A functional micro-pressure oral mucosa morphology prediction method and system

Abstract

The application discloses a functional micro-pressure oral mucosa shape prediction method and system, relates to the field of oral cavity three-dimensional scanning technology, and obtains static gingival alveolar mucosa data of a patient through three-dimensional scanning, obtains functional micro-pressure mucosa data through individual tray impression technology, and registers the two data; feature points are selected on the intraoral static mucosa data, the feature points are copied to the functional micro-pressure mucosa data, and a feature template is constructed; the feature template is trained according to the obtained feature point data, and an elastic deformation prediction model is constructed; the intraoral static mucosa data of a patient to be predicted is input into the elastic deformation prediction model, and feature point coordinates are obtained; based on the feature point coordinates, all points that need to be deformed, including the feature points, in the point cloud data are calculated, and the functional micro-pressure mucosa shape is predicted. The functional micro-pressure mucosa prediction method and system provided by the application can solve the technical problem that the oral cavity direct three-dimensional scanning cannot obtain the functional micro-pressure mucosa shape of the edentulous area of a patient with tooth loss or defect.

Description

Technical Field

[0001] This invention relates to the field of oral cavity three-dimensional scanning technology, and more specifically to a method and system for predicting the morphology of oral mucosa using functional micro-pressure. Background Technology

[0002] As people's living standards continue to improve, oral diseases are attracting increasing attention. In clinical oral diagnosis and treatment, dental impressions are an important source of information; almost every patient needs one or more impressions. By comparing multiple impressions before, during, and after treatment, doctors can understand the treatment's effectiveness and improve the accuracy of examination, diagnosis, and treatment. Dental technicians also need to determine the shape of the prosthesis based on the impressions.

[0003] However, in the field of removable prostheses, including complete dentures and removable partial dentures, there are still certain limitations. One important point is that during direct 3D scanning of the oral cavity, all the mucosa is in a static state without any pressure. However, for impressions of edentulous jaws or edentulous areas of patients, it is necessary to obtain the shape of the mucosa under certain pressure, that is, to imitate the state of the mucosa under pressure during functional chewing. The purpose is to reduce pressure pain when making dentures based on this impression and improve chewing efficiency. Direct 3D scanning technology obviously cannot obtain this shape.

[0004] Therefore, how to transform the static impression of the edentulous area obtained by oral 3D scanning technology into a pressure impression is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method for predicting the morphology of functional micro-pressure oral mucosa, which overcomes the limitation that direct three-dimensional scanning of the oral cavity cannot obtain the morphology of functional micro-pressure mucosa in edentulous areas, making it possible to directly fabricate dentures by obtaining the mucosal morphology through three-dimensional scanning.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for predicting the morphology of the oral mucosa under functional micropressure includes the following steps:

[0008] Step 1: Obtain the patient's intraoral static mucosal data through 3D scanning; obtain functional micro-pressure mucosal data through micro-impression, and register the intraoral static mucosal data with the functional micro-pressure mucosal data;

[0009] Step 2: Select feature points on the intraoral static mucosal data obtained by 3D scanning to obtain feature point data. At the same time, copy the feature points to the functional micro-pressure mucosal data based on the feature point data to construct a feature template.

[0010] Step 3: Based on the feature point data obtained in Step 2, train the feature template to construct a micro-pressure mucosa morphology elastic deformation prediction model;

[0011] Step 4: Input the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain the new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data;

[0012] Step 5: Based on the new coordinates of the obtained feature point data, calculate all points in the point cloud data that need to be deformed using the Laplacian deformation technique, thereby realizing the prediction of the functional micro-pressure mucosa morphology.

[0013] Preferably, step 1 is implemented according to the following steps:

[0014] Step 1.1: Use 3D scanning to acquire static oral mucosal data of the patient;

[0015] Step 1.2: Micro-pressure impressions of patients are taken using individual tray impression technology. After scanning with a dental model 3D scanner, the impression data is obtained. The impression data is then flipped in the software to form functional micro-pressure mucosal data.

