CN111696068A - Method and computer system for generating digital data set representing target tooth layout by using artificial neural network - Google Patents

Method and computer system for generating digital data set representing target tooth layout by using artificial neural network Download PDF

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CN111696068A
CN111696068A CN201910192920.0A CN201910192920A CN111696068A CN 111696068 A CN111696068 A CN 111696068A CN 201910192920 A CN201910192920 A CN 201910192920A CN 111696068 A CN111696068 A CN 111696068A
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data set
digital data
layout
set representing
tooth
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张长庚
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Hangzhou Chaohou Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/20—Finite element generation, e.g. wire-frame surface description, tesselation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10028—Range image; Depth image; 3D point clouds
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30036—Dental; Teeth

Abstract

One aspect of the application provides a method of generating a digital data set representing a target tooth layout using an artificial neural network, comprising: acquiring a digital data set representing an initial tooth layout; and generating a digital data set representing a target tooth layout based on the digital data set representing the initial tooth layout using a trained artificial neural network.

Description

Method and computer system for generating digital data set representing target tooth layout by using artificial neural network
Technical Field
The present application relates generally to methods and computer systems for generating a digital data set representing a target tooth layout using an artificial neural network.
Background
With the rapid development of computer technology, dental treatment is increasingly assisted by computer technology, for example, a dental professional can automatically generate a target tooth layout required for orthodontic treatment (i.e., a tooth layout expected to be achieved at the end of orthodontic treatment) by using a computer, so as to release manpower and improve efficiency. One method is to define the characteristic points of the teeth, then carry out dental arch curve fitting and optimization, and then carry out full-automatic tooth arrangement through collision detection iterative optimization of a local bounding box.
However, the above method has the following disadvantages: firstly, parameters and shapes of an arch curve are directly related to the initial layout of teeth, namely, the parameters and the shapes of the arch curve are determined during calculation initialization, and for a tooth model with a disordered initial layout, the method is difficult to fit to obtain an accurate arch curve; secondly, the method is sensitive to noise, and teeth with large deviation from the fitted arch curve affect the accuracy of the arch curve.
In view of the above, there is a need for a new system and method for generating a target tooth layout.
Disclosure of Invention
One aspect of the application provides a method of generating a digital data set representing a target tooth layout using an artificial neural network, comprising: acquiring a digital data set representing an initial tooth layout; and generating a digital data set representing a target tooth layout based on the digital data set representing the initial tooth layout using a trained artificial neural network.
In some embodiments, the target tooth layout may be a tooth layout desired for orthodontic treatment.
In some embodiments, the artificial neural network is trained with N sets of data, wherein each set of N sets of data may include a digital data set representing an initial layout and a digital data set of a target layout of a pair of upper and lower dentitions in a bite state, where N is an integer greater than 1.
In some embodiments, the artificial neural network may include a GAN network for predicting position coordinates of teeth and an FCNN network for predicting angle coordinates of teeth.
In some embodiments, the method for generating a digital data set representing a target tooth layout using an artificial neural network may further include: generating, with the artificial neural network, a digital data set representing a first tooth layout based on the digital data set representing the initial tooth layout; fitting to obtain a target arch curve based on the first tooth layout; and adjusting the first tooth layout based on the target arch curve to obtain the digital data set representing the target tooth layout.
In some embodiments, the tooth position coordinate information in the digital data set representing the first tooth layout may be generated by the GAN network based on the tooth position coordinate information in the digital data set representing the initial tooth layout, and the tooth angle coordinate information in the digital data set representing the first tooth layout may be generated by the FCNN network based on the tooth angle coordinate information in the digital data set representing the initial tooth layout.
In some embodiments, the target arch curve may be a Beta curve.
In some embodiments, the target arch curve may be iteratively fit according to how far the teeth deviate from the arch curve based on the first tooth layout.
In some embodiments, the method for generating a digital data set representing a target tooth layout using an artificial neural network may further include: fitting to obtain an initial arch curve based on the representative first tooth layout; removing teeth deviating from the initial dental arch curve and exceeding a preset threshold value; and obtaining the target arch curve based on the remaining tooth fitting.
In some embodiments, the method for generating a digital data set representing a target tooth layout using an artificial neural network may further include: adjusting the first tooth layout based on the target arch curve resulting in a digital data set representing a second tooth layout; and iteratively adjusting the tooth layout with collision detection based on the second tooth layout, resulting in the digital data set representing the target tooth layout.
