Source: http://www.google.com/patents/US7247021?dq=6,977,809
Timestamp: 2016-05-06 03:47:14
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Patent US7247021 - Subdividing a digital dentition model - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA computer or other digital circuitry is used to assist in the creation of a digital model of an individual component, such as a tooth or gum tissue, in a patient's dentition. The computer receives a data set that forms a three-dimensional (3D) representation of the patient's dentition, applies a test...http://www.google.com/patents/US7247021?utm_source=gb-gplus-sharePatent US7247021 - Subdividing a digital dentition modelAdvanced Patent SearchPublication numberUS7247021 B2Publication typeGrantApplication numberUS 10/802,124Publication dateJul 24, 2007Filing dateMar 15, 2004Priority dateJun 20, 1997Fee statusPaidAlso published asUS20040175671Publication number10802124, 802124, US 7247021 B2, US 7247021B2, US-B2-7247021, US7247021 B2, US7247021B2InventorsTimothy N. Jones, Muhammad Chishti, Huafeng Wen, Gregory BalaOriginal AssigneeAlign Technology, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (101), Non-Patent Citations (99), Referenced by (11), Classifications (15), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetSubdividing a digital dentition model
US 7247021 B2Abstract
A computer or other digital circuitry is used to assist in the creation of a digital model of an individual component, such as a tooth or gum tissue, in a patient's dentition. The computer receives a data set that forms a three-dimensional (3D) representation of the patient's dentition, applies a test to the data set to identify data elements that represent portions of the individual component, and creates a digital model of the individual component based upon the identified data elements. Many implementations require the computer to identify data elements representing a 2D cross-section of the dentition lying in a 2D plane that is roughly parallel to or roughly perpendicular to the dentition's occlusal plane. The computer analyzes the 2D cross-section to identify dentition features that represent boundaries between individual dentition components.
1. A computer-implemented method for use in creating a digital model of a tooth in a patient's dentition, the method comprising:
(a) receiving a three-dimensional (3D) data set representing the patient's dentition;
(b) applying a computer-implemented test to the 3D data set to identify data elements that represent an interproximal margin between two teeth in the dentition, wherein applying the computer-implemented test includes;
identifying elements of the data set that represent the interproximal margin, and
labeling those data elements as belonging to the interproximal margin; and
(c) applying another computer-implemented test to select data elements that lie on one side of the interproximal margin for inclusion in the digital model of the tooth.
2. The method of claim 1, further comprising creating a set of 2D planes that intersect the dentition roughly perpendicular to an occlusal plane of the dentition, each 2D plane including data elements that form a 2D cross-section of the dentition.
3. The method of claim 2, further comprising identifying the 2D plane with the smallest cross-sectional area.
4. The method of claim 3, further comprising rotating the 2D plane with the smallest cross-sectional area to at least one other orientation to form at least one other 2D cross-section of the dentition.
5. The method of claim 4, further comprising selecting the orientation that gives the rotated plane its smallest possible cross-sectional area.
6. The method of claim 5, further comprising identifying data elements that represent the selected orientation of the rotated plane as lying on an interproximal margin.
7. The method of claim 4, wherein the plane is rotated about two orthogonal lines passing through its center point.
8. The method of claim 3, further comprising creating a set of additional 2D planes in the vicinity of the 2D plane with the smallest cross-sectional area.
9. The method of claim 8, further comprising identifying the plane in the set of additional planes that has the smallest cross-sectional area.
10. The method of claim 9, further comprising rotating the plane with the smallest cross-sectional area to at least one other orientation to form at least one other 2D cross-section of the dentition.
11. The method of claim 10, further comprising selecting the orientation that produces the 2D cross-section with the smallest possible area.
12. The method of claim 2, wherein creating a set of 2D planes includes creating an initial plane near one end of the dentition.
13. The method of claim 12, further comprising selecting a point in the dentition that is a predetermined distance from the initial plane and creating a second plane.
14. The method of claim 13, wherein the second plane is roughly parallel to the initial plane.
15. The method of claim 13, further comprising rotating the second plane to at least one additional orientation to form at least one additional 2D cross-section of the dentition.
