Oral cavity structure information generation method and system, storage medium and oral cavity instrument

Abstract

The invention discloses an oral cavity structure information generation method and system, a storage medium and an oral cavity instrument.The oral cavity structure information generation method comprises the steps that first data information is obtained, and the first data information is used for establishing an oral cavity three-dimensional model; and when the first data information does not meet the condition of establishing the oral cavity three-dimensional model, obtaining oral cavity image data corresponding to the first data information, and supplementing missing oral cavity features in the oral cavity three-dimensional model according to the oral cavity image data to obtain the oral cavity structure information. According to the oral cavity structure information generation method provided by the invention, logic can be simplified, data integrity and accuracy can be improved, and more intuitive and accurate structure information can be obtained.

Description

Technical Field

[0001] This application relates to the technical field of oral digital models, and particularly to a method, system, storage medium, and oral device for generating oral structure information. Background Art

[0002] In today's society, oral health has gradually become a key concern for people. Among them, oral health not only includes oral hygiene achieved by controlling plaque, removing dirt and food residues, but also includes oral health maintenance represented by improving occlusion and orofacial muscle function. For the latter, on the one hand, it will affect dental hygiene, chewing function, the development and function of the temporomandibular joint, and is prone to malocclusion and snoring, which is a double harm to human health and aesthetics; on the other hand, the adjustment of occlusion and orofacial muscle function depends on the high-precision analysis of oral structure information, especially the characteristics of intraoral tissue structures. When there are large errors, problems such as poor treatment effects and user experience will occur. It can be seen that in order to effectively maintain oral health, the analysis and extraction of oral structure information are crucial.

[0003] Common technical solutions in the prior art are to make an oral silicone model of the patient and use it to further make an oral plaster model or an oral three-dimensional model, or to scan the oral cavity with a scanner to obtain an oral scan model of the patient, and then analyze the oral structure information. However, in the process of extracting the above models, on the one hand, too much attention is paid to the shape and position of the dental crown, resulting in the lack of other features in the oral cavity, and it is difficult to provide comprehensive data reference for comprehensive diagnosis; on the other hand, considering the operational errors and patient comfort, etc., it is very likely that the regional features are incompletely collected, and it is necessary to remake or collect the model for local features. Also, because the steps of making an oral silicone model or an oral scan model are cumbersome, and simply using intraoral images cannot meet the accuracy requirements. Based on this, it is necessary to provide a method for generating oral structure information to solve the above problems. Summary of the Invention

[0004] One of the purposes of this application is to provide a method for generating oral structure information to solve the problem that the oral feature analysis steps in the prior art cannot take into account both the comprehensiveness of feature data, high-precision requirements, and the advantages of patient comfort and simplicity of steps, and cannot achieve the automation of judgment logic and information generation.

[0005] One of the purposes of this application is to provide a system for generating oral structure information.

[0006] One of the purposes of this application is to provide a storage medium.

[0007] One of the purposes of this application is to provide an oral device.

[0008] To achieve one of the above objects, an embodiment of the present application provides an oral structure information generation method, including: obtaining first data information for establishing an oral three-dimensional model; when the first data information does not meet the conditions for establishing the oral three-dimensional model, obtaining intraoral image data corresponding to the first data information, and supplementing the missing oral features in the oral three-dimensional model according to the intraoral image data to obtain the oral structure information.

[0009] Optionally, the conditions for establishing the oral three-dimensional model include at least one of the following: the position between the tooth position and the model boundary in the first data information satisfies a first preset position relationship for confirming the data information of the part corresponding to the model boundary in the first data information; the position between the corresponding model boundaries in the first data information satisfies a second preset position relationship for confirming the data information of the part corresponding to the model boundary in the first data information.

[0010] Optionally, the situation that the first data information does not meet the conditions for establishing the oral three-dimensional model includes at least one of the following: determining the corresponding model boundary in the preset direction of the tooth position, and the distance between the tooth position and the corresponding model boundary is less than the preset criterion value; determining the corresponding upper boundary and lower boundary in the preset direction of the tooth position, and the distance between the upper boundary and the lower boundary is less than the preset criterion value.

[0011] Optionally, it includes at least one of the following: obtaining the convex hull contour of the tooth position and determining the corresponding model boundary according to the center of the convex hull contour; obtaining the gingival margin information of the tooth position and determining the corresponding model boundary according to the midpoint of the gingival margin.

[0012] Optionally, obtaining the intraoral tissue area of interest in the first data information

[0013] obtaining the feature data corresponding to the intraoral tissue area;

[0014] when the feature data does not meet the conditions for establishing the oral three-dimensional model, obtaining the intraoral image data including at least the intraoral tissue area.

[0015] Optionally, it includes at least one of the following: the designated intraoral tissue area includes the vestibular sulcus, and the corresponding feature data includes the vestibular sulcus height data, which is determined according to the model boundary points of the incisor tooth position in the extending direction of the long axis of the tooth body; the designated intraoral tissue area includes the vestibular sulcus, and the corresponding feature data includes the dental arch width data, which is determined according to the model boundary points of the molar tooth position in the extending direction of the long axis of the tooth body; the designated intraoral tissue area includes the root eminence, and the corresponding feature data includes the dental arch width data, which is determined according to the model boundary points of the molar tooth position in the extending direction of the long axis of the tooth body; the designated intraoral tissue area includes the labial frenum, and the corresponding feature data includes the labial frenum width data, which is determined according to the model boundary points of the central incisor tooth position in the extending direction of the long axis of the tooth body.

[0016] Optionally, determine the oral feature data of the region of interest in the intraoral image data according to the neural network model. The region of interest corresponds to the region lacking oral features in the oral three-dimensional model. Determine the missing oral features in the oral three-dimensional model according to the oral feature data of the region of interest.

[0017] Optionally, register the intraoral image data and the oral three-dimensional model according to the size data of the teeth at the mutually corresponding tooth positions in the intraoral image and the oral three-dimensional model.

[0018] To achieve one of the above purposes, an embodiment of the present application provides an oral structure information generation method, including: obtaining first data information and the corresponding intraoral image data, where the first data information is used to establish an oral three-dimensional model, and supplementing the missing oral features in the oral three-dimensional model data according to the intraoral image data to obtain the oral structure information.

[0019] To achieve one of the above purposes, an embodiment of the present application provides an oral structure information generation system, including a processor, a memory, and a communication bus. The processor and the memory complete mutual communication through the communication bus; the memory is used to store application programs; the processor is used to implement the steps of any oral structure information generation method when executing the application programs stored on the memory.

[0020] To achieve one of the above purposes, an embodiment of the present application provides a storage medium, on which application programs are stored. When the application programs are executed, the steps of any oral structure information generation method are implemented.

[0021] To achieve one of the above purposes, an embodiment of the present application provides an oral instrument, which is constructed according to oral structure information, and the oral structure information is generated according to any oral structure information generation method.

[0022] Optionally, the oral appliance is used for training orofacial muscle function and / or for treating mouth breathing.

[0023] Compared with the prior art, in the oral structure information generation method provided by the present application, when the obtained data information does not meet the conditions for establishing an oral three-dimensional model, the features are complemented according to the corresponding intraoral image data, so as to obtain complete oral structure information, thus reducing the requirements for the extraction of the oral three-dimensional model. On the basis of being sufficient to obtain high-precision and complete and comprehensive oral structure features, the steps and operation logics are simplified. Brief Description of the Drawings

[0024] Figure 1 It is a schematic structural diagram of an oral three-dimensional model when the oral appliance is not installed in an embodiment of the present application.

[0025] Figure 2 It is a schematic structural diagram of an oral three-dimensional model when the oral appliance is installed in an embodiment of the present application.

[0026] Figure 3 It is a schematic structural diagram of an oral structure information generation system in an embodiment of the present application.

[0027] Figure 4 It is a schematic diagram of the steps of an oral structure information generation method in an embodiment of the present application.

[0028] Figure 5 It is a schematic diagram of the steps of an oral structure information generation method in another embodiment of the present application.

[0029] Figure 6 It is a first schematic structural diagram of another oral three-dimensional model in a front view in another embodiment of the present application.

[0030] Figure 7 It is a schematic diagram of the steps of the first embodiment of an oral structure information generation method in another embodiment of the present application.

[0031] Figure 8 It is a partial schematic diagram of the steps of a specific example of the first embodiment of an oral structure information generation method in another embodiment of the present application.

[0032] Figure 9 It is a partial schematic diagram of the steps of another specific example of the first embodiment of an oral structure information generation method in another embodiment of the present application.

[0033] Figure 10 It is in another embodiment of the present application when implementing the first embodiment of the oral structure information generation method corresponding to Figure 6 a partial enlarged schematic diagram of the Z1 part.

[0034] Figure 11It is a partial step schematic diagram of the second embodiment of the oral structure information generation method in another embodiment of the present application.

[0035] Figure 12 It is a schematic diagram of the structure of another oral three-dimensional model in a lateral view when executing the second embodiment of the oral structure information generation method in another embodiment of the present application.

[0036] Figure 13 It is a schematic diagram of the structure of another oral three-dimensional model in a front view when executing the second embodiment of the oral structure information generation method in another embodiment of the present application.

[0037] Figure 14 It is a partial step schematic diagram of the third embodiment of the oral structure information generation method in another embodiment of the present application.

[0038] Figure 15 It is a schematic diagram of the structure of another oral three-dimensional model in a front view when executing the third embodiment of the oral structure information generation method in another embodiment of the present application.

[0039] Figure 16 It is a partial step schematic diagram of the fourth embodiment of the oral structure information generation method in another embodiment of the present application.

[0040] Figure 17 It is a schematic diagram of the state of the distribution of distance training data on the three-dimensional training model data when executing the fourth embodiment of the oral structure information generation method in another embodiment of the present application.

[0041] Figure 18 It is a partial step schematic diagram of the fifth embodiment of the oral structure information generation method in another embodiment of the present application.

[0042] Figure 19 It is a step schematic diagram of the oral structure information generation method in still another embodiment of the present application.

[0043] Figure 20 It is a partial step schematic diagram of the first embodiment of the oral structure information generation method in still another embodiment of the present application.

[0044] Figure 21 It is a second schematic diagram of the structure of another oral three-dimensional model in a front view when executing the first embodiment of the oral structure information generation method in still another embodiment of the present application.

[0045] Figure 22 It is a partial step schematic diagram of the second embodiment of the oral structure information generation method in still another embodiment of the present application.

[0046] Figure 23It is a partial step schematic diagram of the first embodiment of the oral structure information generation method in an embodiment of the present application.

[0047] Figure 24 It is a partial step schematic diagram of the second embodiment of the oral structure information generation method in an embodiment of the present application.

[0048] Figure 25 It is corresponding to when executing the second embodiment of the oral structure information generation method in an embodiment of the present application Figure 21 A partial enlarged schematic diagram of the Z2 part in and the Z3 part in the corresponding intraoral image.

[0049] Figure 26 It is a partial step schematic diagram of the first specific example of the second embodiment of the oral structure information generation method in an embodiment of the present application.

[0050] Figure 27 It is a partial step schematic diagram of the second specific example of the second embodiment of the oral structure information generation method in an embodiment of the present application.

[0051] Figure 28 It is a partial step schematic diagram of the third embodiment of the oral structure information generation method in an embodiment of the present application.

[0052] Figure 29 It is a step schematic diagram of the oral structure information generation method in another embodiment of the present application. Specific embodiments

[0053] The present application will be described in detail below in conjunction with the specific embodiments shown in the drawings. However, these embodiments do not limit the present application, and any structural, method, or functional transformation made by those of ordinary skill in the art based on these embodiments is included in the protection scope of the present application.

[0054] It should be noted that the term "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article, or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article, or device. In addition, the terms "first", "second", "third", "fourth", "fifth", etc. are only used for descriptive purposes and cannot be construed as indicating or implying relative importance.

[0055] The main idea of this application is that after obtaining the oral three-dimensional model or its data, in order to avoid the incompleteness of the content of the oral three-dimensional model, such as the lack of features corresponding to the target tooth position or other regions, the intraoral image or its data corresponding to the oral three-dimensional model is called. On the one hand, the correspondence between the intraoral image data and the oral three-dimensional model data is established, and on the other hand, the information required in the intraoral image data is extracted, and then the oral three-dimensional model data is complemented or expanded, reducing the actual operation requirements for the extraction of the oral three-dimensional model, and outputting more intuitive model data for medical workers to analyze and diagnose. Preferably, before calling the intraoral image data, a step of judging the integrity of the oral three-dimensional model can also be included, so as to streamline the process of generating oral structure information and enable the step of "generating oral structure information according to the oral three-dimensional model" to be automatically realized.

[0056] The following will further elaborate on various embodiments, technical principles and corresponding technical effects of this application with reference to the accompanying drawings. In one embodiment of this application, an oral instrument is provided. Figure 1 Shows the installation and cooperation environment of the oral instrument or the oral instrument model. Figure 1 The content shown in it can be interpreted as the internal environment of the oral cavity of an actual human body, or an extracted oral solid model or a modeled oral three-dimensional model. Taking Figure 1 The structure shown as the oral three-dimensional model 100 as an example, the left figure shows the rendered three-dimensional structure, and the right figure shows the contour structure corresponding to at least part of the structure of the oral three-dimensional model 100.

[0057] In a specific example of the present application, the oral three-dimensional model 100 specifically includes teeth 11, labiobuccal mucosa 12, vestibular sulcus 13, root eminence 14, and labial frenum 15. Among them, the vestibular sulcus 13, also known as the labiobuccal gingival sulcus, can be interpreted as the upper and lower boundaries of the oral cavity. The vestibular sulcus 13 is overall horseshoe-shaped and is a groove-like tissue structure formed by the transition of the labiobuccal mucosa 12 to the alveolar mucosa. Specifically, when the human head is in an upright state, the tooth positions in the oral three-dimensional model 100 are arranged in the conventional order. At this time, according to the FDI (Fédération Dentaire Internationale) tooth position notation, the tooth No. 11 is located in the upper left of the tooth No. 31, and the tooth No. 41 is located in the lower left of the tooth No. 21. Based on this, the vestibular sulcus 13 can be defined to include the upper vestibular sulcus 131 and the lower vestibular sulcus 132. Among them, the vestibular sulcus 13 that is farther from the teeth No. 41 and No. 31 relative to the teeth No. 11 and No. 21 is the upper vestibular sulcus 131, and the vestibular sulcus 13 that is farther from the teeth No. 11 and No. 21 relative to the teeth No. 41 and No. 31 is the lower vestibular sulcus 132. Of course, when observing the oral three-dimensional model 100 from different perspectives or setting the oral three-dimensional model 100 in different positions and postures, the definitions of up, down, the upper vestibular sulcus 131, and the lower vestibular sulcus 132 can be adjusted correspondingly. This is understood by those skilled in the art and will not be elaborated here.

