A method, device and medium for measuring the three-dimensional morphology of the temporomandibular joint

Abstract

This application discloses a method, device, and medium for measuring the three-dimensional morphology of the temporomandibular joint (TMJ), relating to the field of image processing. The method comprises: inputting target TMJ CBCT data into a mandibular three-dimensional segmentation model to obtain a mandibular three-dimensional segmentation result; determining an image of the TMJ center region based on the mandibular three-dimensional segmentation result and the target TMJ CBCT data; inputting the TMJ center region image into a condyle and fossa two-dimensional segmentation model to obtain two-dimensional segmentation results of the condyle and fossa regions; and calculating condyle morphological information, fossa morphological information, and position information of the condyle in the fossa based on the two-dimensional segmentation results of the condyle and fossa regions; wherein the mandibular three-dimensional segmentation model and the condyle and fossa two-dimensional segmentation model are both obtained by training a deep learning network. This application can achieve automatic and accurate measurement of the three-dimensional morphology and position of the condyle and fossa of the TMJ.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device, and medium for measuring the three-dimensional morphology of a temporomandibular joint. Background Art

[0002] The temporomandibular joint (TMJ), composed of the mandibular condyle, the articular surface of the temporal bone, the articular disc, the joint capsule, and the ligaments, is one of the most complex joints in the body. Temporomandibular joint disorders are common and frequently occurring diseases of the oral and maxillofacial region. Changes in the morphology and position of the TMJ may increase the prevalence of TMJ disorders.

[0003] Currently, quantitative measurement based on cone-beam computed tomography (CBCT) imaging has become the most reliable method for assessing the morphology and position of the temporomandibular joint (TMJ) condyle and glenoid fossa. As a three-dimensional imaging technique, CBCT effectively avoids image overlap and offers advantages such as high spatial resolution, low radiation dose, and the ability to reconstruct images at arbitrary angles and in multiple planes. However, three-dimensional quantitative assessment of the condyle and glenoid fossa based on CBCT images often relies on manual measurement, which presents the following challenges: 1) Most physicians lack or have not received systematic, standardized training in oral and maxillofacial medical imaging. Furthermore, the scarcity of temporomandibular joint specialists complicates TMJ assessment, and measurement accuracy is significantly affected by physician experience. 2) Measurement methods and indicators are not standardized, and accurate and reproducible measurement tools are lacking. 3) Quantitative assessment of the condyle and glenoid fossa is more challenging when condylar development is incomplete or when osteoarthritis, such as bone wear and osteophyte sclerosis, is present. 4) Data volume is limited, measurement is cumbersome and time-consuming, and consistency and reproducibility are relatively poor. Summary of the Invention

[0004] The purpose of this application is to provide a method, device and medium for measuring the three-dimensional morphology of the temporomandibular joint, which can realize automatic and accurate measurement of the three-dimensional morphology and position of the condyle and fossa of the temporomandibular joint.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for measuring the three-dimensional morphology of the temporomandibular joint, comprising:

[0007] Acquire CBCT data of the target temporomandibular joint;

[0008] Inputting the target temporomandibular joint CBCT data into a mandibular three-dimensional segmentation model to obtain a mandibular three-dimensional segmentation result;

[0009] Determining a central position area image of the temporomandibular joint based on the mandibular three-dimensional segmentation result and the target temporomandibular joint CBCT data;

[0010] Inputting the image of the central position area of ​​the temporomandibular joint into the condyle and glenoid fossa two-dimensional segmentation model to obtain two-dimensional segmentation results of the condyle region and glenoid fossa region;

[0011] Calculating three-dimensional morphological information of the temporomandibular joint based on the two-dimensional segmentation results of the condyle region and the fossa region; the three-dimensional morphological information of the temporomandibular joint includes condyle morphological information, fossa morphological information, and position information of the condyle in the fossa;

[0012] Among them, the three-dimensional segmentation model of the mandible is obtained by training the first preset deep learning network using the temporomandibular joint CBCT sample data set; the two-dimensional segmentation model of the condyle and fossa is obtained by training the second preset deep learning network using the temporomandibular joint center position area sample data set.

[0013] In a second aspect, the present application provides a computer device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement a method for measuring the three-dimensional morphology of the temporomandibular joint.

