Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
  • A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
  • A61C7/002—Orthodontic computer assisted systems
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
  • A61C13/00—Dental prostheses; Making same
  • A61C13/0003—Making bridge-work, inlays, implants or the like
  • A61C13/0004—Computer-assisted sizing or machining of dental prostheses
• • G—PHYSICS
  • G06—COMPUTING OR CALCULATING; COUNTING
  • G06F—ELECTRIC DIGITAL DATA PROCESSING
  • G06F30/00—Computer-aided design [CAD]
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
  • A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
  • A61C9/00—Impression cups, i.e. impression trays; Impression methods
  • A61C9/004—Means or methods for taking digitized impressions
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
  • A61C7/00—Orthodontics, i.e. obtaining or maintaining the desired position of teeth, e.g. by straightening, evening, regulating, separating, or by correcting malocclusions
  • A61C7/002—Orthodontic computer assisted systems
  • A61C2007/004—Automatic construction of a set of axes for a tooth or a plurality of teeth

Definitions

• the use of digital 3D models in the dental market is becoming more prevalent. These models can be acquired in vivo using an intra-oral scanner or off-line by laser scanning of a traditional impression.
• the digital 3D models can be used for various clinical tasks including treatment planning, and crown and implant preparation.
• the models can also be used in diagnostic aides, for example to assess tooth wear and gingival recession.
• the digital 3D models are usually obtained in a random orientation and not fixed to a particular coordinate system.
• Methods for aligning a digital 3D model of teeth include receiving a digital 3D model of teeth represented by a 3D mesh in random alignment and changing an orientation of the 3D mesh to align the digital 3D model of teeth with a desired axis within a 3D coordinate system.
• Methods for modifying and aligning a digital 3D model of teeth include receiving a digital 3D model of teeth with associated gingiva represented by a 3D mesh in random alignment, detecting a gum line in the digital 3D model, and removing the gingiva from the digital 3D model.
• the digital 3D model without the gingiva is aligned with a desired axis within a 3D coordinate system.
• FIG. 1 is a diagram of a system for aligning digital 3D models based upon intra-oral 3D scans or 3D scans from impressions;
• FIG. 2 illustrates a 3D model of teeth from intra-oral scans
• FIG. 3 is a flow chart of methods for aligning and modifying digital 3D models
• FIG. 4 is a diagram of a digital 3D model aligned in a desired orientation within a 3D coordinate system and with the gum line detected.
• FIG. 1 is a diagram of a system 10 for aligning digital 3D models based upon intra-oral 3D scans.
• System 10 includes a processor 20 receiving digital 3D models of teeth (12) from intra-oral 3D scans or scans of impressions of teeth.
• System 10 also includes an electronic display device 16, such as a liquid crystal display (LCD) device, for displaying digital 3D models and an input device 18 for receiving user commands or other information.
• LCD liquid crystal display
• FIG. 2 An example of digital 3D model of a patient's teeth from a scan is shown in FIG. 2.
• Systems to generate digital 3D images or models based upon image sets from multiple views are disclosed in U.S. Patent Nos. 7,956,862 and 7,605,817, both of which are incorporated herein by reference as if fully set forth.
• System 10 can be implemented with, for example, a desktop, notebook, or tablet computer. System 10 can receive the 3D scans locally or remotely via a network.
• the 3D scans addressed herein are represented as triangular meshes.
• the triangular mesh is common representation of 3D surfaces and has two components.
• the first component referred to as the vertices of the mesh, are simply the coordinates of the 3D points that have been reconstructed on the surface - i.e., a point cloud.
• the second component the mesh faces, encodes the connections between points on the object and is an efficient way of interpolating between the discrete sample points on the continuous surface.
• Each face is a triangle defined by three vertices, resulting in a surface that can be represented as a set of small triangular planar patches.
• FIG. 3 is a flow chart of methods 1-4 for aligning digital 3D models and optionally removing the gingiva from the models. These methods can be implemented in software or firmware modules, for example, for execution by processor 20. These methods can alternatively be implemented in hardware modules or a combination of software and hardware.
• the methods 1-4 receive a 3D mesh in random alignment (step 30) and generate a 3D mesh aligned with a desired axis in a 3D coordinate system (step 32).
• the 3D mesh is triangular mesh having the components described above.
• the alignment results in an occlusal plane being aligned to a desired orientation within a 3D coordinate system.
• the occlusal plane can be determined by finding points at the top of a tooth or teeth in a digital 3D model of the teeth and fitting a plane to those points.
• a desired orientation aligns the occlusal plane with the Y axis with the teeth pointing up in the model, although the occlusal plane can also be aligned with other axes using the alignment methods.
• An example of a 3D coordinate system includes an X axis, a Y axis, and Z axis with each of the axes being mutually orthogonal with one another. Alignment Method 1 - Aggregation of Normals
• Method 1 involves the following steps: compute the normals of the mesh at each face or vertex, or at a subset of the faces or vertices (step 34); compute an aggregate of the surface normals to determine a representative normal direction by calculating the mean of the surface normals or, alternatively, calculating the sum or the median of the surface normals (step 36); and compute and apply a rotation matrix to align the mean normal with a desired axis (step 38).
• a rotation matrix between two vectors, the mean of the normals with the desired spatial orientation.
• the exemplary method below uses Rodrigues formula.
• Rodrigues' formula yields the rotation matrix [R] to transform from vector a to vector b.
• Table 1 provides exemplary pseudocode for implementing alignment method 1.