[0058] As Figure 1 and Figure 2 shown, the oral instrument 200 provided by the present application can be a physical oral instrument or a corresponding three-dimensional model or physical model. The oral instrument 200 is specifically customized and constructed according to the oral structure information, so that when the oral instrument 200 is matched or installed with the oral three-dimensional model 100 or the corresponding actual human oral environment, it can fit as closely as possible to the oral tissues such as at least one of the above-mentioned teeth 11, labiobuccal mucosa 12, vestibular sulcus 13, root eminence 14, and labial frenum 15, and improve the comfort or matching degree on the premise of realizing the functions of the oral instrument 200 itself.

[0059] The oral appliance 200 can be specifically configured into various types. For example, the oral appliance 200 can be a dental malocclusion appliance or a retainer, or it can be an appliance for training orofacial muscle function (also known as orofacial myofunctional therapy, OMT) and / or for treating mouth breathing. Corresponding to different types of the oral appliance 200, the oral structure information relied on in its construction process may also vary. In the first case, the oral structure information includes the morphological information of all tissues inside the oral cavity, specifically, it can include the morphological characteristics of the dental crowns of the dentition, the morphological distribution of the root eminences 14, and the morphological distribution of the vestibular sulci 13 simultaneously; in the second case, when the oral appliance 200 is a dental malocclusion appliance or a retainer, the oral structure information includes at least the morphological characteristics of the dental crowns of part of the dentition, and preferably includes the morphological distribution of the root eminences 14; in the third case, when the oral appliance 200 is an orofacial muscle barrier, the oral structure information includes at least the morphological characteristics of part of the vestibular sulci, and preferably includes the morphological characteristics of the dental crowns of the dentition.

[0060] Regardless of which of the above application scenarios the oral appliance 200 belongs to, the dimension design of at least one dimension on the oral appliance 200 should have a higher order of magnitude compared to the oral three-dimensional model 100. Thus, there can be a certain clearance distance between the oral appliance 200 and at least the root eminences 14, preventing excessive squeezing of the gums and causing damage to the oral model or a decrease in wearing comfort. Based on this, in one embodiment, the oral three-dimensional model 100 includes a right distal root eminence 141 and a left distal root eminence 142, and the oral appliance 200 includes a right end 21 corresponding to the right distal root eminence 141 and a left end 22 corresponding to the left distal root eminence 142. In the embodiment where the oral appliance 200 is configured for training orofacial muscle function and / or for treating mouth breathing, the right end 21 and the left end 22 can specifically be the ends of the buccal screen on the side away from the lip bumper or the ends of the buccal screen on the side away from the breathing holes.

[0061] Among them, the right end 21 can be defined as the end of the oral appliance 200 located on the side of the right distal root eminence 141 away from the soft palate, and the distance by which the right end 21 is away from the right distal root eminence 141 relative to the soft palate can be defined as the "certain clearance distance"; the left end 22 can be defined as the end of the oral appliance 200 located on the side of the left distal root eminence 142 away from the soft palate, and the distance by which the left end 22 is away from the left distal root eminence 142 relative to the soft palate can be defined as the "certain clearance distance".

[0062] The "certain yielding distance" is freely selected according to the specific type or functional role of the oral instrument 200. For example, when the oral instrument 200 is configured as a dental malocclusion corrector or retainer, the distance between the right end 21 and the left end 22 can be less than or equal to the distance between the right distal root eminence 141 and the left distal root eminence 142, so as to constrain the teeth in the corresponding tooth positions to produce corresponding displacements or maintain their original positions. Another example is when the oral instrument 200 is configured to train orofacial muscle functions and / or treat mouth breathing, or configured as other devices for forming a barrier in the mouth. The distance between the right end 21 and the left end 22 can be greater than the distance between the right distal root eminence 141 and the left distal root eminence 142. Preferably, the difference between the distance between the right end 21 and the left end 22 and the distance between the right root eminence 141 and the left distal root eminence 142 is greater than or equal to 3 mm, so as not to overly interfere with soft tissues such as the gingiva at the root eminence 142, thereby affecting the wearing experience or causing wear of the oral model.

[0063] The left distal root eminence 142 and the right distal root eminence 141 are the root eminences of the tooth positions that are the farthest from the dental midline in terms of orientation. For adults, they usually refer to the root eminences of the maxillary second molar or the mandibular second molar. For children, they usually refer to the root eminences of the maxillary second deciduous molar or the mandibular second deciduous molar. The root eminence 14 at any of the above tooth positions can be interpreted as the oral tissue that wraps the tooth root and protrudes in the direction away from the soft palate relative to the labial surface of the tooth crown. Specifically, it can be the gingival part located outside the dental root canal and the alveolar bone part wrapped by the gingival part.

[0064] Furthermore, when the oral instrument 200 is installed or cooperates with the oral three-dimensional model 100 or the actual oral environment of the human body, the upper end near the maxillary side can be fitted to the upper vestibular sulcus 131, and the lower end near the mandibular side can be fitted to the lower vestibular sulcus 132. That is, the distance between the upper end and the lower end of the oral instrument 200 can be equal to the distance between the upper vestibular sulcus 131 and the lower vestibular sulcus 132.

[0065] Regarding the above size relationship, considering that when extracting the corresponding three-dimensional oral model 100 of the actual oral environment of the human body, the distance between the upper vestibular sulcus 131 and the lower vestibular sulcus 132 may be greater than the distance between the two vestibular sulci in the normal living state of the human body due to stretching. Therefore, the above "equal" relationship can also be "slightly less". Of course, in order to improve the training effect of the oro-facial muscles and / or the treatment effect of mouth breathing, the above "equal" relationship can also be "slightly greater". Preferably, the cross-sectional shape and the extended distribution curve of the upper end of the oral appliance 200 can also conform to the distribution curve and tissue morphology of the upper vestibular sulcus 131, and the cross-sectional shape and the extended distribution curve of the lower end of the oral appliance 200 can also conform to the distribution curve and tissue morphology of the lower vestibular sulcus 132. Thus, the entire oral appliance 200 is also configured in a horseshoe shape.

[0066] Under the overall shape configuration scheme of the horseshoe shape, in order to avoid the labial frenum 15 in the mouth and prevent the oral appliance 200 from pressing on it, the middle parts of the upper end and the lower end of the oral appliance 200 can be correspondingly provided with avoidance parts that are recessed towards the geometric center of the oral appliance 200. At the same time, considering that the morphology of the labial frenum 15 may vary in different oral models and the actual oral environment of the human body, the width of the avoidance part extending along the length direction of the oral appliance 200 should be at least greater than or equal to the width of the labial frenum 15 on the three-dimensional oral model 100 to prevent unnecessary restriction of the soft tissue of the labial frenum 15 and cause pain to the wearer.

[0067] The above description of the characteristics of the oral appliance 200 can, on the one hand, be used as a limitation on the morphological characteristics of the oral appliance 200 itself to achieve the corresponding technical effects mentioned above; on the other hand, in one embodiment, the oral appliance 200 is configured to be constructed based on oral structure information, and the oral structure information is generated according to an oral structure information generation method. Based on this, the above description of the oral appliance 200 can all be interpreted as the beneficial effects of the oral structure information or the oral structure information generation method. In other words, when performing the steps of the oral structure information generation method provided in this application, an oral structure information can be generated so that the corresponding produced oral appliance has any of the above characteristics and technical solutions.

[0068] The present application further provides a storage medium, which may specifically be a computer-readable storage medium. The storage medium may be disposed in a computer and store an application program. At this time, the storage medium may be any available medium that the computer can access data from, or may be a storage device such as a server or a data center that integrates one or more available media. The available medium may be a magnetic medium such as a floppy disk, a hard disk, or a magnetic tape, or an optical medium such as a DVD (Digital Video Disc), or a semiconductor medium such as an SSD (Solid State Disk). When the application program is executed, the steps of an oral structure information generation method are implemented, which at least include: obtaining intraoral image data and oral three-dimensional model data, establishing pixel size relationships, and reconstructing oral feature data on the oral three-dimensional model data. And preferably, a step of judging the integrity of the oral three-dimensional model data is executed.

[0069] A further embodiment of the present application further provides an oral structure information generation system 300 as Figure 3 shown, including a processor 31, a memory 33, and a communication bus 34. Among them, the processor 31 and the memory 33 communicate with each other through the communication bus 34. To further expand the functions of the oral structure information generation system 300, the oral structure information generation system 300 may further include a communication interface 32 for the oral structure information generation system 300 to complete communication with other systems or devices such as a medical worker operating system / device, a patient client, a manufacturer / warehouse management system, or a manufacturing / warehouse management device. Similarly, the processor 31, the communication interface 32, and the memory 33 can communicate with each other through the communication bus 34.

[0070] Correspondingly, the memory 33 is used to store the application program; the processor 31 is used to execute the application program stored on the memory 33. The application program may be the application program stored on the storage medium described above, that is, the storage medium may be configured to be at least included in the memory 33. Based on this, when the application program is executed, the processor 31 can implement an oral structure information generation method, which may specifically include: steps such as obtaining intraoral image data and oral three-dimensional model data, establishing pixel size relationships, and reconstructing oral feature data on the oral three-dimensional model data. And preferably, it includes a step of judging the integrity of the oral three-dimensional model data.

[0071] The communication bus 34 can be a PCI bus (Peripheral Component Interconnect) or an EISA bus (Extended Industry Standard Architecture), etc. This communication bus. The communication bus 34 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 3 it is only represented by a thick line in Figure 3 , but it does not mean that there is only one bus or one type of bus.

[0072] The memory 33 can include a RAM (Random Access Memory), and can also include an NVM (Non-Volatile Memory), such as at least one disk memory. The processor 33 can be a general-purpose processor, including a CPU (Central Processing Unit), an NP (Network Processor), etc., and can also be a DSP (Digital Signal Processing), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0073] Of course, although an oral structure information generation system 300 is provided above in this application, based on the description of the oral structure information generation system 300, it can be known that after the various components inside are combined through embodiments, they can also be integrated into one device. Based on this, the oral structure information generation system 300 does not only refer to a large system such as a fieldbus control system, but can also refer to a small circuit system or a control system in an oral structure information generation device.

[0074] As Figure 4 shown, an embodiment of this application provides an oral structure information generation method. The application program or instruction corresponding to this method can be carried on the above storage medium and / or the above oral structure information generation system 300 to achieve the technical effect of oral structure information generation. The oral structure information generation method can specifically include the following steps.

[0075] Step 40, obtain oral three-dimensional model data, and judge whether a specified intraoral tissue area in the oral three-dimensional model meets a preset integrity condition.

[0076] The oral three-dimensional model data refers to the data information capable of establishing an oral three-dimensional model. The source of this data information can be electronic data information such as intraoral images from multiple angles and oral scan models, or physical models such as oral silicone models and oral plaster models. For electronic data information, through composite recombination and repair, oral three-dimensional model data that is as comprehensive as possible (but not required to completely include all intraoral tissue features) can be formed; for physical models, through CT (Computed Tomography) reconstruction and occlusion processing. Preferably, the oral three-dimensional model can be a three-dimensional model representing the structural characteristics of the mandibular surface of the patient in the occlusal state.

[0077] The "designated intraoral tissue area" can be interpreted as an area on the oral three-dimensional model determined to adapt to different diagnosis and treatment purposes or task types. The determination criteria can be delimited by medical workers or automatically determined by the preset task types of the system. For the former, it can specifically include the step of "obtaining an intraoral tissue selection instruction"; for the latter, it can specifically include the step of "determining the task type and the corresponding intraoral tissue area according to the analysis instruction". The basis for determining the intraoral tissue area can be based on the positions of different tooth positions in the oral three-dimensional model, or determined after segmentation according to indicators such as the point cloud characteristics and gray scale characteristics of other oral tissues. The intraoral tissue area includes, but is not limited to, parts such as the vestibular sulcus, labial frenum, and dental arch.

[0078] The integrity condition can be a preset index requirement for measuring the overall integrity of the oral three-dimensional model. Its judgment result can be obtained by manual identification to get an integrity score, and then handed over to the system for comparison. Based on this, step 40 can include: obtaining the integrity score based on the oral three-dimensional model data, and judging whether at least the intraoral tissue area in the oral three-dimensional model meets the preset integrity condition.

[0079] The judgment result of the integrity condition can also be obtained by first evaluating through artificial intelligence and machine learning means such as a pre-constructed neural network model and then comparing. Based on this, step 40 can include: analyzing the oral three-dimensional model to obtain at least the integrity score corresponding to the intraoral tissue area, and judging whether at least the intraoral tissue area in the oral three-dimensional model meets the preset integrity condition. Among them, the integrity score at least points to the intraoral tissue area.

[0080] The process of evaluating the integrity of the oral three-dimensional model includes, but is not limited to, performing position relationship analysis, gray scale situation analysis, point cloud density analysis, and image or model continuity analysis on at least the intraoral tissue area.

[0081] Of course, the setting of the above integrity condition is not limited to making a local integrity judgment on some important intraoral tissue regions, but can also be set for the entire oral three-dimensional model. Correspondingly, the integrity evaluation process can also be carried out for the entire oral three-dimensional model.

[0082] If not satisfied, jump to step 43, obtain the corresponding intraoral image data, extract the oral feature data corresponding to the intraoral tissue region in the intraoral image data, establish the pixel size relationship between the intraoral image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0083] The intraoral image data corresponds to the oral three-dimensional model data. This correspondence is not limited to the two must point to the same object, but as long as it can represent roughly the same structural features. For example, the oral three-dimensional model data can be based on an oral scan model with the actual oral environment of the human body as the object, while the intraoral image can be established with an oral silicone model, an oral plaster model, etc. as the object, as long as the oral silicone model or the oral plaster model points to the same intraoral tissue structure of the oral cavity as the oral scan model. However, it should be noted that the intraoral image data needs to include the intraoral tissue region corresponding to the oral feature data to be reconstructed.

[0084] The establishment of the pixel size relationship is not limited to scaling the intraoral image and the oral three-dimensional model correspondingly to make them match each other, as long as the oral feature data can be mapped and reflected on the oral three-dimensional model or its data while retaining the relative position relationship.

[0085] In this way, it is possible to judge the integrity of at least part of the regions on the oral three-dimensional model, and selectively reconstruct at least part of the regions on the oral three-dimensional model to complete the missing oral feature data or information on the oral three-dimensional model, so as to generate complete oral structure information for medical workers to analyze and diagnose.

[0086] As Figure 5 shown, another embodiment of the present application provides an oral structure information generation method, specifically providing specific steps 41 and 42 for step 40, preferably by analyzing the target tooth position characteristics and model boundary characteristics to determine whether the oral three-dimensional model meets the above integrity condition. It can be understood that the application program or instruction corresponding to this method can also be carried on the above storage medium and / or the above oral structure information generation system 300. The oral structure information generation method may specifically include the following steps.