[0014] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements a method for measuring the three-dimensional morphology of the temporomandibular joint.

[0015] According to the specific embodiments provided by this application, this application has the following technical effects: This application provides a method, device and medium for measuring the three-dimensional morphology of the temporomandibular joint, inputting the target temporomandibular joint CBCT data into the three-dimensional segmentation model of the mandible to obtain the three-dimensional segmentation result of the mandible, obtaining the image of the central position area of ​​the temporomandibular joint from the segmentation result and inputting it into the two-dimensional segmentation model of the condyle and fossa to obtain the two-dimensional segmentation results of the condyle area and the fossa area, and then calculating the condyle morphological information, fossa morphological information and the position information of the condyle in the fossa based on the two-dimensional segmentation results of the condyle area and the fossa area. The above-mentioned whole process does not require the manual judgment of the doctor, is completed automatically, and will not be affected by the doctor's experience. Moreover, the process is a unified process with good repeatability. In the above process, the three-dimensional segmentation model of the mandible and the two-dimensional segmentation model of the condyle and fossa were used to process the target temporomandibular joint CBCT data. These two models are deep learning networks based on artificial intelligence technology and have been trained in advance to quickly output accurate results. Therefore, this application can quickly, automatically and accurately measure the condyle morphological information, fossa morphological information and the position information of the condyle in the fossa, that is, the three-dimensional morphology and position information of the condyle and fossa of the temporomandibular joint. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 This is a diagram of the application environment of the method for measuring the three-dimensional morphology of the temporomandibular joint in one embodiment of the present application.

[0018] Figure 2 Schematic diagram of the flow of a method for measuring the three-dimensional morphology of the temporomandibular joint in one embodiment of the present application.

[0019] Figure 3 Schematic diagram of the measurement of condylar length and condylar width.

[0020] Figure 4 Schematic diagram for measuring condylar head height.

[0021] Figure 5 Schematic diagram of condylar height measurement.

[0022] Figure 6 Schematic diagram of the measurement of glenoid fossa width, glenoid fossa depth and articular tuberosity inclination.

[0023] Figure 7Schematic diagram of the measurement of the anterior joint space, supra-articular space and posterior joint space.

[0024] Figure 8 Schematic diagram of the measurement of intra-articular space, mid-articular space and extra-articular space.

[0025] Figure 9 、 Figure 10 and Figure 11 They are respectively an original image, the corresponding segmented image, and a schematic diagram of the 3D segmented image in the temporomandibular joint CBCT sample dataset to be used.

[0026] Figure 12 and Figure 13 They are respectively a coronal original image and a corresponding coronal two-dimensional segmentation image in the sample data set of the central position area of ​​the temporomandibular joint to be used.

[0027] Figure 14 and Figure 15 They are respectively a sagittal original image and a corresponding sagittal two-dimensional segmentation image in the sample data set of the center position area of ​​the temporomandibular joint to be used.

[0028] Figure 16 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0029] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0030] This application combines artificial intelligence technology to achieve automatic and precise measurement of the morphology and position of the condyle and fossa of the temporomandibular joint, and can serve as a powerful auxiliary tool to help dentists diagnose diseases, formulate treatment plans and evaluate treatment effects.

[0031] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0032] The method for measuring the three-dimensional morphology of the temporomandibular joint provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send the target temporomandibular joint CBCT data to the server 104. After receiving it, the server 104 inputs it into the mandibular three-dimensional segmentation model to obtain the mandibular three-dimensional segmentation result, and determines the temporomandibular joint center position area image based on the mandibular three-dimensional segmentation result and the target temporomandibular joint CBCT data. The temporomandibular joint center position area image is input into the condyle and fossa two-dimensional segmentation model to obtain the condyle area and fossa area two-dimensional segmentation results. Finally, based on the condyle area and fossa area two-dimensional segmentation results, the condyle morphology information, fossa morphology information and the position information of the condyle in the fossa are calculated. In addition, the mandibular three-dimensional segmentation model and the condyle and fossa two-dimensional segmentation model are trained and saved in the server. The server 104 can feed back the obtained condyle morphology information, glenoid fossa morphology information, and condyle position information in the glenoid fossa to the terminal 102. In addition, in some embodiments, the method for measuring the 3D morphology of the temporomandibular joint can also be implemented by the server 104 or the terminal 102 alone.