• Input a 3D mesh with a set of vertices V specified in 3D coordinate system X,Y and Z.
• Y represents the vertical axis or the general direction in which the teeth point upwards.
• the mesh also has a set of triangulations or faces F based on the vertices.
• Output aligned mesh where the occlusal surface of the teeth are roughly parallel to the XZ plane and perpendicular to the vertical axis, in this case the Y axis.
• Method 2 involves the following steps: compute a grid of vectors aligned with the desired direction of orientation (step 40); project the vectors through the mesh and count the number of intersections with mesh faces (step 42); compute the cost function as the total number of cases, where the number of intersections for each grid line with faces on the mesh is one (step 44); and rotate the model and repeat steps 42 and 44 until the cost function is maximized (step 46).
• this approach makes the assumption that the model represents an open topographic surface, i.e. the surface does not self-intersect or close on itself. This model is often the case with 3D scans of dentitions.
• this method projects a set of parallel rays (also parallel to a fixed desired axis) onto the mesh; the grid spacing of those rays is chosen based on the model resolution and computational limitations.
• the cost function is a ratio of the number of rays that intersected one face to the number of rays that intersected more than one face:
• the method rotates the model around some given axis ( ⁇ ) and maximizes the cost function until a local maximum is found.
• Table 2 provides exemplary pseudocode for implementing alignment method 2.
• Alignment Method 3 involves the following steps: subtract the mean of data points to centralize the data points (step 48); apply a regression or plane fit algorithm (e.g., Support Vector Regression (SVR) function) to find the occlusal plane (step 50); and find the normal direction of the occlusal plane (step 52).
• a regression or plane fit algorithm e.g., Support Vector Regression (SVR) function
• SVR Support Vector Regression
• PCA principal component analysis
• Robust PCA and other forms of regression such as Generalized Linear Models (GLM), Multivariate Regression
• GLM Generalized Linear Models
• Multivariate Regression can also be used.
• This alignment method can be implemented using an SVR method to find the occlusal plane fitted to a mesh of the teeth in the digital 3D model.
• the alignment can be used to have the teeth in the digital 3D model essentially aligned with the Y axis.
• the alignment can use the LIBSVM toolbox and — J ⁇ method.
• the best value of epsilon can be chosen based on many training meshes.
• the training is based on the assumption that teeth are roughly pointing up along the Y axis.
• the output is sample points from the occlusal plane which is given to a simple PCA method to find the normal direction.
• the SVR can also directly return the equation of the plane of best fit. The normal direction can then be computed from this plane.
• SVR uses a linear loss function with a zero part within the margins which performs better for teeth dataset than the quadratic loss function in regular least square regression methods. It helps to decrease the effect of gingiva cut-lines which can be very jagged and bumpy in mesh scans. It also tries to rule out the vertical points on the teeth (buccal part) and give more weight of importance to the horizontal points on teeth (cuspal part) in determining the occlusal plane orientation.
• the RANSAC method and Robust PCA method can alternatively be used for the alignment.
• Table 3 provides exemplary pseudocode for implementing alignment method 3.
• Input a 3D mesh with a set of vertices V specified in 3D coordinate system X,Y and Z.
• Y represents the rough direction of vertical axis in which the teeth point upwards.
• Output the normal vector perpendicular to occlusal plane which represents the desired upward direction of teeth.
• Teeth are roughly pointing up along the Y axis. The mesh has been truncated below the gum line.
• Method 4 involves the following steps: detect the gum line in the model using correlation on curvature or a classification function based upon mesh surface properties (step 54); fit a surface to the gum line points using a modified ridge estimator (step 56); remove all vertices below the surface corresponding to the gingiva (step 58); and align the remaining model using any of the above methods 1-3 (step 60).
• This method 4 can align the model before detecting the gum line and realign the model after gum line detection, or only align the model after gum line detection.
• a classifier using multiple surface features such as curvature, normal direction, mesh local covariance, or other such features can also be used to predict if a vertex lies on the gum line.
• Some examples of the classifiers are linear discriminant classifier, decision tree, support vector machines, and the like.
• the method detects most gum line points, not necessarily all gum line points, or at least a sufficient number of gum line points to fit a surface to the gum line.
• Table 4 provides exemplary pseudocode for implementing alignment and gum line detection method 4 using alignment method 1.
• Input a 3D mesh with a set of vertices V specified in 3D coordinate system X,Y and Z.
• Y represents the vertical axis or the general direction in which the teeth point upwards.
• the mesh also has a set of triangulations or faces F based on the vertices.
• Output aligned mesh where the occlusal surface of the teeth are roughly parallel to the XZ plane and perpendicular to the vertical axis, this case the Y axis, and the mesh below the gum line is truncated.
• ID curvature map Iterate through every point along the border and follow it to the peak of the model storing the curvature at each point, referred to as a ID curvature map.
• step (ii)(c) until all segments have been connected, or the sum of the scores has been maximized.
• FIG. 4 is a diagram of a digital 3D model aligned with a desired orientation and with the gum line detected.
• the digital 3D model of teeth 70 has been aligned with the Y axis (teeth pointing up along with Y axis), and the gum line 72 has been identified with portions of the gingiva below gum line 72 removed from the digital 3D model.
• the alignment results in a desired orientation of the occlusal plane generally parallel with, or extending along, the XZ plane and perpendicular to, or intersecting with, the Y axis.

