Tooth supply universal model design method, system and equipment in autologous tooth transplantation and storage medium

Abstract

The invention belongs to the technical field of oral medicine, and relates to a tooth supply universal model design method, system and device in autologous tooth transplantation and a storage medium. The method comprises the following steps: acquiring a plurality of wisdom tooth images as original data; extracting vertex information from the original data, and removing outliers to obtain point cloud data; classifying the point cloud data according to the growth position and the root shape of the wisdom tooth to obtain a plurality of types of data sets; registering any two point cloud data in the same type of data set to obtain a reference coordinate system of multi-point cloud registration; in the registration process, wisdom tooth point cloud data which cannot be registered are removed, and screened point cloud data are obtained; fusing the registered and screened point cloud data to obtain a fused point cloud, calculating the local density of the fused point cloud and filtering, and reserving the dense part of the point cloud as a preliminary model; and carrying out point cloud boundary extraction on the preliminary model, and then carrying out smoothing processing to obtain tooth supply universal models with different sizes in autologous tooth transplantation.

Description

Technical Field

[0001] The present invention belongs to the technical field of oral medicine, and in particular relates to a method, system, device and storage medium for designing a universal model of a donor tooth in autologous tooth transplantation. Background Art

[0002] Autologous tooth transplantation is one of the treatment options for single tooth loss. It uses the patient's own teeth to restore the missing teeth, maintain the bone volume of the alveolar bone, restore the normal periodontal tissue and tooth proprioception in the recipient area, and achieve a certain aesthetic effect. It is now gradually accepted by oral professionals and patients with edentulous teeth.

[0003] In the process of preparing the socket for autologous tooth transplantation, it is necessary to use teeth or models for trial implantation until the teeth or models can be completely accommodated in the socket. If real teeth are used, it will inevitably increase the time for the donor teeth to be out of the body and damage the healthy periodontal membrane; therefore, most people now use 3D printed personalized donor tooth models to replace the donor teeth for repeated trial implantation, shortening the time for the donor teeth to be out of the body, reducing damage to the periodontal membrane, and improving the survival rate and success rate of the transplanted teeth. However, the production of personalized 3D printed models has the characteristics of long preparation cycle, high cost, high professional knowledge requirements, and lack of health economics. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a method, system, device and storage medium for designing a universal model of donor teeth in autologous tooth transplantation, so as to solve the problems of long preparation cycle, high cost, high professional knowledge requirements and lack of health economics in making personalized 3D printed models.

[0005] The present invention is achieved through the following technical solutions:

[0006] The present invention discloses a method for designing a universal model of a donor tooth in autologous tooth transplantation, comprising the following steps:

[0007] S1, obtaining multiple wisdom tooth images as raw data;

[0008] S2, extracting vertex information from the original data, removing outliers in the vertex information, and obtaining point cloud data;

[0009] S3, classifying the point cloud data obtained in S2 according to the growth position and root morphology of the wisdom tooth to obtain multiple types of data sets;

[0010] S4, registering any two point cloud data in the same type of data set to obtain a reference coordinate system for multi-point cloud registration;

[0011] During the registration process, the wisdom tooth point cloud data that cannot be registered is eliminated to obtain the filtered point cloud data;

[0012] S5, fusing the multiple registered and screened point cloud data to obtain a fused point cloud, calculating the local density of the fused point cloud and filtering it, and retaining the dense part of the point cloud as a preliminary model;

[0013] S6. Extracting the boundary of the point cloud of the preliminary model, thereby obtaining a universal model of the donor tooth in the autologous tooth transplantation;

[0014] S7. Smoothing the surface of the obtained universal model of the donor tooth in the autologous tooth transplantation, scaling it proportionally with different coefficients, and obtaining universal models of the donor tooth in the autologous tooth transplantation of different sizes.

[0015] Furthermore, in S2, outliers in vertex information are removed according to the Laida criterion.

[0016] Furthermore, in S3, the various types of data sets include an upper single-root wisdom tooth data set, an upper multi-root wisdom tooth data set, a lower single-root wisdom tooth data set, and a lower multi-root wisdom tooth data set.

