0200","authenticated-orcid":false,"given":"Lei","family":"Bi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3381-214X","authenticated-orcid":false,"given":"Dagan","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5960-1060","authenticated-orcid":false,"given":"Jinman","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,18]]},"reference":[{"issue":"1-2","key":"24_CR1","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)","journal-title":"Mach. Vis. Appl."},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Meng, M., Liu, S.: High-quality panorama stitching based on asymmetric bidirectional optical flow. In: International Conference on Computational Intelligence and Applications, pp. 118\u2013122 (2020)","DOI":"10.1109\/ICCIA49625.2020.00030"},{"key":"24_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2022.119444","volume":"259","author":"M Meng","year":"2022","unstructured":"Meng, M., Bi, L., Fulham, M., Feng, D.D., Kim, J.: Enhancing medical image registration via appearance adjustment networks. Neuroimage 259, 119444 (2022)","journal-title":"Neuroimage"},{"key":"24_CR4","unstructured":"Baheti, B., Waldmannstetter, D., Chakrabarty, S., Akbari, H., 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)"},{"issue":"3","key":"24_CR5","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1109\/TMI.2013.2293478","volume":"33","author":"D Kwon","year":"2013","unstructured":"Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.M.: PORTR: pre-operative and post-recurrence brain tumor registration. IEEE Trans. Med. Imaging 33(3), 651\u2013667 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7","key":"24_CR6","doi-asserted-by":"publisher","first-page":"1153","DOI":"10.1109\/TMI.2013.2265603","volume":"32","author":"A Sotiras","year":"2013","unstructured":"Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153\u20131190 (2013)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"12","key":"24_CR7","doi-asserted-by":"publisher","first-page":"4895","DOI":"10.21037\/qims-21-175","volume":"11","author":"H Xiao","year":"2021","unstructured":"Xiao, H., et al.: A review of deep learning-based three-dimensional medical image registration methods. Quant. Imaging Med. Surg. 11(12), 4895\u20134916 (2021)","journal-title":"Quant. Imaging Med. Surg."},{"key":"24_CR8","doi-asserted-by":"crossref","unstructured":"Zhao, S., Dong, Y., Chang, E.I., Xu, Y., et al.: Recursive cascaded networks for unsupervised medical image registration. In: IEEE International Conference on Computer Vision, pp. 10600\u201310610 (2019)","DOI":"10.1109\/ICCV.2019.01070"},{"key":"24_CR9","doi-asserted-by":"publisher","unstructured":"Mok, T.C., Chung, A.C.: Large deformation diffeomorphic image registration with laplacian pyramid networks. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 211\u2013221. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59716-0_21","DOI":"10.1007\/978-3-030-59716-0_21"},{"key":"24_CR10","doi-asserted-by":"publisher","unstructured":"Shu, Y., Wang, H., Xiao, B., Bi, X., Li, W.: Medical image registration based on uncoupled learning and accumulative enhancement. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 3\u201313. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-87202-1_1","DOI":"10.1007\/978-3-030-87202-1_1"},{"key":"24_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102379","volume":"78","author":"M Kang","year":"2022","unstructured":"Kang, M., Hu, X., Huang, W., Scott, M.R., Reyes, M.: Dual-stream pyramid registration network. Med. Image Anal. 78, 102374 (2022)","journal-title":"Med. Image Anal."},{"issue":"10","key":"24_CR12","doi-asserted-by":"publisher","first-page":"2788","DOI":"10.1109\/TMI.2022.3170879","volume":"41","author":"J Lv","year":"2022","unstructured":"Lv, J., et al.: Joint progressive and coarse-to-fine registration of brain MRI via deformation field integration and non-rigid feature fusion. IEEE Trans. Med. Imaging 41(10), 2788\u20132802 (2022)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR13","doi-asserted-by":"publisher","unstructured":"Meng, M., Bi, L., Feng, D., Kim, J.: Non-iterative coarse-to-fine registration based on single-pass deep cumulative learning. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 88\u201397. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-16446-0_9","DOI":"10.1007\/978-3-031-16446-0_9"},{"issue":"8","key":"24_CR14","doi-asserted-by":"publisher","first-page":"1788","DOI":"10.1109\/TMI.2019.2897538","volume":"38","author":"G Balakrishnan","year":"2019","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)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"24_CR15","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.media.2019.07.006","volume":"57","author":"AV Dalca","year":"2019","unstructured":"Dalca, A.V., Balakrishnan, G., Guttag, J., Sabuncu, M.R.: Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med. Image Anal. 57, 226\u2013236 (2019)","journal-title":"Med. Image Anal."},{"issue":"1","key":"24_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.media.2007.06.004","volume":"12","author":"BB Avants","year":"2008","unstructured":"Avants, B.B., Epstein, C.L., Grossman, M., Gee, J.C.: Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12(1), 26\u201341 (2008)","journal-title":"Med. Image Anal."},{"key":"24_CR17","doi-asserted-by":"publisher","unstructured":"Kuang, D., Schmah, T.: Faim\u2013a convnet method for unsupervised 3d medical image registration. In: International Workshop on Machine Learning in Medical Imaging, pp. 646\u2013654. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32692-0_74","DOI":"10.1007\/978-3-030-32692-0_74"},{"key":"24_CR18","doi-asserted-by":"publisher","unstructured":"Lee, M.C., Oktay, O., Schuh, A., Schaap, M., Glocker, B.: Image-and-spatial transformer networks for structure-guided image registration. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 337\u2013345. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32245-8_38","DOI":"10.1007\/978-3-030-32245-8_38"},{"issue":"1","key":"24_CR19","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.neuroimage.2007.07.007","volume":"38","author":"J Ashburner","year":"2007","unstructured":"Ashburner, J.: A fast diffeomorphic image registration algorithm. Neuroimage 38(1), 95\u2013113 (2007)","journal-title":"Neuroimage"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Mok, T.C., Chung, A.C.: Robust Image Registration with Absent Correspondences in Pre-operative and Follow-up Brain MRI Scans of Diffuse Glioma Patients. arXiv preprint, arXiv:2210.11045 (2022)","DOI":"10.1007\/978-3-031-33842-7_20"}],"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\/978-3-031-33842-7_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T06:05:14Z","timestamp":1707372314000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33842-7_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031338410","9783031338427"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33842-7_24","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":"18 July 2023","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)"}}]}}