Digital technology assisted the establishment of craniomaxillofacial midsagittal plane

doi:10.19438/j.cjoms.2024.03.014

Abstract

Abstract: The midsagittal plane(MSP), defined as the sagittal plane that intersects the human body by its midline, is one of the most crucial symmetrical planes of the 3D craniomaxillofacial structure, holding huge significance in the analysis and restoration of facial symmetry. In clinic, the commonly-used method for establishing craniomaxillofacial MSPs is subject to such limitations as doctors' subjective factors, the excessive time consumption, and the lack of universal standards for the selection of anatomical landmarks. The aforementioned issues have spawned a series of studies on the establishment of craniomaxillofacial MSPs based on digital technology, indicating the great potential of digital technology in this field. This article systematically reviewed the research progress of digital technology assisted craniomaxillofacial MSP establishment, focusing on the existing research based on landmark-based method, symmetry method and deep learning method, and elaborating the further optimization of craniomaxillofacial MSP establishment by digital technology.

Key words: Midsagittal plane, Digital technology, Surgical planning, Artificial intelligence

CLC Number:

R782.2

FU Yi-jiao, TAO Le-ran, YU Hong-bo. Digital technology assisted the establishment of craniomaxillofacial midsagittal plane[J]. China Journal of Oral and Maxillofacial Surgery, 2024, 22(3): 287-293.