[0033] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, and IoT devices. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0034] In an exemplary embodiment, Figure 2 As shown, a method for measuring the three-dimensional morphology of the temporomandibular joint is provided. The method is executed by a computer device, specifically a computer device such as a terminal or a server, or a terminal and a server. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used for explanation, including the following steps 201 to 205.

[0035] Step 201: Acquire CBCT data of the target temporomandibular joint.

[0036] In step 202, the target temporomandibular joint (TMJ) CBCT data is input into a 3D mandibular segmentation model to obtain a 3D mandibular segmentation result. The 3D mandibular segmentation model is obtained by training a first preset deep learning network using a temporomandibular joint (TMJ) CBCT sample dataset. Processing the 3D mandibular segmentation model allows for rapid location of the center of the TMJ, eliminating the influence of other redundant skull structures.

[0037] Step 203 determines a central region image of the temporomandibular joint based on the mandibular 3D segmentation result and the target temporomandibular joint CBCT data. Specifically, based on the mandibular 3D segmentation result, all corresponding central region images of the temporomandibular joint are cropped from the target temporomandibular joint CBCT data, i.e., the central region image of the temporomandibular joint. The central region image of the temporomandibular joint specifically includes three slice images: a maximum axial image of the condyle, a corrected oblique sagittal image of the condyle center, and a corrected oblique coronal image of the condyle center.

[0038] In step 204, the image of the central region of the temporomandibular joint is input into a two-dimensional segmentation model of the condyle and fossa to obtain a two-dimensional segmentation result of the condyle and fossa regions; wherein the two-dimensional segmentation model of the condyle and fossa is obtained by training a second preset deep learning network using a sample dataset of the central region of the temporomandibular joint. Through processing of the two-dimensional segmentation model of the condyle and fossa, a mask of the condyle and fossa can be obtained for each slice image of the central region of the temporomandibular joint, and the condyle and fossa regions are displayed on the corresponding masks.

[0039] Step 205, based on the two-dimensional segmentation results of the condyle area and the fossa area, calculate the three-dimensional morphological information of the temporomandibular joint; the three-dimensional morphological information of the temporomandibular joint includes condyle morphological information, fossa morphological information and position information of the condyle in the fossa; wherein, the condyle morphological information includes condyle length, condyle width, condyle head height and condyle height; the fossa morphological information includes fossa width, fossa depth and articular tubercle inclination; the position information of the condyle in the fossa includes the anterior articular space, supra-articular space, posterior articular space, intra-articular space, mid-articular space and extra-articular space.

[0040] In a specific application example, in step 205, based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region, the condyle morphology information is calculated, including the following steps (11)-(14).

[0041] (11) Corresponding to the maximum axis of the condyle, the innermost point of the condyle and the outermost point of the condyle are connected to obtain the condyle length.

[0042] (12) Corresponding to the maximum axis of the condyle, the midpoint of the line connecting the innermost point of the condyle and the outermost point of the condyle and perpendicular to the inner and outer diameters of the condyle are used to obtain the anterior point of the condyle and the posterior point of the condyle, and the anterior point of the condyle and the posterior point of the condyle are connected to obtain the condyle width.

[0043] (13) Corresponding to the oblique sagittal position of the center of the corrected condyle, a tangent is drawn through the lowest point of the mandibular sigmoid notch, and a vertical line is drawn from the apex of the condyle to the tangent to obtain the condyle height.

[0044] (14) Corresponding to the oblique coronal position of the center of the corrected condyle, the innermost point of the condyle and the outermost point of the condyle are connected to obtain an inner-outer line, and a vertical line is drawn from the apex of the condyle to the inner-outer line to obtain the height of the condylar head.

[0045] In a specific application example, in step 205, based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region, the glenoid fossa morphological information is calculated, including the following steps (21)-(23).

[0046] (21) Connect the lowest point of the articular tubercle with the lowest point of the posterior wall of the glenoid fossa to obtain the glenoid fossa width.

[0047] (22) Draw a perpendicular line through the apex of the glenoid fossa to the line connecting the lowest point of the articular tubercle and the lowest point of the posterior wall of the glenoid fossa to obtain the depth of the glenoid fossa.

