JP7508152B2 - Method for automating dental 3D data alignment and computer-readable recording medium having a program recorded thereon for executing the method on a computer - Google Patents

Description

The present invention relates to an automated method for aligning dental 3D data, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer. More specifically, the present invention relates to an automated method for aligning dental 3D data, which can be performed automatically, thereby reducing the effort required for matching dental CT images and digital impression models, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer.

In dentistry, 3D patient medical image data and 3D digital impression model scan data are used for diagnosis, analysis, prosthetic production, etc. These data contain different information, so when they are combined into one, it is more effective and various diagnoses, analyses, and productions are possible. However, since the 3D data was acquired in different coordinate systems, a matching process is required to match the two data.

In order to align the 3D patient medical image data and the 3D digital impression model scan data, feature points must be set in each of the 3D patient medical image data and the 3D digital impression model scan data.

Selecting six feature points from two different 3D data, a dental CT image and a digital impression model, can be a time-consuming task. Even if feature points are found using artificial intelligence, they may not match exactly. And when there are missing teeth or when the data areas are different, initial matching results may not be good using feature points alone.

The objective of the present invention is to provide an automated method for aligning dental 3D data that can reduce the effort required for matching dental CT images and digital impression models.

Another object of the present invention is to provide a computer-readable recording medium having a program recorded thereon for causing a computer to execute the method for automating the alignment of dental 3D data.

In one embodiment for achieving the above-mentioned object of the present invention, an automated method for aligning dental 3D data includes the steps of extracting feature points of CT data, extracting feature points of scan data of a digital impression model, determining an upward vector indicating the direction of the patient's eyes and nose and the left and right of the feature points of the scan data, extracting tooth portions of the scan data, searching for source points of the scan data on a spline curve of the CT data to generate candidate target points, and determining a value having the smallest error between the candidate target points and the feature points of the CT data as a final candidate.
The feature points of the CT data include three or more feature points in the upper jaw and three or more feature points in the lower jaw. The feature points of the scan data include three feature points.

The first feature point and the third feature point of the scan data each represent the outermost point of the tooth in the scan data in the lateral direction. The second feature point of the scan data is between the two central incisors.

The step of determining whether the feature point of the scan data is left or right is performed by: a first feature point of the scan data is P ₁₁ , a second feature point is P ₁₂ , and a third feature point is P ₁₃ , and an average vector of normal vectors at all points constituting a mesh of the scan data is calculated as

Then,

Vector and

The cross product of the vectors and the mean vector

is used to determine the left and right of the feature points in the scan data.

In the step of determining whether the feature points of the scan data are left or right, if the scan data is upper jaw data and the discriminant d<0, the left dental feature point P _L indicating the contour point of the patient's left tooth is P ₁₁ , and the right dental feature point P _R indicating the contour point of the patient's right tooth is P _13. If the scan data is upper jaw data and the discriminant d>=0, the left dental feature point P _L is P ₁₃ , and the right dental feature point P _R is P _11. The discriminant is

It is.

In the step of determining whether the feature points of the scan data are left or right, if the scan data is mandibular data and the discriminant d<0, the left dental feature point P _L is P ₁₃ and the right dental feature point P _R is P ₁₁ ; if the scan data is mandibular data and the discriminant d>=0, the left dental feature point P _L is P ₁₁ and the right dental feature point P _R is P ₁₃ .
In the step of determining the upward vector, the upward vector is

a left tooth feature point indicating a contour point of the patient's left tooth is P _L , a right tooth feature point indicating a contour point of the patient's right tooth is P _R , a second feature point of the scan data is P ₁₂ , and the scan data is upper jaw data,

It is.

In the step of determining the upward vector, the upward vector is

a left tooth feature point indicating a contour point of the patient's left tooth is P _L , a right tooth feature point indicating a contour point of the patient's right tooth is P _R , a second feature point of the scan data is P ₁₂ , and the scan data is mandibular data,

It is.

The automated method for dental 3D data registration further includes a step of determining whether the CT data and the scan data have the same region.

where th is a first threshold for determining whether the CT data and the scan data have the same region, p1, p3, and p5 are feature points of the CT data, and _P11 , _P12 , and _P13 are feature points of the scan data,

If the above condition is satisfied, it is determined that the CT data and the scan data have the same area that matches each other.

When the scan data is upper jaw data, the step of extracting the tooth portion of the scan data includes the steps of: extracting the highest point on the upward vector from among the first feature point, the second feature point, and the third feature point of the scan data; cutting out the scan data on an infinite plane having the upward vector as a normal vector from the highest point to a first distance movement point in the positive direction of the upward vector; and cutting out the scan data on an infinite plane having the upward vector as a normal vector from the highest point of the scan data in the negative direction of the upward vector to a second distance movement point.

