{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T09:40:56Z","timestamp":1794476456071,"version":"4.9.1"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004921","name":"Shanghai Jiao Tong University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004921","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1016\/j.media.2024.103350","type":"journal-article","created":{"date-parts":[[2024,9,19]],"date-time":"2024-09-19T20:35:34Z","timestamp":1726778134000},"page":"103350","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["Maxillofacial bone movements-aware dual graph convolution approach for postoperative facial appearance prediction"],"prefix":"10.1016","volume":"99","author":[{"given":"Xinrui","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongming","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4487-6683","authenticated-orcid":false,"given":"Zhenming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3999-1449","authenticated-orcid":false,"given":"Xiaofan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xudong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.media.2024.103350_b1","doi-asserted-by":"crossref","first-page":"249","DOI":"10.3109\/00016357.2010.494618","article-title":"Patients\u2019 perceptions of orthognathic treatment, well-being, and psychological or psychiatric status: a systematic review","volume":"68","author":"Alanko","year":"2010","journal-title":"Acta Odontol. Scand."},{"key":"10.1016\/j.media.2024.103350_b2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","article-title":"Geometric deep learning: going beyond euclidean data","volume":"34","author":"Bronstein","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"10.1016\/j.media.2024.103350_b3","doi-asserted-by":"crossref","first-page":"35929","DOI":"10.1109\/ACCESS.2020.2975067","article-title":"A comprehensive survey on geometric deep learning","volume":"8","author":"Cao","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.media.2024.103350_b4","doi-asserted-by":"crossref","unstructured":"Fan, H., Su, H., Guibas, L.J., 2017. A point set generation network for 3d object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 605\u2013613.","DOI":"10.1109\/CVPR.2017.264"},{"key":"10.1016\/j.media.2024.103350_b5","series-title":"Deep learning-based facial appearance simulation driven by surgically planned craniomaxillofacial bony movement","first-page":"565","author":"Fang","year":"2022"},{"key":"10.1016\/j.media.2024.103350_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2024.103094","article-title":"Correspondence attention for facial appearance simulation","volume":"93","author":"Fang","year":"2024","journal-title":"Medical Image Analysis"},{"key":"10.1016\/j.media.2024.103350_b7","series-title":"Surface simplification using quadric error metrics","first-page":"209","author":"Garland","year":"1997"},{"key":"10.1016\/j.media.2024.103350_b8","first-page":"1","article-title":"Meshcnn: a network with an edge","volume":"38","author":"Hanocka","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.media.2024.103350_b9","series-title":"Deep residual learning for image recognition","first-page":"770","author":"He","year":"2016"},{"key":"10.1016\/j.media.2024.103350_b10","doi-asserted-by":"crossref","unstructured":"Huang, J., Zhang, H., Yi, L., Funkhouser, T., Nie\u00dfner, M., Guibas, L.J., 2019. Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 4440\u20134449.","DOI":"10.1109\/CVPR.2019.00457"},{"key":"10.1016\/j.media.2024.103350_b11","doi-asserted-by":"crossref","unstructured":"Jiang, L., Zhao, H., Liu, S., Shen, X., Fu, C.-W., Jia, J., 2019. Hierarchical point-edge interaction network for point cloud semantic segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 10433\u201310441.","DOI":"10.1109\/ICCV.2019.01053"},{"issue":"8","key":"10.1016\/j.media.2024.103350_b12","doi-asserted-by":"crossref","first-page":"4252","DOI":"10.1002\/mp.12391","article-title":"A clinically validated prediction method for facial soft-tissue changes following double-jaw surgery","volume":"44","author":"Kim","year":"2017","journal-title":"Med. Phys."},{"issue":"2\u20133","key":"10.1016\/j.media.2024.103350_b13","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.pbiomolbio.2010.09.004","article-title":"A new soft-tissue simulation strategy for cranio-maxillofacial surgery using facial muscle template model","volume":"103","author":"Kim","year":"2010","journal-title":"Prog. Biophys. Mol. Biol."