Yet another aspect of the application provides a computer system for generating a digital data set representing a target tooth layout using an artificial neural network, comprising a processor and a storage device, wherein the storage device stores a computer program that, when executed by the processor, controls the processor to: acquiring a digital data set representing an initial tooth layout; and generating a digital data set representing a target tooth layout based on the digital data set representing the initial tooth layout using a trained artificial neural network.
In some embodiments, the target tooth layout may be a tooth layout desired to be achieved by orthodontic treatment.
In some embodiments, the artificial neural network is trained with N sets of data, wherein each set of N sets of data may include a digital data set representing an initial layout of a pair of upper and lower jaw dentitions in a bite state and a digital data set of a target layout, wherein N is an integer greater than 1.
In some embodiments, the artificial neural network may include a GAN network for predicting position coordinates of teeth and an FCNN network for predicting angle coordinates of teeth.
In some embodiments, the computer program may further control the processor to: generating, with the artificial neural network, a digital data set representing a first tooth layout based on the digital data set representing the initial tooth layout; fitting to obtain a target arch curve based on the representative first tooth layout; and adjusting the first tooth layout based on the target arch curve to obtain the digital data set representing the target tooth layout.
In some embodiments, the tooth position coordinate information in the digital data set representing the first tooth layout may be generated by the GAN network based on the tooth position coordinate information in the digital data set representing the initial tooth layout, and the tooth angle coordinate information in the digital data set representing the first tooth layout may be generated by the FCNN network based on the tooth angle coordinate information in the digital data set representing the initial tooth layout.
In some embodiments, the target arch curve may be a Beta curve.
In some embodiments, the target arch curve may be iteratively fit according to how far the teeth deviate from the arch curve based on the first tooth layout.
In some embodiments, the computer program may further control the processor to: fitting to obtain an initial arch curve based on the first tooth layout; removing teeth deviating from the initial dental arch curve and exceeding a preset threshold value; and obtaining the target arch curve based on the remaining tooth fitting.
In some embodiments, the computer program is further capable of controlling the processor to: adjusting the first tooth layout based on the target arch curve resulting in a digital data set representing a second tooth layout; and iteratively adjusting the tooth layout with collision detection based on the second tooth layout, resulting in the digital data set representing the target tooth layout.
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The above and other features of the present application will be further explained with reference to the accompanying drawings and detailed description thereof. It is appreciated that these drawings depict only several exemplary embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope. The drawings are not necessarily to scale and wherein like reference numerals refer to like parts, unless otherwise specified.
FIG. 1 is a schematic flow chart diagram of a method for generating a digital data set representing a target tooth layout using an artificial neural network in one embodiment of the present application;
fig. 2 schematically illustrates a network structure of a generator of a GAN network in an embodiment of the present application;
FIG. 3A schematically illustrates a three-dimensional digital model representing an initial tooth layout in one embodiment of the present application;
FIG. 3B schematically illustrates a three-dimensional digital model representing a first tooth layout in one embodiment of the present application; and
FIG. 3C schematically illustrates a three-dimensional digital model representing a target tooth layout in one embodiment of the present application.
Detailed Description
The following detailed description refers to the accompanying drawings, which form a part of this specification. The exemplary embodiments mentioned in the description and the drawings are only for illustrative purposes and are not intended to limit the scope of the present application. Those skilled in the art, having benefit of this disclosure, will appreciate that many other embodiments can be devised which do not depart from the spirit and scope of the present application. It should be understood that the aspects of the present application, as described and illustrated herein, may be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are within the scope of the present application.
One aspect of the present application provides a method and system for generating a target tooth layout using an artificial neural network with deep learning capabilities.
The method and system of the present application can be used in different scenarios, it being understood that in different application scenarios, the target tooth layout has different meaning, and for orthodontic treatment, the target tooth layout can be the tooth layout that orthodontic treatment is expected to achieve.
Referring to FIG. 1, a schematic flow chart diagram of a method 100 for generating a digital data set representing a target tooth layout using an artificial neural network is shown in one embodiment of the present application.
In 101, a digital data set representing an initial tooth layout is acquired.
It will be appreciated in light of the present application that in one embodiment, the initial tooth layout may be the patient's tooth layout prior to orthodontic treatment; in yet another embodiment, the initial tooth layout may also be the current tooth layout of the patient based on which the target tooth layout was generated using the method of the present application.