16. The method of claim 15, further comprising selecting the orientation that produces the 2D cross-section with the smallest cross-sectional area.
17. The method of claim 15, further comprising selecting a point that is a predetermined distance from the second plane and creating a third plane that includes the selected point.
18. The method of claim 17, further comprising rotating the third plane to at least one other orientation to create at least one additional 2D cross-section of the dentition.
19. The method of claim 17, further comprising creating additional planes, each including a point that is a predetermined distance from a preceding plane, until the other end of the dentition is reached.
20. The method of claim 19, further comprising identifying at least one plane having a local minimum in cross-sectional area.
21. The method of claim 19, further comprising identifying a centerpoint of the cross-section in each of the planes and creating a curve that fits among the identified centerpoints.
22. The method of claim 21, further comprising creating a set of additional 2D planes along the curve, where the curve is roughly normal to each of the additional planes, and where each of the additional planes is roughly perpendicular to the occlusal plane.
23. The method of claim 22, further comprising identifying at least one of the additional planes that has a local minimum in cross-sectional area.
24. A computer-implemented method for use in creating a digital model of a tooth in a patient's dentition, the method comprising:
(a) receiving a 3D dataset representing at least a portion of the patient's dentition, including at least a portion of a tooth and gum tissue surrounding the tooth;
(b) applying a test to the 3D dataset to identify data elements lying on a gingival boundary that occurs where the tooth and the gum tissue meet, wherein applying the computer-implemented test includes;
identifying elements of the data set that represent the gingival boundary, and
labeling those data elements as belonging to the gingival boundary; and
(c) applying a test to the data elements lying on the boundary to identify other data elements representing portions of the tooth, wherein applying the test to identify data elements on the gingival boundary includes creating a series of roughly parallel 2D planes, each intersecting the dentition roughly perpendicular to an occlusal plane of the dentition, and each including data elements that represent a cross-sectional surface of the dentition.
25. The method of claim 24, wherein the cross-sectional surface in each 2D plane includes two cusps that roughly identify the locations of the gingival boundary.
The present application is a continuation of U.S. application Ser. No. 09/264,547, filed Mar. 8, 1999, now U.S. Pat. No. 7,063,532, which is a continuation-in-part of U.S. application Ser. No. 09/169,276, filed on Oct. 8, 1998, (now abandoned), and entitled “Computer Automated Development of an Orthodontic Treatment Plan and Appliance,” which is a continuation-in-part of PCT Application No. PCT/US98/12861, filed on Jun. 19, 1998, and entitled “Method and System for Incrementally Moving Teeth”, which is a continuation-in-part of U.S. application Ser. No. 08/947,080, filed on Oct. 8, 1997, and entitled “Method and System for Incrementally Moving Teeth” (now U.S. Pat. No. 5,975,893), which claims the benefit of U.S. Provisional Application No. 60/050,342, filed on Jun. 20, 1997, all of which are incorporated by reference into this application.
The invention relates generally to the fields of dentistry and orthodontics and, more particularly, to subdividing a digital model of a patient's dentition.
Two-dimensional (2D) and three-dimensional (3D) digital image technology has recently been tapped as a tool to assist in dental and orthodontic treatment. Many treatment providers use some form of digital image technology to study the dentitions of patients. U.S. patent application Ser. No. 09/169,276, incorporated by reference above, describes the use of 2D and 3D image data in forming a digital model of a patient's dentition, including models of individual dentition components. Such models are useful, among other things, in developing an orthodontic treatment plan for the patient, as well as in creating one or more orthodontic appliances to implement the treatment plan.
The inventors have developed several computer automated techniques for subdividing, or segmenting, a digital dentition model into models of individual dentition components. These dentition components include, but are not limited to, tooth crowns, tooth roots, and gingival regions. The segmentation techniques include both human assisted and fully automated techniques. Some of the human assisted techniques allow a human user to provide “algorithmic hints” by identifying certain features in the digital dentition model. The identified features then serve as a basis for automated segmentation. Some techniques act on a volumetric 3D image model, or “voxel representation,” of the dentition, and other techniques act on a geometric 3D model, or “geometric representation.”
