Hill JL, Vasconez HC. Midfacial fractures. Ferraro’s Fundamentals of Maxillofacial Surgery: Springer; 2015. p. 185–190.
Fuessinger MA, Schwarz S, Gass M, Poxleitner P, Brandenburg L, Schlager S, et al. The statistical shape model as a quality assurance measure in the treatment of complex midface fractures: a case control study. Head & Face Medicine. 2021;17(1):1–7.
Article
Google Scholar
Ghosh R, Kulandaswamy G. Facial fractures. J Craniofac Surg. 2018;29:e334–40.
Article
Google Scholar
Oh J-h. Recent advances in the reconstruction of cranio-maxillofacial defects using computer-aided design/computer-aided manufacturing. Maxillofac Plast Reconstr Surg. 2018;40(1):1–7.
Article
Google Scholar
Bell RB. Computer planning and intraoperative navigation in cranio-maxillofacial surgery. Oral and Maxillofacial Surg Clin. 2010;22(1):135–56.
Article
Google Scholar
Kodym O, Španěl M, Herout A. Skull shape reconstruction using cascaded convolutional networks. Comput Biol Med. 2020;123:103886.
Article
Google Scholar
Schramm A, Suarez-Cunqueiro M, Rücker M, Kokemueller H, Bormann KH, Metzger M, et al. Computer-assisted therapy in orbital and mid-facial reconstructions. Int J Med Robot. 2009;5(2):111–24.
Article
CAS
Google Scholar
Ramanathan M, Panneerselvam E, Raja VKK. 3D planning in mandibular fractures using CAD/CAM surgical splints—A prospective randomized controlled clinical trial. J Craniomaxillofac Surg. 2020;48(4):405–12.
Article
Google Scholar
Fuessinger MA, Schwarz S, Neubauer J, Cornelius C-P, Gass M, Poxleitner P, et al. Virtual reconstruction of bilateral midfacial defects by using statistical shape modeling. J Craniomaxillofac Surg. 2019;47(7):1054–9.
Article
Google Scholar
Ehrenfeld M, Manson PN, Prein J. Principles of internal fixation of the craniomaxillofacial skeleton.Trauma and Orthognathic Surgery: Thieme; 2012.
Rückschloß T, Ristow O, Kühle R, Weichel F, Roser C, Aurin K, et al. Accuracy of laser-melted patient-specific implants in genioplasty—A three-dimensional retrospective study. J Craniomaxillofac Surg. 2020;48(7):653–60.
Article
Google Scholar
Buonamici F, Furferi R, Genitori L, Governi L, Marzola A, Mussa F, et al. Reverse engineering techniques for virtual reconstruction of defective skulls: an overview of existing approaches. Comput Aided Des Appl. 2018;16(1):103–12.
Article
Google Scholar
Gass M, Füßinger MA, Metzger MC, Schwarz S, Bähr JD, Brandenburg L, et al. Virtual reconstruction of orbital floor defects using a statistical shape model. J Anat. 2022;240(2):323–9.
Article
Google Scholar
Hierl T, Doerfler HM, Huempfner-Hierl H, Kruber D. Evaluation of the Midface by Statistical Shape Modeling. J Oral Maxillofac Surg. 2021;79(1):202.e1–202.e6.
Article
Google Scholar
Verma S, Gonzalez M, Schow SR, Triplett RG. Virtual Preoperative Planning and Intraoperative Navigation in Facial Prosthetic Reconstruction: A Technical Note. Int J Oral Maxillofac Implants. 2017;32(2):e77–81.
Article
Google Scholar
Egger J, Gall M, Tax A, Ücal M, Zefferer U, Li X, et al. Interactive reconstructions of cranial 3D implants under MeVisLab as an alternative to commercial planning software. PLoS ONE. 2017;12(3):e0172694.
Article
Google Scholar
Kwon T-G, Park H-S, Ryoo H-M, Lee S-H. A comparison of craniofacial morphology in patients with and without facial asymmetry—a three-dimensional analysis with computed tomography. Int J Oral Maxillofac Surg. 2006;35(1):43–8.
Article
Google Scholar
Wagner MEH, Lichtenstein JT, Winkelmann M, Shin HO, Gellrich NC, Essig H. Development and first clinical application of automated virtual reconstruction of unilateral midface defects. J Craniomaxillofac Surg. 2015;43(8):1340–7.