[0016] Step 1.3: Register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in 3D reverse engineering software;

[0017] Step 1.4: Draw the boundary of the edentulous mucosa in the patient's intraoral static mucosal data, extract the boundary as a curve, and project it onto the functional micropressure mucosal data; delete the data outside the boundary line of the patient's intraoral static mucosal data and functional micropressure mucosal data.

[0018] Preferably, step 2 specifically includes: manually selecting feature points and extracting dental model features on the intraoral static mucosal data obtained by three-dimensional scanning to obtain feature point data, and copying the feature point data onto the functional micro-pressure mucosal data to construct a feature template.

[0019] Preferably, step 3 specifically includes: using manually selected feature point data from intraoral static mucosa data obtained by three-dimensional scanning as input data, using the three-dimensional coordinates of feature points manually selected and copied to functional micro-pressure mucosa data as prediction data, using a multi-layer neural network, dividing the input data into training and testing sets, verifying the training results, continuously optimizing the network, and constructing a micro-pressure mucosa morphological elastic deformation prediction model.

[0020] Preferably, step 5 specifically includes: based on the new coordinates of the obtained feature point data, there are control points for deformation in the intraoral static mucosal data obtained by 3D scanning. When all control points are moved to the new position, all deformable points in the point cloud data are calculated using the Laplace deformation technique. According to the average weight, the Cartesian coordinates of the triangular mesh are converted to Laplace coordinates, as shown in the following formula:

[0021]

[0022] Where, δ i Represented as the corresponding Laplace coordinates, v i Represents the Cartesian coordinates of the vertices of the triangular mesh. Representing point v i Cartesian coordinates minus all v i neighboring point v j The average of the Cartesian coordinates, where d i Indicates v i The number of adjacent points of v, N(i) represents the number of adjacent points of v. i The adjacent points; L is the Laplace matrix, expressed as L = ID. -1 A, where I is the identity matrix containing control point weights, D is the diagonal matrix, and A is the adjacency matrix;

[0023] Calculate the new coordinates of all points in the point cloud data that need to be deformed, by... The calculation is performed, where V' represents the new coordinates of all points, δ represents the coordinates of all Laplace points, and W represents the coordinates of all control points, thereby enabling the prediction of functional micro-pressure mucosal morphology.

[0024] To achieve the above objectives, the present invention also provides a functional micro-pressure oral mucosal morphology prediction system, comprising:

[0025] The data collection and registration module is used to acquire intraoral static mucosal data of patients through 3D scanning; functional micro-pressure mucosal data is obtained through micro-imprinting; and the intraoral static mucosal data and functional micro-pressure mucosal data are registered.

[0026] The template construction module is used to select feature points on the intraoral static mucosal data obtained by 3D scanning, obtain feature point data, and copy the feature points to the functional micro-pressure mucosal data to construct a feature template.

[0027] The model building module is used to train the feature template based on the feature point data and build a micro-pressure mucosa morphology elastic deformation prediction model.

[0028] The prediction simulation module inputs the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data.

[0029] The computation module is used to calculate all deformable points in the point cloud data using the Laplace deformation technique based on the new coordinates of the obtained feature point data, thereby realizing the prediction of the functional micro-pressure mucosa morphology.

[0030] Preferably, the data collection and registration module further includes:

[0031] The scanning module is used to acquire three-dimensional scanning data of the patient's intraoral static mucosa.

[0032] The data acquisition module is used to take a patient’s micro-pressure impression using individual tray impression technology, obtain the impression data by scanning with a dental model 3D scanner, and flip the impression data in the software to form functional micro-pressure mucosal data.

