Automatic craniomaxillofacial landmarks detection in CT images of individuals with dentomaxillofacial deformities by a two-stage deep learning model

[1] Cho SM(2018)Reappraisal of neonatal Greenstick Skull Fractures caused by birth injuries: comparison of 3-Dimensional reconstructed computed tomography and simple Skull radiographs World Neurosurg 109 E305-E12

[2] Kim HG(2017)Fully automated quantitative cephalometry using convolutional neural networks J Med Imaging (Bellingham Wash) 4 014501-68

[3] Yoon SH(2020)Artificial intelligence in orthodontics evaluation of a fully automated cephalometric analysis using a customized convolutional neural network J Orofac Orthopedics-Fortschritte Der Kieferorthop 81 52-41

[4] Chang KH(2020)CephaNN: a multi-head attention network for Cephalometric Landmark Detection Ieee Access 8 112633-7

[5] Park MS(2022)Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning J Dent Res 101 1380-66

[6] Park YH(2022)Localization of Craniomaxillofacial Landmarks on CBCT images using 3D mask R-CNN and local dependency learning IEEE Trans Med Imaging 41 2856-14

[7] Choi MS(2021)SkullEngine: a multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection. Machine learning in medical imaging MLMI (Workshop) 12966 606-54

[8] Arik SO(2014)Norms of McNamara’s cephalometric analysis on lateral view of 3D CT imaging in adults from Northeast China J Hard Tissue Biol 23 249-E73

[9] Ibragimov B(2011)Three-dimensional cephalometric norms of chinese adults in Hong Kong with balanced facial profile Oral Surg Oral Med Oral Pathol Oral Radiol Endodontology 112 E56-10

[10] Xing L(2015)Reproducibility of natural head position in normal chinese people Am J Orthod Dentofac Orthop 148 503-8

[1] Cho SM(2018)Reappraisal of neonatal Greenstick Skull Fractures caused by birth injuries: comparison of 3-Dimensional reconstructed computed tomography and simple Skull radiographs World Neurosurg 109 E305-E12

[2] Kim HG(2017)Fully automated quantitative cephalometry using convolutional neural networks J Med Imaging (Bellingham Wash) 4 014501-68

[3] Yoon SH(2020)Artificial intelligence in orthodontics evaluation of a fully automated cephalometric analysis using a customized convolutional neural network J Orofac Orthopedics-Fortschritte Der Kieferorthop 81 52-41

[4] Chang KH(2020)CephaNN: a multi-head attention network for Cephalometric Landmark Detection Ieee Access 8 112633-7

[5] Park MS(2022)Automatic 3-Dimensional Cephalometric Landmarking via Deep Learning J Dent Res 101 1380-66

[6] Park YH(2022)Localization of Craniomaxillofacial Landmarks on CBCT images using 3D mask R-CNN and local dependency learning IEEE Trans Med Imaging 41 2856-14

[7] Choi MS(2021)SkullEngine: a multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection. Machine learning in medical imaging MLMI (Workshop) 12966 606-54

[8] Arik SO(2014)Norms of McNamara’s cephalometric analysis on lateral view of 3D CT imaging in adults from Northeast China J Hard Tissue Biol 23 249-E73

[9] Ibragimov B(2011)Three-dimensional cephalometric norms of chinese adults in Hong Kong with balanced facial profile Oral Surg Oral Med Oral Pathol Oral Radiol Endodontology 112 E56-10

[10] Xing L(2015)Reproducibility of natural head position in normal chinese people Am J Orthod Dentofac Orthop 148 503-8