The step of extracting the teeth portion of the scan data further includes, when the scan data is mandibular data, a step of extracting the lowest point in the upward direction vector from among the first feature point, the second feature point, and the third feature point of the scan data, a step of cutting out the scan data from the lowest point in the positive direction of the upward direction vector to the first distance movement point on an infinite plane having the upward direction vector as its normal vector, and a step of cutting out the scan data from the lowest point of the scan data in the negative direction of the upward direction vector to the second distance movement point on an infinite plane having the upward direction vector as its normal vector.

The step of extracting the dental portion of the scan data further comprises:

and the vector from the second feature point to the left tooth feature point is

Then, from the right tooth feature point

At a point moved a third distance in the vector direction,

a step of extracting the scan data on an infinite plane having a normal vector of the vector;

At a point moved by the third distance in the vector direction,

and cutting out the scan data with an infinite plane having the vector as a normal vector.

The third distance is less than the first distance and the second distance.
The step of extracting the dental portion of the scan data further comprises:

At a point where the third distance has been moved in the vector direction,

a first vector rotated by +90 degrees from the vector is set as a normal vector, and the scan data is cut out on an infinite plane at a point moved a fourth distance from the first vector;

At a point where the third distance has been moved in the vector direction,

a second vector rotated by -90 degrees from the vector is set as a normal vector, and the scan data is cut out on an infinite plane at a point moved by the fourth distance to the second vector;

At a point where the third distance has been moved in the vector direction,

a third vector rotated by -90 degrees from the vector is set as a normal vector, and the scan data is cut out on an infinite plane at a point moved by the fourth distance to the third vector;

At a point where the third distance has been moved in the vector direction,

The method includes a step of setting a fourth vector rotated +90 degrees from the vector as a normal vector, and cutting out the scan data on an infinite plane at a point moved to the fourth vector by the fourth distance.

The fourth distance is greater than the first distance, the second distance, and the third distance.
The step of extracting the dental portion of the scan data further comprises:

Vector and

It is the sum of vectors

At the point where the object has moved a fifth distance in the vector direction,

cutting out the scan data with an infinite plane having a normal vector of the vector; and extracting the second feature point of the scan data from the second feature point of the scan data.

At a point where the fifth distance has been moved in the vector direction,

and cutting out the scan data with an infinite plane having the vector as a normal vector.

The step of searching for source points of the scan data on a spline curve of the CT data to generate candidate target points includes the step of calculating the splinecurve, C(u), based on a plurality of feature points of the upper jaw of the CT data or a plurality of feature points of the lower jaw of the CT data.
The source points include three points: a left dental feature point, a second feature point, and the right dental feature point.

P _L is the left dental feature point, P ₁₂ is the second feature point, and P _R is the right dental feature point. A first target point is found by increasing a parameter u on C(u) by a first value, a second target point is found by increasing the parameter u on C(u) by a second value, and C(u2) is found where d11 = ||C(u1) - C(u2) || - ||P _L - P ₁₂ || is minimized. A third target point is found by increasing the parameter u on C(u) by a third value, and C(u3) is found where d12 = ||C(u2) - C(u3) || - ||P ₁₂ - P _R || is minimized. The d11, d12, and d13 = ||C(u3) - C(u1) || - ||P _R - P _L If all of || are smaller than a second threshold, the target points C(u1), C(u2), and C(u3) are selected as the candidate target points, and the candidate target points include the three points C(u1), C(u2), and C(u3).

The step of determining the value having the smallest error between the candidate target point and the feature point of the CT data as the final candidate includes the steps of transforming the candidate target point into the domain of the CT data using a transformation matrix, and measuring the transformation error as the average distance between the transformed candidate target point and the feature point of the CT data.
A program for causing a computer to execute the method for automating dental 3D data registration is recorded in a computer-readable recording medium.

The automated method for aligning dental 3D data according to the present invention can obtain initial alignment results without user input, even if the data contains different regions. This allows for quick alignment up to the final precision alignment without user input.

The automated method for aligning dental 3D data according to the present invention can dramatically reduce the effort required to align patient medical image data (CT, CBCT) and digital impression model scan data, which is frequently performed in dentistry for diagnosis, analysis, prosthetic production, etc.