[0033] The registration module is used to register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in three-dimensional reverse engineering software;

[0034] The boundary extraction module is used to draw the boundary of the edentulous mucosa in the static mucosal data of the patient's mouth, extract the boundary as a curve, and project it onto the functional micro-pressure mucosal data; and delete the data outside the boundary line of the static mucosal data and the functional micro-pressure mucosal data of the patient's mouth.

[0035] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for predicting the morphology of functional micro-pressure oral mucosa. By acquiring static mucosal data and functional micro-pressure mucosal data from the patient's mouth, feature points are selected from the static mucosal data, and then copied to the functional micro-pressure mucosal data. A multilayer neural network is used to establish a connection between the static mucosal data containing feature point data and the functional micro-pressure mucosal data containing feature point data, thereby establishing a micro-pressure mucosal morphology elastic deformation prediction model. Based on the model output data, all points in the point cloud are calculated using Laplace deformation technology to achieve the prediction of functional micro-pressure mucosal morphology. This overcomes the limitation of existing technologies where 3D scanning cannot directly obtain the morphology of functional micro-pressure mucosa in edentulous areas, making it possible to directly fabricate dentures using mucosal morphology obtained from 3D scanning. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0037] Figure 1 The attached figure is a flowchart of a method for predicting the morphology of a functional micro-pressure mucosa;

[0038] Figure 2 The attached diagram illustrates the selection of characteristic points in patients with missing upper and lower jaw teeth.

[0039] Figure 3 The attached diagram illustrates the selection of characteristic points in patients with missing upper and lower jaw teeth.

[0040] Figure 4 The attached figure is a schematic diagram of a micro-pressure mucosa morphology elastic deformation prediction model. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] This invention discloses a method for predicting the morphology of the oral mucosa under functional micropressure, such as... Figure 1 As shown, it includes the following steps:

[0043] Step 1: Obtain the patient's intraoral static mucosal data through 3D scanning; obtain functional micro-pressure mucosal data through micro-impression, and register the intraoral static mucosal data with the functional micro-pressure mucosal data;

[0044] Step 2: Select feature points on the intraoral static mucosal data obtained by 3D scanning to obtain feature point data. At the same time, copy the feature points to the functional micro-pressure mucosal data based on the feature point data to construct a feature template.

[0045] Step 3: Based on the feature point data obtained in Step 2, train the feature template to construct a micro-pressure mucosa morphology elastic deformation prediction model;

[0046] Step 4: Input the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain the new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data;

[0047] Step 5: Based on the new coordinates of the obtained feature point data, calculate all deformable points in the point cloud data using the Laplace deformation technique, thereby realizing the prediction of the functional micro-pressure mucosa morphology.

[0048] In one specific embodiment, step 1 specifically includes:

[0049] Step 1.1: Use 3D scanning to acquire static oral mucosal data of the patient;

[0050] Step 1.2: Micro-pressure impressions of patients are taken using individual tray impression technology. After scanning with a dental model 3D scanner, the impression data is obtained. The impression data is then flipped in the software to form functional micro-pressure mucosal data.

[0051] Step 1.3: Register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in 3D reverse engineering software;

[0052] Step 1.4: Draw the boundary of the edentulous mucosa in the patient's intraoral static mucosal data, extract the boundary as a curve, and project it onto the functional micropressure mucosal data; delete the data outside the boundary line of the patient's intraoral static mucosal data and functional micropressure mucosal data.

[0053] Furthermore, the three-dimensional scanning methods in step 1.1 include: intraoral three-dimensional scanning, three-dimensional scanning of dental models without pressure impressions, or tomographic three-dimensional scanning such as CT / MRI.

[0054] Furthermore, in step 1.2, CT / MRI three-dimensional tomographic scan data of the gingiva after compression deformation following the patient's wearing of dentures can also be used as flip data in the software.

[0055] Furthermore, in step 1.3, if there are points in the area that do not deform under pressure during registration in the 3D reverse engineering software (including but not limited to: implants for edentulous patients, remaining teeth for patients with missing teeth, dental attachments rigidly bonded or fixed to remaining teeth, etc.), these points are used as common areas for best-fit registration.

