[1] |
张紫涵, 熊鑫, 王军. 三维头影测量的研究现状和应用发展[J]. 国际口腔医学杂志, 2020, 47(6): 739-744.
|
[2] |
崇煜明, 龙笑. 三维摄影测量技术在面部软组织形态评估中的应用与展望[J]. 中国美容整形外科杂志, 2023, 34(6): 366-369.
|
[3] |
Lee M, Kanavakis G, Miner RM. Newly defined landmarks for a three-dimensionally based cephalometric analysis: A retrospective cone-beam computed tomography scan review[J]. Angle Orthod, 2015, 85(1): 3-10.
doi: 10.2319/021814-120.1
pmid: 24866835
|
[4] |
Zamora N, Cibrián R, Gandia JL, et al. A new 3D method for measuring cranio-facial relationships with cone beam computed tomography (CBCT)[J]. Med Oral Patol Oral Cir Bucal, 2013, 18(4): e706-e713.
|
[5] |
Dobai A, Vizkelety T, Markella Z, et al. Lower face cephalometry based on quadrilateral analysis with cone-beam computed tomography: A clinical pilot study[J]. Oral Maxillofac Surg, 2017, 21(2): 207-218.
doi: 10.1007/s10006-017-0620-7
pmid: 28337564
|
[6] |
Pinheiro M, Ma XH, Fagan MJ, et al. A 3D cephalometric protocol for the accurate quantification of the craniofacial symmetry and facial growth[J]. J Biol Eng, 2019, 13: 42.
doi: 10.1186/s13036-019-0171-6
pmid: 31131023
|
[7] |
Leung MY, Leung YY. Three-dimensional evaluation of mandibular asymmetry: A new classification and three-dimensional cephalometric analysis[J]. Int J Oral Maxillofac Surg, 2018, 47(8): 1043-1051.
|
[8] |
Hikosaka Y, Koizumi S, Kim YI, et al. Comparison of mandibular volume and linear measurements in patients with mandibular asymmetry[J]. Diagnostics, 2023, 13(7): 1331.
|
[9] |
You KH, Kim KH, Lee KJ, et al. Three-dimensional computed tomography analysis of mandibular morphology in patients with facial asymmetry and mandibular retrognathism[J]. Am J Orthod Dentofacial Orthop, 2018, 153(5): 685-691.
|
[10] |
Nur RB, Çakan DG, Arun T. Evaluation of facial hard and soft tissue asymmetry using cone-beam computed tomography[J]. Am J Orthod Dentofacial Orthop, 2016, 149(2): 225-237.
|
[11] |
Kwon SM, Baik HS, Jung HD, et al. Diagnosis and surgical outcomes of facial asymmetry according to the occlusal cant and Menton deviation[J]. J Oral Maxillofac Surg, 2019, 77(6): 1261-1275.
|
[12] |
Chung M, Lee JY, Song W, et al. Automatic registration between dental cone-beam CT and scanned surface via deep pose regression neural networks and clustered similarities[J]. IEEE Trans Med Imaging, 2020, 39(12): 3900-3909.
|
[13] |
Fang Y, Cui ZM, Ma L, et al. Curvature-enhanced implicit function network for high-quality tooth model generation from CBCT images[M]// Lecture Notes in Computer Science. Cham: Springer Nature Switzerland, 2022: 225-234.
|
[14] |
Xu B, Chen ZZ. Multi-level fusion based 3D object detection from monocular images[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23, 2018, Salt Lake City, UT, USA. IEEE, 2018: 2345-2353.
|
[15] |
Payer C, Štern D, Bischof H, et al. Integrating spatial configuration into heatmap regression based CNNs for landmark localization[J]. Med Image Anal, 2019, 54: 207-219.
doi: S1361-8415(18)30578-4
pmid: 30947144
|
[16] |
Dot G, Schouman T, Chang S, et al. Automatic 3-dimensional cephalometric landmarking via deep learning[J]. J Dent Res, 2022, 101(11): 1380-1387.
|
[17] |
Chen RN, Ma YX, Chen NL, et al. Structure-aware long short-term memory network for 3D cephalometric landmark detection[J]. IEEE Trans Med Imaging, 2022, 41(7): 1791-1801.
|
[18] |
Lang YK, Lian CF, Xiao DQ, et al. Localization of craniomaxillofacial landmarks on CBCT images using 3D mask R-CNN and local dependency learning[J]. IEEE Trans Med Imaging, 2022, 41(10): 2856-2866.
|
[19] |
Chen RN, Ma YX, Liu LJ, et al. Semi-supervised anatomical landmark detection via shape-regulated self-training[J]. Neurocomputing, 2022, 471: 335-345.
|
[20] |
Wu TH, Lian CF, Lee S, et al. Two-stage mesh deep learning for automated tooth segmentation and landmark localization on 3D intraoral scans[J]. IEEE Trans Med Imaging, 2022, 41(11): 3158-3166.
|
[21] |
Lang YK, Deng HH, Xiao DQ, et al. DLLNet: An attention-based deep learning method for dental landmark localization on high-resolution 3D digital dental models[M]// Lecture notes in computer science. Cham: Springer International Publishing, 2021: 478-487.
|
[22] |
Sullivan EO, Zafeiriou S. 3D landmark localization in point clouds for the human ear[C]// 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). November 16-20, 2020, Buenos Aires, Argentina. IEEE, 2020: 402-406.
|
[23] |
Hong Y, Peng B, Xiao HY, et al. HeadNeRF: A realtime NeRF-based parametric head model[C]// 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 18-24, 2022, New Orleans, LA, USA. IEEE, 2022: 20342-20352.
|
[24] |
Cheung LK, Chan YM, Jayaratne YSN, et al. Three-dimensional cephalometric norms of Chinese adults in Hong Kong with balanced facial profile[J]. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 2011, 112(2): e56-e73.
|
[25] |
Liang CK, Liu SH, Liu Q, et al. Norms of McNamara's cephalometric analysis on lateral view of 3D CT imaging in adults from NorthEast China[J]. J Hard Tissue Biol, 2014, 23(2): 249-254.
|
[26] |
Wong RK, Chau AM, Hägg U. 3D CBCT McNamara's cephalometric analysis in an adult southern Chinese population[J]. Int J Oral Maxillofac Surg, 2011, 40(9): 920-925.
|
[27] |
Ho CT, Denadai R, Lai HC, et al. Computer-aided planning in orthognathic surgery: A comparative study with the establishment of burstone analysis-derived 3D norms[J]. J Clin Med, 2019, 8(12): 2106.
|