1 is a flowchart showing an automated method for registering three-dimensional dental data according to the present embodiment. FIG. 1 shows dental CT images and scan data of a digital impression model. FIG. 13 is a diagram showing feature points of a dental CT image. FIG. 1 shows feature points of the scan data of the digital impression model. FIG. 2 is a conceptual diagram showing left and right partitioning steps of the up vector and feature points of the scan data in FIG. 1 . FIG. 2 is a conceptual diagram showing left and right partitioning steps of the up vector and feature points of the scan data in FIG. 1 . FIG. 2 is a conceptual diagram showing left and right partitioning steps of the up vector and feature points of the scan data in FIG. 1 . FIG. 2 is a conceptual diagram showing left and right partitioning steps of the up vector and feature points of the scan data in FIG. 1 . FIG. 13 is a conceptual diagram showing an upward vector when the scan data is upper jaw data. FIG. 13 is a conceptual diagram showing an upward vector when the scan data is lower jaw data. FIG. 2 is a conceptual diagram showing a step of determining whether or not regions of the CT data and the scan data match in FIG. 1 . FIG. 2 is a conceptual diagram showing a step of determining whether or not regions of the CT data and the scan data match in FIG. 1 . FIG. 2 is a conceptual diagram illustrating a tooth portion extraction step of the scan data of FIG. 1 . FIG. 2 is a conceptual diagram illustrating a tooth portion extraction step of the scan data of FIG. 1 . 2 is a diagram showing a tooth portion of the scan data of FIG. 1 extracted by a tooth portion extraction step of the scan data of FIG. 1; FIG. 2(a)-(c) show the results of the initial alignment step (COARSE REGISTRATION) of FIG. 1; 2(a)-(c) show the results of the fine registration step of FIG. 1;

Specific structural or functional descriptions of the embodiments of the present invention shown herein are merely exemplary for purposes of describing the embodiments of the present invention, and the example embodiments of the present invention may be embodied in various forms and should not be construed as being limited to the embodiments described herein.

The present invention can be modified in various ways and can have various forms, and specific embodiments are illustrated in the drawings and described in detail in the text. However, this is not intended to limit the invention to the specific disclosed form, and it should be understood that the invention includes all modifications, equivalents, and alternatives within the spirit and technical scope of the invention.

Terms such as first and second are used to describe various components, but the components should not be limited by the terms. The terms are used for the purpose of distinguishing one component from another. For example, a first component can be referred to as a second component, and similarly, a second component can be referred to as a first component, without departing from the scope of the present invention.

When a component is said to be "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to the other component, but there may also be other components in between. On the other hand, when a component is said to be "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "directly between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

The terms used in this application are merely used to describe certain embodiments and are not intended to limit the present invention. The singular expressions include the plural expressions unless the context clearly indicates otherwise. In this application, the terms "include" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and not as having an idealized or overly formal meaning, unless expressly defined in this application.

However, when an embodiment can be implemented differently, the functions or operations specified in a particular block may occur differently than the sequence specified in the flowchart. For example, two consecutive blocks may in fact occur substantially simultaneously, or the blocks may occur in reverse depending on the functions or operations involved.

Below, a preferred embodiment of the present invention will be described in more detail with reference to the attached drawings. The same components in the drawings are given the same reference numerals, and duplicated descriptions of the same components will be omitted.

Figure 1 is a flowchart showing an automated method for aligning dental 3D data according to the present embodiment. Figure 2 is a diagram showing a dental CT image and scan data of a digital impression model. Figure 3 is a diagram showing feature points of the dental CT image. Figure 4 is a diagram showing feature points of the scan data of the digital impression model.

As shown in Figures 1 to 4, to automatically align the dental CT image and the digital impression model, feature points are extracted from the dental CT image (step S100), and feature points are extracted from the scan data of the digital impression model (step S200).

For example, the dental CT image is a CBCT (ConeBeam CT) image. The dental CT image is an image including teeth, bones, and nerve canals. For example, the scan data of the digital impression model is an image obtained by scanning the inside of a patient's oral cavity with a scanner. For example, the scan data is an image obtained by scanning a plaster replica of the inside of a patient's oral cavity with a scanner.

The left image in Figure 2 is the scan data of the digital impression model. The right image in Figure 2 is the dental CT image. In this embodiment, the digital impression model is data corresponding to either the patient's upper jaw or lower jaw. In this embodiment, the dental CT image can include information about both the patient's upper jaw and lower jaw.

For example, in FIG. 3, the feature points of the dental CT image are points indicating specific positions of teeth. The feature points of the dental CT image include five feature points (p1, p2, p3, p4, p5) in the upper jaw and five feature points (p6, p7, p8, p9, p10) in the lower jaw. For example, the first feature point (p1) and the fifth feature point (p5) of the upper jaw each indicate the outermost point of the upper jaw teeth in the lateral direction. The third feature point (p3) of the upper jaw indicates the space between the two upper central incisors. The second feature point (p2) of the upper jaw is located between the first feature point (p1) and the third feature point (p3), and the fourth feature point (p4) of the upper jaw is located between the third feature point (p3) and the fifth feature point (p5). For example, the sixth feature point (p6) and the tenth feature point (p10) of the mandible indicate the outermost points of the teeth of the mandible in the horizontal direction, respectively. The eighth feature point (p8) of the mandible indicates the space between the two mandibular central incisors. The seventh feature point (p7) of the mandible is located between the sixth feature point (p6) and the eighth feature point (p8), and the ninth feature point (p9) of the mandible is located between the eighth feature point (p8) and the tenth feature point (p10).