[0056] If the patient is edentulous or has implant restorations, the intraoral static mucosal data and functional micro-pressure mucosal data are registered in three-dimensional reverse engineering software through overall data ICP or by registering with the alveolar bone as a common area.

[0057] In this process, the impression data is flipped in the software to obtain the model data made from the impression data, and the functional micro-pressure mucosa data is obtained based on the model data.

[0058] In one specific embodiment, such as Figure 2 , Figure 3As shown, step 2 specifically includes manually selecting feature points and extracting dental model features from the intraoral static mucosal data D1. 3-4 feature points are selected for the unilateral alveolar ridge crest, and 4-5 feature points are selected for the unilateral secondary support area (buccal / lingual). The maxillary buffer zone (incisive papillae, palatal folds, and mid-palatal suture) can be omitted. The selected feature points are X1, X2, X3...Xn. The number and position of these points can be adjusted later based on the training results. The selected feature points are then copied to the functional micro-pressure mucosal data D2, resulting in Y1, Y2, Y3...Yq, thus constructing a feature template.

[0059] Among them, the dental model features include, but are not limited to: feature point coordinates (x1, y1, z1), mucosal texture data around the feature point, mucosal color data around the feature point, mucosal curvature around the feature point, and mucosal smoothness around the feature point.

[0060] In one specific embodiment, the relationship between the location of feature points and the model is learned, and the automatic generation method of feature points is studied. In the later stage of training, the automatic generation of feature points can be learned.

[0061] In one specific embodiment, such as Figure 4 As shown, step 3 specifically includes using manually selected feature points X1, X2, X3...Xn from the intraoral static mucosal data acquired by 3D scanning as input data for the neural network, representing the coordinates of the feature points in the pre-prediction model. Y1, Y2, Y3...Yq represent the predicted data, which are the displacements of the corresponding feature points in the pre-prediction to post-prediction model, also known as elastic deformation. ij w jk These are the weights of the neural network. Let w be the weights. ij For example, i is the number of the neuron in the previous layer, and j is the number of the neuron in the next layer, using a multi-layer neural network.

[0062] Furthermore, 80% of the input data was used as the training set to train the network, and 20% of the input data was used as the test set to verify the training results. The network was continuously optimized to construct a micro-pressure mucosal morphology elastic deformation prediction model. The fully trained neural network is a nonlinear mucosal elastic deformation prediction model with the ability to predict the elastic deformation of the mucosal morphology in dentate areas.

[0063] In one specific embodiment, step 5 specifically includes the elastic deformation prediction model predicting the elastic deformation amount of the feature points in the preoperative model, i.e., Y1, Y2, Y3...Yq, and calculating the new coordinates of the feature points. Xi in the intraoral static mucosal data are control points used for deformation. When all control points move to the new positions, all deformable points in the point cloud data are calculated using the Laplace deformation technique. Based on the average weight, the Cartesian coordinates of the triangular mesh are converted to Laplace coordinates, as shown in the following formula:

[0064]

[0065] Where, δ i Represented as the corresponding Laplace coordinates, v i Represents the Cartesian coordinates of the vertices of the triangular mesh. Representing point v i Cartesian coordinates minus all v i neighboring point v j The average of the Cartesian coordinates, where d i Indicates v i The number of adjacent points of v, N(i) represents the number of adjacent points of v. i The adjacent points; L is the Laplace matrix, expressed as L = ID. -1 A, where I is the identity matrix containing control point weights, D is the diagonal matrix, and A is the adjacency matrix;

[0066] Calculate the new coordinates of all points in the point cloud data that need to be deformed, by... The calculation is performed, where V' represents the new coordinates of all points, δ represents the coordinates of all Laplace points, and W represents the coordinates of all control points, thereby enabling the prediction of functional micro-pressure mucosal morphology.