[0087] Step 41: Obtain the three-dimensional oral model data. Based on the target tooth position features in the three-dimensional oral model data and the corresponding model boundary features, calculate the positional relationship between the target tooth position and the model boundary and / or the positional relationship between the upper jaw model boundary and the lower jaw model boundary.

[0088] The target tooth position features have two meanings. Firstly, the "target" indicates that the target tooth position features actually point to the part in the three-dimensional oral model that needs to be analyzed, regarding which specific position needs to be analyzed. There are various technical solutions for the steps of position analysis and determination. In other words, there are various technical solutions for determining the target tooth position features. In one technical solution, the position pointed to by the target tooth position features can be defined by medical workers. For example, if it is necessary to check the caries condition of a specific tooth position, the first molar tooth position can be selected as the target tooth position, and a certain feature of this tooth position can be further analyzed as the target tooth position feature. Another example is that if it is necessary to check the numerical value of the vestibular sulcus in a certain dimension, the tooth positions as a whole or around a part of the vestibular sulcus can be selected as the target tooth position, and then the target tooth position features can be obtained. In another technical solution, the position pointed to by the target tooth position features can be determined according to the type of target task. The target task type can be a task of making oral appliances such as orthodontic appliances or oral muscle barriers, or a task of checking the integrity of the dental arch in the mouth (such as whether there are missing teeth). For the former, it can specifically include the steps of: obtaining the information of the target appliance type, determining the target oral tissue; according to the target oral tissue, determining the corresponding tooth position as the target tooth position.

[0089] Secondly, the "features" indicate their functional roles, that is, the target tooth position features are actually a feature information that can characterize the relative position of the target tooth position. For example, the coordinates of a certain feature point on the crown of the target tooth position or the coordinates of a certain feature point on the root bulge of the target tooth position. The feature point and its coordinates can not only be the coordinates of a certain point on the edge or surface of an oral tissue corresponding to the tooth position, but also the coordinates of the geometric center point of an oral tissue on the tooth position, or the coordinates of the set center point of the whole formed by multiple oral tissues on the tooth position. Before forming the target tooth position features, the process of determining the target tooth position number is usually included (this process can also be reused when determining the position in the model pointed to by the target tooth position), and thus it can specifically include the steps of: traversing all the tooth position data in the three-dimensional oral model data, determining the number of each tooth position; according to the number of each tooth position, respectively determining a feature coordinate point on each tooth position as the target tooth position feature.

[0090] The model boundary feature, in terms of the meaning of "boundary", can be a certain boundary point or boundary distribution curve on the three-dimensional oral model. In terms of the "model boundary" and its corresponding relationship with the target tooth position feature, it can be a point or coordinate located within a certain area or volume range of the target tooth position feature and on the boundary of the three-dimensional oral model; it can also be a model boundary point or coordinate obtained by traversing and searching according to the target tooth position feature in a preset direction. Therefore, the corresponding relationship between the two can be used to reflect the positional relationship between the tooth position and the model boundary, or the positional relationship between the upper and lower jaw model boundaries, and further used to determine whether the three-dimensional oral model meets the integrity condition at least in the corresponding intraoral tissue area, and even used for operations such as reconstructing features on the three-dimensional oral model.

[0091] Combined with Figure 6 As shown, it can be determined that the first tooth position 11a in the three-dimensional oral model 100 is the target tooth position, and its first gingival margin midpoint 11A is the coordinate point representing the target tooth position feature. When adopting the implementation method of traversing the model boundary feature corresponding to the target tooth position feature along a preset direction, the first model boundary point 10A corresponding to the first gingival margin midpoint 11A can be retrieved on the three-dimensional oral model 100 along the first reference direction D11, and the first model boundary point 10A is used to represent the model boundary feature. Of course, there can be multiple groups of the target tooth position, model boundary, target tooth position feature, and model boundary feature. For example, if the second tooth position 11b is used as the target tooth position, the second gingival margin midpoint 11B and the second model boundary point 10B can be obtained in sequence based on the same scheme. Another example is that if the fourth tooth position 11d is used as the target tooth position, the fourth gingival margin midpoint and the fourth model boundary point can be determined in sequence in the mandible of the three-dimensional oral model 100 based on the same scheme. Based on this, the positional relationship between the upper jaw model boundary and the lower jaw model boundary in step 41 can be calculated using the first model boundary point 10A or the second model boundary point 10B and the fourth model boundary point. Here, it can be understood that the first reference direction D11 can of course include its corresponding first reference reverse direction D11'. The two directions can be defined as one direction, and adaptively, when retrieving the upper jaw model boundary feature, it is retrieved in the direction away from the occlusal plane (the imaginary plane formed by the mesial adjacent point of the maxillary central incisor to the mesial buccal cusp tip of the bilateral first molars) along the first reference direction D11, and when retrieving the lower jaw model boundary feature, it is retrieved in the direction away from the occlusal plane along the first reference reverse direction D11'.

[0092] Among them, on the oral three-dimensional model 100, there are a first model boundary 10a corresponding to the first model boundary point 10A and a second model boundary 10b corresponding to the second model boundary point 10B. The first model boundary 10a and the second model boundary 10b can be interpreted as model boundaries formed by fitting the model boundary points obtained by traversing and searching multiple dental position feature coordinate points, or can be interpreted as model boundaries that can intersect with the reference lines corresponding to multiple dental position feature coordinate points to form corresponding model boundary points. The former enables it to be known on the user side, and the latter reflects its essence of actually existing in the oral three-dimensional model 100.

[0093] Correspondingly, it can also be determined that the first dental position 11a in the oral three-dimensional model 100 is the target dental position, but the coordinate points representing the target dental position features can be determined according to the geometric center of the area enclosed by the convex hull contour of the crown of the first dental position 11a, that is, the center point 11A' of the labial surface of the first crown can be used to represent the target dental position features. At the same time, a second reference direction D12 different from the first reference direction D11 can also be adopted to perform traversal and retrieval with an inclination angle relative to the tooth length extension direction. Thus, a first reference boundary point 10A' corresponding to the center point 11A' of the second crown labial surface can be retrieved to represent the model boundary features. The second reference direction D12 can be the direction used by all dental positions to determine the model boundary features. Of course, the directions used by each dental position to determine the model boundary features can also be distinguished from each other.

[0094] In one implementation, the actual directions reflected by the second reference direction D12 at each dental position can be different from each other, thereby generating derivative directions such as a third reference direction D13. The determination of the second reference direction D12 and the third reference direction D13 can include the steps of: determining the global reference center point C1 on the labial surface side of the dental crown of the oral three-dimensional model; making a reference line with the global reference center point C1 and the center point 11A' of the first crown labial surface, and using the extension direction of this reference line as the second reference direction D12 corresponding to the first dental position 11a. Based on this, for the corresponding second dental position 11b, a reference line can be made with the global reference center point C1 and the corresponding center point of the second crown labial surface to determine the third reference direction D13. Among them, the global reference center point C1 is preferably the midpoint of at least part of the line segment formed on the dental midline, specifically, the midpoint of the projection line segment of the dental midline on the oral three-dimensional model 100.

[0095] In another embodiment, the direction for traversing and retrieving the boundary features of the model can also be determined by combining the determination of the local center point and the edge point. Preferably, the local center point can be the center point of the labial surface of the dental crown, and the edge point can be the midpoint of the gingival margin. For example, the third tooth position 11c in the oral three-dimensional model 100 can be determined as the target tooth position. According to the method steps provided above, the third center point C2 of the labial surface of the third dental crown corresponding to the third tooth position 11c is determined as the local center point, and according to the method steps provided above, the third midpoint 11C of the gingival margin corresponding to the third tooth position 11c is determined as the edge point. Then, a reference line is drawn through the third center point C2 of the labial surface of the dental crown and the third midpoint 11C of the gingival margin, and the extension direction of this reference line is used as the fourth reference direction D14 corresponding to the third tooth position 11c. In this way, the third model boundary point 10C corresponding to the third tooth position 11c can be obtained.

[0096] So far, those skilled in the art can understand that in the aspect of determining the characteristics representing the target tooth position, the present application includes at least various embodiments such as the midpoint of the gingival margin and the center point of the labial surface of the dental crown; in the aspect of determining the model boundary features, it includes at least various embodiments such as determining by dividing regions and determining by direction; in the embodiment of determining the model boundary features by direction, it includes at least various embodiments such as determining according to a fixed direction, determining according to the center point of the labial surface of the dental crown and the global reference center point, and determining according to the midpoint of the gingival margin and the local reference center point. In addition, any embodiment that can be simply deduced from the above various embodiments is included in the technical solution provided by the present application.

[0097] It should be noted that in step 41, there are three technical solutions in the calculation part. The first is to calculate the positional relationship between the target tooth position and the model boundary, the second is to calculate the positional relationship between the maxillary model boundary and the mandibular model boundary, and the third is to calculate the positional relationship between the target tooth position and the model boundary, and calculate the positional relationship between the maxillary model boundary and the mandibular model boundary.

[0098] Step 42: Compare the positional relationship with the preset position condition corresponding to the positional relationship to determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition.

[0099] Combined with Figure 6As shown, the preset position condition can have various forms, such as "upper", "lower", "left", "right" indicating orientation, or intervals, thresholds, etc. indicating numerical magnitude relationships. For the former, it can be judged according to a single feature. For example, it is judged based on the position of the model boundary feature coordinate points retrieved on the oral three-dimensional model 100. On the premise that the intraoral tissue area points to the height of the vestibular sulcus, for the maxilla, it can be set that when the model boundary feature coordinate points are below the set standard coordinate points, it is determined that at least the preset position condition is not met and the integrity condition may not be satisfied; on the premise that the intraoral tissue area points to the width of the vestibular sulcus, for the left side of the oral cavity (the side where the first tooth position 11a and the third tooth position 11c are located), it can be set that when the model boundary feature coordinate points are to the left of the set standard coordinate points, it is determined that at least the preset position condition is not met and the integrity condition may not be satisfied. It can be seen that the setting of the preset position condition can be related to the index pointed to by the intraoral tissue area or the position of the target tooth position in the oral three-dimensional model 100. In addition, it can also be judged according to the geometric figure or distribution range jointly formed by the model boundary feature coordinate points and the target tooth position feature coordinate points. For example, whether the connection line between the model boundary feature coordinate points and the target tooth position feature coordinate points intersects with a preset line segment or area, or a circle is made with the connection line between the model boundary feature coordinate points and the target tooth position feature coordinate points as the diameter, and it is judged whether the area range of the circle falls within a preset area range or overlaps with a preset line segment.

[0100] For the latter, it is preferably to use the Euclidean distance between the model boundary feature coordinate points and the target tooth position feature coordinate points, or the length of the projection line segment of the connection line in a certain direction as the analysis object, and compare the numerical value of the analysis object with the set threshold or range to determine whether the intraoral tissue area corresponding to the target tooth position meets the preset position condition. The integrity condition is preferably a derivative condition for the situation of whether the preset position condition is met. For example, a threshold is set for the number of parts that do not meet the preset position condition. After the number of intraoral tissue parts that do not meet the preset position condition reaches this threshold, it is determined that the oral three-dimensional model cannot be directly put into use, or it is meaningless to continue analyzing the oral three-dimensional model.

[0101] Based on this, the present application provides various implementation manners for calculating the "positional relationship". The integrity condition and the position condition are at least indicators corresponding to this positional relationship. For example, in a preferred implementation manner, taking the first tooth position 11a as an example, if the parameter for characterizing the positional relationship is the projected line segment length of the midpoint 11A of the first gingival margin and the first model boundary point 10A in the first reference direction D11, then the corresponding preset position condition should also be the projected line segment length of the midpoint of the gingival margin of the central incisor corresponding to the first tooth position 11a (which can be limited to the left maxillary central incisor when a higher precision is required) and a certain intraoral tissue feature point in its first reference direction D11 in the first reference direction D11. Generally speaking, the preset position condition can at least be consistent with the positional relationship in terms of direction, tooth position, and point selection. The integrity condition is preferably a judgment condition formed by comprehensively considering all judgment results relative to the preset position condition, thereby establishing an indirect correlation with the positional relationship.

[0102] If not satisfied, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0103] When it is determined through steps 41 to 42 that the vestibular sulcus feature above the maxillary central incisor is missing and does not meet the preset integrity condition, the obtained intraoral photo image data should at least include the vestibular sulcus tissue area above the maxillary central incisor. Thus, it can be seen that the contribution of step 42 to step 43 is not only "whether the integrity condition is met", but also the specific basis and relevant situations for making such a judgment result of the integrity condition, which can at least include the position of the part or tooth position that does not meet the preset position condition in the oral cavity.

[0104] Combined with Figure 6 For example, referring to the first tooth position 11a, when traversing and retrieving along the first reference direction D11, if it is determined that the midpoint 11A of the first gingival margin and the first model boundary point 10A do not meet the preset position condition (for example, the preset position for the first model boundary point 10A should be at point 13A and above it along the first reference direction D11), then the oral feature data corresponding to the vestibular sulcus analyzed from the intraoral photo image data is the first vestibular sulcus bottom coordinate point 13A, or the distance length between the first vestibular sulcus bottom coordinate point 13A and the midpoint 11A of the first gingival margin. At this time, according to the position of the first vestibular sulcus bottom coordinate point 13A in the intraoral photo image data and the pixel size relationship, for example, at least reconstruct the first vestibular sulcus bottom coordinate point 14A on the oral three-dimensional model 100 to form Figure 6The "dot" pointed to by the coordinate point 13A at the bottom of the first vestibular sulcus. Obviously, in this process, it is not required to magnify the intraoral image to the same size as the oral three-dimensional model 100. In addition, based on the same or similar steps, the reference point 13A' at the bottom of the first vestibular sulcus, the coordinate point 13B at the bottom of the second vestibular sulcus, the coordinate point 13C at the bottom of the third vestibular sulcus, etc. can be correspondingly formed.

[0105] Of course, this does not mean that this application excludes the technical solution of unifying the size of the intraoral image and the oral three-dimensional model 100, especially when it is necessary to fit a curved surface (such as Figure 6 the first tissue space distribution curved surface Sa shown, or the third tissue space distribution curved surface at the third tooth position 11c), or a curve (such as Figure 6 the first tissue space distribution curve 13a shown, or the second tissue space distribution curve 13b), a pixel size relationship can also be established for multiple oral feature data, achieving an effect similar to scaling transformation of the intraoral tissue area.

[0106] As Figure 7 shown, this application provides a first embodiment of an oral structure information generation method based on the above-described embodiment, and this first embodiment specifically includes the following steps.

[0107] Step 410, obtain oral three-dimensional model data.

[0108] Step 411, determine at least one tooth position on the oral three-dimensional model as the target tooth position, and based on a preset feature recognition rule, calculate the reference feature coordinates of the target tooth position to characterize the target tooth position feature.