In one aspect, a computer implementing the invention receives a data set that forms a three-dimensional (3D) representation of the patient's dentition, applies a test to the data set to identify data elements that represent portions of the individual component, and creates a digital model of the individual component based upon the identified data elements. Some implementations require the computer to identify data elements that form one or more 2D cross-sections of the dentition in one or more 2D planes intersecting the dentition. In many of these embodiments, these 2D planes are roughly parallel to the dentition's occlusal plane. The computer analyzes the features of the 2D cross-sections to identify data elements that correspond to the individual component to be modeled. For example, one technique requires the computer to identify cusps in the 2D cross-sectional surface of the dentition, where the cusps represent the locations of an interproximal margin between teeth in the dentition. One variation of this technique allows the computer to confine its search for cusps in one 2D plane to areas in the vicinity of cusps already identified on another 2D plane. Another variation allows the computer to link cusps on adjacent 2D planes to form a solid surface representing the interproximal margin. Some embodiments allow the computer to receive input from a human user identifying the cusp locations in one or more of the 2D cross sections.
Other embodiments require the computer to identify data elements that represent a structural core, or skeleton, of each individual component to be modeled. The computer creates the model by linking other data elements representing the individual component to the structural core.
In another aspect, a computer implementing the invention receives a three-dimensional (3D) data set representing the patient's dentition, applies a test to identify data elements that represent an interproximal margin between two teeth in the dentition, and applies another computer-implemented test to select data elements that lie on one side of the interproximal margin for inclusion in the digital model. Some implementations require the computer to identify data elements that form one or more 2D cross-sections of the dentition in one or more 2D planes intersecting the dentition roughly parallel to the dentition's occlusal plane.
In another aspect, a computer implementing the invention receives a 3D data set representing at least a portion of the patient's dentition, including at least a portion of a tooth and gum tissue surrounding the tooth; applies a test to identify data elements lying on a gingival boundary that occurs where the tooth and the gum tissue meet; and applies a test to the data elements lying on the boundary to identify other data elements representing portions of the tooth.
FIGS. 1A, 1B, and 2 are partial views of a dentition model as displayed on a computer monitor and segmented with a human operated saw tool.
FIG. 3 is a partial view of a dentition model as displayed on a computer monitor and segmented with a human operated eraser tool.
FIGS. 5 and 6 are flow diagram for a feature skeleton analysis technique used in segmenting a dentition model.
FIG. 8 is a flow diagram for a 2D slice analysis technique used in segmenting a dentition model.
FIG. 11 is a flow chart for an automatic cusp detection technique used in segmenting a dentition model.
FIG. 12 is a horizontal 2D cross-section of a dentition model illustrating a neighborhood filtered automatic cusp detection technique used in segmenting the dentition model.
FIG. 13 is shows two groups of voxels in a 2D slice of a dentition model illustrating the neighborhood filtered automatic cusp detection technique.
FIG. 14 is a flow chart for the neighborhood filtered automatic cusp detection technique.
FIG. 15 is a horizontal 2D cross-section of a dentition model illustrating an arch curve fitting technique used in segmenting the dentition model.
FIG. 16 is a flow chart for the arch curve fitting technique.
FIG. 17 is a horizontal 2D cross-section of a dentition model illustrating a curve creation technique for use with the arch curve fitting technique.
FIG. 18 is a flow diagram for the curve creation technique.
FIGS. 20 and 21 are flow diagrams of this technique.
FIG. 22 is a vertical 2D cross-sectional view of a dentition model illustrating a gingival margin detection technique for use in segmenting the dentition model.
FIG. 24 is a flow diagram for the gingival margin detection technique.
U.S. patent application Ser. No. 09/169,276 describes techniques for generating a 3D digital model of a patient's dentition, including the crowns and roots of the patients teeth as well as the surrounding gum tissue. One such technique involves creating a physical model of the dentition from a material such as plaster and then digitally imaging the model with a laser scanner or a destructive scanning system. The described techniques are used to produce a volumetric 3D image model (“volume element representation” or “voxel representation”) and a geometric 3D surface model (“geometric model”) of the dentition. The techniques described below act on one or both of these types of 3D dentition models. In creating a voxel representation, the physical model is usually embedded in a potting material that contrasts sharply with the color of the model to enhance detection of the dentition features. A white dentition model embedded in a black potting material provides the sharpest contrast. A wide variety of information is used to enhance the 3D model, including data taken from photographic images, 2D and 3D x-rays scans, computed tomography (CT) scans, and magnetic resonance imaging (MRI) scans of the patient's dentition.