In one embodiment, the digital data set representing the tooth layout may include position coordinate information and angle coordinate information of the teeth, i.e., the digital data set representing the tooth pose (position and posture). It will be appreciated that based on such a digital data set representing a tooth layout and a digital data set representing a tooth geometry, a three-dimensional digital model representing the corresponding tooth layout is obtained.
In one embodiment, the digital data set representing the initial arrangement of teeth may be obtained based on a three-dimensional digital model representing the initial arrangement of teeth.
In one embodiment, a three-dimensional digital model representing the initial tooth layout may be obtained by directly scanning the patient's dental jaws. In yet another embodiment, a solid model, such as a plaster model, of the patient's dental jaw can be scanned to obtain a three-dimensional digital model representing the initial tooth layout. In yet another embodiment, a three-dimensional digital model representing the initial tooth layout may be obtained by scanning the bite of the patient's jaw.
In one embodiment, a three-dimensional digital model may be constructed based on a triangular mesh, and the following embodiments are all described based on such a three-dimensional digital model. It is understood that the dental three-dimensional digital model can be constructed based on other types of meshes besides triangular meshes, such as quadrilateral meshes, pentagonal meshes, hexagonal meshes, and the like, and a description thereof is omitted.
In one embodiment, the three-dimensional digital model representing the initial tooth layout may be a stl file that is segmented (i.e., independent of each other).
In one embodiment, after obtaining the three-dimensional digital model representing the initial tooth layout, a coordinate system may be set for it, based on which the position coordinates and the angle coordinates of the teeth may be represented. In one embodiment, for ease of calculation, a world coordinate system may be set for the three-dimensional digital model of the entire dentition and a local coordinate system may be set for the three-dimensional digital model of each tooth in the dentition.
In 103, a digital data set representing a first tooth layout is generated based on the digital data set representing the initial tooth layout using the trained artificial neural network.
Through a large amount of research and tests, the inventor of the application finds that a generative confrontation network (GAN) can relatively accurately fit the real distribution of data in the statistical sense, and the accuracy performance is better in the aspect of predicting the tooth position; in the aspect of predicting the tooth angle, the convergence speed and accuracy of a full convolution Network (FCNN) are superior to those of a convolution and full-connection common CNN Network, and the stability and accuracy of the full convolution Network are superior to those of a GAN Network. Therefore, in a preferred embodiment of the present application, the GAN network is used to predict the tooth position, the FCNN network is used to predict the tooth angle, and the advantages of the two networks are fully combined to obtain a better prediction result.
In one embodiment, the artificial neural network may be implemented in Python.
In one embodiment, the pose of each tooth may be expressed in combination with position and angle coordinates. Here, the position coordinates may be expressed in the form of (x, y, z), and the angle coordinates may be expressed in the form of a quaternion (i, j, k, w). Through a large number of tests, the inventors found that the precision of the rotation matrix in terms of angular coordinates is superior to quaternions in many cases, and that the euler angle, although having the benefit of a small amount of data, is a situation where the gimbal may lock up. Thus, in one embodiment, the position coordinates and the rotation matrix may be used as input data to a neural network.
In one embodiment, the rotation matrix M may be expressed by the following equation (1):
Figure BDA0001994900900000071
thus, for each tooth, there are a 3-dimensional vector and a 9-dimensional vector, respectively, with respect to position and angle.
In orthodontic treatment of teeth, one of the requirements is proper coverage and apposition of the upper and lower anterior teeth, wherein coverage refers to the horizontal positional relationship between the incisors of the upper and lower anterior teeth (generally, a distance from the incisor to the labial surface of the lower anterior tooth of 3mm is considered as normal coverage), and apposition refers to the vertical positional relationship between the incisors of the upper and lower anterior teeth (generally, the incisor margin of the upper anterior tooth covers the lower anterior tooth no more than the crown length 1/3 of the lower anterior tooth, or the occlusal side 1/3 of the lower anterior tooth is considered as normal apposition).
In order for the artificial neural network to incorporate this point in the calculation, in one embodiment, a digital data set representing the upper and lower jaw dentitions in a bite state may be used as input to the artificial neural network. Then, for the position coordinates, there are 6 dimensions for each set of upper and lower teeth, and the corresponding 14 sets of teeth form a matrix of 6 x 14 without taking into account the missing tooth. In one embodiment, the matrix may be normalized as an input to the GAN network.
