CN115205245A - Occlusion algorithm for intraoral three-dimensional scanning system - Google Patents

Occlusion algorithm for intraoral three-dimensional scanning system Download PDF

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CN115205245A
CN115205245A CN202210824137.3A CN202210824137A CN115205245A CN 115205245 A CN115205245 A CN 115205245A CN 202210824137 A CN202210824137 A CN 202210824137A CN 115205245 A CN115205245 A CN 115205245A
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algorithm
occlusion
contact points
intraoral
model
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张佰春
陈泽锋
吕广志
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Fussen 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
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/004—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for image acquisition of a particular organ or body part
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
    • A61B5/0082—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes
    • A61B5/0088—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence adapted for particular medical purposes for oral or dental tissue
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61C—DENTISTRY; APPARATUS OR METHODS FOR ORAL OR DENTAL HYGIENE
    • A61C19/00—Dental auxiliary appliances
    • A61C19/04—Measuring instruments specially adapted for dentistry
    • A61C19/05—Measuring instruments specially adapted for dentistry for determining occlusion
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • 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
    • G06T2207/10004—Still image; Photographic image
    • 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
    • G06T2207/10004—Still image; Photographic image
    • G06T2207/10012—Stereo images
    • 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

The invention discloses an occlusion algorithm for an intraoral three-dimensional scanning system, which mainly comprises occlusion contact point determination, an occlusion contact point automatic identification algorithm and a nonlinear occlusion numerical optimization algorithm. This an interlock algorithm for intraoral three-dimensional scanning system, through based on artificial intelligence matching (AI) algorithm, obtain more accurate interlock contact point and record, and through a nonlinear interlock numerical optimization algorithm, establish corresponding optimization equation and improve global optimization interlock precision, and this an interlock algorithm for intraoral three-dimensional scanning system, use through artificial intelligence matching algorithm and nonlinear interlock numerical optimization algorithm combination, obtain the interlock relation between more accurate upper and lower jaw model, help the clinician to improve the interlock precision in carrying out the interlock treatment process, reduce work load and improved the efficiency of oral treatment simultaneously, make the patient obtain better faster treatment.