Article
Google Scholar
Buonamici F, Furferi R, Genitori L, Governi L, Marzola A, Mussa F, et al. Reverse Engineering Techniques for Virtual Reconstruction of Defective Skulls: an Overview of Existing Approaches. Comput-Aided Des Appl. 2018;16(1):103–12.
Article
Google Scholar
Barragan-Montero A, Javaid U, Valdes G, Nguyen D, Desbordes P, Macq B, et al. Artificial intelligence and machine learning for medical imaging: A technology review. Phys Med. 2021;83:242–56.
Article
Google Scholar
Chang AX, Funkhouser T, Guibas L, Hanrahan P, Huang Q, Li Z, et al. Shapenet: An information-rich 3d model repository. Technical Report arXiv. 2015:1512.03012.
Lan L, You L, Zhang Z, Fan Z, Zhao W, Zeng N, et al. Generative adversarial networks and its applications in biomedical informatics. Front Public Health. 2020;8:164.
Article
Google Scholar
Litany O, Bronstein A, Bronstein M, Makadia A, editors. Deformable shape completion with graph convolutional autoencoders. Proceedings of the IEEE conference on computer vision and pattern recognition. 2018;1886–95.
Yuan Z, Jiang M, Wang Y, Wei B, Li Y, Wang P, et al. SARA-GAN: Self-Attention and Relative Average Discriminator Based Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction. Front Neuroinform. 2020;14:58.
Article
Google Scholar
He K, Zhang X, Ren S, Sun J, editors. Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770–8.
He K, Zhang X, Ren S, Sun J, editors. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. Proceedings of the IEEE international conference on computer vision. 2015;1026–34.
Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, et al. Automatic differentiation in pytorch. In: NIPS Workshop. 2017. p. 4–9.
Google Scholar
Kingma DP, Ba J. Adam: a method for stochastic optimization. arXiv preprint arXiv. 2014:1412.6980.
Gui H, Yang H, Zhang S, Shen SG, Ye M, Schmelzeisen R. Mirroring Tool: The Simplest Computer-Aided Simulation Technology? J Craniofac Surg. 2015;26(7):2115–9.
Article
Google Scholar
Wei G, Wang J, Lu M, Wu J, Wei C. Similarity measures of spherical fuzzy sets based on cosine function and their applications. IEEE Access. 2019;7:159069–80.
Article
Google Scholar
Xiao D, Lian C, Wang L, Deng H, Lin HY, Thung KH, et al. Estimating Reference Shape Model for Personalized Surgical Reconstruction of Craniomaxillofacial Defects. IEEE Trans Biomed Eng. 2021;68(2):362–73.
Article
Google Scholar
Steinbacher DM. Three-Dimensional Analysis and Surgical Planning in Craniomaxillofacial Surgery. J Oral Maxillofac Surg. 2015;73(12):S40–56.
Article
Google Scholar
Semper-Hogg W, Fuessinger MA, Schwarz S, Ellis E 3rd, Cornelius CP, Probst F, et al. Virtual reconstruction of midface defects using statistical shape models. Journal of Cranio-Maxillofacial Surgery. 2017;45(4):461–6.
Article
Google Scholar
Xiao D, Wang L, Deng H, Thung KH, Zhu J, Yuan P, et al. Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects. Med Image Comput Comput Assist Interv. 2019;11768:327–35.
PubMed
PubMed Central
Google Scholar
Kodym O, Spanel M, Herout A. Deep learning for cranioplasty in clinical practice: Going from synthetic to real patient data. Comput Biol Med. 2021;137:104766.
Article
Google Scholar
Kim T-Y, Baik J-S, Park J-Y, Chae H-S, Huh K-H, Choi S-C. Determination of midsagittal plane for evaluation of facial asymmetry using three-dimensional computed tomography. Imaging science in dentistry. 2011;41(2):79–84.
Article
CAS
Google Scholar
Wong R, Chau A, Hägg U. 3D CBCT McNamara’s cephalometric analysis in an adult southern Chinese population. Int J Oral Maxillofac Surg. 2011;40(9):920–5.
Article
CAS
Google Scholar
Özer CM, Öz II, Serifoglu I, Büyükuysal MÇ, Barut Ç. Evaluation of eyeball and orbit in relation to gender and age. J Craniofac Surg. 2016;27(8):e793–800.
Article
Google Scholar