[0048] (23) A first connecting line is made through the apex of the articular fossa and the lowest point of the articular tubercle, and the angle between the first connecting line and the orbitoauricular plane is used as the inclination of the articular tubercle.

[0049] In a specific application example, in step 205, based on the two-dimensional segmentation results of the condyle region and the fossa region, the position information of the condyle in the fossa is calculated, including the following steps (31) to (32).

[0050] (31) Corresponding to the oblique sagittal position of the center of the corrected condyle, the anterior point of the condyle is connected to the anterior point of the fossa to obtain the anterior joint space; the apex of the condyle is connected to the apex of the fossa to obtain the supra-articular space; and the posterior point of the condyle is connected to the posterior point of the fossa to obtain the posterior joint space.

[0051] (32) In the coronal oblique position corresponding to the center of the corrected condyle, the point inside the articular fossa is connected with the point inside the condyle top to obtain the intra-articular space; the middle point of the articular fossa is connected with the middle point of the condyle top to obtain the mid-articular space; the point outside the articular fossa is connected with the point outside the condyle top to obtain the extra-articular space.

[0052] The above describes the process of measuring the 3D morphology of the temporomandibular joint (TMJ). In practical applications, prior to measurement, a 3D segmentation model of the mandible and a 2D segmentation model of the condyle and glenoid fossa must be constructed and prepared. This process requires preparing a CBCT sample dataset of the TMJ, a sample dataset of the TMJ center region, a first preset deep learning network, and a second preset deep learning network.

[0053] Any temporomandibular joint CBCT sample data in the temporomandibular joint CBCT sample data set includes historical temporomandibular joint CBCT data and corresponding mandibular three-dimensional segmentation result labels. Any temporomandibular joint center position area sample data in the temporomandibular joint center position area sample data set includes historical temporomandibular joint center position area images and corresponding condyle segmentation labels and glenoid fossa area segmentation labels.

[0054] The acquisition process for historical TMJ CBCT data involves maintaining the orbitoauricular plane parallel to the ground plane during the TMJ CBCT scan. After the CBCT scan is complete, the original DICOM data is imported into SmartVPro software. Each patient's TMJ CBCT scan serves as a historical data set, and a total of 150 or more cases can be collected. This data set can be adjusted as needed by relevant technicians. After obtaining multiple historical data sets, the data sets are screened according to the following inclusion and exclusion criteria to obtain the final set of historical TMJ CBCT data.

[0055] Inclusion criteria: ① clear images without motion artifacts or sclerosis artifacts; ② normal bone structure of the condyle and glenoid fossa.

[0056] Exclusion criteria: ① Incomplete or unclear images of the condyle and fossa; ② History of temporomandibular joint tumors, trauma, ankylosis, or systemic diseases; ③ History of temporomandibular joint trauma and surgery; ④ Systemic diseases involving the condyle, such as rheumatoid arthritis and systemic lupus erythematosus.

[0057] On this basis, for each historical TMJ CBCT data set, the medial-lateral long axis of the condyle was determined at the plane of the maximum condylar cross-section in the axial plane. This plane was designated as the maximum axial plane of the condyle. The sagittal plane was adjusted perpendicular to the long axis of the condyle, and the center plane of this sagittal plane was designated as the corrected condylar center oblique sagittal plane. The coronal plane was adjusted parallel to the long axis of the condyle, and the center plane of this coronal plane was designated as the corrected condylar center oblique coronal plane. The slice images of these three planes constituted the historical TMJ center position regional image, namely, the historical TMJ center position regional image, which includes the maximum axial plane of the condyle, the corrected condylar center oblique sagittal plane, and the corrected condylar center oblique coronal plane.

[0058] The 3D mandibular segmentation result labels were obtained using Mimics Research 19.0. The segmentation results included the mandible and teeth. Labelme, a 3D medical image processing software, was used for 2D segmentation of the condyle and glenoid fossa. Because accurate assessment of the 3D structure and morphology of the temporomandibular joint relies on precise segmentation of the mandible, condyle, and glenoid fossa, physicians were trained in software usage and methods for 3D mandibular segmentation and 2D condyle and glenoid fossa segmentation prior to segmentation to ensure accuracy.