Description

Occlusion algorithm for intraoral three-dimensional scanning system
Technical Field
The invention relates to the technical field of photoelectric information, in particular to a meshing algorithm for an intraoral three-dimensional scanning system.
Background
With the improvement of the living standard of people in China and the increase of population base, the demand of people on oral health is gradually increased, and about half of people in China suffer from various oral diseases according to incomplete statistics. Meanwhile, in recent years, the technology of domestic computer aided design and manufacture (CAD/CAM) is gradually mature to be commercial, and people also put forward a new demand on oral cosmetology in order to seek better appearance. The digital impression technology is a research hotspot in recent years, and particularly in the aspect of treatment of oral clinical indications, such as orthodontics, repair, implantation and the like, the digital impression technology can greatly improve the accuracy and treatment efficiency of oral treatment. The intraoral scanner device is a terminal device for data acquisition in the process of digital impression, has the characteristics of non-contact measurement, real restoration of intraoral three-dimensional form, good experience and the like, and thus becomes a main device for digital treatment such as orthodontics, restoration, implantation and the like in the oral industry at present. Meanwhile, due to the complex clinical oral environment, the technology also faces many problems in the aspects of solving intraoral optical imaging, accuracy of scanning data, experience of patients and the like.
The intraoral three-dimensional digitization technology is a technology integrating photoelectric equipment and a visual algorithm, projects the photoelectric equipment to the surface of a tooth, acquires two-dimensional image data of the surface of the tooth by means of sensing equipment, and performs three-dimensional modeling on intraoral tooth data by the visual algorithm so as to meet the requirement of clinical dental design. For 3D model data acquired by an intraoral digital scanner, the occlusion relationship between the upper jaw and the lower jaw is the key for successfully designing a treatment scheme, but due to the complexity of an actual measurement environment (problems of occlusion looseness, serious missing teeth and the like), the occlusion relationship is inaccurate, and subsequent processing design cannot be performed. Therefore, whether the right occlusion relation of the upper jaw and the lower jaw can be obtained affects the success rate of the whole oral cavity digital treatment process, and becomes the difficulty of the existing oral cavity digital treatment, so that the occlusion algorithm for the intraoral three-dimensional scanning system is provided to solve the problem by combining the problems.
Disclosure of Invention
Technical problem to be solved
In view of the deficiencies of the prior art, the present invention provides a bite algorithm for intraoral three-dimensional scanning systems, which solves the problems set forth in the background art above.
(II) technical scheme
2. In order to achieve the purpose, the invention provides the following technical scheme: a bite algorithm for an intraoral three-dimensional scanning system mainly comprises bite contact point determination, a bite contact point automatic identification algorithm and a nonlinear bite numerical optimization algorithm, and specifically comprises the following operations:
the first step is as follows: snap contact determination
(1) Acquiring upper and lower jaw models of the dental jaw by using a mouth scanning device, and identifying an accurate occlusion relation established by the upper and lower jaws;
(2) Observing the occlusion relation between the upper jaw and the lower jaw in the occlusion process, and acquiring a 2D image of the dental jaw by means of an acquisition imaging device, so as to judge the position of an occlusion contact point and mark the position;
the second step is that: automatic identification algorithm for occlusion contact points
(1) Model learning, namely manually marking a model occlusion contact point through a large amount of clinical occlusion data, inputting the model occlusion contact point into a general AI learning algorithm library, and completing the learning of the occlusion contact point by the model by contacting with the powerful experience prediction capability of an AI algorithm;
(2) Identifying contact points, namely identifying the occlusal contact points of the upper jaw 3D model and the lower jaw 3D model through the model obtained by training in the model learning step;
(3) (ii) a Intelligent matching, namely performing intelligent matching on the occluded contact points acquired in the step of identifying the contact points to finish the intelligent matching process of the occluded contact points;
the third step: nonlinear occlusion numerical optimization algorithm
(1) Selecting the selection of an optimization solver, solving the optimization problem by using a Newton algorithm, and optimizing the memory and the iteration process of the solver by using the algorithm according to the change of the gradient between two continuous iterations;
(2) The method is characterized in that a solving equation is established, the solving equation of the algorithm is mainly divided into two types, namely an occlusion equation and an upper and lower jaw equation, and a loss function is defined as follows:
Figure BDA0003743401690000031
merit is the whole loss function value of the current optimization model, unit is the loss function value of each equation in the current optimization model, and n is the number of the equations.
The fourth step is that the first step is that,
substituting the obtained occlusion contact points into a global optimization algorithm to establish a solver and a solution equation, further establishing an optimal occlusion transformation matrix RT, then acting on an upper jaw model and a lower jaw model established by the occlusion contact point determination time in the first step to obtain an optimal occlusion relation, and if the occlusion precision is not met, returning to obtain the occlusion contact points again and continuously executing global optimization to obtain the optimal occlusion relation.
Preferably, in the step of determining the snap contact points in the first step, the plurality of judged snap contact points may be respectively marked as A1-A2, B1-B2, and C1-C2, so as to form three corresponding sets of snap contact points.
Preferably, the automatic snap contact identification algorithm in the second step is based on an artificial intelligence matching algorithm, and the artificial intelligence matching algorithm comprises an existing open source deep learning algorithm library, such as 3D data processing models PointNet + +, KCNet, SO-Net, pointCNN, A-CNN and PointConv.