[0017] Further, in S4, any two point cloud data in the same type of data set are registered to obtain a reference coordinate system for multi-point cloud registration, specifically:

[0018] By introducing scale transformation into the ICP algorithm for registration, the point cloud undergoes both rigid transformation and geometric scaling transformation during the registration process. After continuous iteration, the degree of fit of the point cloud approaches the local optimum.

[0019] Furthermore, the error function constructed in the ICP algorithm is:

[0020] E=∑ i (R·s·p i +tq i ) 2

[0021] Among them, R is the rotation transformation, t is the translation transformation, s is the scale transformation; E is the error value; p i and q i For any two point cloud data;

[0022] After continuous iteration of R, t, s, when E→min, p i Transformed point cloud As close as possible i , to achieve registration;

[0023]

[0024] error=min E

[0025] After all point clouds are registered, the root mean square error between the registered point clouds is calculated:

[0026]

[0027] N represents the number of point cloud data; i = 1, 2, ..., N; RMSE represents the root mean square error;

[0028] According to the point cloud q corresponding to the minimum root mean square error aim The spatial coordinate system is the reference coordinate system for multi-point cloud registration, so that all point clouds can find the best transformation R, t, s in a unified reference coordinate system.

[0029] Further, S5 is specifically:

[0030] The multiple registered and screened point clouds are fused to obtain the fused point cloud Q. According to the distribution characteristics of the fused point cloud, a hollow structure is presented from sparse to dense and then to sparse from the outside to the inside.

[0031] Q = {q aim ,q1,q2,…,q aim-1 ,q aim+1 ,…,q n}

[0032] n is the number of point cloud data after screening;

[0033] Calculate the local density of the fused point cloud and filter it to retain the dense part of the point cloud as the preliminary model, specifically:

[0034] Construct a KD tree based on the point cloud Q, and for any point (x l ,y l ,z l ), calculate its local density ρ based on the constructed KD tree l , let the density threshold be φ, if ρ l >φ, then the point is retained and the filtered point cloud is obtained. The filtered point cloud is recorded as

[0035] Furthermore, in S6, the point cloud boundary is extracted, specifically:

[0036] For point clouds Any point in The neighborhood relationship is calculated by the K nearest neighbor algorithm. For each point in the neighborhood, the distance from all points to the nearest neighbor is calculated. The distance d between the planes formed j , find the minimum sum of the squares of the distances to find the normal vector and determine the tangent plane;

[0037]

[0038] Project all points in the neighborhood onto the tangent plane and use the angle rule to determine whether the point is a contour point. Select any point in the neighborhood as the starting point, and calculate the angle formed by each of the remaining points and the two points in a clockwise or counterclockwise direction, denoted as θ, and the formed set is denoted as Ω;

[0039] Ω=(θ1,θ2,…,θ n …,θ k )

[0040] Then every two adjacent points and points The angle formed is denoted as θ′, and the formed set is denoted as Ω′;

[0041] Ω′=(θ1′,θ2′,…θ′ n-1 …θ k ′); where θ′ n-1 =θ n -θ n-1 ;

[0042] Let the threshold of contour points be σ, when θ′ max >σ, it is considered that the point is the contour point;

[0043] Among them, θ′ max Represents the maximum value of Ω′.

[0044] The present invention also discloses a universal model design system for a tooth in autologous tooth transplantation, comprising:

[0045] A data acquisition module, used for acquiring multiple wisdom tooth images as raw data;

[0046] The original data includes vertex information and normal information of the wisdom tooth;

[0047] Point cloud data extraction module, used to extract vertex information from raw data, remove outliers in the vertex information, and obtain point cloud data;

[0048] The data classification module is used to classify the point cloud data according to the growth position and root morphology of the wisdom teeth to obtain various types of data sets;

[0049] The registration module is used to register any two point cloud data in the same type of data set to obtain the reference coordinate system for multi-point cloud registration;

[0050] During the registration process, the wisdom tooth point cloud data that cannot be registered is eliminated to obtain the filtered point cloud data;

[0051] The fusion module is used to fuse the multiple registered and screened point cloud data to obtain a fused point cloud, calculate the local density of the fused point cloud and perform filtering, and retain the dense part of the point cloud as the preliminary model;

[0052] The boundary extraction module is used to extract the point cloud boundary of the preliminary model, thereby obtaining a universal model of the donor tooth in the autologous tooth transplantation;

[0053] The smoothing processing module is used to smooth the surface of the universal model of the donor tooth in the autologous tooth transplantation, and scale it proportionally with different coefficients to obtain the universal model of the donor tooth in the autologous tooth transplantation with different sizes.

