{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:46:07Z","timestamp":1773326767865,"version":"3.50.1"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030718268","type":"print"},{"value":"9783030718275","type":"electronic"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-71827-5_9","type":"book-chapter","created":{"date-parts":[[2021,3,12]],"date-time":"2021-03-12T00:02:36Z","timestamp":1615507356000},"page":"74-79","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Variable Fraunhofer MEVIS RegLib Comprehensively Applied to Learn2Reg Challenge"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1900-3530","authenticated-orcid":false,"given":"Stephanie","family":"H\u00e4ger","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9206-2086","authenticated-orcid":false,"given":"Stefan","family":"Heldmann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7602-803X","authenticated-orcid":false,"given":"Alessa","family":"Hering","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4374-0880","authenticated-orcid":false,"given":"Sven","family":"Kuckertz","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2392-2051","authenticated-orcid":false,"given":"Annkristin","family":"Lange","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,13]]},"reference":[{"key":"9_CR1","unstructured":"Learn2Reg: 2020 MICCAI registration challenge. https:\/\/learn2reg.grand-challenge.org\/"},{"key":"9_CR2","unstructured":"Learn2Reg challenge, metrics and evaluation. https:\/\/learn2reg.grand-challenge.org\/Submission\/"},{"key":"9_CR3","doi-asserted-by":"publisher","unstructured":"Dalca, A., et al.: Learn2Reg - the challenge, March 2020. https:\/\/doi.org\/10.5281\/zenodo.3715652","DOI":"10.5281\/zenodo.3715652"},{"issue":"1","key":"9_CR4","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1023\/A:1021897212261","volume":"18","author":"B Fischer","year":"2003","unstructured":"Fischer, B., Modersitzki, J.: Curvature based image registration. J. Math. Imaging Vis. 18(1), 81\u201385 (2003)","journal-title":"J. Math. Imaging Vis."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. In: Medical Image Computing and Computer-Assisted Intervention - MICCAI 2006, vol. 3216, pp. 591\u2013598 (2006)","DOI":"10.1007\/11866763_89"},{"key":"9_CR6","series-title":"Informatik aktuell","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1007\/978-3-658-25326-4_69","volume-title":"Bildverarbeitung f\u00fcr die Medizin 2019","author":"A Hering","year":"2019","unstructured":"Hering, A., Kuckertz, S., Heldmann, S., Heinrich, M.P.: Enhancing label-driven deep deformable image registration with local distance metrics for state-of-the-art cardiac motion tracking. Bildverarbeitung f\u00fcr die Medizin 2019. I, pp. 309\u2013314. Springer, Wiesbaden (2019). https:\/\/doi.org\/10.1007\/978-3-658-25326-4_69"},{"key":"9_CR7","doi-asserted-by":"publisher","unstructured":"Hering, A., Murphy, K., van Ginneken, B.: Learn2Reg Challenge: CT Lung Registration - Test Data, September 2020. https:\/\/doi.org\/10.5281\/zenodo.4048761","DOI":"10.5281\/zenodo.4048761"},{"key":"9_CR8","doi-asserted-by":"publisher","unstructured":"Hering, A., Murphy, K., van Ginneken, B.: Learn2Reg Challenge: CT Lung Registration - Training Data, May 2020. https:\/\/doi.org\/10.5281\/zenodo.3835682","DOI":"10.5281\/zenodo.3835682"},{"key":"9_CR9","doi-asserted-by":"crossref","unstructured":"Kuckertz, S., Papenberg, N., Honegger, J., Morgas, T., Haas, B., Heldmann, S.: Deep learning based CT-CBCT image registration for adaptive radio therapy. In: Medical Imaging 2020: Image Processing, vol. 11313, pp. 149\u2013154. International Society for Optics and Photonics, SPIE (2020)","DOI":"10.1117\/12.2549531"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"R\u00fchaak, J., Polzin, T., Heldmann, S., Simpson, I.J., Handels, H., Modersitzki, J., Heinrich, M.P.: Estimation of large motion in lung CT by integrating regularized keypoint correspondences into dense deformable registration. IEEE Trans. Med. Imaging 36(8), 1746\u20131757 (2017)","DOI":"10.1109\/TMI.2017.2691259"}],"container-title":["Lecture Notes in Computer Science","Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71827-5_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T01:04:02Z","timestamp":1773277442000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71827-5_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030718268","9783030718275"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71827-5_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"13 March 2021","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":"Lima","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Peru","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.miccai2020.org\/en\/","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":"1809","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":"542","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":"30% - 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":"4","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":"The conference was held virtually due to the COVID-19 pandemic.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}