Tooth segmentation and gingival tissue deformation framework for 3D orthodontic treatment planning and evaluating

In this study, we propose an integrated tooth segmentation and gingival tissue deformation simulation framework used to design and evaluate the orthodontic treatment plan especially with invisible aligners. Firstly, the bio-characteristics information of the digital impression is analyzed quantitatively and demonstrated visually. With the derived information, the transitional regions of tooth-tooth and tooth-gingiva are extracted as the solution domain of the segmentation boundaries. Then, a boundary detection approach is proposed, which is used for the tooth segmentation and region division of the digital impression. After tooth segmentation, we propose the deformation simulation framework driven by energy function based on the biological deformation properties of gingival tissues. The correctness and availability of the proposed segmentation and gingival tissue deformation simulation framework are demonstrated with typical cases and qualitative analysis. Experimental results show that segmentation boundaries calculated by the proposed method are accurate, and local details of the digital impression under study are preserved well during deformation simulation. Qualitative analysis results of the gingival tissues’ surface area and volume variations indicate that the proposed gingival tissue deformation simulation framework is consistent with the clinical gingival tissue deformation characteristics, and it can be used to predict the rationality of the treatment plan from both visual inspection and numerical simulation. The proposed tooth segmentation and gingival tissue deformation simulation framework is shown to be effective and has good practicability, but accurate quantitative analysis based on clinical results is still an open problem in this study. Combined with tooth rearrangement steps, it can be used to design the orthodontic treatment plan, and to output the data for production of invisible aligners. Graphical abstract Graphical abstract

[1]  Yi Su,et al.  Modeling and Simulation of Soft Tissue Deformation , 2013, Abdominal Imaging.

[2]  Olof Torgersson,et al.  Digital casts in orthodontics: a comparison of 4 software systems. , 2015, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[3]  Qian Yan,et al.  A Review of 3D Printing Technology for Medical Applications , 2018, Engineering.

[4]  Thomas W. Sederberg,et al.  Free-form deformation of solid geometric models , 1986, SIGGRAPH.

[5]  Cagatay Basdogan,et al.  Surgical Simulation: An Emerging Technology for Training in Emergency Medicine , 1997, Presence: Teleoperators & Virtual Environments.

[6]  Ayellet Tal,et al.  Hierarchical mesh decomposition using fuzzy clustering and cuts , 2003, ACM Trans. Graph..

[7]  Mark Meyer,et al.  Discrete Differential-Geometry Operators for Triangulated 2-Manifolds , 2002, VisMath.

[8]  Qing Yu,et al.  Single-Tooth Modeling for 3D Dental Model , 2010, Int. J. Biomed. Imaging.

[9]  C. Flores‐Mir,et al.  Patient satisfaction and quality of life changes after Invisalign treatment , 2018, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[10]  Tung Nguyen,et al.  3D technologies for precision in orthodontics , 2018, Seminars in Orthodontics.

[11]  Mongi A. Abidi,et al.  Perception-based 3D triangle mesh segmentation using fast marching watersheds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[12]  Hao Wang,et al.  Interactive Tooth Separation from Dental Model Using Segmentation Field , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  Salina Sulaiman,et al.  Effect of time complexities and variation of mass spring model parameters on surgical simulation , 2014 .

[14]  Leif Kobbelt,et al.  Extraction of feature lines on triangulated surfaces using morphological operators , 2000 .

[15]  S. Cunningham,et al.  Quality of life and its importance in orthodontics. , 2001, Journal of orthodontics.

[16]  Sim Heng Ong,et al.  Tooth segmentation of dental study models using range images , 2004, IEEE Transactions on Medical Imaging.

[17]  Tosiyasu L. Kunii,et al.  Ridges and ravines on implicit surfaces , 1998, Proceedings. Computer Graphics International (Cat. No.98EX149).

[18]  P. Vig.  Orthodontics, current principles and techniques , 1985 .

[19]  L. Joffe,et al.  Current Products and Practices OrthoCAD™: Digital Models for a Digital Era , 2004 .

[20]  Kyung-Min Lee,et al.  Reproducibility of an intraoral scanner: A comparison between in‐vivo and ex‐vivo scans , 2018, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[21]  Yu Qing,et al.  Bio-Information Based Segmentation of 3D Dental Models , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[22]  Alan Liu,et al.  A Survey of Surgical Simulation: Applications, Technology, and Education , 2003, Presence: Teleoperators & Virtual Environments.

[23]  W SederbergThomas,et al.  Free-form deformation of solid geometric models , 1986 .

[24]  Shenghui Liao,et al.  Interactive tooth partition of dental mesh base on tooth-target harmonic field , 2015, Comput. Biol. Medicine.

[25]  Suvranu De,et al.  Physically Realistic Virtual Surgery Using the Point-Associated Finite Field (PAFF) Approach , 2006, PRESENCE: Teleoperators and Virtual Environments.

[26]  A. Jacobson,et al.  An evaluation of the use of digital study models in orthodontic diagnosis and treatment planning. , 2009, The Angle orthodontist.

[27]  Christian Rössl,et al.  Adaptive and robust curve smoothing on surface meshes , 2014, Comput. Graph..

[28]  Daniel Cohen-Or,et al.  A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics, and Image Processing , 2015 .

[29]  Mathieu Desbrun,et al.  Animation of Deformable Models Using Implicit Surfaces , 1997, IEEE Trans. Vis. Comput. Graph..

[30]  Reinhard Klein,et al.  Efficient RANSAC for Point‐Cloud Shape Detection , 2007, Comput. Graph. Forum.

[31]  Ralph R. Martin,et al.  Rapid and effective segmentation of 3D models using random walks , 2009, Comput. Aided Geom. Des..

[32]  Gianluca De Leo,et al.  Simulation of the biomechanical behavior of the skin in virtual surgical applications by finite element method , 2005, IEEE Transactions on Biomedical Engineering.

[33]  Randall Moles.  The SureSmile system in orthodontic practice. , 2009, Journal of clinical orthodontics : JCO.

[34]  Budi Kusnoto,et al.  How well does Invisalign work? A prospective clinical study evaluating the efficacy of tooth movement with Invisalign. , 2009, American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics.

[35]  Christian Rössl,et al.  Laplacian surface editing , 2004, SGP '04.

[36]  Nour Eldin Tarraf,et al.  Present and the future of digital orthodontics✰ , 2018, Seminars in Orthodontics.

[37]  Petros Koumoutsakos,et al.  A Lagrangian particle method for the simulation of linear and nonlinear elastic models of soft tissue , 2008, J. Comput. Phys..

[38]  Thomas Kronfeld,et al.  Snake-Based Segmentation of Teeth from Virtual Dental Casts , 2010 .

[39]  A. Johal,et al.  Orthodontic measurements on digital study models compared with plaster models: a systematic review. , 2011, Orthodontics & craniofacial research.

[40]  John D. Muir,et al.  Orthodontics: Current principles and techniques , 1985 .

[41]  Anssi J. Mäkynen,et al.  Recent advances in dental optics – Part I: 3D intraoral scanners for restorative dentistry , 2014 .