[0054] The present invention also discloses a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for designing a universal model of a tooth to be supplied in autologous tooth transplantation are implemented.

[0055] The present invention also discloses a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for designing a universal model of a tooth to be supplied in autologous tooth transplantation are implemented.

[0056] Compared with the prior art, the present invention has the following beneficial technical effects:

[0057] The present invention discloses a method for designing a universal model of a tooth donor in an autologous tooth transplantation. The method comprises the following steps: taking a wisdom tooth image as original data, performing preprocessing to remove outliers, and obtaining point cloud data; preclassifying the point cloud data according to different positions and forms of the wisdom teeth; performing pairwise registration on all point clouds in each data set, taking the point cloud with the smallest RMSE as a registration reference, so that all other point clouds can find the best transformation in the coordinate system where the point cloud is located; then fusing the registered point clouds, obtaining the distribution of the point clouds under a unified reference, and obtaining a preliminary model after filtering; and then performing point cloud boundary extraction on the preliminary model, so as to obtain a universal model of a tooth donor in an autologous tooth transplantation.

[0058] After the universal model is designed, it can replace the individualized model for the preparation of the alveolar socket in autologous tooth transplantation. Specifically, the universal model is divided into three types: large, medium and small according to the crown; and divided into single-root and multi-root according to the root. 3D printing technology is used to make a universal model of the supply tooth using cobalt-chromium alloy that can be sterilized repeatedly. When surgery is required clinically, the imaging image of the patient's supply tooth can be matched with the universal model, and then the universal model of the appropriate model can be selected and used immediately when preparing the alveolar socket in autologous tooth transplantation. This eliminates the need to prepare an individualized model, simplifies the diagnosis and treatment process, shortens the treatment cycle, and has good health economic characteristics. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A flowchart of a method for designing a universal model of a donor tooth in an autologous tooth transplantation according to the present invention;

[0060] Figure 2This is a visualization image of the original wisdom tooth data;

[0061] Figure 3 It is an illustration of outliers in the original data;

[0062] Figure 4 Illustration of the pre-classification results for four different types of wisdom teeth;

[0063] Figure 5 Comparison diagram before and after pairwise registration of point clouds;

[0064] Figure 6 This is a schematic diagram of the principle of multi-point cloud registration;

[0065] Figure 7 In order to integrate the distribution characteristics of the point cloud, a hollow structure is presented, which gradually becomes dense and then sparse from the outside to the inside.

[0066] Figure 8 It is the universal model of donor teeth in autologous tooth transplantation obtained through fusion filtering;

[0067] Fig. 9 The large, medium and small models are obtained after smoothing with different coefficients;

[0068] Fig.10 The present invention is a schematic diagram of a universal model design system for supplying teeth in autologous tooth transplantation. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solution and advantages of the present invention more clear, the following is further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention, that is, the embodiments described are only part of the embodiments of the present invention, not all embodiments.

[0070] The components described and shown in the drawings and embodiments of the present invention may be arranged and designed in various configurations. Therefore, the detailed description of the embodiments of the present invention provided in the following drawings is not intended to limit the scope of the claimed invention, but merely represents a selected embodiment of the present invention. Based on the drawings and embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0071] The features and performances of the present invention are further described in detail below in conjunction with the embodiments.

[0072] Based on the vertex information in the original data of wisdom teeth and the physiological characteristics of wisdom teeth, the present invention designs a universal three-dimensional wisdom tooth model through data preprocessing, preclassification, multi-point cloud registration, fusion point cloud filtering, point cloud boundary extraction and other methods to replace the individualized model for the preparation of the alveolar socket in autologous tooth transplantation.

[0073] like Figure 1 As shown, the present invention discloses a method for designing a universal model of a donor tooth in autologous tooth transplantation, comprising the following steps:

[0074] S1, obtaining wisdom tooth images as raw data;

[0075] Specifically, DICOM format data containing wisdom tooth images and related information is obtained based on medical images, which are converted into STL format data for storage and regarded as raw data, including vertex information and normal information, and its visualization is as follows: Figure 2 shown.