For example, in Fig. 4, the feature points of the scan data are points indicating specific positions of teeth. The feature points of the scan data include three feature points ( _P11 , _P12 , _P13 ). Here, the scan data is data indicating both the upper jaw and the lower jaw of a patient. For example, the first feature point ( _P11 ) and the third feature point ( _P13 ) of the scan data respectively indicate the outermost points of the teeth of the scan data in the horizontal direction. The second feature point ( _P12 ) of the scan data indicates the space between the two central incisors.

In this embodiment, the feature points of the CT image (e.g., p1 to p10) can be automatically extracted using artificial intelligence (deep learning) technology. Also, the feature points of the scan data (e.g., _P11 to _P13 ) can be automatically extracted using artificial intelligence (deep learning) technology. Whether the scan data shows the upper or lower jaw can be determined from a user's input or automatically determined through additional information of the scan data.

5 to 8 show the upward vector of FIG.

13 is a conceptual diagram showing the left and right division steps of feature points of scan data. FIG.
As shown in FIGS. 5 to 8, an up vector indicates the up direction of the scan data (the direction in which the patient's eyes and nose are located).

Next, a method for dividing the left and right of the feature points (P ₁₁ , P ₁₃ ) of the scan data will be described (step S300).

5 shows a case where the teeth are projected downward in the scan data, a first feature point ( _P11 ) is displayed on the left side of the scan data, and a third feature point ( _P13 ) is displayed on the right side of the scan data. Although the teeth in the scan data of FIG. 5 are projected downward, the scan data of FIG. 5 does not mean upper jaw data. Whether the scan data of FIG. 5 is upper jaw data or lower jaw data can be determined from a user's input or from additional information of the scan data.

Calculate the normal vector for all points that make up the mesh of the scan data in Figure 5 (all points on the surface of the scan data), and calculate the unit vector of the average vector

If normal vectors are calculated for all points constituting the mesh and their average vector is calculated, the average vector will be directed downward in the drawing in the case of scan data such as that shown in Figure 5. In Figure 5, the unit vector of the average vector

has unit length in the downward direction.

The discriminant for distinguishing between the left and right of the feature points (P ₁₁ , P ₁₃ ) of the scan data is given by Equation 1 below.

When the scan data is upper jaw data and the discriminant d<0, the left tooth feature point P _L = _P11 indicating the contour points of the patient's left tooth, and the right tooth feature point P _R = _P13 indicating the contour points of the patient's right tooth. On the other hand, when the scan data is upper jaw data and the discriminant d>=0, the left tooth feature point P _L = _P13 and the right tooth feature point P _R = _P11 .

If the scan data is mandibular data and the discriminant d<0, the left dental feature point P _L = _P13 and the right dental feature point P _R = _P11 . On the other hand, if the scan data is mandibular data and the discriminant d>=0, the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 .

In FIG.

is the downward direction, so if the upward direction is the positive direction, then

=-1.

and

The cross product of

has a downward direction (negative number). Therefore, if the scan data of Fig. 5 is upper jaw data, d>=0, so the left dental feature point P _L = _P13 and the right dental feature point P _R = _P11 . On the other hand, if the scan data of Fig. 5 is lower jaw data, d>=0, so the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 .

FIG. 6 shows a case where the teeth are extracted downward in the scan data, the third feature point (P ₁₃ ) is displayed on the left side of the scan data, and the first feature point (P ₁₁ ) is displayed on the right side of the scan data.

In FIG.

is the downward direction, so if the upward direction is the positive direction, then

=-1.

and

The cross product of

has an upward direction (positive number). Therefore, if the scan data of Fig. 6 is upper jaw data, d<0, so the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 . On the other hand, if the scan data of Fig. 6 is lower jaw data, d<0, so the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 .

FIG. 7 shows a case where the teeth are extracted upward in the scan data, the third feature point (P ₁₃ ) is displayed on the left side of the scan data, and the first feature point (P ₁₁ ) is displayed on the right side of the scan data.

In FIG.

is the upward direction, so if the upward direction is the positive direction,

=1.

and

The cross product of

has an upward direction (positive number). Therefore, if the scan data of Fig. 7 is mandibular data, d>=0, so the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 . On the other hand, if the scan data of Fig. 7 is maxillary data, d>=0, so the left dental feature point P _L = _P13 and the right dental feature point P _R = _P11 .

FIG. 8 shows a case where the teeth are extracted upward in the scan data, a first feature point (P ₁₁ ) is displayed on the left side of the scan data, and a third feature point (P ₁₃ ) is displayed on the right side of the scan data.

In FIG.

is the upward direction, so if the upward direction is the positive direction,

=1.

and

The cross product of

has a downward direction (negative number). Therefore, if the scan data of Fig. 8 is mandibular data, d<0, so the left dental feature point P _L = _P13 and the right dental feature point P _R = _P11 . On the other hand, if the scan data of Fig. 8 is maxillary data, d<0, so the left dental feature point P _L = _P11 and the right dental feature point P _R = _P13 .