Preferably, the nonlinear occlusion numerical optimization algorithm in the third step solves the optimization problem through a quasi-newton algorithm and the like, and the algorithm optimizes the memory and the iteration process of the solver according to the change of the gradient between two continuous iterations.
Preferably, in the nonlinear occlusion numerical optimization algorithm in the third step, the occlusion equation in the equation solving step is established to optimize the relationship between occlusion data and the upper and lower jaws, and the upper and lower jaw equations in the equation solving step is established to optimize the relationship between the upper and lower jaws.
Preferably, the general bite algorithm of the present application can be referenced to currently commercialized oral products, such as intraoral scanners in use today.
(III) advantageous effects
The invention provides a meshing algorithm for an intraoral three-dimensional scanning system, which has the following beneficial effects:
(1) According to the occlusion algorithm for the intraoral three-dimensional scanning system, more accurate occlusion contact points are obtained and recorded through an Artificial Intelligence (AI) algorithm, and the overall optimization occlusion precision is improved through a nonlinear occlusion numerical optimization algorithm and a corresponding optimization equation.
(2) According to the occlusion algorithm for the intraoral three-dimensional scanning system, the artificial intelligence matching (AI) algorithm and the nonlinear occlusion numerical optimization algorithm are combined for use, so that a more accurate occlusion relation between the upper jaw model and the lower jaw model is obtained, a clinician is helped to improve the occlusion precision in the occlusion treatment process, and the oral treatment efficiency is improved.
Drawings
FIG. 1 is a schematic perspective view of a 3D model of an upper and lower jaw according to the present invention;
FIG. 2 is a schematic flow chart of the principle of the present invention.
Detailed Description
All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention provides a technical scheme that: an occlusion algorithm for an intraoral three-dimensional scanning system mainly comprises occlusion contact point determination, an occlusion contact point automatic identification algorithm and a nonlinear occlusion numerical optimization algorithm, and specifically comprises the following operations:
the first step is as follows: snap contact determination
(1) Acquiring upper and lower jaw models of the dental jaw by using an oral scanning device, and identifying an accurate occlusion relation established by the upper and lower jaws;
(2) The occlusion relation between the upper jaw and the lower jaw is observed in the occlusion process, meanwhile, 2D images of the dental jaw are obtained through the acquisition imaging equipment, so that the positions of occlusion contact points are judged and marked, a plurality of judged occlusion contact points can be respectively marked as A1-A2, B1-B2 and C1-C2, three groups of corresponding occlusion contact point groups are formed for subsequent recording and identification, and the general occlusion algorithm can refer to the current commercial oral scanning products, such as the currently used oral scanner.
The second step: automatic identification algorithm for occlusion contact points
(1) Model learning, namely manually marking a model occlusion contact point through a large amount of clinical occlusion data, inputting the marked model occlusion contact point into a general AI learning algorithm library, completing the learning of the occlusion contact point by the model by contacting with the powerful experience prediction capability of an AI algorithm, wherein an automatic identification algorithm of the occlusion contact point is based on an artificial intelligence matching algorithm, and the artificial intelligence matching algorithm comprises the existing open source deep learning algorithm library, such as 3D data processing models PointNet + +, KCNet, SO-Net, pointCNN, A-CNN and PointConv, and various options;
(2) Identifying contact points, namely identifying the occlusal contact points of the upper jaw 3D model and the lower jaw 3D model through the model obtained by training in the model learning step;
(3) (ii) a Intelligent matching, namely performing intelligent matching on the occluded contact points acquired in the step of identifying the contact points to finish the intelligent matching process of the occluded contact points;
the third step: nonlinear occlusion numerical optimization algorithm
(1) Selecting the selection of an optimization solver, solving the optimization problem by using a Newton algorithm, and optimizing the memory and the iteration process of the solver by using the algorithm according to the change of the gradient between two continuous iterations;
the algorithm solves the optimization problem through a quasi-Newton algorithm and the like, and optimizes the memory and the iteration process of a solver according to the change of the gradient between two continuous iterations;
(2) Establishing a solving equation, wherein the solving equation of the algorithm is mainly divided into two types, namely an occlusion equation and an upper jaw and lower jaw equation, the occlusion equation is a relationship between optimized occlusion data and an upper jaw and a lower jaw, the upper jaw and lower jaw equation is a relationship between optimized upper jaw and lower jaw, and a loss function is defined as follows:
Figure BDA0003743401690000051
where Merit is the overall loss function value of the current optimization model, merit is the loss function value of each equation in the current optimization model, and n is the number of equations.
The fourth step is that the first step is that,
substituting the obtained occlusion contact points into a global optimization algorithm to establish a solver and a solution equation, further establishing an optimal occlusion transformation matrix RT, then acting on an upper jaw model and a lower jaw model established by the occlusion contact point determination time in the first step to obtain an optimal occlusion relation, and if the occlusion precision is not met, returning to obtain the occlusion contact points again and continuously executing global optimization to obtain the optimal occlusion relation.
In conclusion, the occlusion algorithm for the intraoral three-dimensional scanning system obtains the optimal occlusion transformation matrix RT by selecting occlusion contact points and carrying out occlusion global optimization, establishing a solver and solving an equation by substituting the obtained occlusion contact points into the global optimization algorithm, and finally applying the optimal occlusion transformation matrix RT to the maxilla model and the maxilla model to obtain the optimal occlusion relation. And if the occlusion precision is not met, returning to obtain the occlusion contact point again and continuously executing global optimization to obtain the optimal occlusion relation.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (6)

