{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:24:32Z","timestamp":1783034672302,"version":"3.54.6"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030597184","type":"print"},{"value":"9783030597191","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-59719-1_70","type":"book-chapter","created":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T14:02:56Z","timestamp":1601647376000},"page":"724-734","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Non-Rigid Volume to Surface Registration Using a Data-Driven Biomechanical Model"],"prefix":"10.1007","author":[{"given":"Micha","family":"Pfeiffer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carina","family":"Riediger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Leger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jens-Peter","family":"K\u00fchn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danilo","family":"Seppelt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ralf-Thorsten","family":"Hoffmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00fcrgen","family":"Weitz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanie","family":"Speidel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"70_CR1","unstructured":"Bilic, P., Christ, P.F., Vorontsov, E., Chlebus, G., Chen, H., Dou, Q., et al.: The liver tumor segmentation benchmark (LiTS). ArXiv abs\/1901.04056 (2019)"},{"key":"70_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-030-32254-0_16","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"J-N Brunet","year":"2019","unstructured":"Brunet, J.-N., Mendizabal, A., Petit, A., Golse, N., Vibert, E., Cotin, S.: Physics-based deep neural network for augmented reality during liver surgery. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 137\u2013145. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_16"},{"key":"70_CR3","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning. vol. 48 (2016)"},{"issue":"11","key":"70_CR4","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1002\/nme.2579","volume":"79","author":"C Geuzaine","year":"2009","unstructured":"Geuzaine, C., Remacle, J.F.: Gmsh: A 3-d finite element mesh generator with built-in pre- and post-processing facilities. Int. J. Numer. Meth. Eng. 79(11), 1309\u20131331 (2009)","journal-title":"Int. J. Numer. Meth. Eng."},{"issue":"12","key":"70_CR5","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.3390\/rs11121499","volume":"11","author":"D Griffiths","year":"2019","unstructured":"Griffiths, D., Boehm, J.: A review on deep learning techniques for 3d sensed data classification. Remote Sensing 11(12), 1499 (2019)","journal-title":"Remote Sensing"},{"issue":"2","key":"70_CR6","doi-asserted-by":"publisher","first-page":"021203","DOI":"10.1117\/1.JMI.5.2.021203","volume":"5","author":"J Heiselman","year":"2017","unstructured":"Heiselman, J., Clements, L., Collins, J., Weis, J., Simpson, A., Geevarghese, S.: Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery. J. Med. Imaging 5(2), 021203 (2017)","journal-title":"J. Med. Imaging"},{"key":"70_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"326","DOI":"10.1007\/978-3-319-66182-7_38","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013MICCAI 2017","author":"B Koo","year":"2017","unstructured":"Koo, B., \u00d6zg\u00fcr, E., Le Roy, B., Buc, E., Bartoli, A.: Deformable registration of a preoperative 3d liver volume to a laparoscopy image using contour and shading cues. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10433, pp. 326\u2013334. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-66182-7_38"},{"key":"70_CR8","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (ICLR) (2017)"},{"key":"70_CR9","volume-title":"Elmer Finite Element Solver for Multiphysics and Multiscale Problems","author":"M Malinen","year":"2013","unstructured":"Malinen, M., R\u00e5back, P.: Elmer Finite Element Solver for Multiphysics and Multiscale Problems. Multiscale Modelling Methods for Applications in Materials Science, Forschungszentrum J\u00fclich (2013)"},{"key":"70_CR10","doi-asserted-by":"publisher","first-page":"101569","DOI":"10.1016\/j.media.2019.101569","volume":"59","author":"A Mendizabal","year":"2019","unstructured":"Mendizabal, A., M\u00e1rquez-Neila, P., Cotin, S.: Simulation of hyperelastic materials in real-time using deep learning. Med. Image Anal. 59, 101569 (2019)","journal-title":"Med. Image Anal."