Some computer-implemented techniques for segmenting a 3D dentition model into models of individual dentition components require a substantial amount of human interaction with the computer. One such technique, which is shown in FIGS. 1A, 1B, and 2, provides a graphical user interface with a feature that imitates a conventional saw, allowing the user to identify components to be cut away from the dentition model 100. The graphical user interface provides a rendered 3D image 100 of the dentition model, either at one or more static views from predetermined positions, as shown in FIGS. 1A and 1B, or in a “full 3D ” mode that allows the user to alter the viewing angle, as shown in FIG. 2. The saw tool is implemented as a set of mathematical control points 102, represented graphically on the rendered image 100, which define a 3D cutting surface 104 that intersects the volumetric or geometric dentition model. The computer subdivides the data elements in the dentition model by performing a surface intersection operation between the 3D cutting surface 104 and the dentition model. The user sets the locations of the mathematical control points, and thus the geometry and position of the 3D cutting surface, by manipulating the control points in the graphical display with an input device, such as a mouse. The computer provides a visual representation 104 of the cutting surface on the display to assist the user in fitting the surface around the individual component to be separated. Once the intersection operation is complete, the computer creates a model of the individual component using the newly segmented data elements.
FIG. 5 describes a particular technique for forming a skeleton in the dentition model. The computer first identifies the voxels in the dentition model that represent the tooth surfaces (step 130). For a voxel representation that is created from a physical model embedded in a sharply contrasting material, identifying the tooth surfaces is as simple as identifying the voxels at which sharp changes in image value occur, as described in U.S. patent application Ser. No. 09/169,276. The computer then calculates, for each voxel in the model, a distance measure indicating the physical distance between the voxel and the nearest tooth surface (step 132). The computer identifies the voxels with the largest distance measures and labels each of these voxels as forming a portion of the skeleton (step 134). Feature skeleton analysis techniques are described in more detail in the following publications: (1) Gagvani and Silver, “Parameter Controlled Skeletons for 3D Visualization,” Proceedings of the IEEE Visualization Conference (1997); (2) Bertrand, “A Parallel Thinning Algorithm for Medial Surfaces,” Pattern Recognition Letters, v. 16, pp. 979–986 (1995); (3) Mukherjee, Chatteiji, and Das, “Thinning of 3-D Images Using the Safe Point Thinning Algorithm (SPTA),” Pattern Recognition Letters, v. 10, pp. 167–173 (1989); (4) Niblack, Gibbons, and Capson, “Generating Skeletons and Centerlines from the Distance Transform,” CVGIP: Graphical Models and Image Processing, v. 54, n. 5, pp. 420–437 (1992).
Once a skeleton has been identified, the computer uses the skeleton to divide the dentition model into 3D models of the individual teeth. FIG. 6 shows one technique for doing so. The computer first identifies those portions of the skeleton that are associated with each individual tooth. To do so, the computer defines a plane that is roughly parallel to the dentition's occlusal surface and that intersects the skeleton near its base (step 136). The computer then identifies points at which the plane and the skeleton intersect by identifying each voxel that lies on both the skeleton and the plane (step 138). In general, a single tooth includes all of the voxels that lie in a particular branch of the skeleton; and because the plane intersects the skeleton near its base, voxels that lie together in a branch of the skeleton usually cluster together on the intersecting plane. The computer is able to locate the branches by identifying voxels on the skeleton that lie within a particular distance of each other on the intersecting plane (step 140). The computer then identifies and labels all voxels on the skeleton that belong to each branch (step 142).