[0109] The reference feature coordinates may be the coordinates of the midpoint of the gingival margin or the coordinates of the center point of the labial surface of the tooth crown as described above, so as to at least characterize the position feature of the target tooth position. Of course, the coordinates of multiple points distributed on the gingival margin and the coordinates of multiple points distributed on the labial surface of the tooth crown can also be used to characterize the morphological feature of the target tooth position; the position feature of the target tooth position can also be characterized by using the coordinates of the midpoint of the incisal ridge of the incisor, the cusp point coordinates of the canine, or the coordinates of the center point of the occlusal surface of the molar as the reference feature coordinates.

[0110] The preset feature recognition rule may be to determine the contour feature of the tooth crown of the target tooth position through pixel gray values or the spatial relative position of the point cloud set, and based on the spatial points on the convex hull contour, find the coordinates of a spatial point that is farthest from the occlusal plane as the reference feature coordinates that can reflect the coordinates of the midpoint of the gingival margin. Of course, when the reference feature coordinates point to other positions of the target tooth position, there may be other derived technical solutions in its extraction process.

[0111] Step 412: According to the reference feature coordinates, determine the boundary feature coordinates corresponding to the reference feature coordinates on the three-dimensional oral model along the first direction to characterize the model boundary features.

[0112] Combined with Figure 6 As shown, the first direction can be correspondingly defined as D1. When adopting different schemes for determining the direction, the first direction D1 can point to any one of the above-mentioned first reference direction D11, first reference reverse direction D11’, second reference direction D12, third reference direction D13 or fourth reference direction D14, or can also point to other directions that are not clearly proposed in the foregoing but are within the capabilities of those skilled in the art.

[0113] The boundary feature coordinates can be the above-mentioned model boundary points or model boundary coordinate points. In this way, through traversal retrieval, feature points corresponding to the target tooth position features and characterizing the model boundary features can be obtained, and the relative position relationship between the target tooth position and the model boundary can be formed by using the reference feature coordinates and the boundary feature coordinates, quantifying the abstract position relationship into computable and clear numerical values, which is convenient for subsequent use in judging the integrity of the model.

[0114] Step 413: Calculate the feature distance value between the reference feature coordinates and the boundary feature coordinates to characterize the position relationship between the target tooth position and the model boundary.

[0115] The feature distance value can be the Euclidean distance between the reference feature coordinates and the boundary feature coordinates, or the Euclidean distance or the projection of the line connecting two points in a certain direction. This application does not limit this. As described above, as long as the calculation method of the corresponding preset position condition is set to be consistent with the calculation method of the feature distance value characterizing the position relationship, the expected technical effect can be achieved. Quantifying the position relationship between the target tooth position and the model boundary by using the feature distance value can facilitate setting the preset position condition and also facilitate the operation in the subsequent judgment process. Compared with using other spatial indicators such as volume or area for fitting the position relationship, it can avoid the reduction of data credibility in the transformation and mapping process due to the dimensional differences between the intraoral images and the three-dimensional oral model.

[0116] Step 421: Compare the numerical magnitudes of the feature distance value and the distance integrity criterion value characterizing the preset position condition.

[0117] In other words, the technical solution provided in this application sets a distance integrity criterion value to characterize the preset position condition. This can be interpreted as that the distance integrity criterion value is a part of the preset position condition. This application does not exclude setting integrity criterion values in multiple dimensions under the preset position condition, so as to comprehensively judge from multiple dimensions whether the positional relationship between the target tooth position and the model boundary meets the preset positional relationship. Among them, the integrity criterion value can also be replaced and selected according to the types of the target tooth position and the target oral tissue. For example, when judging the maxillofacial protrusion amplitude (corresponding to the incisor tooth positions of the upper jaw and the incisor tooth positions of the lower jaw) or the dental arch curvature (corresponding to all tooth positions of the upper jaw or all tooth positions of the lower jaw), an arc integrity criterion value is set.

[0118] Step 422: Judge whether the oral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0119] Regarding the association relationship between the integrity condition and the comparison result, when it is only necessary to judge whether the oral tissue area corresponding to a single target tooth position is complete, the comparison result can be directly used as a sufficient condition for judging whether the integrity condition is met; when it is necessary to perform integrity judgment on the oral tissue areas corresponding to multiple target tooth positions respectively using the characteristic distance value, an integrity condition needs to be set for all comparison results, so as to measure whether the overall oral tissue areas corresponding to multiple target tooth positions meet the expected integrity requirements.

[0120] Combined with Figure 6 As shown, taking the left maxillary central incisor tooth position, the left maxillary lateral incisor tooth position, and the left maxillary canine tooth position as the target tooth positions, the comparison results corresponding to the three tooth positions may all not meet the preset position condition. At this time, it can be determined that the oral tissue areas corresponding to the above three tooth positions are incomplete. Taking the right maxillary central incisor tooth position, the right maxillary lateral incisor tooth position, and the right maxillary canine tooth position as the target tooth positions, it is possible that only the comparison result of the right maxillary lateral incisor tooth position shows that it does not meet the preset position condition. At this time, if the preset integrity condition is set relatively loose, the overall oral tissue areas corresponding to the above three tooth positions can be considered to meet the integrity condition. It can be understood that the setting of the preset integrity condition focuses on judging the number of non-compliance with the preset position condition. Of course, it can also be judged whether it meets the preset integrity condition according to the difference between the sum of the characteristic distances and the distance integrity criterion value, such as calculating indicators such as its average value and variance.

[0121] If not satisfied, jump to step 43, obtain the corresponding intraoral image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral image data, establish the pixel size relationship between the intraoral image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0122] The first embodiment provided by the present application can simplify the expression of the positional relationship, concretize the abstract content into the distance between two coordinates, and can judge the integrity of the oral three-dimensional model based on the simple coordinate relationship, so as to realize the judgment of complex and abstract content on the basis of streamlining the algorithm logic.

[0123] Preferably, the "calculating the reference feature coordinates of the target tooth position based on a preset feature recognition rule" specifically includes: determining and using the coordinates of the midpoint of the gingival margin of the target tooth position as the reference feature coordinates of the target tooth position. Since the midpoint of the gingival margin is located near the position on the crown of the target tooth position that is farthest from the occlusal plane, relatively, the relative distance from other intraoral tissues on the oral three-dimensional model is closer. Using the coordinates of this point as the reference feature coordinates can reduce the number of spatial feature points traversed on the oral three-dimensional model and speed up the operation speed.

[0124] In a specific example of this first embodiment, the reference feature coordinates are located in a preset model coordinate system, and the origin of the model coordinate system is located in the occlusal plane. Based on this, as Figure 7 and Figure 8 shown, the step 411 may specifically include the following steps. It should be noted that this specific example includes steps 410 to 43, but will not be described in detail below.

[0125] Step 51, determine all the spatial points on the crown of the target tooth position on the labial surface side to form a spatial point set.

[0126] The external presentation of the oral three-dimensional model is a three-dimensional figure composed of multiple faces, but its essence is still formed by a computer software through fitting multiple spatial points. Based on this, the oral three-dimensional model can be analyzed by setting a certain density to obtain the point cloud data or point set data distributed on the oral three-dimensional model, that is, obtaining the spatial point set.

[0127] It should be noted here that the phrase "determining all the spatial points on the crown of the target tooth position on the labial side" can be interpreted as being able to at least confirm the above-mentioned spatial points on the crown of the target tooth position. On the one hand, step 51 can be to analyze the point cloud data of the overall oral three-dimensional model according to a preset density, and execute the following steps 52 to 56 to extract the set of spatial points corresponding to the target tooth position obtained from the analysis; on the other hand, before step 51, a neural network algorithm can be called or other methods can be used to first select the region of interest on the oral three-dimensional model, such as selecting the region where the target tooth position or the entire dentition is located, and the set of spatial points can also be obtained through analysis, and the complexity of the algorithm can be reduced.

[0128] The reason for choosing all the spatial points on the labial side depends on the requirements of the oral feature data. In this embodiment, the focus is mainly on the features of the intraoral tissues such as the vestibular sulcus, the prominence amplitude of the occlusal surface, and the labial frenum, which are located on the side of the dentition close to the lips. Among them, the above-mentioned labial surface can also be interpreted as the buccal surface for molars. Of course, in other embodiments, when it is necessary to focus on the characteristics of the alveolar bone development or intraoral tissues such as the hard palate and the soft palate, all the spatial points on the lingual side or the palatal side of the crown of the target tooth position can be calculated based on the preset feature recognition rules to form the set of spatial points.

[0129] Step 52: Select the spatial point with the smallest coordinate value in the set of spatial points as the pole to establish a polar coordinate system, and arrange the other spatial points in ascending order of polar angle and polar radius to form a feature traversal sequence.

[0130] Combined Figure 6 and Figure 10 As shown, the above technical solutions with the reference feature coordinates and the boundary feature coordinates as the operation objects are all based on the above coordinates being in a preset model coordinate system. The model coordinate system can be a number axis, a plane rectangular coordinate system, a space rectangular coordinate system, etc. Preferably, the model rectangular coordinate system includes at least two coordinate axes to reflect the relative position relationship between the reference feature coordinates and the boundary feature coordinates. In order to form the symmetry between the upper jaw and the lower jaw, and preferably form the symmetry between the left dentition and the right dentition, the origin of the model coordinate system can be set in the occlusal plane, and preferably at the intersection of the occlusal plane and the dental midline. Of course, in other embodiments, the origin can also be set at the above-mentioned global reference center point C1. Based on this, the "spatial point with the smallest coordinate value" represents at least the spatial point closest to the occlusal plane among all the spatial points on the labial side of the crown of the target tooth position. At this time, we can consider that this spatial point is on the edge of the crown of the target tooth position, that is, it can be considered as a point on the contour of the crown of the target tooth position.

[0131] For example, the model coordinate system can be a plane rectangular coordinate system including the first coordinate axis Rx and the third coordinate axis Rz, or a spatial rectangular coordinate system including the first coordinate axis Rx, the third coordinate axis Rz, and the second coordinate axis Ry at the same time. The origin Ro is located within the occlusal plane ( Figure 6 In order to avoid interference and make the attached drawings unclear, it is placed outside the three-dimensional oral model). Isolation Figure 6 In the Z1 part forms Figure 10 A partial enlarged schematic diagram. It can be seen that when the first tooth position 11a is selected as the target tooth position, the origin Ro is on the incisal side of the first tooth position 11a. When the first tooth position 11a is the maxillary left central incisor and the origin Ro is preferably the intersection of the occlusal plane and the dental midline, the origin Ro is on the mesial surface side of the first tooth position 11a. Based on this, a model coordinate system can be established where the first coordinate axis Rx extends along the width direction of the first tooth position 11a, and the third coordinate axis Rz extends along the length direction of the first tooth position 11a (or the extension direction of the tooth body axis, or specifically for the central incisor, along the dental midline direction).

[0132] The "minimum coordinate value" can refer to the minimum coordinate value in the direction of the first coordinate axis Rx, can refer to the minimum coordinate value in the direction of the third coordinate axis Rz, can refer to the minimum sum of the coordinate values in the direction of the first coordinate axis Rx and the coordinate values in the direction of the third coordinate axis Rz, or can refer to other parameters generated after different weightings or other operations on the two coordinate values being the smallest. Based on the establishment of the above model coordinate system, after comprehensively considering the coordinate values in the directions of the first coordinate axis Rx and the third coordinate axis Rz, usually the vertex of the mesial incisal angle on the crown of the target tooth position is selected as the pole, especially the above vertex of the central incisor is selected as the pole. When only considering the coordinate values in the direction of the third coordinate axis Rz, it is also possible to select the fifth spatial coordinate point m5 as the pole. In particular, when considering the special case where the first tooth position 11a is blocked, it is also possible to select Figure 10 The point m0 in it as the pole. To illustrate the general applicability of the present application, the following description of the pole selection will be based on considering this special case of occlusion, but for the selection of other spatial points, this special case of occlusion is not considered.

[0133] A polar coordinate system is established with the pole m0. The horizontal axis of the polar coordinate system can be parallel to the first coordinate axis Rx to form the first polar coordinate horizontal axis Px, and the vertical axis can be parallel to the third coordinate axis Rz. The horizontal axis and the vertical axis of the polar coordinate system can be set to have the same positive direction as the first coordinate axis Rx and the third coordinate axis Rz, that is, the first polar coordinate horizontal axis Px extends from the mesial surface to the distal surface of the crown of the target tooth position, and the vertical axis of the polar coordinate system extends from the incisal end to the gingiva of the crown of the target tooth position.

[0134] The polar angle and polar radius are arranged in ascending order. Preferably, they are first arranged in ascending order of the polar angle. When two spatial points have the same polar angle, they are arranged in ascending order of the polar radius. For example, the sixth spatial point m6 and the seventh spatial point m7 have the same polar angle. However, since the polar radius of the sixth spatial point m6 is shorter than that of the seventh spatial point m7, the sixth spatial point m6 is arranged before the seventh spatial point m7. Another example is that the polar radius of the fourth spatial point m4 is shorter than that of the third spatial point m3. However, since the polar angle of the fourth spatial point m4 is greater than that of the third spatial point m3, the third spatial point m3 is arranged before the fourth spatial point m4. Thus, at least a feature traversal sequence including the first spatial point m1, the second spatial point m2, the third spatial point m3, the fourth spatial point m4, the fifth spatial point m5, the sixth spatial point m6, and the seventh spatial point m7 in sequence can be formed, which can be expressed as [m1, m2, m3, m4, m5, m6, m7,......].

[0135] Step 53: Extract the coordinates of the first two spatial points in the feature traversal sequence. Taking the pole as the starting point, calculate the first polar coordinate vector and the second polar coordinate vector in sequence, and calculate the cross product of the first polar coordinate vector and the second polar coordinate vector, and determine whether the cross product is less than 0.

[0136] The first polar coordinate vector can be expressed as The second polar coordinate vector can be expressed as The cross product can be expressed as If the cross product CP(1) < 0, it can be considered that the direction of rotating the line segment m0m1 to obtain the line segment m0m2 is counterclockwise. At this time, it can be determined that the first spatial point m1 and the second spatial point m2 may exist at the edge of the target tooth crown at the same time, that is, they belong to a convex hull reference point in the convex hull contour. However, if the cross product CP(1) > 0, it can be considered that the direction of rotating the line segment m0m1 to obtain the line segment m0m2 is clockwise. At this time, it is necessary to further check whether one of the first spatial point m1 and the second spatial point m2 contains the convex hull reference point.

[0137] If so, jump to step 54A, and update the starting point to the first spatial point corresponding to the first polar coordinate vector;

[0138] If not, jump to step 54B, and update the starting point to the second spatial point corresponding to the second polar coordinate vector.

[0139] When it conforms to the counterclockwise rotation direction, it can be determined that the first spatial point m1 is closer to the pole point m0 than the second spatial point m2, and the arrangement of the first spatial point m1 and the second spatial point m2 conforms to the arrangement of the convex hull reference points on the convex hull contour data relative to the pole point m0. Thus, it is determined that the first spatial point m1 is the next starting point, and it continues to be determined whether the second spatial point m2 and subsequent spatial points are within the convex hull contour data range. When it does not conform to the counterclockwise rotation direction, it can be determined that there may be errors in steps such as point selection, and the first spatial point m1 does not belong to the convex hull reference points in the convex hull contour data. Thus, the second spatial point m2 is used as the next starting point to continue the traversal and judgment.

