{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,22]],"date-time":"2025-12-22T04:31:42Z","timestamp":1766377902158,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031439957"},{"type":"electronic","value":"9783031439964"}],"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\/978-3-031-43996-4_54","type":"book-chapter","created":{"date-parts":[[2023,9,30]],"date-time":"2023-09-30T23:07:48Z","timestamp":1696115268000},"page":"566-575","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Spatiotemporal Incremental Mechanics Modeling of\u00a0Facial Tissue Change"],"prefix":"10.1007","author":[{"given":"Nathan","family":"Lampen","sequence":"first","affiliation":[]},{"given":"Daeseung","family":"Kim","sequence":"additional","affiliation":[]},{"given":"Xuanang","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xi","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Jungwook","family":"Lee","sequence":"additional","affiliation":[]},{"given":"Tianshu","family":"Kuang","sequence":"additional","affiliation":[]},{"given":"Hannah H.","family":"Deng","sequence":"additional","affiliation":[]},{"given":"Michael A. K.","family":"Liebschner","sequence":"additional","affiliation":[]},{"given":"James J.","family":"Xia","sequence":"additional","affiliation":[]},{"given":"Jaime","family":"Gateno","sequence":"additional","affiliation":[]},{"given":"Pingkun","family":"Yan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,1]]},"reference":[{"key":"54_CR1","doi-asserted-by":"crossref","unstructured":"Buoso, S., Joyce, T., Kozerke, S.: Personalising left-ventricular biophysical models of the heart using parametric physics-informed neural networks. Med. Image Anal. 71 (2021)","DOI":"10.1016\/j.media.2021.102066"},{"key":"54_CR2","doi-asserted-by":"crossref","unstructured":"Chabanas, M., Luboz, V., Payan, Y.: Patient specific finite element model of the face soft tissues for computer-assisted maxillofacial surgery. Med. Image Anal. 7(2), 131\u2013151 (2003)","DOI":"10.1016\/S1361-8415(02)00108-1"},{"key":"54_CR3","unstructured":"Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems 2017-December, pp. 1025\u20131035 (2017)"},{"key":"54_CR4","doi-asserted-by":"crossref","unstructured":"Karami, M., Lombaert, H., Rivest-H\u00e9nault, D.: Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning. Comput. Med. Imaging Graph. 104, 102165 (2023)","DOI":"10.1016\/j.compmedimag.2022.102165"},{"key":"54_CR5","doi-asserted-by":"crossref","unstructured":"Kim, D., et al.: A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery. Med. Phys. 44(8), 4252\u20134261 (2017)","DOI":"10.1002\/mp.12391"},{"key":"54_CR6","doi-asserted-by":"crossref","unstructured":"Kim, D., et al.: A new approach of predicting facial changes following orthognathic surgery using realistic lip sliding effect. In: Medical Image Computing and Computer-Assisted Intervention: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, vol. 11768, pp. 336\u2013344 (2019)","DOI":"10.1007\/978-3-030-32254-0_38"},{"key":"54_CR7","doi-asserted-by":"crossref","unstructured":"Kim, D., et al.: A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect. Med. Image Anal. 72, 102095 (2021)","DOI":"10.1016\/j.media.2021.102095"},{"key":"54_CR8","doi-asserted-by":"crossref","unstructured":"Knoops, P.G., et al.: Three-dimensional soft tissue prediction in orthognathic surgery: a clinical comparison of Dolphin, ProPlan CMF, and probabilistic finite element modelling. Int. J. Oral Maxillofac. Surgery 48(4), 511\u2013518 (2019)","DOI":"10.1016\/j.ijom.2018.10.008"},{"key":"54_CR9","doi-asserted-by":"crossref","unstructured":"Knoops, P.G., et al.: A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling. PLOS ONE 13(5), e0197209 (2018)","DOI":"10.1371\/journal.pone.0197209"},{"issue":"5","key":"54_CR10","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1007\/s11548-022-02596-1","volume":"17","author":"N Lampen","year":"2022","unstructured":"Lampen, N., et al.: Deep learning for biomechanical modeling of facial tissue deformation in orthognathic surgical planning. Int. J. Comput. Assist. Radiol. Surgery 17(5), 945\u2013952 (2022)","journal-title":"Int. J. Comput. Assist. Radiol. Surgery"},{"key":"54_CR11","doi-asserted-by":"crossref","unstructured":"Liu, M., Liang, L., Sun, W.: A generic physics-informed neural network-based constitutive model for soft biological tissues. Comput. Methods Appl. Mech. Eng. 372, 113402 (2020)","DOI":"10.1016\/j.cma.2020.113402"},{"key":"54_CR12","doi-asserted-by":"crossref","unstructured":"Mendizabal, A., M\u00e1rquez-Neila, P., Cotin, S.: Simulation of hyperelastic materials in real-time using deep learning. Med. Image Anal. 59, 101569 (2020)","DOI":"10.1016\/j.media.2019.101569"},{"key":"54_CR13","unstructured":"Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., Battaglia, P.: Learning mesh-based simulation with graph networks. In: International Conference on Learning Representations (2021)"},{"key":"54_CR14","doi-asserted-by":"crossref","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. Surgery 14(7), 1147\u20131155 (2019)","DOI":"10.1007\/s11548-019-01965-7"},{"key":"54_CR15","doi-asserted-by":"crossref","unstructured":"Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J. Comput. Phys. 378, 686\u2013707 (2019)","DOI":"10.1016\/j.jcp.2018.10.045"},{"key":"54_CR16","unstructured":"Salehi, Y., Giannacopoulos, D.: PhysGNN: a physics-driven graph neural network based model for predicting soft tissue deformation in image-guided neurosurgery. arXiv preprint arXiv:2109.04352 (2021)"},{"key":"54_CR17","unstructured":"Wu, J.Y., Munawar, A., Unberath, M., Kazanzides, P.: Learning Soft-Tissue Simulation from Models and Observation. In: 2021 International Symposium on Medical Robotics, ISMR 2021 (2021)"},{"key":"54_CR18","unstructured":"Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K.I., Jegelka, S.: representation learning on graphs with jumping knowledge networks. In: 35th International Conference on Machine Learning, ICML 2018, vol. 12, pp. 8676\u20138685 (2018)"}],"container-title":["Lecture Notes in Computer Science","Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-43996-4_54","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,4]],"date-time":"2024-07-04T16:09:56Z","timestamp":1720109396000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43996-4_54"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031439957","9783031439964"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43996-4_54","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":"1 October 2023","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":"Vancouver, BC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"miccai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/conferences.miccai.org\/2023\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2250","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":"730","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":"32% - 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":"5","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)"}}]}}