[0059] After the two-dimensional segmentation results of the condyle and glenoid regions are known, Figure 3 As shown, the condyle length and condyle width were measured, where the condyle length is the distance between the innermost point and the outermost point of the condyle at the maximum axial plane of the condyle, corresponding to Figure 3 The condylar width is the distance between the anterior and posterior points of the condyle, perpendicular to the condyle's inner and outer diameters, at the maximum axial plane of the condyle. Figure 3 in the CD.

[0060] Ke Ru Figure 4 As shown, the condylar head height is measured, where the condylar head height is: on the oblique coronal image of the corrected condyle center, the vertical distance from the condyle apex to the innermost point of the condyle and the outermost point of the condyle, corresponding to Figure 4 HI in.

[0061] Ke Ru Figure 5 As shown, the condylar height is measured, where the condylar height is: the vertical distance from the condylar vertex to the tangent of the lowest point of the mandibular sigmoid notch on the oblique sagittal image of the corrected condylar center, corresponding to Figure 5 JK in.

[0062] Ke Ru Figure 6 As shown, the glenoid fossa width, glenoid fossa depth and articular tubercle inclination were measured, wherein the glenoid fossa width is the distance between the lowest point of the articular tubercle and the lowest point of the posterior wall of the glenoid fossa, corresponding to Figure 6 NM in the glenoid fossa; the depth of the glenoid fossa is the shortest distance between the glenoid fossa vertex perpendicular to the line connecting the lowest point of the articular tubercle and the lowest point of the posterior wall of the glenoid fossa, corresponding to Figure 6 The SO in the articular tubercle is the angle between the first connecting line between the apex of the glenoid fossa and the lowest point of the articular tubercle and the orbitoauricular plane, corresponding to Figure 6 The angle ∠α in .

[0063] Ke Ru Figure 7 As shown, the anterior joint space, supra-articular space and posterior joint space are measured, wherein the anterior joint space corresponds to Figure 7 Q1-Q2, denoted as Q; the joint gap corresponds to Figure 7 S1-S2, denoted as S; the posterior joint space corresponds to Figure 7 In a specific application, the position of the condyle can be evaluated according to the method of Pullinger and Hollender: linear percentage = (PQ) / (P + Q) × 100%. For example, if the linear percentage is less than -12%, it indicates that the condyle is posterior; if the linear percentage is greater than +12%, it indicates that the condyle is anterior; and if the linear percentage is between -12% and +12%, it indicates that the condyle is centered.

[0064] Ke Ru Figure 8 As shown, the intra-articular space, the mid-articular space and the extra-articular space are measured, wherein the intra-articular space corresponds to Figure 8 V1-V2 in the joint is denoted as V; the gap in the joint corresponds to Figure 8 T1-T2 in the joint is denoted as T; the extra-articular space corresponds to Figure 8 U1-U2 in is denoted as U.

[0065] In addition, according to Figure 7 and Figure 8 The process of determining the condyle apex, glenoid fossa apex, etc. can be known, specifically: Figure 7 In the middle, draw a horizontal line Line1 parallel to the orbitoauricular plane and tangent to the upper edge of the articular fossa at the apex S2 of the articular fossa. Draw a tangent to the anterior edge of the condyle through the apex S2 and tangent to the anterior point Q1 of the condyle. Draw a tangent to the posterior edge of the condyle and tangent to the posterior point P1 of the condyle. A perpendicular line Line2 through the apex S2 of the articular fossa intersects with the upper edge of the condyle at the apex S1 of the condyle. Draw a perpendicular line to the tangent to the anterior edge of the condyle through the anterior point Q1 of the condyle and intersects with the anterior edge of the articular fossa at the anterior point Q2. Draw a perpendicular line to the tangent to the posterior edge of the condyle through the posterior point P1 of the condyle and intersects with the posterior edge of the articular fossa at the posterior point P2 of the articular fossa. Figure 8 In the figure, the line connecting the innermost point G of the condyle and the outermost point F of the condyle is recorded as Line3, and a perpendicular line is drawn through the midpoint R of Line3, recorded as Line4. Line4 intersects with the superior edge of the condyle at the middle point T1 of the condyle top and intersects with the articular fossa at the middle point T2 of the articular fossa. The medial angle bisector of Line3 and Line4 intersects with the inner edge of the condyle at the inner 1 / 4 point V1 of the condyle top and intersects with the articular fossa at the inner point V2 of the articular fossa. The lateral angle bisector of Line3 and Line4 intersects with the outer edge of the condyle at the outer 1 / 4 point U1 of the condyle top and intersects with the articular fossa at the outer point U2.