Similarly, for angular coordinates, each set of upper and lower teeth has 18 dimensions, and the corresponding 14 sets of teeth form an 18 x 14 matrix, without regard to missing teeth. In one embodiment, this matrix may be used as input to the FCNN network.
In one embodiment, the GAN network can be constructed by referencing the network structure of Pix2Pix, please refer to Image-to-Image transformation with Conditional Access Networks (referred to as "ref I" below) published by IEEE Conference on Computer Vision and Pattern recognition, IEEE Computer Society,2017: 5967-.
Pix2Pix uses a conditional generative countermeasure network (cGAN) to learn the mapping from input pictures to output pictures, rather than the mapping of noise to pictures. Wherein the loss function does not need to be artificially defined but is learned by a discriminant (discriminator).
In one embodiment, a U-Net structure (instead of an Encoder-Decoder network structure) as shown in fig. 2 can be adopted in the GAN network as a network structure of the generator to promote the details of the picture-to-picture conversion.
The GAN network in this embodiment differs from the Pix2Pix network in reference one in the following: the picture in reference one is based on three colors of RGB, so there are 3 channels in its network, while in this embodiment, there are only 1 channel in its network because the picture is monochrome; the convolution kernel size of the generator in the network in reference one is 5 × 5, and since the picture is small in this embodiment and is not suitable for using the convolution kernel, a convolution kernel of 3 × 3 size is used.
In one embodiment, a two-layer encoder-decoder structure can be adopted as the structure of the FCNN network, and the input and the output are in one-to-one correspondence and equal in size.
In one embodiment, 2850 sets of dental data are used to train the GAN network and the FCNN network, wherein each set of dental data may include a digital data set representing an initial layout of the set of teeth and a digital data set representing a target layout of the set of teeth. In one embodiment, these target digital data sets in the dental data used for training may be obtained by a medical professional by manually manipulating a three-dimensional digital model of the teeth.
After inputting the three-dimensional digital model representing the initial tooth layout into the trained GAN network and FCNN network, a digital data set representing the first tooth layout can be generated. In some cases, the first tooth layout is deemed not close enough to the desired target layout and, therefore, may be further optimized.
At 105, an arch curve is fitted based on the first tooth layout, and the first tooth layout is adjusted based on the arch curve to obtain a digital data set representing a second tooth layout.
The purpose of this operation is to further optimize the first tooth layout generated by the artificial neural network to be closer to the desired target tooth layout based on the fitted arch curve.
As known to those skilled in the art, there are many ways to fit the dental arch curve, and the Beta curve will be described as an example.
In one embodiment, a three-dimensional digital model representing the first tooth layout may be derived based on the digital data set representing the first tooth layout and the digital data set representing the tooth morphology.
First, feature points on a three-dimensional digital model representing a first tooth layout are acquired, then these feature points are projected onto the XOY plane, and then an arch curve is fitted based on the projection of these feature points on XOY.
In one embodiment, the equation for the arch curve based on the Beta curve may be expressed by the following equation (2):
f(x)＝D[1-(2x/W)2]eequation (2)
Wherein D represents the arch depth, W represents the arch width, and e is the arch curve parameter.
In one embodiment, the arch curve may be fitted based on the following energy function, i.e., find parameters D, W and e.
Figure BDA0001994900900000091
Wherein m represents the number of teeth, ωiRepresents the weight of the ith tooth,irepresenting the amount of movement to adjust the ith tooth to the arch curve.
Since posterior molars are less likely to need to be moved during orthodontic treatment than other teeth, in one embodiment, posterior molars may be weighted higher than anterior molars.
Since teeth having a large number of teeth deviating from the fitted arch curve participate in the fitting of the arch curve, this may have a certain adverse effect on the accuracy of the arch curve, and in order to eliminate this effect as much as possible, teeth deviating from the arch curve by more than a predetermined threshold may be excluded from participating in the fitting of the arch curve.
In one embodiment, a first arch curve is obtained by fitting all teeth involved in the fitting, then the shortest distance between the projection of the feature points and the first arch curve is calculated based on the following equation (4), and then equations (2) are combined to obtain the translation amounts Δ x and Δ y required for adjusting the teeth to the arch curve.
Figure BDA0001994900900000101
Wherein, PxRepresenting the coordinates of the projection of the feature points.