[0140] Step 55, traverse all other spatial points in the feature traversal sequence that are after the second spatial point. According to the polar coordinate vectors formed by the spatial points and the starting point, and the cross product between the polar coordinate vectors, selectively update the starting point and determine at least two spatial points that meet the determination conditions as the convex hull reference points.

[0141] After the next starting point after the selected pole point m0, it is possible to continue to use the second spatial point m2 and all other spatial points after it as the object for cross product judgment, or it is possible to continue to use all other spatial points after the second spatial point m2 as the object for cross product judgment, and finally obtain at least two qualified ones that can be considered to fall into the convex hull reference points in the convex hull contour data.

[0142] Step 56, fit the convex hull contour data of the target tooth position according to the convex hull reference points and the pole point, and calculate the reference feature coordinates of the target tooth position according to the convex hull contour data.

[0143] At least two convex hull reference points and the pole point m0 can form convex hull contour data that can enclose or fit to form a convex hull. The larger the data volume of the convex hull reference points, the more the enclosed convex hull fits the contour of the crown of the target tooth position. Based on this, preferably, when selecting the gingival margin midpoint coordinates of the crown of the target tooth position as the reference feature coordinates, one convex hull reference point or convex hull contour fitting point with the largest coordinate value on the first coordinate axis Rx can be selected as the reference feature coordinates. Among them, the convex hull contour fitting point can be obtained by performing the following steps: Interpolate and fit the convex hull contour data composed of the pole point and the convex hull reference points to form a convex hull contour curve; among them, the convex hull contour curve includes convex hull reference points and convex hull contour fitting points obtained by interpolation. For example, in the case where both the first spatial point m1 and the second spatial point m2 are convex hull reference points, a first convex hull contour fitting point can be interpolated between the pole point m0 and the first spatial point m1, and / or a second convex hull contour fitting point can be interpolated between the first spatial point m1 and the second spatial point m2, so as to obtain a smoother convex hull contour curve.

[0144] Such asFigure 7 , Figure 8 and Figure 9 As shown in Figure 9 , as another specific example independent of the above specific examples, or as a preferred implementation provided for step 55 in the above specific examples, step 55 may further specifically include the following steps. It can be understood that Figure 7 Steps 410 to 43 in Figure 7 can be combined with the above description to form a technical solution; Figure 8 Steps 51 to 56 in Figure 8 are used as Figure 7 Part of step 411 in Figure 7 can be combined with the above description to form another technical solution; Figure 9 Steps 551 to 553 in Figure 9 are used as Figure 8 Part of step 55 in Figure 8 , and further as Figure 7 Part of step 411 in Figure 7 can be combined with the above description to form yet another technical solution. It should be noted that this specific example includes steps 410 to 43, and steps 51 to 56, but they will not be further described below.

[0145] Step 551: Extract the third spatial point after the second spatial point in the feature traversal sequence, calculate the reference polar coordinate vectors between the non-starting reference spatial points and the starting point among the first spatial point and the second spatial point in sequence, and the third polar coordinate vector between the third spatial point and the starting point, and calculate the cross product of the reference polar coordinate vector and the third polar coordinate vector, and determine whether the cross product is less than 0.

[0146] Combined with Figure 6 and Figure 10 As shown, for example, when it is determined that the cross product CP(1) formed by the first polar coordinate vector and the second polar coordinate vector is less than 0, the first spatial point m1 is selected as the starting point after the pole m0, and the second spatial point m2 is defined as the reference spatial point. At this time, further calculate the cross product CP(2) of the reference polar coordinate vector and the third polar coordinate vector , and determine whether the cross product CP(2) is less than 0, thereby judging the rotation direction of the line segment m1m2 to the line segment m1m3.

[0147] If so, jump to step 552A, update the starting point to the reference spatial point, and determine the reference spatial point as the convex hull reference point.

[0148] If not, jump to step 552B, do not update the starting point, delete the reference spatial point, use the third spatial point as the new reference spatial point, and selectively update the starting point and determine the convex hull reference point according to the cross product of the new reference polar coordinate vector and the polar coordinate vector formed by the starting point and the next spatial point of the new reference spatial point.

[0149] If the cross product CP(2) < 0, it is determined that the second spatial point m2 as the reference spatial point is a new starting point after the first spatial point m1, and the second spatial point m2 is determined as one of the convex hull reference points. If the cross product CP(2) > 0, the second spatial point m2 as the reference spatial point is deleted, and starting from the first spatial point m1, the third polar coordinate vector is calculated with the polar coordinate vector formed by the first spatial point m1 and the fourth spatial point m4 to calculate the cross product CP between them, and iterate. Based on the subsequent cross product judgment results, determine whether to update the starting point and the convex hull reference point.

[0150] Step 553: Repeat the iteration until all spatial points in the feature traversal sequence are judged, obtaining at least two convex hull reference points.

[0151] The implementation processes for the fourth spatial point m4, the fifth spatial point m5, the sixth spatial point m6, and the seventh spatial point m7 will be described below. After step 552, taking the situations shown in Figure 6 and Figure 10 as examples, it can be determined that the second spatial point m2 is the starting point after the first spatial point m1. Based on this, the third spatial point m3 and the fourth spatial point m4 are extracted, and the fourth polar coordinate vector and the fifth polar coordinate vector are respectively formed and Taking the third spatial point m3 as the reference spatial point, calculate the corresponding cross product CP(3) and judge its relationship with 0. Since the cross product CP(3) < 0, the third spatial point m3 is updated as the new starting point, and the third spatial point m3 is determined as the convex hull reference point.

[0152] Further extract the fourth spatial point m4 and the fifth spatial point m5, and respectively form the sixth polar coordinate vector and the seventh polar coordinate vector Taking the fourth spatial point m4 as the reference spatial point, calculate the corresponding cross product CP(4) and judge its relationship with 0. Since the cross product CP(4) > 0, the starting point is not updated, the fourth spatial point m4 as the reference spatial point is deleted, taking the fifth spatial point m5 as the new reference spatial point, extract the next spatial point of the fifth spatial point m5, the sixth spatial point m6, and form the eighth polar coordinate vector According to the seventh polar coordinate vector as the new reference polar coordinate vector and the relationship between the cross product CP(5) of the eighth polar coordinate vector and 0, determine whether to update the starting point or determine the convex hull reference point.

[0153] Since CP(5) < 0, the fifth space point m5 is updated as the new starting point, and the fifth space point m5 is determined as the convex hull reference point. Further, the sixth space point m6 and the seventh space point m7 are extracted, and the ninth polar coordinate vector is respectively formed and the tenth polar coordinate vector Taking the sixth space point m6 as the reference space point, the corresponding cross product CP(5) is calculated and its relationship with 0 is judged. Since the cross product CP(5) > 0, the starting point is not updated, the sixth space point m6 as the reference space point is deleted, and the seventh space point m7 is used as the new reference space point.

[0154] After the above steps, at least the first space point m1, the second space point m2, the third space point m3, and the fifth space point m5 are extracted as the convex hull reference points. The above technical solution is repeatedly iterated until all space points in the feature traversal sequence are judged, and then all convex hull reference points for the target tooth position are obtained, and then the corresponding convex hull contour and its data can be formed.

[0155] Although the above process forms a loop nested algorithm in terms of expression, in actual operation, it can be implemented through algorithms such as stacks and stack top pops. In this application, no exhaustive enumeration of specific operation methods is carried out.

[0156] As Figure 7 and Figure 11 shown, the present application provides a second embodiment of an oral structure information generation method based on the above embodiment. This second embodiment refines step 412 and specifically provides step 4121A and step 4122A. It can be understood that for other steps except the refined steps, they will not be described in detail below. This second embodiment specifically includes the following steps.

[0157] Step 410, obtain oral three-dimensional model data.

[0158] Step 411, determine at least one tooth position on the oral three-dimensional model as the target tooth position, and based on a preset feature recognition rule, calculate the reference feature coordinates of the target tooth position to characterize the target tooth position feature.

[0159] Step 4121A, taking the reference feature coordinates as the starting point, draw a reference extension line in the first direction away from the target tooth position, and continuously analyze the formation of the projection endpoints of the reference extension end other than the reference feature coordinates on the jaw surface of the oral three-dimensional model.

[0160] The reference extension line may be an auxiliary line actually shown on the three-dimensional oral model, so as to present the process of retrieving and determining the boundary feature coordinates to the medical worker for monitoring. Of course, the reference extension line may also be interpreted as the internal logic of the traversal process when the system executes the oral structure information generation method, that is, the traversal of the spatial points on the three-dimensional oral model and the judgment of the formation of the end points are carried out in the direction approximately parallel to the reference extension line. Generally speaking, the reference extension line does not necessarily need to actually exist, but exists as an internal logic such as an auxiliary line or a direction reference line in the oral structure information generation method provided in this application.

[0161] Combined with Figure 12 and Figure 13 As shown, taking the fifth tooth position 11e as the target tooth position as an example, if the coordinates of the midpoint 11E of the fifth gingival margin corresponding to the fifth tooth position 11e are used as the reference feature coordinates, when the first direction is the extension direction of the long axis of the tooth body, the fifth standard reference extension line Le will be correspondingly generated. Among them, one end of the fifth standard reference extension line Le is the midpoint 11E of the fifth gingival margin, and the projection of the other end on the oral three-dimensional model 100 corresponds to the projection end point. Of course, when the first direction is defined as the extension direction of the long axis of the target tooth position or the projection of the extension direction of the long axis of the tooth body on the jaw surface of the oral three-dimensional model 100, the corresponding reference extension line will form a certain inclination angle with the fifth standard reference extension line Le. The above three implementation manners are all feasible and can form the required projection end points. For the last technical solution, the first direction corresponds to the fifth projection direction D1e, and the fifth reference extension line Le' can be constructed; when the target tooth position is in the lower jaw, it can correspond to the fifth projection reverse direction D1e' opposite to the fifth projection direction D1e.

[0162] Similarly, taking the sixth tooth position 11f as the target tooth position as an example, if the sixth tooth position 11f is used as the target tooth position, it correspondingly includes the midpoint 11F of the sixth gingival margin. When the first direction is the extension direction of the long axis of the tooth body, the sixth standard reference extension line Lf will be generated through the above technical solution. When the first direction is the projection of the extension direction of the long axis of the tooth body on the jaw surface of the oral three-dimensional model 100, the sixth reference extension line Lf' will be generated through the above technical solution, and the corresponding first direction is the sixth projection direction D1f. When the target tooth position is in the lower jaw, the first direction corresponds to the sixth projection reverse direction D1f'.

[0163] It can be understood that although the reference feature coordinates will be determined as the coordinates of the midpoint of the gingival margin of the target tooth position in the following text of this application, according to the previous description, those skilled in the art can obtain the technical solutions when the reference feature coordinates are determined as other coordinates on the target tooth position.

[0164] Step 4122A: When the reference extension end no longer forms a projection end point with the occlusal surface of the oral three-dimensional model, determine the coordinates of the last formed projection end point as the boundary feature coordinates.

[0165] During the process of gradually extending any of the above reference extension lines away from the target tooth position, the reference extension line will at least continuously generate projection end points on the oral three-dimensional model 100. When the reference extension line extends beyond the boundary of the oral three-dimensional model 100, it can no longer generate the projection end points with the oral three-dimensional model 100. Based on this, according to the formation situation of the projection end points, when no new projection end points are formed, it is considered that the last formed projection end point has been on the boundary of the oral three-dimensional model 100.

[0166] For example, the fifth reference extension line Le’ or the fifth standard reference extension line Le starts from the midpoint 11E of the fifth gingival margin and gradually extends, continuously generating projection end points on the oral three-dimensional model 100. The last projection end point is the corresponding fifth model boundary point 10E. Another example is that the sixth reference extension line Lf’ or the sixth standard reference extension line Lf starts from the midpoint 11F of the sixth gingival margin and gradually extends, continuously generating projection end points on the oral three-dimensional model 100. The last projection end point is the corresponding sixth model boundary point 10F.

[0167] Step 413: Calculate the characteristic distance value between the reference feature coordinates and the boundary feature coordinates to characterize the positional relationship between the target tooth position and the model boundary.

[0168] Step 421: Compare the numerical magnitudes of the characteristic distance value and the distance integrity criterion value characterizing the preset position condition.

[0169] Step 422: Determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0170] If it does not meet the condition, jump to Step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0171] In this way, based on the above technical solutions, not only can the boundary position of the model be determined based on a simple algorithm, avoiding the problems of excessive data volume, long operation time, and difficult fitting process caused by analyzing point cloud data, but also the model boundary features corresponding to the tooth position features can be actively established based on the tooth position features, facilitating subsequent judgment of integrity and determination of missing positions.

[0172] In an application scenario, preferably, the intraoral tissue region includes the vestibular sulcus, the oral cavity feature data includes the vestibular sulcus height data, the first direction is the projection of the extension direction of the tooth long axis on the jaw surface of the oral cavity three-dimensional model, and the target tooth positions include the maxillary incisor tooth positions and the mandibular incisor tooth positions. In this way, aiming at the anatomical features of the vestibular sulcus, based on the fact that the highest point of the upper vestibular sulcus is usually above the maxillary central incisor or lateral incisor along the extension direction of the tooth long axis, and the lowest point of the lower vestibular sulcus is usually below the mandibular central incisor or lateral incisor along the extension direction of the tooth long axis, the corresponding model boundary features can be selected to determine whether the oral cavity three-dimensional model includes the regional parts necessary for calculating the vestibular sulcus height data. It should be emphasized that the incisor tooth positions include the central incisor tooth positions and the lateral incisor tooth positions, and further include four orientations: the left side of the maxilla, the left side of the mandible, the right side of the maxilla, and the right side of the mandible.

[0173] In another application scenario, preferably, the intraoral tissue region includes the vestibular sulcus, or the root eminence, or includes both the vestibular sulcus and the root eminence. The oral cavity feature data includes the dental arch width data, the first direction is the projection of the extension direction of the tooth long axis on the jaw surface of the oral cavity three-dimensional model, and the target tooth positions include the corresponding first maxillary left molar tooth position and the first maxillary right molar tooth position. Among them, the first jaw surface is the maxilla, or the mandible, or both the maxilla and the mandible are selected as the first jaw surface at the same time. In this way, aiming at the anatomical features of the whole dental arch, based on the fact that the farthest point on the left side of the dental arch is usually on the left side of the projection direction of the maxillary left second molar along the tooth long axis, or on the left side of the projection direction of the mandibular left second molar along the tooth long axis, and based on the fact that the farthest point on the right side of the dental arch is usually on the right side of the projection direction of the maxillary right second molar along the tooth long axis, or on the right side of the projection direction of the mandibular right second molar along the tooth long axis, the corresponding model boundary features can be selected to determine whether the oral cavity three-dimensional model includes the regional parts necessary for calculating the dental arch width data.