Landscapes

• Health & Medical Sciences (AREA)
• Oral & Maxillofacial Surgery (AREA)
• Dentistry (AREA)
• Epidemiology (AREA)
• Life Sciences & Earth Sciences (AREA)
• Animal Behavior & Ethology (AREA)
• General Health & Medical Sciences (AREA)
• Public Health (AREA)
• Veterinary Medicine (AREA)
• Engineering & Computer Science (AREA)
• Physics & Mathematics (AREA)
• Theoretical Computer Science (AREA)
• General Engineering & Computer Science (AREA)
• General Physics & Mathematics (AREA)
• Geometry (AREA)
• Evolutionary Computation (AREA)
• Computer Hardware Design (AREA)
• Dental Tools And Instruments Or Auxiliary Dental Instruments (AREA)
• Software Systems (AREA)
• Architecture (AREA)
• Data Mining & Analysis (AREA)
• Mathematical Physics (AREA)
• Algebra (AREA)
• Computational Mathematics (AREA)
• Mathematical Analysis (AREA)
• Mathematical Optimization (AREA)
• Pure & Applied Mathematics (AREA)
• Databases & Information Systems (AREA)

Priority Applications (8)

Applications Claiming Priority (2)

Publications (1)

Family

ID=55437715

Family Applications (1)

Country Status (7)

Cited By (1)

Families Citing this family (23)

Citations (5)

Family Cites Families (36)

• 2014
  • 2014-09-08 US US14/479,692 patent/US10192003B2/en active Active
• 2015
  • 2015-08-31 EP EP20197192.6A patent/EP3785662B1/en active Active
  • 2015-08-31 EP EP15839898.2A patent/EP3191011B1/en active Active
  • 2015-08-31 WO PCT/US2015/047720 patent/WO2016040032A1/en not_active Ceased
  • 2015-08-31 CN CN201580048036.XA patent/CN106604692B/zh active Active
  • 2015-08-31 AU AU2015315653A patent/AU2015315653A1/en not_active Abandoned
  • 2015-08-31 CN CN202010261171.5A patent/CN111462333B/zh active Active
  • 2015-08-31 JP JP2017513055A patent/JP6766033B2/ja active Active
  • 2015-08-31 DK DK15839898.2T patent/DK3191011T3/da active
• 2018
  • 2018-10-19 AU AU2018250487A patent/AU2018250487B2/en not_active Ceased
  • 2018-12-12 US US16/217,094 patent/US10713396B2/en active Active
• 2020
  • 2020-09-16 JP JP2020155060A patent/JP7245809B2/ja active Active

Patent Citations (5)

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events