References

[1] 张彦琦, 白玉兴, 张楠, 等. 基于锥形束CT的颅颌正中矢状面的选择评估[J]. 北京口腔医学, 2013, 21(5): 277-280.
Zhang YQ, Bai YX, Zhang N, et al.Evaluation of craniofacial midsagittal plane selection based on cone beam computed tomography[J]. Beijing Journal of Stomatology, 2013, 21(5): 277-280.
[2] Choi HW, Kim B, Kim JY, et al.Three-dimensional computed tomography evaluation of craniofacial characteristics according to lateral deviation of chin[J]. Maxillofac Plast Reconstr Surg, 2019, 41(1): 57-64.
[3] 欧阳王涛. 建立及验证基于三维颅底对称模型的正中矢状面[C]. 2020年中华口腔医学会口腔颌面放射专业委员会第十八次全国口腔颌面医学影像学专题研讨会, 2020: 88.
Ouyang WT.Establishment and verification of median sagittal plane based on three-dimensional skull base symmetric model[C]. The 18th National Oral and maxillofacial Medical Imaging Symposium of the Oral and maxillofacial Radiation Professional Committee of the Chinese Stomatological Association In 2020, 2020:88.
[4] Willing RT, Roumeliotis G, Jenkyn TR, et al.Development and evaluation of a semi-automatic technique for determining the bilateral symmetry plane of the facial skeleton[J]. Med Eng Phys, 2013, 35(12): 1843-1849.
[5] De Momi E, Chapuis J, Pappas I, et al.Automatic extraction of the mid-facial plane for cranio-maxillofacial surgery planning[J]. Int J Oral Maxillofac Surg, 2006, 35(7): 636-642.
[6] Damstra J, Fourie Z, De Wit M, et al.A three-dimensional comparison of a morphometric and conventional cephalometric midsagittal planes for craniofacial asymmetry[J]. Clin Oral Investig, 2012, 16(1): 285-294.
[7] Xiong Y, Zhao Y, Yang H, et al.Comparison between interactive closest point and procrustes analysis for determining the median sagittal plane of three-dimensional facial data[J]. J Craniofac Surg, 2016, 27(2): 441-444.
[8] Zhu YJ, Zheng SW, Yang GS, et al.A novel method for 3D face symmetry reference plane based on weighted Procrustes analysis algorithm[J]. BMC Oral Health, 2020, 20(1): 319-330.
[9] Zhu YJ, Fu XL, Zhang L, et al.A mathematical algorithm of the facial symmetry plane: application to mandibular deformity 3D facial data[J]. J Anat, 2022, 240(3): 556-566.
[10] 朱玉佳, 赵一姣, 郑盛文, 等. 基于赋权形态学分析的三维面部对称参考平面构建方法[J].北京大学学报(医学版), 2021, 53(1): 220-226.
Zhu Y, Zhao Y, Zheng S, et al.A method for constructing three-dimensional face symmetry reference plane based on weighted shape analysis algorithm[J]. Journal of Peking University (Health Sciences), 2021, 53(1): 220-226.
[11] 朱玉佳. 基于标志点赋权的三维颜面对称参考平面算法研究[C]. 2021年度全国口腔颌面创伤及正颌研究生优秀论文专题研讨会, 2021: 36.
Zhu YJ.Research on 3D facial symmetry reference plane algorithm based on weighted marker points[C]. National Symposium on Oral and Maxillofacial Trauma and Orthognathic Postgraduate Excellent Papers in 2021, 2021:36.
[12] 邱淑婷, 朱玉佳, 王时敏, 等. 姿势微笑位口唇对称参考平面的数字化构建及初步应用验证[J]. 北京大学学报(医学版), 2022, 54(1): 193-199.
Qiu ST, Zhu YJ, Wang SM, et al.Preliminary clinical application verification of complete digital workflow of design lips symmetry reference plane based on posed smile[J]. Journal of Peking University: Health Sciences, 2022, 54(1): 193-199.
[13] Moorrees CFA, Kean MR.Natural head position, a basic consideration in the interpretation of cephalometric radiographs[J]. Am J Phys Anthropol, 1958, 16(2): 213-234.
[14] Wong TY, Liu JK, Fang JJ, et al. Use of the matching optimal symmetry plane method in planning surgical correction of facial asymmetry-a preliminary report of 20 patients[J]. J Oral Maxillofac Surg, 2014, 72(6): 1180.e1-13.
[15] Hsiao YC, Fang JJ.An automatic voxel-based method for optimal symmetry plane generation for the maxillofacial region in severe asymmetry cases[J]. J Clin Med, 2022, 11(19): 5689-5700.
[16] Ji PL, Liu XG.A fast and efficient 3D reflection symmetry detector based on neural networks[J]. Multimed Tools Appl, 2019, 78(24): 35471-35492.
[17] Zhou Y, Liu S, Ma Y. Learning to detect 3D reflection symmetry for single-view reconstruction[J]. ArXiv, 2020, abs/2006.10042.
[18] Gao L, Zhang LX, Meng HY, et al.PRS-Net: planar reflective symmetry detection net for 3D models[J]. IEEE Trans Vis Comput Graph, 2021, 27(6): 3007-3018.
[19] Stoian M C, Cavallari T.Recurrently Estimating reflective symmetry planes from partial pointclouds[C]. The CVPR 2021 Workshop on 3D Vision and Robotics. arXiv e-prints, 2021: 2106.16129.
[20] Zhou Y, Liu S, Ma Y.NeRD: neural 3D reflection symmetry detector[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021: 15935-15944.
[21] Nagar R.Robust extrinsic symmetry estimation in 3D point clouds[C]. The CVPR 2021 Workshop on 3D Vision and Robotics. arXiv e-prints, 2021: 2109.09927v2.
[22] Lin HH, Chiang WC, Yang CT, et al.On construction of transfer learning for facial symmetry assessment before and after orthognathic surgery[J]. Comput Methods Programs Biomed, 2021, 200: 105928.
[23] Lo LJ, Yang CT, Ho CT, et al.Automatic assessment of 3-dimensional facial soft tissue symmetry before and after orthognathic surgery using a machine learning model a preliminary experience[J]. Ann Plast Surg, 2021, 86(3S Suppl 2): S224-S228.
[24] Dalvit Carvalho da Silva R, Jenkyn TR, Carranza VA. Convolutional neural networks and geometric moments to identify the bilateral symmetric midplane in facial skeletons from CT scans[J]. Biology (Basel), 2021, 10(3): 182-197.
[25] 朱玉佳, 许晴, 赵一姣, 等. 深度学习算法辅助构建三维颜面正中矢状平面[J]. 北京大学学报(医学版), 2022, 54(1): 134-139.
Zhu YJ, Xu Q, Zhao YJ, et al.Deep learning-assisted construction of three-demensional facial midsagittal plane[J]. Journal of Peking University(Health Sciences), 2022, 54(1): 134-139.