[0174] In yet another application scenario, preferably, the intraoral tissue region includes the labial frenum, and the oral cavity feature data includes the labial frenum width data. Based on this, as Figure 7 and Figure 14 shown, the present application provides a third embodiment of the oral cavity structure information generation method based on the above embodiment. This third embodiment refines steps 411 and 412 to adapt to the above application scenario, and specifically provides steps 411B, 4121B, and 4122B. It can be understood that for other steps except the refined steps, they will not be described in detail below. This third embodiment specifically includes the following steps.

[0175] Step 410, obtain the oral cavity three-dimensional model data.

[0176] Step 411B: Determine the tooth positions of the maxillary left central incisor and the maxillary right central incisor on the oral three-dimensional model as target tooth positions, and based on a preset feature recognition rule, calculate the reference feature coordinates of the target tooth positions and several relative feature coordinates between the two reference feature coordinates on the target tooth positions to characterize the target tooth position features.

[0177] Wherein, the first maxillofacial region is the maxilla, or the mandible, or both the maxilla and the mandible are selected simultaneously as the first maxillofacial region. Combining Figure 14 As shown, taking the first maxillofacial region being the maxilla as an example, the tooth position of the maxillary right central incisor points to the fifth tooth position 11e, and the tooth position of the maxillary left central incisor points to the seventh tooth position 11g. Then, correspondingly, the coordinates of the midpoint 11E of the fifth gingival margin can be used as the reference feature coordinates of this tooth position, and the coordinates of the midpoint 11G of the seventh gingival margin can be used as the reference feature coordinates of this tooth position. Since the central incisors on the same maxillofacial region are arranged in sequence along a direction perpendicular to the dental midline or the long axis of the tooth, similar to extracting crown edge points such as convex hull reference points, there must be multiple spatial points distributed on the target tooth position between the midpoint 11E of the fifth gingival margin and the midpoint 11G of the seventh gingival margin. As a preferred implementation manner, the coordinates of the spatial points that are simultaneously located between the midpoint 11E of the fifth gingival margin and the midpoint 11G of the seventh gingival margin and are distributed on the gingival margin of the fifth tooth position 11e or the gingival margin of the seventh tooth position 11g can be used as the relative feature coordinates. It can be understood that the spatial points pointed to by the relative feature coordinates are also located on the target tooth position. Therefore, the relative feature coordinates and the reference feature coordinates can jointly characterize the features of the target tooth position.

[0178] For example, the coordinates of the fifth gingival margin marking point 11Em located on the fifth tooth position 11e can be selected as the relative feature coordinates. Of course, the spatial points pointed to by the relative feature coordinates are not limited to this fifth gingival margin marking point 11Em, and can be not only any point located on the gingival margin, but also any point located on the tooth crown.

[0179] Step 4121B: Respectively use the reference feature coordinates and the relative feature coordinates as starting points, and draw reference extension lines in the first direction away from the target tooth position, and continuously analyze the formation of the projection endpoints of the reference extension ends other than the reference feature coordinates or the relative feature coordinates on the oral three-dimensional model on the reference extension lines.

[0180] Wherein, the first direction is the projection of the extension direction of the tooth long axis on the jaw surface of the oral three-dimensional model. Based on this, corresponding to the fifth tooth position 11e and the midpoint 11E of the fifth gingival margin, a fifth reference extension line Le' can be generated; corresponding to the seventh tooth position 11g and the midpoint 11G of the seventh gingival margin, a seventh reference extension line Lg' can be generated; corresponding to the fifth tooth position 11e and the fifth gingival margin marking point 11Em, a fifth marked reference extension line Lem' can be generated as one of the reference extension lines. Similarly, the target tooth positions corresponding to the above reference extension lines are in the upper jaw, and the first direction D1 is from the incisal ridge of the central incisor tooth position upwards; when the target tooth position is selected as the central incisor in the lower jaw, the extension direction on which the corresponding reference extension line depends can be the first reverse direction D1' (for example, any of the above reference reverse directions).

[0181] Step 4122B, when the reference extension end no longer forms a projection end point with the oral three-dimensional model, determine that the coordinates of the finally formed projection end point are the boundary feature coordinates.

[0182] During the elongation process of the reference extension line, the projection end points will continuously be generated on the oral three-dimensional model 100. When the above at least three reference extension lines no longer generate new projection end points with the oral three-dimensional model 100, it is considered that the boundary of the oral three-dimensional model 100 has been touched. Based on this, the fifth model boundary point 10E corresponding to the fifth tooth position 11e, the seventh model boundary point 10G corresponding to the seventh tooth position 11g, and the fifth marked boundary point 10Em corresponding to the fifth gingival margin marking point 11Em on the fifth tooth position 11e can be determined, so that the coordinates of the above model boundary points and marked boundary points are jointly used as the boundary feature coordinates.

[0183] Step 413, calculate the feature distance value between the reference feature coordinates and the boundary feature coordinates to characterize the positional relationship between the target tooth position and the model boundary.

[0184] Step 421, compare the numerical magnitude of the feature distance value with the distance integrity criterion value representing the preset position condition.

[0185] Step 422, judge whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0186] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0187] The principle of the above technical solution is that, based on the anatomical characteristics of the labial frenum, it is usually located in the middle area above the two maxillary central incisors and the middle area below the two mandibular central incisors. Therefore, the positions of the central incisors can be selected as the target tooth positions, and the range of the area where the labial frenum is located can be defined accordingly, thus improving the operation speed. Since the boundary features of the model corresponding to the relative feature coordinates are retrieved, it is possible to further determine whether the area of the labial frenum on the three-dimensional oral model is missing based on the positional relationship between the relative feature coordinates and the model boundary features. The technical solution for determining whether there is a missing part can use the same method for generating oral structure information as that for determining whether there is a missing part in the vestibular sulcus described above, which will not be elaborated here.

[0188] It can be seen that on this basis, those skilled in the art can obtain the positional relationship between the target tooth position and the model boundary corresponding to different regions on the three-dimensional oral model based on the anatomical characteristics of different oral tissues. In this application, the technical solutions corresponding to all oral tissues are not exhaustively listed.

[0189] As Figure 7 and Figure 16 shown, the fourth embodiment of the method for generating oral structure information based on the above embodiment is provided in this application. This fourth embodiment provides the pre-steps 61 to 62 for step 421A. Steps 61 to 62 can be set at any position before step 421A, and this application does not limit this. In the following, this application will take the fourth embodiment formed by setting the pre-steps before step 410 as an example for expansion. It can be understood that for other steps except these pre-steps, they will not be expanded in the following. This fourth embodiment specifically includes the following steps.

[0190] Step 61, obtain at least two sets of three-dimensional training model data, and at least two sets of distance training data corresponding to the three-dimensional training model data.

[0191] The "preset position condition" can be directly obtained and set according to experimental and network data. Of course, the technical solution provided in the fourth embodiment of this application can also be used to obtain a more accurate and practical preset position condition.

[0192] In this embodiment, it is preferable to calculate the preset position condition by using three-dimensional training model data that at least includes the corresponding features of the complete intraoral tissue region. For example, when the generation of oral structure information pays more attention to the vestibular sulcus, the three-dimensional training model data used to construct the preset position condition preferably includes all the vestibular sulcus features and dental position features. However, for the position and shape of, for example, the root eminence, missing is allowed. It should be noted at this time that although the data used to formulate the preset position condition in the fourth embodiment is three-dimensional training model data, when only local features such as the labial frenum and the prominence amplitude of the maxillofacial region are concerned, it can also be partial or local intraoral image data in the three-dimensional training model. When applying planar image data, steps of establishing the pixel size relationship and converting two-dimensional features into three-dimensional features need to be added.

[0193] Wherein, the distance training data includes the distance between the conditional training coordinates corresponding to the reference feature coordinates on the three-dimensional training model and the target tissue coordinates corresponding to the intraoral tissue region on the three-dimensional training model. The connection line between the conditional training coordinates and the target tissue coordinates extends along the first direction.

[0194] As Figure 6 and Figure 17 shown, taking the intraoral tissue region as the vestibular sulcus region and the first dental position 11a corresponding to the dental position of the left maxillary central incisor as an example, the reference feature coordinates corresponding to the first dental position 11a in the actual oral three-dimensional model 100 point to the coordinates of the first gingival margin midpoint 11A, the conditional training coordinates corresponding to the dental position of the left maxillary central incisor in the three-dimensional training model point to the coordinates of the first training gingival margin midpoint 1121U, and moreover, the target tissue coordinates corresponding to the dental position of the left maxillary central incisor in the three-dimensional training model point to the coordinates of the first training vestibular sulcus bottom point 1321U. Based on this, the distance training data can be jointly determined according to the coordinates of the first training gingival margin midpoint 1121U and the coordinates of the first training vestibular sulcus bottom point 1321U.

[0195] For the establishment of the corresponding relationship between the above data, it is established respectively with the first gingival margin midpoint 11A and the first training gingival margin midpoint 1121U as the benchmarks according to the first direction D1. Since there is a natural corresponding relationship for the gingival margin midpoints, the corresponding relationship between the first gingival margin midpoint 11A and the first training gingival margin midpoint 1121U retrieved by traversing and searching according to the first direction D1 can be quickly and stably established.

[0196] The distance training data is multiple data formed for a single dental position in multiple three-dimensional training model data. In other words, one distance training data corresponding to the first dental position can be obtained in the first three-dimensional training model, and another distance training data corresponding to the second dental position can be obtained in the second three-dimensional training model.

[0197] Step 62: Calculate the average distance data from the training data and the training distance standard deviation, and calculate the distance integrity criterion value based on the difference between the average distance data and the product of the training distance standard deviation and a preset distribution probability coefficient.

[0198] The distribution probability coefficient refers to a preset probability distribution, which can be a discrete distribution such as a geometric distribution, a binomial distribution, a Poisson distribution, etc., or a continuous distribution such as a uniform distribution, an exponential distribution, and a normal distribution. The distance integrity criterion value established thereby is used to characterize the preset position condition, and can estimate and cover various situations that may occur on the three-dimensional oral model to a greater extent, thereby reducing the probability of misjudgment.

[0199] Step 410: Obtain three-dimensional oral model data.

[0200] Step 411: Determine at least one tooth position on the three-dimensional oral model as the target tooth position, and calculate the reference feature coordinates of the target tooth position based on a preset feature recognition rule to characterize the target tooth position feature.

[0201] Step 412: According to the reference feature coordinates, determine the boundary feature coordinates corresponding to the reference feature coordinates on the three-dimensional oral model in the first direction to characterize the model boundary feature.

[0202] Step 413: Calculate the feature distance value between the reference feature coordinates and the boundary feature coordinates to characterize the positional relationship between the target tooth position and the model boundary.

[0203] Step 421: Compare the numerical magnitudes of the feature distance value and the distance integrity criterion value characterizing the preset position condition.

[0204] Step 422: Determine whether the intraoral tissue area corresponding to the target tooth position in the three-dimensional oral model meets the preset integrity condition according to the comparison result.

[0205] If not, jump to Step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the three-dimensional oral model data, and reconstruct the part representing the oral feature data on the three-dimensional oral model data to obtain the oral structure information.

[0206] Thus, based on the three-dimensional training model data with a relatively high degree of data integrity, an integrity criterion value with relatively high reference value can be formed, which can better determine whether feature loss occurs in the regional part of the corresponding target tooth position. At the same time, during the implementation of the above-mentioned fourth embodiment, the first direction can also be adjusted to establish other positional relationships and preset position conditions based on the actual oral three-dimensional model and the three-dimensional training model, so as to complete the integrity judgment of the intraoral tissue region in dimensions such as the width of the dental arch, the curvature of the dental arch, the protrusion amplitude of the maxillofacial region, and the width of the labial frenum.

[0207] In an application scenario, preferably, the intraoral tissue region includes the vestibular sulcus, the oral feature data includes the vestibular sulcus height data, the target tooth positions include the maxillary central incisor tooth position and the mandibular lateral incisor tooth position, and the distance training data includes the first sulcus bottom distance parameter corresponding to the maxillary central incisor tooth position and the second sulcus bottom distance parameter corresponding to the mandibular lateral incisor tooth position. Based on this, on the basis of the above-mentioned fourth embodiment, as Figure 16 shown, the step 62 may specifically include the following steps as part of it.

[0208] Step 621A, calculate the first sulcus bottom average distance value of all the first sulcus bottom distance parameters in all the distance training data, and the second sulcus bottom average distance value of all the second sulcus bottom distance parameters in all the distance training data, to obtain the average distance data.

[0209] Step 622A, calculate the first sulcus bottom distance standard deviation of all the first sulcus bottom distance parameters in all the distance training data, and the second sulcus bottom distance standard deviation of all the second sulcus bottom distance parameters in all the distance training data, to obtain the training distance standard deviation.

[0210] Taking Figure 17 the three-dimensional training model data represented by a set of distance training data as an object, and taking the left maxillary central incisor as the maxillary central incisor and the right mandibular lateral incisor as the mandibular lateral incisor as an example. The first sulcus bottom distance parameter points to the distance between the first training gingival margin midpoint 1121U and the first training vestibular sulcus bottom point 1321U that are sequentially distributed along the first direction D1 of the maxillary central incisor tooth position; the second sulcus bottom distance parameter points to the distance between the second training gingival margin midpoint 1142L and the second training vestibular sulcus bottom point 1342L that are sequentially distributed along the first direction D1 of the mandibular lateral incisor tooth position.

[0211] Based on this, the distance training data of all three-dimensional training model data (for example, there are n groups in total) can be integrated to obtain n groups of first sulcus bottom distance parameters corresponding to the maxillary left central incisor and n groups of second sulcus bottom distance parameters corresponding to the mandibular right lateral incisor. From this, their average values can be calculated and used as the first average sulcus bottom distance value and the second average sulcus bottom distance value respectively to form average distance data, and their standard deviations can be calculated and used as the first sulcus bottom distance standard deviation and the second sulcus bottom distance standard deviation respectively to form training distance standard deviations.