[0076] Vertex information can determine the shape and outline of an object. Through the coordinate positions of these vertices, the external shape of the wisdom tooth can be outlined.

[0077] Normal information helps determine the lighting effects and visual performance of the object surface during rendering or simulation. For example, when visualizing a wisdom tooth image in 3D, normal information can make the light reflection on the wisdom tooth surface look more realistic and natural.

[0078] S2. Data preprocessing

[0079] Extract vertex information from the original data to obtain point cloud data;

[0080] There are noise points and sparse outliers in the obtained point cloud data, such as Figure 3 As shown in Figure 2, outliers in the original data are removed according to the Laida criterion.

[0081] The Raida criterion is a statistical method used to identify and remove outliers (also known as gross errors or bad values) in data. It is also called the standard deviation method or the 3σ method. It calculates the arithmetic mean and standard deviation of a set of data, and defines a confidence interval within the range of three standard deviations above and below the mean. Data outside this range is considered an outlier. For data that presents an approximately normal distribution and when the data volume is large enough, this method can effectively find outliers. For all points {P1,P2,…,P i …,P n}, perform statistical analysis on the neighborhood of each point, calculate the average distance d from the point to all neighboring points, and calculate its arithmetic mean and standard deviation σ, for a point P i The average distance d to all neighboring points i , if it satisfies:

[0082]

[0083] It is considered to contain gross errors and is an outlier and should be eliminated.

[0084] S3, wisdom teeth pre-classification

[0085] In the actual formation process of wisdom teeth, their morphology will vary greatly depending on whether they have a single root, multiple roots, and their location. It is not reasonable to use a unified model for wisdom teeth with great differences. Therefore, they are pre-classified according to the position of wisdom teeth and root morphology to obtain a data set.

[0086] Generally, there are four types: upper single-root wisdom tooth dataset, upper multi-root wisdom tooth dataset, lower single-root wisdom tooth dataset, and lower multi-root wisdom tooth dataset.

[0087] Specifically, according to the position of wisdom teeth growth, the wisdom teeth located at the 18th and 28th teeth in the maxilla are distinguished from the wisdom teeth located at the 38th and 48th teeth in the mandible; according to the morphology of the wisdom teeth roots, single roots and multiple roots are distinguished, such as Figure 4 shown.

[0088] After pre-classification, the point cloud data are divided into four data sets: a single-root wisdom tooth data set at teeth 18 and 28, a multi-root wisdom tooth data set at teeth 18 and 28, a single-root wisdom tooth data set at teeth 38 and 48, and a multi-root wisdom tooth data set at teeth 38 and 48.

[0089] The present invention obtains the wisdom tooth data of 1003 patients, including 269 single-root wisdom teeth at the 18th and 28th teeth, 215 multi-root wisdom teeth at the 18th and 28th teeth, 169 single-root wisdom teeth at the 38th and 48th teeth, and 350 multi-root wisdom teeth at the 38th and 48th teeth. The saved data is in stl format and contains a lot of content. Only a part of the data is taken here as a schematic illustration, as shown in the following table.

[0090]

[0091]

[0092]

[0093] S4, multi-point cloud registration

[0094] In order to obtain a universal model, the present invention needs to have universal applicability, and the obtained tooth data comes from different individuals, and is unique in size, position, and posture (for example, some people's wisdom teeth grow horizontally or even backwards). Therefore, these teeth need to be aligned and these data need to be unified into a coordinate system of the same scale and the same reference.

[0095] The ICP algorithm is an algorithm that finds the optimal coordinate transformation through iterative optimization. Its basic idea is to align two point clouds in the same coordinate system by minimizing the error between the point clouds. The algorithm is based on the nearest point search method and uses an iterative process to gradually approach the optimal solution.

[0096] For any point cloud p in the same dataset i and q i By introducing scale transformation into the ICP algorithm for registration, the point cloud undergoes both rigid transformation and geometric scaling transformation during the registration process. After continuous iteration, the degree of fit of the point cloud continues to approach the local optimum, such as Figure 5 As shown. The error function constructed is:

[0097] E=∑ i (R·s·p i +tq i ) 2

[0098] Among them, R is the rotation transformation, t is the translation transformation, and s is the scale transformation.