[0066] In a specific application, the first preset deep learning network is a UNet model. Correspondingly, the training process of the mandibular three-dimensional segmentation model includes the following steps (41)-(42).

[0067] (41) Performing data expansion processing and data enhancement processing on the temporomandibular joint CBCT sample dataset to obtain a temporomandibular joint CBCT sample dataset to be used; wherein, the expansion method can adopt random cropping, random rotation, horizontal flipping, vertical flipping and contrast adjustment, and the data enhancement method can adopt an online enhancement mode to improve the generalization of the model.

[0068] (42) The temporomandibular joint CBCT sample dataset to be used is input into the UNet model for 3D segmentation, and the mandibular 3D segmentation model is obtained through iterative optimization training. Among them, a lightweight mandibular segmentation model detection model, namely the UNet model, is built based on the Pytorch deep learning framework; secondly, the temporomandibular joint CBCT sample dataset to be used is divided into a training set, a validation set and a test set according to a ratio of 3:1:1, and then the training set is input into the UNet3D positioning model to output the predicted position heat map, so as to perform model training to achieve rough positioning and find the location of the temporomandibular joint. The above training process uses a focal loss function to ensure sensitivity to position, which is defined as:

[0069] .

[0070] Among them, Loss is the value of the loss function, is the number of samples in the temporomandibular joint CBCT sample dataset to be used, To predict pixel points, is 0.5, To mark the pixel points, is 0.5, x, y, and z are coordinate values.

[0071] In a practical application, the image sizes of the training set, validation set, and test set can be unified to 72×72×72. Figure 9 、 Figure 10 and Figure 11 As shown in the figure, they are an original image, the corresponding segmented image, and the 3D segmented image in the temporomandibular joint CBCT sample data set to be used. Figure 11 After viewing the image shown, the maximum axial plane of the condyle can be located and the maximum axial-coronal plane can be output.

[0072] In another specific application, the second preset deep learning network is a nnUNet model. Correspondingly, the training process of the condyle and glenoid fossa two-dimensional segmentation model includes the following steps (51)-(52).

[0073] (51) Data enhancement processing is performed on the sample dataset of the central position area of ​​the temporomandibular joint to obtain a sample dataset of the central position area of ​​the temporomandibular joint to be used. In practical applications, the process of obtaining the sample dataset of the central position area of ​​the temporomandibular joint is as follows: after the condyle and fossa are roughly positioned using the coarse positioning model of the condyle and fossa, this process corresponds to steps (41) to (42) above, the image of the corresponding central area of ​​the temporomandibular joint is cropped, and then the condyle and fossa are annotated as a dataset of two-dimensional segmentation of the condyle and fossa, thereby obtaining the sample dataset of the central position area of ​​the temporomandibular joint. In addition, the data enhancement processing in this step includes random rotation, random scaling, noise addition, etc.

[0074] (52) The sample data set of the central position area of ​​the temporomandibular joint to be used is input into the nnUNet model for 2D segmentation training to obtain a two-dimensional segmentation model of the condyle and glenoid fossa. In an application example, the data set is divided into a training set, a validation set and a test set with a ratio of 3:1:1. The nnUNet model is selected to train the 2D segmentation model. The training process uses cross entropy as the loss function to achieve two-dimensional segmentation of the condyle and glenoid fossa on 2D slices, obtain the mask of the condyle and glenoid fossa on each slice, and display the two-dimensional segmentation results of the condyle area and glenoid fossa area on the corresponding mask. Figure 12 and Figure 13 As shown in , they are a coronal original image and the corresponding coronal two-dimensional segmentation image in the sample data set of the center position area of ​​the temporomandibular joint to be used, as shown in Figure 14 and Figure 15 As shown in FIG, there is a sagittal original image and a corresponding sagittal two-dimensional segmentation image in the sample data set of the center position area of ​​the temporomandibular joint to be used.