[0212] In a preferred embodiment, the distance training data includes sulcus bottom distance parameters corresponding to the positions of the maxillary left lateral incisor (i.e., Figure 17 the distance between the fifth training gingival margin midpoint 1122U and the fifth training vestibular sulcus bottom point 1322U along its first direction D1), the sulcus bottom distance parameter of the maxillary left central incisor position ( Figure 17 the distance between the first training gingival margin midpoint 1121U and the first training vestibular sulcus bottom point 1321U along its first direction D1), the sulcus bottom distance parameter of the maxillary right central incisor position ( Figure 17 the distance between the fourth training gingival margin midpoint 1111U and the fourth training vestibular sulcus bottom point 1311U along its first direction D1), the sulcus bottom distance parameter of the maxillary right lateral incisor position ( Figure 17 the distance between the third training gingival margin midpoint 1112U and the third training vestibular sulcus bottom point 1312U along its first direction D1), the sulcus bottom distance parameter of the mandibular left lateral incisor position ( Figure 17 the distance between the eighth training gingival margin midpoint 1132L and the eighth training vestibular sulcus bottom point 1332L along its first direction D1), the sulcus bottom distance parameter of the mandibular left central incisor position ( Figure 17 the distance between the seventh training gingival margin midpoint 1131L and the seventh training vestibular sulcus bottom point 1331L along its first direction D1), the sulcus bottom distance of the mandibular right central incisor position ( Figure 17 the distance between the sixth training gingival margin midpoint 1141L and the sixth training vestibular sulcus bottom point 1341L along its first direction D1) and the sulcus bottom distance parameter of the mandibular right lateral incisor position ( Figure 17 the distance between the second training gingival margin midpoint 1142L and the second training vestibular sulcus bottom point 1342L along its first direction D1). Among them, the first direction D1 corresponding to different target tooth positions may be different. Here, for the sake of simplifying the description, it is not specifically marked and distinguished. In this way, more accurate and reliable distance integrity criterion values can be calculated based on the sulcus bottom distance parameters of eight incisor tooth positions.

[0213] It should be emphasized that the bottom groove distance parameter characterizes the distance between the conditional training coordinates of the midpoint of the gingival margin corresponding to the incisor tooth position and the target tissue coordinates of the bottom groove feature point corresponding to the midpoint of the gingival margin. Therefore, in other scenarios, the bottom groove distance parameter or other parameters of other target tooth positions can be calculated based on the same logic.

[0214] In another application scenario, preferably, the intraoral tissue area includes the vestibular groove, or the root eminence, or both the vestibular groove and the root eminence. The oral feature data includes dental arch width data. The target tooth positions include the distal molar tooth positions on both sides of the upper jaw and the distal molar tooth positions on both sides of the lower jaw. The distance training data includes the upper dental arch width parameters corresponding to the distal molar tooth positions on both sides of the upper jaw, and the lower dental arch width parameters corresponding to the distal molar tooth positions on both sides of the lower jaw. Based on this, on the basis of the fourth embodiment above, as Figure 16 shown, step 62 may specifically include the following steps as a part thereof.

[0215] Step 621B, calculate the upper average width value of all the upper dental arch width parameters in all the distance training data, and the lower average width value of all the lower dental arch width parameters in all the distance training data, to obtain average distance data.

[0216] Step 622B, calculate the upper width standard deviation of all the upper dental arch width parameters in all the distance training data, and the lower width standard deviation of all the lower dental arch width parameters in all the distance training data, to obtain the training distance standard deviation.

[0217] Step 621B and step 622B correspond to step 621A and step 621A, with the dental arch width parameters as the calculation objects, so as to judge the integrity of the vestibular groove or the root eminence. The basic idea is the same as that of the previous application scenario, and it will not be described in detail here.

[0218] For the distal molar tooth positions on both sides of the upper jaw, preferably the left second molar tooth position and the right second molar tooth position of the upper jaw; for the distal molar tooth positions on both sides of the lower jaw, preferably the left second molar tooth position and the right second molar tooth position of the lower jaw. Of course, in the case of missing teeth, multiple teeth (the third molar erupts), etc., the tooth position farthest from the dental midline can be selected as the "distal molar tooth position".

[0219] For any specific example or derivative technical solution in the fourth embodiment, further preferably, the distribution probability coefficient is 2.

[0220] For example, in an application scenario where the oral feature data includes the height data of the vestibular sulcus, taking the tooth position of the maxillary central incisor as an object, if the average distance value of the first sulcus bottom is calculated as μ21 and the standard deviation of the first sulcus bottom distance is calculated as σ21, then in a preferred embodiment where the distribution probability coefficient m = 2, the first distance integrity criterion value ζ21 corresponding to the tooth position of the maxillary central incisor can be configured to at least satisfy:

[0221] ζ21 = μ21 - 2 * σ21.

[0222] Thus, the first characteristic distance value corresponding to the tooth position of the central incisor can be compared with the first distance integrity criterion value ζ21 to achieve the effect of "comparing the positional relationship with the preset position condition corresponding to the positional relationship".

[0223] In a preferred implementation manner, corresponding to step 413 described in any of the technical solutions herein, for "calculating the characteristic distance value between the reference feature coordinate and the boundary feature coordinate" therein, it can be preferably configured to include: calculating the distance value between the reference feature coordinate and the boundary feature coordinate in the extending direction of the tooth long axis to obtain the characteristic distance value.

[0224] Corresponding to Figure 6 in, for example, the characteristic distance value corresponding to the first tooth position 11a can be the distance value between the coordinate of the first gingival margin midpoint 11A and the coordinate of the first model boundary point 10A in the first reference direction D11.

[0225] Based on this, in an embodiment, the simplification of the characteristic distance value calculation step can be further achieved by establishing a special coordinate system. In this embodiment, the first direction D1 can be defined as the projection of the extending direction of the tooth long axis on the oral three-dimensional model 100. Correspondingly, the boundary feature coordinate and the reference feature coordinate are located in a preset model coordinate system, and the model coordinate system includes at least a first coordinate axis Rx and a third coordinate axis Rz. The third coordinate axis Rz extends along the extending direction of the tooth midline, and the first coordinate axis Rx extends along the extending direction of the central incisor width. At this time, the "calculating the distance value between the reference feature coordinate and the boundary feature coordinate in the extending direction of the tooth midline" can be specifically configured to include: calculating the coordinate difference between the reference feature coordinate and the boundary feature coordinate on the third coordinate axis. For example, that is, calculating the coordinate difference between the coordinate of the first gingival margin midpoint 11A and the coordinate of the first model boundary point 10A on the third coordinate axis Rz.

[0226] Such as Figure 7 and Figure 18As shown in the figure, the present application provides a fifth embodiment of the oral structure information generation method based on the above-described embodiment. In this fifth embodiment, step 421 is refined, specifically providing step 4211, step 4212A, and step 4212B, and step 422 is refined to provide step 4220 for step 4212A and step 4212B. It can be understood that for other steps except the refined steps, no further description will be given below. Similarly, since steps 4211 to 4220 belong to steps 421 and 422 in sequence, the descriptions of steps 421 and 422 themselves will be omitted below. The fifth embodiment specifically includes the following steps.

[0227] Step 410, obtain oral three-dimensional model data.

[0228] Step 411, determine at least one tooth position on the oral three-dimensional model as the target tooth position, and calculate the reference feature coordinates of the target tooth position based on a preset feature recognition rule to characterize the target tooth position feature.

[0229] Step 412, according to the reference feature coordinates, determine the boundary feature coordinates corresponding to the reference feature coordinates on the oral three-dimensional model in the first direction to characterize the model boundary feature.

[0230] Step 413, calculate the feature distance value between the reference feature coordinates and the boundary feature coordinates to characterize the positional relationship between the target tooth position and the model boundary.

[0231] Step 4211, determine whether the feature distance value is less than the distance integrity criterion value.

[0232] If so, jump to step 4212A to determine that the position in the oral three-dimensional model pointed to by the feature distance value is the feature missing position.

[0233] If not, jump to step 4212B to continue comparing the numerical size of the next feature distance value with the distance integrity criterion value.

[0234] Step 4220, according to the number of feature missing positions, determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the integrity condition.

[0235] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0236] Based on the above-mentioned fifth embodiment, it is possible to first determine the position where features are missing, and based on the number of positions where features are missing, evaluate whether the oral three-dimensional model falls within the range where the integrity condition is not satisfied.

[0237] As Figure 6 shown, following the definition of the first feature distance value and the first distance integrity criterion value ζ21 for the first tooth position 11a (i.e., the first incisor tooth position, or the maxillary left central incisor tooth position) in the previous text, when it is determined that the position at the first tooth position 11a is a position where features are missing. Following the definition of the fourth feature distance value and the fourth distance integrity criterion value ζ41 for the fourth tooth position 11d (i.e., the sixth incisor tooth position, or the mandibular right central incisor tooth position) in the previous text, when it is determined that the position at the fourth tooth position 11d is not a position where features are missing, and thus the judgment of the next target tooth position is carried out.

[0238] Figure 6 shows the distribution of intraoral tissues at a total of eight incisor tooth positions in the maxilla and mandible. It can be seen that the positions corresponding to the left lateral incisor, left central incisor, and right lateral incisor in the maxilla are positions where features are missing. At this time, the "number of positions where features are missing" corresponds to 3. Based on this, a fixed threshold or a dynamic threshold can be set for this number, so as to further determine whether the intraoral tissue region corresponding to the vestibular sulcus height indicated by the above eight incisor tooth positions satisfies the integrity condition.

[0239] For the specific judgment rule of the integrity condition provided in step 4220, in a specific example based on this embodiment, it can be specifically configured to include the following steps. However, it can be understood that this judgment rule is not limited to this fifth embodiment, and those skilled in the art are capable of combining the judgment rule with steps 42, step 422, or step 4220 in other embodiments, examples, or specific examples, so as to form a preferred technical solution of the corresponding embodiment.

[0240] Step 4221, judge the numerical magnitude relationship between the number of positions where features are missing and the allowable error number value.

[0241] If the number of positions where features are missing is greater than or equal to the allowable error number value, jump to step 4222A and determine that the intraoral tissue region corresponding to the target tooth position in the oral three-dimensional model does not satisfy the integrity condition.

[0242] If the number of positions where features are missing is less than the allowable error number value, jump to step 4222B and determine that the intraoral tissue region corresponding to the target tooth position in the oral three-dimensional model satisfies the integrity condition.

[0243] In this way, it is possible to directly determine whether the oral tissue area meets the integrity condition by comparing the preset allowable error quantity value with the quantity of feature missing positions, thereby transforming the abstract concept of the integrity condition into data content that can be obtained through actual operations.

[0244] For any technical solution including the above specific examples, further preferably, the allowable error quantity value is an integer greater than or equal to one-half of the quantity of the target tooth positions. In this way, by setting the allowable error quantity value as a threshold that dynamically changes with the quantity of the target tooth positions, it can reflect the proportion of the quantity of feature missing positions in the quantity of all target tooth positions for which the feature distance values are calculated, and overall control the requirements for the integrity of the oral three-dimensional model.

[0245] For example, if a total of eight tooth positions including the maxillary left and right central incisor positions, the maxillary left and right lateral incisor positions, the mandibular left and right central incisor positions, and the mandibular left and right lateral incisor positions are selected as the target tooth positions, then when the quantity of feature missing positions is greater than or equal to 4, it is determined that the oral tissue area corresponding to the target tooth positions in the oral three-dimensional model does not meet the integrity condition. Continuing the previous description, since Figure 6 in the shown oral three-dimensional model, there are 3 feature missing positions corresponding to the maxillary left lateral incisor, the maxillary left central incisor, and the maxillary right lateral incisor, which is less than one-half of the total quantity of the target tooth positions, so it can be considered to meet the integrity condition.

[0246] Preferably, after determining that the oral tissue area corresponding to the target tooth positions in the oral three-dimensional model data meets the integrity condition, it is also possible to further perform feature extraction and fitting on the oral three-dimensional model itself. For feature extraction, since the oral tissue areas corresponding to the maxillary right central incisor position, the mandibular right central incisor position, and the mandibular right lateral incisor position all contain relatively complete vestibular sulcus features, thus, the vestibular sulcus height data included in the oral feature data can be obtained accordingly, that is, the oral feature data is extracted based on the non-feature missing positions. For fitting, the structures on the left and right sides of the maxillofacial region are usually symmetrical, so at least the vestibular sulcus features corresponding to the maxillary right central incisor position can be filled into the feature missing positions corresponding to the maxillary left central incisor position after mirroring and other processes, that is, based on the non-feature missing positions, the oral structure information is reconstructed based on the oral anatomical features.

[0247] Such as Figure 19As shown in the figure, another embodiment of the present application provides an oral structure information generation method. The application program or instructions corresponding to this method can be carried on the above storage medium and / or the above oral structure information generation system 300 to achieve the technical effect of oral structure information generation. This another embodiment mainly refines steps 41 and 42 in the previous embodiment. Corresponding to step 41, steps 410 to 413' are provided, and corresponding to step 42, steps 421' to 422 are provided. The oral structure information generation method may specifically include the following steps.

[0248] Step 410, obtain oral three-dimensional model data.

[0249] Step 411', determine that at least the incisor tooth positions on the oral three-dimensional model are target tooth positions, and based on a preset feature recognition rule, calculate the reference feature coordinates of the target tooth positions to characterize the target tooth position features.

[0250] Step 412', according to the reference feature coordinates, determine the upper boundary extreme coordinates and the lower boundary extreme coordinates located on the oral three-dimensional model along the first direction.

[0251] Step 413', calculate the feature height value between the upper boundary extreme coordinates and the lower boundary extreme coordinates to characterize the positional relationship between the upper jaw model boundary and the lower jaw model boundary.

[0252] Step 421', compare the feature height value with the height integrity criterion value representing the preset position condition, and judge whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0253] Step 422, judge whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0254] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0255] Wherein, the upper boundary extreme coordinates are located on the upper jaw of the oral three-dimensional model and have a maximum value of the distance from the occlusal plane, and the lower boundary extreme coordinates are located on the lower jaw of the oral three-dimensional model and have a maximum value of the distance from the occlusal plane.

[0256] Another embodiment provided by the present application provides a technical solution different from the above embodiment, which uses the positional relationship between the upper jaw model boundary and the lower jaw model boundary to determine whether the integrity condition is met. For steps such as determining the target tooth position and calculating the reference feature coordinates, the technical solutions provided above can be alternatively applied. Further, the other further improved technical solutions described in the previous embodiment can also be appropriately adjusted and alternatively applied to this another embodiment, thereby forming more derivative technical solutions.

[0257] Combined with Figure 21 As shown, in this another embodiment, at least eight incisor tooth positions are determined as target tooth positions, and the upper jaw model boundary features and the lower jaw model boundary features are traversed and retrieved according to the corresponding at least eight reference feature coordinates, and finally the upper boundary extreme value coordinate point 101M and the lower boundary extreme value coordinate point 102M that are respectively the farthest from the occlusal plane are determined. Since the positional relationship between the upper boundary extreme value coordinate point 101M and the lower boundary extreme value coordinate point 102M can be used to characterize the positional relationship between the upper jaw model boundary and the lower jaw model boundary, thus, the abstract positional relationship between the boundaries can be transformed into the positional relationship between specific coordinate points, which is convenient for algorithm design and judgment.