FIGS. 7A and 7B illustrate another technique for identifying and segmenting individual teeth in the dentition model. This technique, called “2D slice analysis,” involves dividing the voxel representation of the dentition model into a series of parallel 2D planes 160, or slices, that are each one voxel thick and that are roughly parallel to the dentition's occlusal plane. Each of the 2D slices 160 includes a 2D cross-section 162 of the dentition, the surface 164 of which represents the lingual and buccal surfaces of the patient's teeth and/or gums. The computer inspects the cross-section 162 in each 2D slice 160 to identify voxels that approximate the locations of the interproximal margins 166 between the teeth. These voxels lie at the tips of cusps 165 in the 2D cross-sectional surface 164. The computer then uses the identified voxels to create 3D surfaces 168 intersecting the dentition model at these locations. The computer segments the dentition model along these intersecting surfaces 168 to create individual tooth models.
FIG. 8 describes a particular implementation of the 2D slice analysis technique. The computer begins by identifying the voxels that form each of the 2D slices (step 170). The computer then identifies, for each 2D slice, the voxels that represent the buccal and lingual surfaces of the patient's teeth and gums (step 172) and defines a curve that includes all of these voxels (step 174). This curve represents the surface 164 of the 2D cross-section 162.
Automated segmentation is enhanced through a technique known as “seed cusp detection.” The term “seed cusp” refers to a location at which an interproximal margin meets the patient's gum tissue. In a volumetric representation of the patient's dentition, a seed cusp for a particular interproximal margin is found at the cusp voxel that lies closest to the gumline. By applying the seed cusp detection technique of the 2D slice analysis, the computer is able to identify all of the seed cusp voxels in the 3D model automatically.
FIG. 11 shows a particular implementation of the seed cusp detection technique, in which the computer detects the seed cusps by identifying each 2D slice in which the rate of curvature of a cusp first falls below a predetermined threshold value. The computer begins by selecting a 2D slice that intersects all of the teeth in the arch (step 220). The computer attempts to select a slice that is near the gingival regions but that does not include any voxels representing gingival tissue. The computer then identifies all of the cusp voxels in the 2D slice (step 222). If the rate of curvature of the 2D cross-section at any of the cusp voxels is less than a predetermined threshold value, the computer labels that voxel as a seed cusp (step 224). The computer then selects the next 2D slice, which is one voxel layer closer to the gingival region (step 226), and identifies all of the cusp voxels that are not associated with a cusp for which the computer has already identified a seed cusp (step 228). If the rate of curvature of the 2D cross-section is less than the predetermined threshold value at any of these cusp voxels, the computer labels the voxel as a seed cusp (step 230) and proceeds to the next 2D slice. The computer continues in this manner until a seed cusp voxel has been identified for each cusp associated with an interproximal margin (step 232).
FIGS. 12, 13, and 14 illustrate a technique, known as “neighborhood-filtered cusp detection,” by which the computer focuses its search for cusps on one 2D slice to neighborhoods 244, 246 of voxels defined by a pair of previously detected cusp voxels 240, 242 on another 2D slice. Upon detecting a pair of cusp voxels 240, 242 in a 2D slice at level N (step 250), the computer defines one or more neighborhoods 244, 246 that include a predetermined number of voxels surrounding the pair (step 252). The computer then projects the neighborhoods onto the next 2D slice at level N+1 by identifying the voxels on the next slice that are immediately adjacent the voxels in the neighborhoods on the original slice (step 254). The neighborhoods are made large enough to ensure that they include the cusp voxels on the N+1 slice. In the example of FIG. 13, each cusp voxel 240, 242 lies at the center of a neighborhood 244, 246 of twenty-five voxels arranged in a 5�5 square.
FIGS. 15 and 16 illustrate another technique, known as “arch curve fitting,” for identifying interproximal margins between teeth in the dentition. The arch curve fitting technique, which also applies to 2D cross-sectional slices of the dentition, involves the creation of a curve 260 that fits among the voxels on the 2D cross-sectional surface 262 of the dentition arch 264. A series of closely-spaced line segments 268, each bounded by the cross-sectional surface 268, are formed along the curve 260, roughly perpendicular to the curve 260, throughout the 2D cross-section 264. In general, the shortest of these line segments 268 lie on or near the interproximal margins; thus computer identifies the cusps that define the interproximal margins by determining the relative lengths of the line segments 268.