},{"key":"70_CR11","doi-asserted-by":"crossref","unstructured":"Mendizabal, A., Tagliabue, E., Brunet, J.N., Dall\u00e1lba, D., Fiorini, P., Cotin, S.: Physics-based deep neural network for real-time lesion tracking in ultrasound-guided breast biopsy. In: Computational Biomechanics for Medicine XIV. Shenzhen, China (2019)","DOI":"10.1007\/978-3-030-42428-2_4"},{"issue":"5","key":"70_CR12","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","volume":"33","author":"R Mur-Artal","year":"2017","unstructured":"Mur-Artal, R., Tard\u00f3s, J.D.: ORB-SLAM2: an open-source SLAM system for monocular, stereo and RGB-D cameras. IEEE Trans. Robot. 33(5), 1255\u20131262 (2017)","journal-title":"IEEE Trans. Robot."},{"key":"70_CR13","doi-asserted-by":"publisher","first-page":"1629","DOI":"10.1007\/s11548-018-1842-3","volume":"13","author":"E \u00d6zg\u00fcr","year":"2018","unstructured":"\u00d6zg\u00fcr, E., Koo, B., Le Roy, B., Buc, E., Bartoli, A.: Preoperative liver registration for augmented monocular laparoscopy using backward-forward biomechanical simulation. Int. J. Comput. Assist. Radiol. Surg. 13, 1629\u20131640 (2018)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"70_CR14","doi-asserted-by":"publisher","first-page":"113083","DOI":"10.1016\/j.eswa.2019.113083","volume":"143","author":"OJ Pellicer-Valero","year":"2020","unstructured":"Pellicer-Valero, O.J., Rup\u00e9rez, M.J., Mart\u00ednez-Sanchis, S., Mart\u00edn-Guerrero, J.D.: Real-time biomechanical modeling of the liver using machine learning models trained on finite element method simulations. Expert Syst. Appl. 143, 113083 (2020)","journal-title":"Expert Syst. Appl."},{"key":"70_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/978-3-030-32254-0_14","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2019","author":"M Pfeiffer","year":"2019","unstructured":"Pfeiffer, M., et al.: Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11768, pp. 119\u2013127. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32254-0_14"},{"issue":"7","key":"70_CR16","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1007\/s11548-019-01965-7","volume":"14","author":"M Pfeiffer","year":"2019","unstructured":"Pfeiffer, M., Riediger, C., Weitz, J., Speidel, S.: Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks. Int. J. Comput. Assist. Radiol. Surg. 14(7), 1147\u20131155 (2019). https:\/\/doi.org\/10.1007\/s11548-019-01965-7","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"70_CR17","first-page":"113083","volume":"143","author":"R Plantefeve","year":"2015","unstructured":"Plantefeve, R., Peterlik, I., Haouchine, N., Cotin, S.: Patient-specific biomechanical modeling for guidance during minimally-invasive hepatic surgery. Ann. Biomed. Eng. 143, 113083 (2015)","journal-title":"Ann. Biomed. Eng."},{"key":"70_CR18","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)"},{"key":"70_CR19","unstructured":"Smith, L.N., Topin, N.: Super-convergence: very fast training of residual networks using large learning rates. CoRR abs\/1708.07120 (2017)"},{"key":"70_CR20","doi-asserted-by":"crossref","unstructured":"Suwelack, S., et al.: Physics-based shape matching for intraoperative image guidance. Med. phys. 41, (2014)","DOI":"10.1118\/1.4896021"},{"key":"70_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01237-3_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Wang","year":"2018","unstructured":"Wang, H., Guo, J., Yan, D.-M., Quan, W., Zhang, X.: Learning 3D Keypoint descriptors for non-rigid shape matching. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11212, pp. 3\u201320. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01237-3_1"},{"key":"70_CR22","doi-asserted-by":"crossref","unstructured":"Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)","DOI":"10.1109\/CVPR.2019.00566"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-59719-1_70","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T22:10:44Z","timestamp":1759356644000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-59719-1_70"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030597184","9783030597191"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-59719-1_70","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","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)"}}]}}