[0067] In one specific embodiment, a functional micro-pressure oral mucosal morphology prediction system is provided, comprising:

[0068] The data collection and registration module is used to acquire intraoral static mucosal data of patients through 3D scanning; functional micro-pressure mucosal data is obtained through micro-imprinting; and the intraoral static mucosal data and functional micro-pressure mucosal data are registered.

[0069] The template construction module is used to select feature points on the intraoral static mucosal data obtained by 3D scanning, obtain feature point data, and copy the feature points to the functional micro-pressure mucosal data to construct a feature template.

[0070] The model building module is used to train the feature template based on the feature point data and build a micro-pressure mucosa morphology elastic deformation prediction model.

[0071] The prediction simulation module inputs the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data.

[0072] The computation module is used to calculate the morphology of functional micro-pressure mucosa by using the Laplace deformation technique on all points in the point cloud data that need to be deformed based on the new coordinates of the obtained feature point data.

[0073] In one specific embodiment, the data collection and registration module further includes:

[0074] The scanning module is used to acquire three-dimensional scanning data of the patient's intraoral static mucosa.

[0075] The data acquisition module is used to take a patient’s micro-pressure impression using individual tray impression technology, obtain the impression data by scanning with a dental model 3D scanner, and flip the impression data in the software to form functional micro-pressure mucosal data.

[0076] The registration module is used to register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in three-dimensional reverse engineering software;

[0077] The boundary extraction module is used to draw the boundary of the edentulous mucosa in the static mucosal data of the patient's mouth, extract the boundary as a curve, and project it onto the functional micro-pressure mucosal data; and delete the data outside the boundary line of the static mucosal data and the functional micro-pressure mucosal data of the patient's mouth.

[0078] This invention provides a method and system for predicting the morphology of functional micro-pressure oral mucosa, overcoming the limitation of existing technologies where 3D scanning cannot obtain the morphology of functional micro-pressure mucosa in edentulous areas. This makes it possible to directly fabricate dentures using mucosal morphology obtained through 3D scanning.

[0079] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0080] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the morphology of the oral mucosa under functional micro-pressure, characterized in that, include: Step 1: Obtain static oral mucosal data of the patient using 3D scanning; Functional micro-pressure mucosal data are obtained by micro-pressure impression, and intraoral static mucosal data are registered with functional micro-pressure mucosal data; Step 2: Select feature points on the intraoral static mucosal data obtained by 3D scanning to obtain feature point data. At the same time, copy the feature points to the functional micro-pressure mucosal data based on the feature point data to construct a feature template. Step 3: Based on the feature point data obtained in Step 2, train the feature template to construct a micro-pressure mucosa morphology elastic deformation prediction model; Step 4: Input the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain the new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data; Step 5: Based on the new coordinates of the obtained feature point data, the three-dimensional point coordinates of the intraoral static mucosa data are calculated using the Laplace deformation technique, thereby realizing the prediction of the functional micro-pressure mucosa morphology.

2. The method for predicting the morphology of the oral mucosa under functional micro-pressure according to claim 1, characterized in that, Step 1 specifically includes: Step 1.1: Use 3D scanning to acquire static mucosal data of the patient's oral cavity; Step 1.2: Micro-pressure impressions of patients are taken using individual tray impression technology. After scanning with a dental model 3D scanner, the impression data is obtained. The impression data is then flipped in the software to form functional micro-pressure mucosal data. Step 1.3: Register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in 3D reverse engineering software; Step 1.4: Draw the boundary of the edentulous mucosa in the patient's intraoral static mucosal data, extract the boundary as a curve, and project it onto the functional micropressure mucosal data; delete the data outside the boundary line of the patient's intraoral static mucosal data and functional micropressure mucosal data.