[0099] After continuous iteration of R, t, s, when E→min, p i Transformed point cloud As close as possible i , to achieve registration.

[0100] in,

[0101] After all point clouds are registered, the root mean square error between the registered point clouds is calculated:

[0102]

[0103] Where error = minE, N represents the number of point cloud data in this dataset. error is the error after registration, which is the minimum value of E after continuous learning iterations.

[0104] According to the point cloud q corresponding to the minimum root mean square error aim The spatial coordinate system is the reference coordinate system for multi-point cloud registration. aim As the reference point cloud, all point clouds find the best transformation R, t, s in a unified reference coordinate system to achieve q aim Multi-point cloud registration based on . Figure 6 shown.

[0105] S5, fusion point cloud filtering

[0106] Because there are individual teeth whose morphology is quite different from that of all other teeth, the error with other teeth is always large after ICP algorithm registration.

[0107] The error judgment threshold is set to α. When the error between the point cloud to be registered and the reference point cloud satisfies error>α, the tooth corresponding to the point cloud is considered to be an error tooth, and this type of special tooth is removed. The point cloud p retained after removal jAfter registering to the reference coordinate system, the corresponding point cloud q is obtained j , where j = 1, 2, ..., n, n is the number of point cloud data after screening; the multiple point clouds after registration and screening are fused to obtain the fused point cloud Q. According to the distribution characteristics of the fused point cloud, a hollow structure is presented from sparse to dense and then sparse from the outside to the inside, such as Figure 7 shown.

[0108] Q = {q aim ,q1,q2,…,q aim-1 ,q aim+1 ,…,q n}

[0109] Calculate the local density of the fused point cloud and filter it to retain the dense part of the point cloud as the preliminary model, specifically:

[0110] Based on the point cloud Q, the corresponding KD tree is constructed. l ,y l ,z l ), calculate its local density ρ based on the constructed KD tree l , set the density threshold φ, if ρ l >φ, then the point is retained and the filtered point cloud is obtained. The filtered point cloud is recorded as

[0111] S6. Point cloud boundary extraction

[0112] The fused and filtered point cloud already has the relevant features of the general model, so the point cloud boundary is extracted to generate an STL model that can be used for 3D printing, and the general model of the donor tooth in autologous tooth transplantation is obtained, such as Figure 8 shown.

[0113] The point cloud boundary is extracted by analyzing the neighborhood relationship of each point in the point cloud, calculating the curvature and normal vector of each point in the point cloud based on the neighborhood relationship, and extracting the outline of the point cloud by analyzing these features.

[0114] Specifically, for point clouds Any point in The neighborhood relationship is calculated by the K nearest neighbor algorithm. For each point in the neighborhood, the distance from all points to the nearest neighbor is calculated. The distance d between the planes formed j , find the minimum sum of the squares of the distances to find the normal vector and thus determine the tangent plane.

[0115]

[0116] Project all points in the neighborhood onto the tangent plane and use the angle rule to determine whether the point is a contour point. Select any point in the neighborhood as the starting point, and calculate the angle formed by each of the remaining points and these two points in a clockwise or counterclockwise direction, recorded as θ.

[0117] Ω=(θ1,θ2,…,θ n …,θ k )

[0118] Then every two adjacent points and points The angle formed is denoted as θ′, and the formed set is denoted as Ω′;

[0119] Ω′=(θ1′,θ2′,…θ′ n-1 …θ k ′); where θ′ n-1 =θ n -θ n-1 ;

[0120] Let the threshold of contour points be σ, when θ′ max >σ, it is considered that the point is the contour point;

[0121] Among them, θ′ max Represents the maximum value of Ω′.

[0122] S7, smoothing

[0123] The surface of the STL model is smoothed and scaled by coefficients of 0.98, 1.03, and 1.08, respectively, to obtain the following: Fig. 9 The large, medium, and small models shown are used to adapt to different customer groups. When surgery is required clinically, the radiographic images of the patient's donated teeth can be matched with the universal model, and then the appropriate universal model can be selected and used immediately when preparing the alveolar socket in the autologous tooth transplantation. This eliminates the need to prepare individual models, simplifies the diagnosis and treatment process, shortens the treatment cycle, and has good health economic characteristics.