If the scan data is upper jaw data, an upward vector indicating the direction in which the patient's eyes and nose are located is

The formula representing this is as shown in Formula 2 below.

For example, in Fig. 5, when the scan data is upper jaw data, the left dental feature point P _L =P ₁₃ and the right dental feature point P _R =P ₁₁ according to the discriminant formula.

and

is the cross product of

will have an upward direction (a positive number).

If the scan data is mandibular data, an upward vector indicating the direction in which the patient's eyes and nose are located is

The formula showing this is as shown in Formula 3 below.

For example, when the scan data of Fig. 7 is mandibular data, the left dental feature point P _L =P ₁₁ and the right dental feature point P R =P ₁₃ according to the discriminant formula.

and

is the cross product of

will have an upward direction (a positive number).

Fig. 9 is a conceptual diagram showing an upward direction vector when the scan data is upper jaw data, and Fig. 10 is a conceptual diagram showing an upward direction vector when the scan data is lower jaw data.
As shown in FIG. 9, when the scan data is upper jaw data, the upward vector

The direction is obtained in the opposite direction to the crystallization direction of the tooth.

Also, as shown in FIG. 10, when the scan data is mandibular data, the upward vector

The direction of the crystallization is obtained in substantially the same direction as the crystallization direction of the tooth.

11 and 12 are conceptual diagrams showing steps for determining whether or not the regions of the CT data and the scan data in FIG. 1 match.
1 to 12, in matching CT data and scan data, it is possible to distinguish between case 1 where the two data have the same area and case 2 where the two data do not have the same area (step S400). If the following formula 4 is satisfied, it can be determined that the CT data and scan data have the same area.

here,

where th is a first threshold for determining whether the CT data and the scan data have the same region. For example, the first threshold (th) is 5 mm.

Figures 13 and 14 are conceptual diagrams showing the tooth portion extraction step of the scan data of Figure 1. Figure 15 is a diagram showing the tooth portion of the scan data of Figure 1 extracted in the tooth portion extraction step of the scan data of Figure 1.

1 to 15, based on the tooth portion, which is a common region between the two data, only the tooth portion is extracted from the scan data in order to perform matching (step S500). If the scan data is upper jaw data, the first feature point ( _P11 ), the second feature point ( _P12 ), and the third feature point ( _P13 ) of the scan data are extracted from the upper direction vector

In the case where the scan data is mandibular data, the highest point is extracted in the upward direction from among the first feature point (P ₁₁ ), the second feature point (P ₁₂ ), and the third feature point (P ₁₃ ) of the scan data.

Extract the lowest point in the direction of.

When the scan data is upper jaw data, the upper direction vector

to a point where the first distance (+a) has been moved in the positive direction,

The scan data is cut out on an infinite plane with a normal vector of

to a point where it moves a second distance (-b) in the negative direction,

The scan data is cut out on an infinite plane with a normal vector of a (FIG. 13). For example, the first distance (+a) is 6 mm. For example, the second distance (-b) is -6 mm. For example, the absolute values of the first distance (+a) and the second distance (-b) are the same. Alternatively, the absolute values of the first distance (+a) and the second distance (-b) may be different.

When the scan data is mandibular data, the upper direction vector

to a point where the first distance (+a) has been moved in the positive direction,

The scan data is cut out on an infinite plane (CP1) with a normal vector of

to a point where it moves a second distance (-b) in the negative direction,

The scan data is cut out by an infinite plane (CP2) with the normal vector as shown in Figure 13.

As shown in FIG. 14, the vector from the second feature point _P12 to the right tooth feature point PR is

The vector from the second feature point _P12 to the left tooth feature point _P12 is expressed as

From the right tooth feature point P _R

At a point moved a third distance in the vector direction,

The scan data is cut out on an infinite plane (CP3) with the vector as a normal vector, and the left tooth feature point P _L is

At a point moved the third distance in the vector direction,

The scan data is cut out by an infinite plane (CP4) having a normal vector of the vector. For example, the third distance is smaller than the first distance and the second distance. Here, the third distance is 1 mm.

From the right tooth feature point P _R

At a point where the object is moved a third distance in the vector direction,

A first vector rotated by -90 degrees from the vector is used as a normal vector, and the scan data is cut out on an infinite plane (CP5) at a point moved a fourth distance to the first _vector .

At a point where the third distance has been moved in the vector direction,

A second vector rotated +90 degrees from the vector is set as a normal vector, and the scan data is cut out on an infinite plane (CP6) at a point moved by the fourth distance to the second vector. For example, the fourth distance is greater than the first distance, the second distance, and the third distance. Here, the fourth distance is 10 mm.