3. The method for predicting the morphology of the oral mucosa under functional micro-pressure according to claim 1, characterized in that, Step 2 specifically includes manually selecting feature points on the intraoral static mucosal data obtained from 3D scanning and extracting dental model features to obtain feature point data. The feature point data is then copied onto the functional micro-pressure mucosal data to construct a feature template.

4. The method for predicting the morphology of the oral mucosa under functional micro-pressure according to claim 3, characterized in that, Step 3 specifically includes using manually selected feature point data from intraoral static mucosa data obtained by 3D scanning as input data, using the 3D coordinates of feature points manually selected and copied to functional micro-pressure mucosa data as prediction data, using a multi-layer neural network, dividing the input data into training and testing sets, verifying the training results, continuously optimizing the network, and constructing a micro-pressure mucosa morphological elastic deformation prediction model.

5. A method for predicting the morphology of the oral mucosa using functional micro-pressure according to claim 1, characterized in that, Step 5 specifically includes the new coordinates based on the obtained feature point data. The intraoral static mucosal data obtained from the 3D scan contains control points for deformation. When all control points are moved to new positions, the 3D point coordinates of the intraoral static mucosal data are calculated using the Laplace deformation technique. Based on the average weight, the Cartesian coordinates of the triangular mesh are converted to Laplace coordinates, as shown in the following formula: Where, δ i Represented as the corresponding Laplace coordinates, v i Represents the Cartesian coordinates of the vertices of the triangular mesh. Indicates v i Cartesian coordinates minus all v i neighboring point v j The average of the Cartesian coordinates, where d i Indicates v i The number of adjacent points; L is the Laplace matrix, expressed as L = ID. -1 A, where I is the identity matrix containing control point weights, D is the diagonal matrix, and A is the adjacency matrix; Calculate the new coordinates of all points in the point cloud data that need to be deformed, by... The calculation is performed, where V' represents the new coordinates of all points, δ represents the coordinates of all Laplace points, and W represents the coordinates of all control points, thereby enabling the prediction of functional micro-pressure mucosal morphology.

6. A functional micro-pressure oral mucosal morphology prediction system, characterized in that, include: The data collection and registration module is used to acquire static oral mucosal data of patients through 3D scanning. Functional micro-pressure mucosal data are obtained by micro-pressure impression, and intraoral static mucosal data are registered with functional micro-pressure mucosal data; The template construction module is used to select feature points on the intraoral static mucosal data obtained by 3D scanning, obtain feature point data, and copy the feature points to the functional micro-pressure mucosal data to construct a feature template. The model building module is used to train the feature template based on the feature point data and build a micro-pressure mucosa morphology elastic deformation prediction model. The prediction simulation module inputs the intraoral static mucosal data of the patient to be predicted into the micro-pressure mucosal morphology elastic deformation prediction model to obtain new coordinates of the corresponding feature point data of the intraoral static mucosal data based on the functional micro-pressure mucosal morphology data. The calculation module is used to calculate the three-dimensional coordinates of intraoral static mucosa data using Laplace deformation technology based on the new coordinates of the obtained feature point data, thereby realizing the prediction of functional micro-pressure mucosa morphology.

7. A functional micro-pressure oral mucosal morphology prediction system according to claim 6, characterized in that, The data collection and registration module also includes: The scanning module is used for three-dimensional scanning to acquire static oral mucosal data of patients; The data acquisition module is used to take a patient’s micro-pressure impression using individual tray impression technology, obtain the impression data by scanning with a dental model 3D scanner, and flip the impression data in the software to form functional micro-pressure mucosal data. The registration module is used to register the patient's intraoral static mucosal data and functional micro-pressure mucosal data in three-dimensional reverse engineering software; The boundary extraction module is used to draw the boundary of the edentulous mucosa in the static mucosal data of the patient's mouth, extract the boundary as a curve, and project it onto the functional micro-pressure mucosal data; and delete the data outside the boundary line of the static mucosal data and the functional micro-pressure mucosal data of the patient's mouth.

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