},{"key":"10.1016\/j.media.2024.103350_b14","doi-asserted-by":"crossref","unstructured":"Kim, D., Kuang, T., Rodrigues, Y.L., Gateno, J., Shen, S.G., Wang, X., Deng, H., Yuan, P., Alfi, D.M., Liebschner, M.A., et al., 2019. A new approach of predicting facial changes following orthognathic surgery using realistic lip sliding effect. In: Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13\u201317, 2019, Proceedings, Part V 22. pp. 336\u2013344.","DOI":"10.1007\/978-3-030-32254-0_38"},{"key":"10.1016\/j.media.2024.103350_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2021.102095","article-title":"A novel incremental simulation of facial changes following orthognathic surgery using FEM with realistic lip sliding effect","volume":"72","author":"Kim","year":"2021","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2024.103350_b16","series-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014"},{"key":"10.1016\/j.media.2024.103350_b17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0197209","article-title":"A novel soft tissue prediction methodology for orthognathic surgery based on probabilistic finite element modelling","volume":"13","author":"Knoops","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.media.2024.103350_b18","doi-asserted-by":"crossref","unstructured":"Koch, R.M., Gross, M.H., Carls, F.R., von B\u00fcren, D.F., Fankhauser, G., Parish, Y.I., 1996. Simulating facial surgery using finite element models. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques. pp. 421\u2013428.","DOI":"10.1145\/237170.237281"},{"key":"10.1016\/j.media.2024.103350_b19","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neucom.2021.01.135","article-title":"Automatic segmentation of organs-at-risk from head-and-neck ct using separable convolutional neural network with hard-region-weighted loss","volume":"442","author":"Lei","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.media.2024.103350_b20","article-title":"One-shot weakly-supervised segmentation in 3d medical images","author":"Lei","year":"2023","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.1016\/j.media.2024.103350_b21","series-title":"Pointcnn: Convolution on x-transformed points","author":"Li","year":"2018"},{"key":"10.1016\/j.media.2024.103350_b22","doi-asserted-by":"crossref","unstructured":"Li, Y., Chen, H., Cui, Z., Timofte, R., Pollefeys, M., Chirikjian, G.S., Van Gool, L., 2021. Towards efficient graph convolutional networks for point cloud handling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 3752\u20133762.","DOI":"10.1109\/ICCV48922.2021.00373"},{"key":"10.1016\/j.media.2024.103350_b23","doi-asserted-by":"crossref","unstructured":"Li, J., Chen, B.M., Lee, G.H., 2018a. So-net: Self-organizing network for point cloud analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 9397\u20139406.","DOI":"10.1109\/CVPR.2018.00979"},{"key":"10.1016\/j.media.2024.103350_b24","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X.-M., 2018b. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence.","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"10.1016\/j.media.2024.103350_b25","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1145\/37402.37422","article-title":"Marching cubes: A high resolution 3D surface construction algorithm","volume":"21","author":"Lorensen","year":"1987","journal-title":"ACM Siggraph Comput. Graph."},{"key":"10.1016\/j.media.2024.103350_b26","series-title":"Deep simulation of facial appearance changes following craniomaxillofacial bony movements in orthognathic surgical planning","first-page":"459","author":"Ma","year":"2021"},{"key":"10.1016\/j.media.2024.103350_b27","article-title":"Simulation of postoperative facial appearances via geometric deep learning for efficient orthognathic surgical planning","author":"Ma","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"11","key":"10.1016\/j.media.2024.103350_b28","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.media.2024.103350_b29","doi-asserted-by":"crossref","DOI":"10.1115\/1.4043290","article-title":"Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis","volume":"141","author":"Madani","year":"2019","journal-title":"J. Biomech. Eng."},{"key":"10.1016\/j.media.2024.103350_b30","doi-asserted-by":"crossref","unstructured":"Masci, J., Boscaini, D., Bronstein, M., Vandergheynst, P., 2015. Geodesic convolutional neural networks on riemannian manifolds. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 37\u201345.","DOI":"10.1109\/ICCVW.2015.112"},{"key":"10.1016\/j.media.2024.103350_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101569","article-title":"Simulation of hyperelastic materials in real-time using deep learning","volume":"59","author":"Mendizabal","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2024.103350_b32","series-title":"Physics-based deep neural network for real-time lesion tracking in ultrasound-guided breast biopsy","first-page":"33","author":"Mendizabal","year":"2020"},{"key":"10.1016\/j.media.2024.103350_b33","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1002\/mp.14602","article-title":"Real-time biomechanics using the finite element method and machine learning: Review and perspective","volume":"48","author":"Phellan","year":"2021","journal-title":"Med. Phys."},{"key":"10.1016\/j.media.2024.103350_b34","unstructured":"Qi, C.R., Su, H., Mo, K., Guibas, L.J., 2017a. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 652\u2013660."},{"key":"10.1016\/j.media.2024.103350_b35","series-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","author":"Qi","year":"2017"},{"key":"10.1016\/j.media.2024.103350_b36","doi-asserted-by":"crossref","unstructured":"Ran, H., Liu, J., Wang, C., 2022. Surface representation for point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 18942\u201318952.","DOI":"10.1109\/CVPR52688.2022.01837"},{"key":"10.1016\/j.media.2024.103350_b37","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Bolkart, T., Sanyal, S., Black, M.J., 2018. Generating 3D faces using convolutional mesh autoencoders. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 704\u2013720.","DOI":"10.1007\/978-3-030-01219-9_43"},{"issue":"7","key":"10.1016\/j.media.2024.103350_b38","doi-asserted-by":"crossref","first-page":"2140","DOI":"10.1097\/01.PRS.0000060105.63335.0C","article-title":"Perceived functional impact of abnormal facial appearance","volume":"111","author":"Rankin","year":"2003","journal-title":"Plast. Reconstr. Surg."},{"key":"10.1016\/j.media.2024.103350_b39","series-title":"U-net: Convolutional networks for biomedical image segmentation","first-page":"234","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.media.2024.103350_b40","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-78114-8_29","article-title":"Multi-resolution 3D approximations for rendering complex scenes","author":"Rossignac","year":"1993","journal-title":"Model .Comput. Graph."},{"key":"10.1016\/j.media.2024.103350_b41","series-title":"Proceedings Third International Conference on 3-D Digital Imaging and Modeling","first-page":"145","article-title":"Efficient variants of the ICP algorithm","author":"Rusinkiewicz","year":"2001"},{"key":"10.1016\/j.media.2024.103350_b42","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1007\/s11548-008-0271-0","article-title":"Maxillofacial surgery simulation using a mass-spring model derived from continuum and the scaled displacement method","volume":"4","author":"San Vicente","year":"2009","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"10.1016\/j.media.2024.103350_b43","doi-asserted-by":"crossref","unstructured":"Schult, J., Engelmann, F., Kontogianni, T., Leibe, B., 2020. Dualconvmesh-net: Joint geodesic and euclidean convolutions on 3d meshes. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 8612\u20138622.","DOI":"10.1109\/CVPR42600.2020.00864"},{"key":"10.1016\/j.media.2024.103350_b44","doi-asserted-by":"crossref","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., Guibas, L.J., 2019. Kpconv: Flexible and deformable convolution for point clouds. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp. 6411\u20136420.","DOI":"10.1109\/ICCV.2019.00651"},{"key":"10.1016\/j.media.2024.103350_b45","first-page":"1","article-title":"Dynamic graph cnn for learning on point clouds","volume":"38","author":"Wang","year":"2019","journal-title":"Acm Trans. Graph. (tog)"},{"key":"10.1016\/j.media.2024.103350_b46","series-title":"Pixel2mesh: Generating 3d mesh models from single rgb images","first-page":"52","author":"Wang","year":"2018"},{"key":"10.1016\/j.media.2024.103350_b47","series-title":"3D dense face alignment via graph convolution networks","author":"Wei","year":"2019"},{"key":"10.1016\/j.media.2024.103350_b48","doi-asserted-by":"crossref","unstructured":"Wu, W., Qi, Z., Fuxin, L., 2019. Pointconv: Deep convolutional networks on 3d point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9621\u20139630.","DOI":"10.1109\/CVPR.2019.00985"},{"key":"10.1016\/j.media.2024.103350_b49","doi-asserted-by":"crossref","unstructured":"Xu, Y., Fan, T., Xu, M., Zeng, L., Qiao, Y., 2018. Spidercnn: Deep learning on point sets with parameterized convolutional filters. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 87\u2013102.","DOI":"10.1007\/978-3-030-01237-3_6"},{"key":"10.1016\/j.media.2024.103350_b50","doi-asserted-by":"crossref","unstructured":"Yang, Z., Litany, O., Birdal, T., Sridhar, S., Guibas, L., 2021. Continuous geodesic convolutions for learning on 3d shapes. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision. pp. 134\u2013144.","DOI":"10.1109\/WACV48630.2021.00018"},{"key":"10.1016\/j.media.2024.103350_b51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3197517.3201288","article-title":"P2p-net: Bidirectional point displacement net for shape transform","volume":"37","author":"Yin","year":"2018","journal-title":"ACM Trans. Graph."},{"key":"10.1016\/j.media.2024.103350_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.gmod.2021.101120","article-title":"Graph-PBN: Graph-based parallel branch network for efficient point cloud learning","volume":"119","author":"Zhang","year":"2022","journal-title":"Graph. Models"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524002755?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841524002755?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T10:06:33Z","timestamp":1741082793000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841524002755"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":52,"alternative-id":["S1361841524002755"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2024.103350","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Maxillofacial bone movements-aware dual graph convolution approach for postoperative facial appearance prediction","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2024.103350","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"103350"}}