[0124] like Fig.10 As shown, the present invention also discloses a universal model design system for a tooth in autologous tooth transplantation, comprising:

[0125] A data acquisition module, used for acquiring multiple wisdom tooth images as raw data;

[0126] The original data includes vertex information and normal information of the wisdom tooth;

[0127] Point cloud data extraction module, used to extract vertex information from raw data, remove outliers in the vertex information, and obtain point cloud data;

[0128] The data classification module is used to classify the point cloud data according to the growth position and root morphology of the wisdom teeth to obtain various types of data sets;

[0129] The registration module is used to register any two point cloud data in the same type of data set to obtain the reference coordinate system for multi-point cloud registration;

[0130] During the registration process, the wisdom tooth point cloud data that cannot be registered is eliminated to obtain the filtered point cloud data;

[0131] The fusion module is used to fuse the multiple registered and screened point cloud data to obtain a fused point cloud, calculate the local density of the fused point cloud and perform filtering, and retain the dense part of the point cloud as the preliminary model;

[0132] The boundary extraction module is used to extract the point cloud boundary of the preliminary model, thereby obtaining a universal model of the donor tooth in the autologous tooth transplantation;

[0133] The smoothing processing module is used to smooth the surface of the universal model of the donor tooth in the autologous tooth transplantation, and scale it proportionally with different coefficients to obtain the universal model of the donor tooth in the autologous tooth transplantation with different sizes.

[0134] The system can also continuously add wisdom tooth model data to make the universal model more accurate.

[0135] The method for designing a universal model of a tooth for autologous tooth transplantation of the present invention can be implemented in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention can be implemented in the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0136] If the method for designing a universal model of a tooth for autologous tooth transplantation of the present invention is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable storage media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be a computer-readable instruction, a data structure, a module of a program or other data. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals. Among them, the computer storage medium can be any available medium or data storage device that can be accessed by the computer, including but not limited to magnetic storage (such as floppy disks, hard disks, magnetic tapes, magneto-optical disks (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NANDFLASH), solid-state drive (SSD)), etc.

[0137] In an exemplary embodiment, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for designing a universal model of a tooth for autologous tooth transplantation when executing the computer program. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for designing a universal model of a donor tooth in autologous tooth transplantation, characterized in that: The following steps are involved: S1, obtaining multiple wisdom tooth images as raw data; S2, extracting vertex information from the original data, removing outliers in the vertex information, and obtaining point cloud data; S3, classifying the point cloud data obtained in S2 according to the growth position and root morphology of the wisdom tooth to obtain multiple types of data sets; S4, registering any two point cloud data in the same type of data set to obtain a reference coordinate system for multi-point cloud registration; During the registration process, the wisdom tooth point cloud data that cannot be registered is eliminated to obtain the filtered point cloud data; S5, fusing the multiple registered and screened point cloud data to obtain a fused point cloud, calculating the local density of the fused point cloud and filtering it, and retaining the dense part of the point cloud as a preliminary model; S6. Extracting the boundary of the point cloud of the preliminary model, thereby obtaining a universal model of the donor tooth in the autologous tooth transplantation; S7. Smoothing the surface of the obtained universal model of the donor tooth in the autologous tooth transplantation, scaling it proportionally with different coefficients, and obtaining universal models of the donor tooth in the autologous tooth transplantation of different sizes.

2. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 1, characterized in that: In S2, outliers in vertex information are removed according to the Laida criterion.

3. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 1, characterized in that: In S3, various types of datasets include upper single-root wisdom tooth dataset, upper multi-root wisdom tooth dataset, lower single-root wisdom tooth dataset, and lower multi-root wisdom tooth dataset.

4. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 1, characterized in that: In S4, any two point cloud data in the same type of data set are registered to obtain the reference coordinate system for multi-point cloud registration, specifically: By introducing scale transformation into the ICP algorithm for registration, the point cloud undergoes both rigid transformation and geometric scaling transformation during the registration process. After continuous iteration, the degree of fit of the point cloud approaches the local optimum.

5. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 4, characterized in that: The error function constructed in the ICP algorithm is: E=∑ i (R·s·p i +t-q i ) 2 Among them, R is the rotation transformation, t is the translation transformation, s is the scale transformation; E is the error value; p i and q i For any two point cloud data; After continuous iteration of R, t, s, when E→min, the point cloud after pi transformation Get as close to qi as possible to achieve registration; errcr=minE After all point clouds are registered, the root mean square error between the registered point clouds is calculated: N represents the number of point cloud data; i = 1, 2, ..., N; RMSE represents the root mean square error; According to the point cloud q corresponding to the minimum root mean square error aim The spatial coordinate system is the reference coordinate system for multi-point cloud registration, so that all point clouds can find the best transformation R, t, s in a unified reference coordinate system.

6. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 1, characterized in that: S5 is as follows: The multiple registered and screened point clouds are fused to obtain the fused point cloud Q. According to the distribution characteristics of the fused point cloud, a hollow structure is presented from sparse to dense and then sparse from the outside to the inside. Q={q aim ,q1,q2,…,q aim -1,q aim+1 ,…,q n } n is the number of point cloud data after screening; Calculate the local density of the fused point cloud and filter it to retain the dense part of the point cloud as the preliminary model, specifically: Construct a KD tree based on the point cloud Q, and for any point (x l ,y l , z l ), calculate its local density ρ based on the constructed KD tree l , let the density threshold be φ, if ρ l >φ, then the point is retained and the filtered point cloud is obtained. The filtered point cloud is recorded as 7. The method for designing a universal model of a donor tooth in autologous tooth transplantation according to claim 6, characterized in that: In S6, the point cloud boundary is extracted, specifically: For point clouds Any point in The neighborhood relationship is calculated by the K nearest neighbor algorithm. For each point in the neighborhood, the distance from all points to the nearest neighbor is calculated. The distance d between the planes formed j , find the minimum sum of the squares of the distances to find the normal vector and determine the tangent plane; Project all points in the neighborhood onto the tangent plane and use the angle rule to determine whether the point is a contour point. Select any point in the neighborhood as the starting point, and calculate the angle formed by each of the remaining points and the two points in a clockwise or counterclockwise direction, denoted as θ, and the formed set is denoted as Ω; Ω=(θ1,θ2,…,θ n …,i k ) Then every two adjacent points and points The angle formed is denoted as θ′, and the formed set is denoted as Ω′; Ω′=(θ1′,θ2′,…θ′ n-1 …i k ′); n-1 =θ n -θ n-1 ; Assume that the threshold of contour points is σ, when θ′ max >σ, it is considered that point is the contour point; Among them, θ′ max Represents the maximum value of Ω′.

8. A universal model design system for donor teeth in autologous tooth transplantation, characterized in that: include: A data acquisition module, used for acquiring multiple wisdom tooth images as raw data; The original data includes vertex information and normal information of the wisdom tooth; Point cloud data extraction module, used to extract vertex information from raw data, remove outliers in the vertex information, and obtain point cloud data; The data classification module is used to classify the point cloud data according to the growth position and root morphology of the wisdom teeth to obtain various types of data sets; The registration module is used to register any two point cloud data in the same type of data set to obtain the reference coordinate system for multi-point cloud registration; During the registration process, the wisdom tooth point cloud data that cannot be registered is eliminated to obtain the filtered point cloud data; The fusion module is used to fuse the multiple registered and screened point cloud data to obtain a fused point cloud, calculate the local density of the fused point cloud and perform filtering, and retain the dense part of the point cloud as the preliminary model; The boundary extraction module is used to extract the point cloud boundary of the preliminary model, thereby obtaining a universal model of the donor tooth in the autologous tooth transplantation; The smoothing processing module is used to smooth the surface of the universal model of the donor tooth in the autologous tooth transplantation, and scale it proportionally with different coefficients to obtain the universal model of the donor tooth in the autologous tooth transplantation with different sizes.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for designing a universal model of a donor tooth in autologous tooth transplantation as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for designing a universal model of a donor tooth in autologous tooth transplantation as claimed in any one of claims 1 to 7 are implemented.

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