[0075] After training the condyle and glenoid fossa 2D segmentation model, the segmentation results can be compared with the gold standard to calculate the accuracy of the segmentation and measurement indicators. If the requirements are not met, it is necessary to re-select the temporomandibular joint CBCT sample dataset and the temporomandibular joint center region sample dataset for training.

[0076] In summary, this application aims to establish a CBCT image automatic measurement system for the three-dimensional morphology and position of the condyle and fossa of the temporomandibular joint based on deep learning artificial intelligence technology, thereby realizing three-dimensional automatic and accurate measurement of the temporomandibular joint. The establishment of an automatic measurement model for the three-dimensional morphology and position of the condyle and fossa of the temporomandibular joint has significant beneficial effects in improving measurement accuracy, achieving rapid measurement, supporting personalized treatment, assisting diagnosis and scientific research, and promoting interdisciplinary cooperation. This will provide strong technical support and guarantee for the diagnosis and treatment of temporomandibular joint-related diseases.

[0077] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 16 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O) and a communication interface. The processor, memory and input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for measuring the three-dimensional morphology of the temporomandibular joint is implemented.

[0078] Those skilled in the art will understand that Figure 16 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present application and does not constitute a limitation on the computer device to which the solution of the present application is applied. A specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement. In an exemplary embodiment, a computer device is provided, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of each of the above-mentioned method embodiments.

[0079] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0080] In an exemplary embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0081] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0082] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments. In particular, any reference to memory, database, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0083] The databases involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processors involved in the various embodiments provided herein may include, but are not limited to, general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic units, data processing logic units based on quantum computing, and the like.

[0084] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0085] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core concept of this application. At the same time, for those skilled in the art, based on the concept of this application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for measuring the three-dimensional morphology of the temporomandibular joint, characterized in that: Methods include: Acquire CBCT data of the target temporomandibular joint; Inputting the target temporomandibular joint CBCT data into a mandibular three-dimensional segmentation model to obtain a mandibular three-dimensional segmentation result; Determine an image of the center position area of ​​the temporomandibular joint based on the mandibular three-dimensional segmentation result and the target temporomandibular joint CBCT data; the image of the center position area of ​​the temporomandibular joint specifically includes three slice images, namely, an image of the maximum axial position of the condyle, an image of the center of the corrected condyle oblique sagittal position, and an image of the center of the corrected condyle oblique coronal position; Inputting the image of the central position area of ​​the temporomandibular joint into the condyle and glenoid fossa two-dimensional segmentation model to obtain two-dimensional segmentation results of the condyle region and glenoid fossa region; Calculating the three-dimensional morphological information of the temporomandibular joint based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region; The three-dimensional morphological information of the temporomandibular joint includes the morphological information of the condyle, the morphological information of the articular fossa, and the position information of the condyle in the articular fossa; The three-dimensional mandibular segmentation model is obtained by training a first preset deep learning network using a temporomandibular joint CBCT sample dataset; the two-dimensional condyle and fossa segmentation model is obtained by training a second preset deep learning network using a temporomandibular joint center area sample dataset; the first preset deep learning network is a UNet model; and the second preset deep learning network is a nnUNet model. During the training of the mandibular 3D segmentation model, a focal loss function was used to ensure sensitivity to position, which is defined as: ； Among them, Loss is the value of the loss function, is the number of samples in the temporomandibular joint CBCT sample dataset to be used, To predict pixel points, is 0.5, To mark the pixel points, is 0.5, x, y, and z are coordinate values.

2. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 1, characterized in that: The condyle morphology information includes condyle length, condyle width, condyle head height and condyle height; the fossa morphology information includes fossa width, fossa depth and articular tubercle inclination; the condyle position information in the fossa includes anterior articular space, supra-articular space, posterior articular space, intra-articular space, mid-articular space and extra-articular space; In the measurement of the anterior joint space, supra-articular space and posterior joint space, the anterior joint space is recorded as Q; the supra-articular space is recorded as S; The posterior joint space was recorded as P, and the position of the condyle was evaluated according to the method of Pullinger and Hollender: linear percentage = (PQ) / (P+Q)×100%. If the linear percentage is less than -12%, it indicates that the condyle is in a posterior position; if the linear percentage is greater than +12%, it indicates that the condyle is in an anterior position; if the linear percentage is between -12% and +12%, it indicates that the condyle is in a neutral position.

3. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 2, characterized in that: Any temporomandibular joint CBCT sample data in the temporomandibular joint CBCT sample data set includes historical temporomandibular joint CBCT data and corresponding mandibular three-dimensional segmentation result labels; The training process of the mandibular 3D segmentation model includes: performing data expansion processing and data enhancement processing on the temporomandibular joint CBCT sample dataset to obtain a stand-by temporomandibular joint CBCT sample dataset; The temporomandibular joint CBCT sample dataset to be used is input into the UNet model for 3D segmentation, and a three-dimensional segmentation model of the mandible is obtained through iterative optimization training.

4. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 3, characterized in that: Any temporomandibular joint center position area sample data in the temporomandibular joint center position area sample data set includes a historical temporomandibular joint center position area image and a corresponding condyle area segmentation label and fossa area segmentation label; The training process of the condyle and glenoid fossa two-dimensional segmentation model includes: performing data enhancement processing on the temporomandibular joint center position region sample dataset to obtain a stand-by temporomandibular joint center position region sample dataset; The sample data set of the center position area of ​​the temporomandibular joint to be used is input into the nnUNet model for 2D segmentation training to obtain a two-dimensional segmentation model of the condyle and glenoid fossa.

5. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 4, characterized in that: The historical images of the central position area of ​​the temporomandibular joint include images of the maximum axial position of the condyle, the oblique sagittal position of the center of the corrected condyle, and the oblique coronal position of the center of the corrected condyle; Based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region, the condyle morphology information is calculated, including: Corresponding to the maximum axis of the condyle, connecting the innermost point of the condyle and the outermost point of the condyle to obtain the condyle length; Corresponding to the maximum axis of the condyle, a line passing through the midpoint of the line connecting the innermost point of the condyle and the outermost point of the condyle and perpendicular to the inner and outer diameters of the condyle is obtained to obtain the anterior point of the condyle and the posterior point of the condyle, and the anterior point of the condyle is connected with the posterior point of the condyle to obtain the condyle width; Corresponding to the oblique sagittal position of the center of the corrected condyle, a tangent line is drawn through the lowest point of the mandibular sigmoid notch, and a perpendicular line is drawn from the apex of the condyle to the tangent line to obtain the condyle height; Corresponding to the corrected condyle center oblique coronal position, the innermost point of the condyle and the outermost point of the condyle are connected to obtain an inner-outer line, and a vertical line is drawn from the condyle apex to the inner-outer line to obtain the condyle head height.

6. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 5, characterized in that: Based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region, calculating glenoid fossa morphological information includes: The lowest point of the articular tuberosity was connected with the lowest point of the posterior wall of the glenoid fossa to obtain the glenoid fossa width; A perpendicular line is drawn through the apex of the glenoid fossa to the line connecting the lowest point of the articular tubercle and the lowest point of the posterior wall of the glenoid fossa to obtain the glenoid fossa depth; A first connecting line is made through the apex of the articular fossa and the lowest point of the articular tubercle, and the angle between the first connecting line and the orbitoauricular plane is used as the inclination of the articular tubercle.

7. The method for measuring the three-dimensional morphology of the temporomandibular joint according to claim 6, characterized in that: Based on the two-dimensional segmentation results of the condyle region and the glenoid fossa region, calculating the position information of the condyle in the glenoid fossa includes: Corresponding to the corrected condyle center oblique sagittal position, the anterior point of the condyle is connected with the anterior point of the fossa to obtain the anterior joint space; the apex of the condyle is connected with the apex of the fossa to obtain the supra-articular space; the posterior point of the condyle is connected with the posterior point of the fossa to obtain the posterior joint space; Corresponding to the corrected condylar center oblique coronal position, connect the inner point of the articular fossa and the inner 1 / 4 point of the condylar top to obtain the intra-articular space; connect the middle point of the articular fossa and the middle point of the condylar top to obtain the mid-articular space; connect the outer point of the articular fossa and the outer 1 / 4 point of the condylar top to obtain the extra-articular space.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for measuring the three-dimensional morphology of the temporomandibular joint according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for measuring the three-dimensional morphology of the temporomandibular joint according to any one of claims 1 to 7 is implemented.

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