When the distance of one tooth deviating from the first arch curve is larger than a preset threshold value, the tooth can be deleted, the rest teeth are fitted again to obtain a second arch curve, and the first tooth layout is adjusted by taking the second arch curve as the reference to obtain a second tooth layout.
In yet another embodiment, the teeth may be weighted differently according to how far they deviate from the first arch curve to reduce the effect of the large deviation on the fit of the second arch curve, then the second arch curve may be obtained by re-fitting based on the new weights, and finally the first tooth layout may be adjusted based on the second arch curve to obtain the second tooth layout.
That is, a target arch curve may be obtained by iterative fitting based on the first tooth layout and the degree to which the teeth deviate from the arch curve, and the first tooth layout may be readjusted based on the target arch curve to obtain the second tooth layout. It is understood that the iterative fitting may be one iteration or a plurality of iterations.
In one embodiment, the iterative fitting process of the target arch curve may be described in the following pseudo-code:
Figure BDA0001994900900000102
Figure BDA0001994900900000111
because the first tooth layout obtained by the artificial neural network prediction is closer to the target tooth layout than the initial tooth layout, the arch curve fitted based on the first tooth layout is also more accurate.
In 107, the tooth layout is iteratively adjusted with collision detection based on the second tooth layout, resulting in a digital data set representing a target tooth layout.
In one embodiment, in this operation, a method of a rectangular bounding box may be used, in which the first molar is used as a reference, the teeth on both left and right sides of the upper and lower jaws are adjusted one by one from the far center to the near center, the distance between the tooth to be adjusted and the adjacent tooth is calculated according to the collision detection, so as to determine whether the tooth to be adjusted is adjusted in the near center direction or the far center direction, and the second tooth layout is adjusted based on the determination, so as to obtain a digital data set representing the target tooth layout.
In one embodiment, the process of iteratively adjusting the tooth layout based on collision detection may be described in the following pseudo-code:
Figure BDA0001994900900000112
Figure BDA0001994900900000121
Figure BDA0001994900900000131
referring to fig. 3A-3C, a three-dimensional digital model representing an initial tooth layout, a three-dimensional digital model representing a first tooth layout (predicted by an artificial neural network based on the initial tooth layout), and a target tooth layout according to an embodiment of the present application are graphically illustrated, respectively. From the evolution of fig. 3A-3C, it can be seen that the method of the present application works well for generating a digital data set representing a target tooth layout based on a three-dimensional digital model representing an initial target tooth layout.
Yet another aspect of the application provides a computer system for generating a digital data set representing a target tooth layout, comprising a processor and a storage device, wherein the storage device stores a computer program that, when executed by the processor, is capable of performing the method 100 of generating a digital data set representing a target tooth layout using an artificial neural network.
While various aspects and embodiments of the disclosure are disclosed herein, other aspects and embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification. The various aspects and embodiments disclosed herein are for purposes of illustration only and are not intended to be limiting. The scope and spirit of the application are to be determined only by the claims appended hereto.
Likewise, the various diagrams may illustrate an exemplary architecture or other configuration of the disclosed methods and systems that is useful for understanding the features and functionality that may be included in the disclosed methods and systems. The claimed subject matter is not limited to the exemplary architectures or configurations shown, but rather, the desired features can be implemented using a variety of alternative architectures and configurations. In addition, to the extent that flow diagrams, functional descriptions, and method claims do not follow, the order in which the blocks are presented should not be limited to the various embodiments which perform the recited functions in the same order, unless the context clearly dictates otherwise.
Unless otherwise expressly stated, the terms and phrases used herein, and variations thereof, are to be construed as open-ended as opposed to limiting. In some instances, the presence of an extensible term or phrases such as "one or more," "at least," "but not limited to," or other similar terms should not be construed as intended or required to imply a narrowing in instances where such extensible terms may not be present.

Claims (20)

1. A method of generating a digital data set representing a target tooth layout using an artificial neural network, comprising:
acquiring a digital data set representing an initial tooth layout; and
a digital data set representing a target tooth layout is generated based on the digital data set representing the initial tooth layout using a trained artificial neural network.
2. The method of generating a digital data set representing a target tooth layout using an artificial neural network of claim 1, wherein the target tooth layout is a tooth layout that orthodontic treatment is desired to achieve.
3. The method of generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 1, wherein the artificial neural network is trained with N sets of data, wherein each set of the N sets of data includes a digital data set representing an initial layout of a pair of upper and lower dentitions in a bite state and a digital data set of the target layout, wherein N is an integer greater than 1.