In applying this technique, the computer first locates an end 265 of the arch (step 300) and creates a line segment 291 that passes through the arch 264 near this end 265 (step 301). The line segment 291 is bounded by voxels 292 a–b lying on the surface of the arch. The computer then determines the midpoint 293 of the line segment 291 (step 302), selects a voxel 294 located particular distance from the midpoint 293 (step 304), and creates a second line segment 295 that is parallel to the initial line segment 291 and that includes the selected voxel 294 (step 306). The computer then calculates the midpoint 296 of the second segment 295 (step 308) and rotates the second segment 295 to the orientation 295′ that gives the segment its minimum possible length (step 309). In some cases, the computer limits the second segment 295 to a predetermined amount of rotation (e.g., �10�).
FIGS. 19A, 19B and 20 illustrate an alternative technique for creating 3D surfaces that approximate the geometries and locations of the interproximal margins in the patient's dentition. This technique involves the creation of 2D planes that intersect the 3D dentition model at locations that approximate the interproximal margins. In general, the computer defines a series of planes, beginning with an initial plane 330 at one end 331 of the arch 332, that are roughly perpendicular to the occlusal plane of the dentition model (“vertical” planes). Each plane intersects the dentition model to form a 2D cross-section 334. If the planes are spaced sufficiently close to each other, the planes with the smallest cross-sectional areas approximate the locations of the interproximal margins in the dentition. The computer locates the interproximal regions more precisely by rotating each plane about two orthogonal axes 336, 338 until the plane reaches the orientation that yields the smallest possible cross-sectional area.
In one implementation of this technique, the computer first identifies one end of the arch in the dentition model (step 340). The computer then creates a vertical plane 330 through the arch near this end (step 342) and identifies the centerpoint 331 of the plane 330 (step 344). The computer then selects a voxel located a predetermined distance from the centerpoint (step 345) and creates a second plane 333 that is parallel to the initial plane and that includes the selected voxel (step 346). The computer calculates the midpoint of the second plane (step 348) and rotates the second plane about two orthogonal axes that intersect at the midpoint (step 350). The computer stops rotating the plane upon finding the orientation that yields the minimum cross-sectional area. In some cases, the computer limits the plane to a predetermined amount of rotation (e.g., �10� about each axis). The computer then selects a voxel located a particular distance from the midpoint of the second plane (step 352) and creates a third plane that is parallel to the second plane and that includes the selected voxel (step 354). The computer calculates the midpoint of the third plane (step 356) and rotates the plane to the orientation that yields the smallest possible cross-sectional area (step 357). The computer continues adding and rotating planes in this manner until the other end of the arch is reached (step 358). The computer identifies the planes at which local minima in cross-sectional area occur and labels these planes as “interproximal planes,” which approximate the locations of the interproximal margins (step 360).
FIGS. 22, 23, and 24 illustrate a technique for identifying the gingival margin that defines the boundary between tooth and gum in the patient's dentition. This technique involves the creation of a series of horizontal 2D planes 380, or slices, that intersect the dentition model roughly perpendicular to the occlusal plane (see FIG. 19A). The cross-sectional surface 382 of the dentition model in each of these planes 380 includes cusps 384, 386 that represent the gingival margin. The computer identifies the gingival margin by applying one or more of the cusp detection techniques described above.
Many of these automated segmentation techniques are even more useful and efficient when used in conjunction with human-assisted techniques. For example, techniques that rely on the identification of the interproximal or gingival margins function more quickly and effectively when a human user first highlights the interproximal or gingival cusps in a graphical representation of the dentition model. One technique for receiving this type of information from the user is by displaying a 2D or 3D representation and allowing the user to highlight individual voxels in the display. Another technique allows the user to scroll through a series of 2D cross-sectional slices, identifying those voxels that represent key features such as interproximal or gingival cusps. Some of these techniques rely on user interface tools such as cursors and bounding-box markers.
In many instances, the computer creates proposals for segmenting the dentition model and then allows the user to select the best alternative. For example, one version of the arch curve fitting technique requires the computer to create a candidate catenary or spline curve, which the user is allowed to modify by manipulating the mathematical control parameters. Other techniques involve displaying several surfaces that are candidate cutting surfaces and allowing the user to select the appropriate surfaces.
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