From the left tooth feature point P _L

At a point where the third distance has been moved in the vector direction,

A third vector rotated by +90 degrees from the vector is used as a normal vector, and the scan data is cut out on an infinite plane (CP7) at a point moved a fourth distance to the _third vector.

At a point where the third distance has been moved in the vector direction,

A fourth vector rotated -90 degrees from the vector is set as a normal vector, and the scan data is cut out on an infinite plane (CP8) at a point moved to the fourth vector by the fourth distance.

Furthermore, from the second feature point (P ₁₂ ),

Vector and

It is the sum of vectors

At a point where the object is moved a fifth distance in the vector direction,

The scan data is cut out by an infinite plane ( _CP10 ) with the vector as the normal vector.

At a point where the object is moved a fifth distance in the vector direction,

The scan data is cut out by an infinite plane (CP9) having a normal vector of the vector. For example, the fifth distance is greater than the third distance and less than the fourth distance. Here, the fifth distance is 6 mm.

FIG. 15 shows tooth portions obtained by cutting the scan data using cutting planes (CP1 to CP10).
A parametric spline curve, C(u), is calculated using the first to fifth feature points (p1 to p5) of the upper jaw of the CT data as control points, where u satisfies 0<=u<=1, and u=0 at the leftmost point relative to the patient and u=1 at the rightmost point relative to the patient.

The parametricsplinecurveC(u) refers to an arch spline curve that connects the five feature points (p1 to p5) of the upper jaw in the CT data. The parametricsplinecurveC(u) can also be calculated using the five feature points (p6 to p10) of the lower jaw in the CT data as control points.

The source points of the scan data are searched on the CTsplinecurve C(u) to generate target points (step S600).
The source points of the scan data include three points: the left dental feature point (P _L ), the second feature point (P ₁₂ ), and the right dental feature point (PR).

A first one of the target points can be searched for on C(u) by increasing the parameter u by a first value. The first one of the target points is denoted as C(u1). The first value is 0.05.

The second point of the target point is to increase the parameter u on C(u) by a second value and search for C(u2) where d11 = ∥C(u1)-C(u2)∥-∥P _L -P ₁₂ ∥ is minimum, where u>u1 and the second value is 0.001.

The third target point is to search for C(u3) where d12 = ∥C(u2) - C(u3)∥ - ∥P ₁₂ - P _R ∥ is minimized while increasing the parameter u on C(u) by a third value, where u>u2 and the third value is 0.001.

If d11, d12, and d13 = ||C(u3)-C(u1)||-||P _R -P _L || are all smaller than a second threshold, the target points C(u1), C(u2), and C(u3) are selected as candidates. The second threshold is 8 mm.

16a to 16c show the results of the initial alignment step (COARSE REGISTRATION) of FIG.
1 to 16c, in step S500, the candidate target points include six points, and a plurality of the candidate target points are generated.

For the multiple candidate target points, a transformation matrix M is calculated through landmarktransform. The transformation error can be measured as the average distance between the feature points pi' = Mpi, (i = L, 12, R) of the transformed scan data and the feature points pk (k = 1, 3, 5 or k = 6, 8, 10) of the CT data. The transformation matrix M moves the feature points of the scan data into the domain of the CT data.

In step S400, in case 1 where the two data, the CT data and the scan data, have the same area, the candidate target point with the smallest conversion error is determined as the final candidate.

The step of moving the candidate target points of the scan data into the domain of the CT data using the transformation matrix M and determining the final candidate with the smallest transformation error is called the initial registration step (coarse registration, step S700).

In Figs. 16a to 16c, the basic image showing the shape of the teeth is CT data. In Figs. 16a to 16c, the solid lines are the outlines of the scan data aligned to the CT data. Fig. 16a shows an axial view of the initially aligned CT data and the scan data, Fig. 16b shows a sagittal view of the initially aligned CT data and the scan data, and Fig. 16c shows a coronal view of the initially aligned CT data and the scan data.

17a to 17c show the results of the FINE REGISTRATION step of FIG.
1 to 17c, after the initial matching step (S700), a precision matching step (S800) is performed to further match the tooth region of the CT data with the tooth region of the scan data. In the precision matching step, the source data may be only the extracted tooth portion of the scan data, and the target data may be the CT image of the patient.

In Figs. 17a to 17c, the basic image showing the shape of the teeth is CT data. In Figs. 17a to 17c, the solid lines are the outlines of the scan data aligned to the CT data. Fig. 17a shows an axial view of the precisely aligned CT data and the scan data, Fig. 17b shows a sagittal view of the precisely aligned CT data and the scan data, and Fig. 17c shows a coronal view of the precisely aligned CT data and the scan data.

17a to 17c, it can be seen that the tooth portion of the CT data and the tooth portion of the scan data are more precisely aligned compared to FIGS. 16a to 16c.
According to the present embodiment, an initial matching result can be obtained without user input, even if the regions included in the data are different from each other, so that matching can be performed quickly without user input until the final precise matching.