1. A bite algorithm for an intraoral three-dimensional scanning system, characterized by: the occlusion algorithm mainly comprises occlusion contact point determination, an occlusion contact point automatic identification algorithm and a nonlinear occlusion numerical optimization algorithm, and specifically comprises the following operations:
the first step is as follows: snap contact determination
(1) Acquiring upper and lower jaw models of the dental jaw by using a mouth scanning device, and identifying an accurate occlusion relation established by the upper and lower jaws;
(2) Observing the occlusion relation between the upper jaw and the lower jaw in the occlusion process, and acquiring a 2D image of the dental jaw by means of an acquisition imaging device, so as to judge the position of an occlusion contact point and mark the position;
the second step: automatic identification algorithm for occlusion contact points
(1) Model learning, namely manually marking the occluded contact points of the model through a large amount of clinical occlusion data, inputting the labeled occluded contact points into a general AI learning algorithm library, and completing the learning of the occluded contact points by the model through the strong experience predicting capability of an exposed AI algorithm;
(2) Identifying contact points, namely identifying occlusion contact points of the 3D models of the upper jaw and the lower jaw through the model obtained by training in the model learning step;
(3) (ii) a Intelligent matching, namely performing intelligent matching on the occluded contact points acquired in the step of identifying the contact points to finish the intelligent matching process of the occluded contact points;
the third step: nonlinear occlusion numerical optimization algorithm
(1) Selecting the selection of an optimization solver, solving the optimization problem by using a Newton algorithm, and optimizing the memory and the iteration process of the solver by using the algorithm according to the change of the gradient between two continuous iterations;
(2) A solution equation is established, the algorithm solution equation is mainly divided into two types, namely an occlusion equation and an upper and lower jaw equation, and a loss function is defined as follows:
Figure FDA0003743401680000011
where Merit is the overall loss function value of the current optimization model, merit is the loss function value of each equation in the current optimization model, and n is the number of equations.
The fourth step is that the first step is that,
and substituting the obtained occluding contact points into a global optimization algorithm to establish a solver and a solution equation, further establishing an optimal occluding transformation matrix RT, then acting on an upper jaw model and a lower jaw model established by the occluding contact point determination time in the first step to obtain an optimal occluding relation, and if the occluding precision is not met, returning to obtain the occluding contact points again and continuously executing global optimization to obtain the optimal occluding relation.
2. A bite algorithm for an intraoral three-dimensional scanning system according to claim 1, characterized in that: in the step of determining the snap contact points in the first step, the plurality of judged snap contact points can be respectively marked as A1-A2, B1-B2 and C1-C2 to form three corresponding snap contact point groups.
3. A bite algorithm for an intraoral three-dimensional scanning system according to claim 1, characterized in that: the automatic identification algorithm of the occlusion contact points in the second step is based on an artificial intelligence matching algorithm, and the artificial intelligence matching algorithm comprises an existing open source deep learning algorithm library, such as a 3D data processing model PointNet + +, KCNet, SO-Net, pointCNN, A-CNN and PointConv.
4. A bite algorithm for an intraoral three-dimensional scanning system according to claim 1, wherein: in the nonlinear occlusion numerical optimization algorithm in the third step, the optimization problem is solved through a quasi-Newton algorithm and the like, and the algorithm optimizes the memory of a solver and the iteration process according to the change of the gradient between two continuous iterations.
5. A bite algorithm for an intraoral three-dimensional scanning system according to claim 1, characterized in that: in the nonlinear occlusion numerical optimization algorithm in the third step, the occlusion equation in the step of establishing and solving the equation is to optimize the relationship between occlusion data and the upper and lower jaws, and the upper and lower jaw equations in the step of establishing and solving the equation is to optimize the relationship between the upper and lower jaws.
6. A bite algorithm for an intraoral three-dimensional scanning system according to claim 1, wherein: the general bite algorithm described herein may be referenced to currently commercialized oral products, such as intraoral scanners in use today.
CN202210824137.3A 2022-07-13 2022-07-13 Occlusion algorithm for intraoral three-dimensional scanning system Pending CN115205245A (en)

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