4. The method of generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 1, wherein the artificial neural network comprises a GAN network and an FCNN network, wherein the GAN network is used to predict the position coordinates of the teeth and the FCNN network is used to predict the angle coordinates of the teeth.
5. A method for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 1 or 4, further comprising:
generating, with the artificial neural network, a digital data set representing a first tooth layout based on the digital data set representing the initial tooth layout;
fitting to obtain a target arch curve based on the first tooth layout; and
adjusting the first tooth layout based on the target arch curve results in the digital data set representing a target tooth layout.
6. The method according to claim 5, wherein the tooth position coordinate information in the digital data set representing the first tooth layout is generated by the GAN network based on the tooth position coordinate information in the digital data set representing the initial tooth layout, and the tooth angle coordinate information in the digital data set representing the first tooth layout is generated by the FCNN network based on the tooth angle coordinate information in the digital data set representing the initial tooth layout.
7. The method of generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 5, wherein the target arch curve is a Beta curve.
8. The method of generating a digital data set representing a target tooth layout using an artificial neural network of claim 5, wherein the target arch curve is iteratively fit according to how far teeth deviate from an arch curve based on the first tooth layout.
9. The method of generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 8, further comprising:
fitting to obtain an initial arch curve based on the representative first tooth layout;
removing teeth deviating from the initial dental arch curve and exceeding a preset threshold value; and
the target arch curve is derived based on the remaining tooth fits.
10. The method of generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 8, further comprising:
adjusting the first tooth layout based on the target arch curve resulting in a digital data set representing a second tooth layout; and
iteratively adjusting the dental layout with collision detection based on the second dental layout resulting in the digital data set representing the target dental layout.
11. A computer system for generating a digital data set representing a target tooth layout using an artificial neural network, comprising a processor and a storage device, wherein the storage device stores a computer program that, when executed by the processor, controls the processor to:
acquiring a digital data set representing an initial tooth layout; and
a digital data set representing a target tooth layout is generated based on the digital data set representing the initial tooth layout using a trained artificial neural network.
12. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network of claim 11, wherein the target tooth layout is a tooth layout that is desired to be achieved by orthodontic treatment.
13. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 11, wherein the artificial neural network is trained with N sets of data, wherein each set of the N sets of data includes a digital data set representing an initial layout of a pair of upper and lower dentitions in a bite state and a digital data set of the target layout, wherein N is an integer greater than 1.
14. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 11, wherein the artificial neural network comprises a GAN network and an FCNN network, wherein the GAN network is used to predict the position coordinates of the teeth and the FCNN network is used to predict the angle coordinates of the teeth.
15. A computer system for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 11 or 14, wherein the computer program is further operable to control the processor to:
generating, with the artificial neural network, a digital data set representing a first tooth layout based on the digital data set representing the initial tooth layout;
fitting to obtain a target arch curve based on the representative first tooth layout; and
adjusting the first tooth layout based on the target arch curve results in the digital data set representing a target tooth layout.
16. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 15, wherein the tooth position coordinate information in the digital data set representing the first tooth layout is generated by the GAN network based on the tooth position coordinate information in the digital data set representing the initial tooth layout, and the tooth angle coordinate information in the digital data set representing the first tooth layout is generated by the FCNN network based on the tooth angle coordinate information in the digital data set representing the initial tooth layout.
17. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network as claimed in claim 15, wherein said target arch curve is a Beta curve.
18. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network of claim 15, wherein the target arch curve is iteratively fit according to how far teeth deviate from an arch curve based on the first tooth layout.
19. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network of claim 18, wherein the computer program further controls the processor to:
fitting to obtain an initial arch curve based on the first tooth layout;
removing teeth deviating from the initial dental arch curve and exceeding a preset threshold value; and
the target arch curve is derived based on the remaining tooth fits.
20. The computer system for generating a digital data set representing a target tooth layout using an artificial neural network of claim 18, wherein the computer program further controls the processor to:
adjusting the first tooth layout based on the target arch curve resulting in a digital data set representing a second tooth layout; and
iteratively adjusting the dental layout with collision detection based on the second dental layout resulting in the digital data set representing the target dental layout.
CN201910192920.0A 2019-03-14 2019-03-14 Method and computer system for generating digital data set representing target tooth layout by using artificial neural network Pending CN111696068A (en)

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