This embodiment can dramatically reduce the effort required to match patient medical image data (CT, CBCT) with digital impression model scan data, which is frequently performed in dentistry for diagnosis, analysis, prosthetic production, etc.

According to one embodiment of the present invention, a computer-readable recording medium is provided having a program recorded thereon for executing the above-mentioned automated method for dental 3D data registration on a computer. The above-mentioned method can be created as a computer-executable program and can be embodied in a general-purpose digital computer that runs the program using a computer-readable medium. In addition, the data structure used in the above-mentioned method can be recorded on the computer-readable medium through various means. The computer-readable medium can include program instructions, data files, data structures, etc., alone or in combination. The program instructions recorded on the medium can be those specifically designed and configured for the present invention, or those that are known and usable by ordinary technicians in the computer software field. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, flash memories, etc. Examples of program instructions include not only machine language codes such as those created by a compiler, but also high-level language codes executed by a computer using an interpreter, etc. The hardware devices described above can be configured to operate as one or more software modules to perform the operations of the present invention.

The above-described automated method for automating registration of dental 3D data may also be embodied in the form of a computer program or application that is stored in a recording medium and executed by a computer.
[Industrial Applicability]

The present invention relates to an automated method for aligning dental 3D data, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer, which can reduce the effort required for matching dental CT images and digital impression models.

Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will appreciate that the present invention may be modified and changed in various ways without departing from the spirit and scope of the present invention as set forth in the following claims.

Claims (19)

Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate ,
The feature points of the CT data include three or more feature points on the upper jaw and three or more feature points on the lower jaw,
The feature points of the scan data include three feature points;
the first feature point and the third feature point of the scan data each indicate an outermost point of the tooth of the scan data in a horizontal direction,
The method for automating registration of dental 3D data , wherein the second feature point of the scan data is between two central incisors. Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
The step of determining left and right of the feature point of the scan data includes:
The first feature point of the scan data is P ₁₁ , the second feature point is P ₁₂ , and the third feature point is P ₁₃ , and the average vector of the normal vectors at all points constituting the mesh of the scan data is



Then,



Vector and

The cross product of the vectors and the mean vector

The method for automating registration of three-dimensional dental data is characterized in that the left and right of feature points of the scan data are determined using the above-mentioned method. In the step of determining left and right of the feature point of the scan data,
If the scan data is upper jaw data and the discriminant d<0, the left tooth feature point P _L indicating the contour point of the left tooth of the patient is P ₁₁ , and the right tooth feature point P _R indicating the contour point of the right tooth of the patient is P ₁₃ ;
If the scan data is upper jaw data and the discriminant d>=0, the left dental feature point P _L is P ₁₃ and the right dental feature point P _R is P ₁₁ ;
The discriminant is



3. The method for automating registration of dental three-dimensional data according to claim 2, wherein: In the step of determining left and right of the feature point of the scan data,
If the scan data is mandibular data and the discriminant d<0, the left tooth feature point P _L Is P ₁₃ and the right tooth feature point P _R Is P ₁₁ and
If the scan data is mandibular data and the discriminant d>=0, the left tooth feature point P _L Is P ₁₁ and the right tooth feature point P _R Is P ₁₃ 4. The method for automating registration of dental three-dimensional data according to claim 3, wherein: Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
In the step of determining the upward vector, the upward vector is

a left tooth feature point indicating a contour point of the patient's left tooth is P _L , a right tooth feature point indicating a contour point of the patient's right tooth is P _R , a second feature point of the scan data is P ₁₂ , and the scan data is upper jaw data,



13. An automated method for aligning three-dimensional dental data, comprising: Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
In the step of determining the upward vector, the upward vector is

a left tooth feature point indicating a contour point of the patient's left tooth is P _L , a right tooth feature point indicating a contour point of the patient's right tooth is P _R , a second feature point of the scan data is P ₁₂ , and the scan data is mandibular data,



13. An automated method for aligning three-dimensional dental data, comprising: Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
Further comprising the step of determining whether the CT data and the scan data have identical regions of agreement;



where th is a first threshold for determining whether the CT data and the scan data have the same region, p1, p3, and p5 are feature points of the CT data, and P11 _, P12 _, and P13 _are feature points of the scan data,



and determining that the CT data and the scan data have the same area that matches when the above condition is satisfied. Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
The step of extracting a dental portion of the scan data includes:
When the scan data is upper jaw data, extracting a maximum point on the upward vector from among a first feature point, a second feature point, and a third feature point of the scan data;
cutting out the scan data from the highest point in a positive direction of the upward vector to a first distance moving point on an infinite plane having the upward vector as a normal vector;
and cutting out the scan data from the highest point of the scan data in the negative direction of the upward vector to a second distance movement point with an infinite plane having the upward vector as its normal vector. The step of extracting a dental portion of the scan data further comprises:
When the scan data is mandibular data, extracting a lowest point in the upward vector from among a first feature point, a second feature point, and a third feature point of the scan data;
cutting out the scan data from the lowest point in a positive direction of the upward vector to the first distance movement point on an infinite plane having the upward vector as a normal vector;
The method for automating registration of dental 3D data as described in claim 8, further comprising a step of extracting the scan data from the lowest point of the scan data in the negative direction of the upward vector to the second distance movement point with an infinite plane having the upward vector as its normal vector. The step of extracting a dental portion of the scan data further comprises:
A vector from the second feature point of the scan data to the right dental feature point is

and the vector from the second feature point to the left tooth feature point is

Then,
From the right dental feature point

At a point moved a third distance in the vector direction,

a step of extracting the scan data on an infinite plane having a normal vector of the vector;



At a point moved by the third distance in the vector direction,



10. The method for automating registration of dental three-dimensional data according to claim 9, further comprising a step of cutting out the scan data with an infinite plane having the vector as a normal vector. The method for automating registration of dental 3D data according to claim 10, wherein the third distance is smaller than the first distance and the second distance. The step of extracting a dental portion of the scan data further comprises:
From the right dental feature point

At a point where the third distance has been moved in the vector direction,

a step of cutting out the scan data on an infinite plane at a point where a first vector rotated by +90 degrees from the vector is set as a normal vector and moved a fourth distance to the first vector;
From the right dental feature point



At a point where the third distance has been moved in the vector direction,

a step of setting a second vector rotated by −90 degrees from the vector as a normal vector, and cutting out the scan data on an infinite plane at a point moved by the fourth distance to the second vector;
From the left dental feature point



At a point where the third distance has been moved in the vector direction,

a step of cutting out the scan data on an infinite plane at a point where a third vector obtained by rotating the vector by −90 degrees is set as a normal vector and the third vector is moved by the fourth distance;
From the left dental feature point



At a point where the third distance has been moved in the vector direction,

The method for automating registration of dental 3D data according to claim 10, further comprising a step of setting a fourth vector rotated +90 degrees from the vector as a normal vector, and extracting the scan data on an infinite plane at a point moved the fourth distance to the fourth vector. The method for automating registration of dental 3D data according to claim 12, wherein the fourth distance is greater than the first distance, the second distance, and the third distance. The step of extracting a dental portion of the scan data further comprises:
The second feature point of the scan data is

Vector and

It is the sum of vectors

At the point where the object has moved a fifth distance in the vector direction,

cutting out the scan data with an infinite plane having a normal vector of the vector; and extracting the vector from the second feature point of the scan data.



At a point where the fifth distance has been moved in the vector direction,



11. The method for automating registration of dental three-dimensional data according to claim 10, further comprising a step of cutting out the scan data with an infinite plane having the vector as a normal vector. Extracting feature points from the CT data;
Extracting feature points from the scan data of the digital impression model;
determining an up vector indicating the direction in which the patient's eyes and nose are located and the left and right of feature points in the scan data;
extracting a dental portion of the scan data;
searching for source points of the scan data on a spline curve of the CT data to generate candidate target points;
A step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate,
The step of searching for source points of the scan data on a spline curve of the CT data to generate candidate target points includes:
A method for automating registration of 3D dental data, comprising the step of calculating the spline curve, C(u), based on a plurality of feature points of the upper jaw in the CT data or a plurality of feature points of the lower jaw in the CT data. The method for automating registration of 3D dental data according to claim 15, wherein the source points include three points: a left dental feature point, a second feature point, and a right dental feature point. P _L is the left tooth feature point, P ₁₂ is the second feature point, and P _R is the right dental feature point,
A first target point C(u1) is searched for by increasing the parameter u on C(u) by a first value, and a second target point C(u2) is searched for by increasing the parameter u on C(u) by a second value, d11 = ||C(u1)-C(u2)||-||P _L -P ₁₂ The point where || is the smallest is searched for, and the third point C(u3) of the target point is obtained by increasing the parameter u on C(u) by a third value, and d12 = ||C(u2) - C(u3)|| - ||P ₁₂ -P _R The point where || is minimal is searched for,
The d11, d12, and d13 are ||C(u3)-C(u1)||-||P _R -P _L If || is smaller than a second threshold, the target points C(u1), C(u2), and C(u3) are selected as the candidate target points;
The automated method for registering 3D dental data according to claim 16, wherein the candidate target points include three points C(u1), C(u2), and C(u3). The step of determining a value having the smallest error between the candidate target point and the feature point of the CT data as a final candidate includes:
transforming the candidate target points into the domain of the CT data using a transformation matrix;
The method for automating registration of dental 3D data according to claim 15, further comprising a step of measuring a transformation error by an average distance between the transformed candidate target points and feature points of the CT data. A computer-readable recording medium having a program recorded thereon for causing a computer to execute the method according to any one of claims 1 to 18.

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