[0258] As Figure 19 and Figure 20 As shown, the present application provides a first embodiment of an oral structure information generation method based on the above embodiment. This first embodiment refines step 412', specifically provides step 4121' and step 4122', and refines step 413', providing step 4130' corresponding to step 4121' to step 4122'. It can be understood that for other steps except the refined steps, they will not be described in detail below. Similarly, since step 4121' to step 4130' belong to step 412' and step 413' in sequence, the descriptions of step 412' and step 413' themselves will be omitted below. This first embodiment specifically includes the following steps.

[0259] Step 410, obtain oral three-dimensional model data.

[0260] Step 411', determine at least the incisor tooth positions on the oral three-dimensional model as target tooth positions, and calculate the reference feature coordinates of the target tooth positions based on a preset feature recognition rule to characterize the target tooth position features.

[0261] Step 4121', calculate the upper boundary coordinate set corresponding to the upper jaw target tooth positions on the oral three-dimensional model, and the lower boundary coordinate set corresponding to the lower jaw target tooth positions on the oral three-dimensional model to characterize the model boundary features.

[0262] Step 4122', traverse and determine that the upper boundary coordinate corresponding to the third coordinate axis with the maximum coordinate value in the set of upper boundary coordinates is the upper boundary extreme coordinate, and traverse and determine that the lower boundary coordinate corresponding to the third coordinate axis with the minimum coordinate value in the set of lower boundary coordinates is the lower boundary extreme coordinate.

[0263] Step 4130', calculate the coordinate difference between the upper boundary extreme coordinate and the lower boundary extreme coordinate on the third coordinate axis as the characteristic height value.

[0264] Step 421', compare the characteristic height value with the height integrity criterion value representing the preset position condition, and determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0265] Step 422, determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0266] If not, jump to Step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0267] Wherein, the first direction is the projection of the extension direction of the tooth long axis on the jaw surface of the oral three-dimensional model, the reference feature coordinate, the upper boundary extreme coordinate and the lower boundary extreme coordinate are located in the preset model coordinate system, the origin of the model coordinate system is located on the line where the incisal ridge of the incisor is located, and at least includes a first coordinate axis and a third coordinate axis, the third coordinate axis extends along the extension direction of the tooth midline, and the first coordinate axis extends along the extension direction of the width of the central incisor.

[0268] Combined Figure 6 and Figure 21 As shown, the first coordinate axis is Rx, the third coordinate axis is Rz, the origin Ro of the model coordinate system is located on the line where the incisal ridge of the central incisor or lateral incisor is located, and is preferably located at a position between the mesial incisal angle of the left maxillary central incisor and the mesial incisal angle of the right maxillary central incisor. Of course, the origin Ro of the model coordinate system can also be interpreted as preferably located at the intersection of the tooth midline and the occlusal plane.

[0269] In this first embodiment, by traversing all the upper boundary coordinates and all the lower boundary coordinates, the boundary feature coordinates that are respectively the farthest from the occlusal plane are determined. When the target tooth position is determined to be an incisor or the target intraoral tissue area is determined to be the vestibular sulcus area, based on the above technical solution, eight boundary feature coordinates corresponding to the eight incisor tooth positions can be determined. Further, among the four boundary feature coordinates located in the maxilla, the spatial point coordinate with the largest coordinate value on the third coordinate axis Rz is selected as the upper boundary extreme value coordinate 101M, and among the four boundary feature coordinates located in the mandible, the spatial point coordinate with the smallest coordinate value on the third coordinate axis Rz is selected as the lower boundary extreme value coordinate 102M, so as to calculate the coordinate difference between the two on the third coordinate axis Rz as the feature height value ΔH.

[0270] As Figure 19 and Figure 22 shown, the present application provides a second embodiment of an oral structure information generation method based on the above embodiment. This second embodiment provides pre-steps 61' to 62' for step 421'. Steps 61' to 62' can be set at any position before step 421', and the present application does not limit this. In the following, the present application will take the second embodiment formed by setting the pre-steps before step 410 as an example for elaboration. It can be understood that for other steps except these pre-steps, no further elaboration will be provided below. This second embodiment specifically includes the following steps.

[0271] Step 61', obtain at least two groups of three-dimensional training model data and at least two groups of height training data corresponding to the three-dimensional training model data.

[0272] Step 62', calculate the average height data and the training height standard deviation of the height training data, and calculate the height integrity criterion value according to the difference between the product of the average height data and the training height standard deviation and the preset distribution probability coefficient.

[0273] Step 410, obtain oral three-dimensional model data.

[0274] Step 411', determine at least the incisor tooth position on the oral three-dimensional model as the target tooth position, and calculate the reference feature coordinates of the target tooth position based on the preset feature recognition rule to characterize the target tooth position feature.

[0275] Step 412', determine the upper boundary extreme value coordinate and the lower boundary extreme value coordinate located on the oral three-dimensional model along the first direction according to the reference feature coordinates.

[0276] Step 413', calculate the feature height value between the upper boundary extreme value coordinate and the lower boundary extreme value coordinate to characterize the positional relationship between the maxillary model boundary and the mandibular model boundary.

[0277] Step 421’, compare the feature height value with the height integrity criterion value representing the preset position condition in terms of numerical magnitude, and determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0278] Step 422, determine whether the intraoral tissue area corresponding to the target tooth position in the oral three-dimensional model meets the preset integrity condition according to the comparison result.

[0279] If not, jump to Step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information.

[0280] Wherein, the height training data includes the distance between the upper tissue extreme coordinate corresponding to the intraoral tissue area on one side of the first direction on the three-dimensional training model and the lower tissue extreme coordinate corresponding to the intraoral tissue area on the other side of the first direction on the three-dimensional training model.

[0281] Combined Figure 6 、 Figure 17 and Figure 21 As shown, when the intraoral tissue area is determined to be the vestibular sulcus area, when the third training vestibular sulcus bottom point 1312U is the highest point relative to the occlusal plane on the upper vestibular sulcus of the three-dimensional training model data, and the sixth training vestibular sulcus bottom point 1341L is the lowest point relative to the occlusal plane on the lower vestibular sulcus of the three-dimensional training model data, it can be considered that the coordinate of the third training vestibular sulcus bottom point 1312U is the upper tissue extreme coordinate, and the coordinate of the sixth training vestibular sulcus bottom point 1341L is the lower tissue extreme coordinate, so as to obtain the distance between the two as the height training data. Of course, the determination of the upper tissue extreme coordinate and the lower tissue extreme coordinate above only represents a special specific situation, and for different three-dimensional training model data, there may be other situations for the positions pointed to by the above extreme coordinates.

[0282] The second embodiment and the first embodiment of this implementation manner are not necessarily independent of each other. When combined with each other, they can better establish the correspondence between the three-dimensional training model data and the oral three-dimensional model data, and provide a more specific and accurate solution for the calculation of the integrity condition. For example, in this second embodiment, the calculation process of the height training data can be consistent with the characteristic height value. Preferably, when the characteristic height value ΔH is configured as the coordinate difference between the upper boundary extreme point 101M and the lower boundary extreme point 102M in the direction of the third coordinate axis Rz, and the third coordinate axis Rz extends along the dental midline direction, the height training data can be correspondingly configured as the distance value between the upper tissue extreme coordinate and the lower tissue extreme coordinate in the extension direction of the dental midline, or the projection length value of the line connecting the two extreme coordinate points in the dental midline direction. Of course, if the calculation steps of the characteristic height value are adjusted, the selection of the height training data can be adjusted correspondingly.

[0283] The definitions of the average height data and the training height standard deviation can be similar to the average height data and the training distance standard deviation described above. The distribution probability coefficient can also be similar to the distribution probability coefficient corresponding to the distance training data described above. It can be explained that there are only differences in the data between the two. The data basis of one of them points to the positional relationship between the dental position feature and the oral tissue feature, and the data basis of the other points to the positional relationship between the upper and lower oral tissue features, or between the left and right oral tissue features, or between at least two oral tissues at other different positions.

[0284] For any specific example or derivative technical solution in the second embodiment, further preferably, the distribution probability coefficient is 3.

[0285] For example, in the application scenario where the oral feature data includes the vestibular sulcus height data, if the calculated average height data corresponding to all height training data Dul is μ Dul and the training height standard deviation is σ Dul , then in the preferred embodiment where the distribution probability coefficient p = 3, the height integrity criterion value ζ Dul can be configured to at least satisfy:

[0286] ζ Dul = μ Dul - 3 * σ Dul .

[0287] Thus, the characteristic height value ΔH can be compared with the height integrity criterion value ζ DulTo achieve the effect of "comparing the positional relationship with the preset position conditions corresponding to the positional relationship". It should be noted that since the characteristic height value and the height integrity criterion value are already data representing the integrity of the corresponding intraoral tissue, thus, there is no need to count their quantities and set the allowable error quantity value. If the comparison result is that the characteristic height value is less than the height integrity criterion value, it can be directly determined that the oral three-dimensional model does not meet the preset integrity condition.

[0288] When combining the above two embodiments, the steps for determining whether the preset integrity condition is met can also be specifically performed as follows: If the number of feature missing positions is greater than or equal to the allowable error quantity value, or the characteristic height value is less than the height integrity criterion value, it is determined that the oral three-dimensional model does not meet the preset integrity condition; if the number of feature missing positions is less than the allowable error quantity value, and the characteristic height is greater than the height integrity criterion value, it is determined that the oral three-dimensional model meets the preset integrity condition. The feature extraction steps after meeting the preset integrity condition can refer to the first embodiment and will not be elaborated here.

[0289] Based on any of the above embodiments, as Figure 4 and Figure 23 shown, the first embodiment of a method for generating oral structure information provided by the present application refines step 43 and specifically provides steps 431 to 433. It can be understood that for other steps except the refined steps, they will not be further described below. For example, the step 40 may specifically include steps 41 and 42. The first embodiment specifically includes the following steps.

[0290] Step 40, obtain oral three-dimensional model data, and determine whether the specified intraoral tissue area in the oral three-dimensional model meets the preset integrity condition.

[0291] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area from the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information. The step 43 specifically includes:

[0292] Step 431, call the corresponding first neural network model and second neural network model according to the intraoral tissue area.

[0293] Step 432, input the intraoral photo image data into the first neural network model for extracting the region of interest to obtain the feature region image data.

[0294] Step 433, input the feature region image data into the second neural network model for feature recognition to obtain the oral feature data corresponding to the intraoral tissue area.

[0295] In this way, the steps of feature extraction from intraoral image data can be divided into two steps, reducing the amount of basic data for feature recognition, thereby accelerating the speed of obtaining oral feature data through the analysis of intraoral image data. Performing the above-mentioned steps of feature extraction using a neural network model can adapt to various analysis scenarios, and has higher accuracy and stronger universality compared to simply using parameters such as chroma, RGB values, or grayscale in the image.

[0296] In a specific example of this first embodiment, model training steps 71 to 74 for step 43 (i.e., the above-mentioned steps 431 to 433) can be provided as the model training steps. Specifically, the steps 71 to 74 can be set at any position before step 431, and this application does not limit this. In the following, this application will take the specific example formed by setting the above-mentioned steps before step 41 as an example for expansion. It can be understood that for other steps except the above-mentioned steps, no further expansion will be described below. This specific example can include the following steps.

[0297] Step 71, obtain at least two sets of training image data, and the region of interest markings corresponding to the training image data.

[0298] Step 72, call a preset convolutional neural network model, and perform iterative training with the training image data and the region of interest markings as the model inputs to obtain the first training parameters and the corresponding first neural network model.

[0299] Step 73, obtain at least two sets of training image data, and the tooth position markings and the markings of the bottom of the vestibular sulcus corresponding to the training image data.

[0300] Step 74, call a preset convolutional neural network model, and perform iterative training with the training image data, the tooth position markings, and the markings of the bottom of the vestibular sulcus as the model inputs to obtain the second training parameters and the corresponding second neural network model.

[0301] Step 40, obtain oral three-dimensional model data, and determine whether a specified intraoral tissue region in the oral three-dimensional model meets a preset integrity condition.

[0302] If not, jump to step 43, obtain the corresponding intraoral image data, extract the oral feature data corresponding to the intraoral tissue region from the intraoral image data, establish the pixel size relationship between the intraoral image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information. The step 43 specifically includes:

[0303] Step 431, call the corresponding first neural network model and second neural network model according to the intraoral tissue region.

[0304] Step 432: Input the intraoral image data into the first neural network model for extracting the region of interest to obtain the feature region image data.

[0305] Step 433: Input the feature region image data into the second neural network model for feature recognition to obtain the oral cavity feature data corresponding to the intraoral tissue region.

[0306] Wherein, the extended range of the region of interest marked in the corresponding training image covers at least the upper lip, lower lip, and dental arch in the training image.

[0307] Building the first neural network model and the second neural network model using convolutional neural networks can better adapt to their use for local feature analysis. On the one hand, it can reduce the amount of data in the operation process by using its characteristics of sparse connection, weight sharing, and downsampling; on the other hand, it can ensure the accuracy of feature recognition by using its translational invariance.

[0308] In this embodiment, the extended range of the region of interest marked is defined, and the position for feature extraction is determined in the region where the upper lip, lower lip, and dental arch are located, which can avoid the influence of human facial features on the feature extraction process of the intraoral image and also avoid the increase in the amount of operation data caused by the extraction of unnecessary features.

[0309] Based on any of the above embodiments, as Figure 24 shown, the second embodiment of the method for generating oral cavity structure information provided by this application refines step 43 and specifically provides steps 434 to 435. It can be understood that for other steps except the refined steps, they will not be elaborated below. For example, step 40 can specifically include steps 41 and 42. This second embodiment specifically includes the following steps.

[0310] Step 40: Obtain the oral cavity three-dimensional model data, and determine whether the specified intraoral tissue region in the oral cavity three-dimensional model meets the preset integrity condition.

[0311] If it does not meet the condition, jump to step 43, obtain the corresponding intraoral image data, extract the oral cavity feature data corresponding to the intraoral tissue region in the intraoral image data, establish the pixel size relationship between the intraoral image data and the oral cavity three-dimensional model data, and reconstruct the part representing the oral cavity feature data on the oral cavity three-dimensional model data to obtain the oral cavity structure information. The specific steps of step 43 include:

[0312] Step 434: Determine at least one target tooth position corresponding to each other in the intraoral image and the oral cavity three-dimensional model as the relative reference tooth position, and calculate the size data of the tooth crown of the relative reference tooth position in at least one same direction in the intraoral image and the oral cavity three-dimensional model, respectively obtaining the reference pixel size data and the reference physical size data.

[0313] Step 435: According to the reference pixel size data and the reference physical size data, a size mapping factor is obtained by fitting to characterize the pixel size relationship.

[0314] Two points need to be explained. First, the second embodiment can be combined with the first embodiment described above and the third embodiment below to form a more complete Step 43. Of course, taking the three as different embodiments can also achieve corresponding technical effects respectively. Second, the following will be combined with Figure 25 to give examples of the detailed parts. Figure 25 The Z2 part and the Z3 part in Figure 21 are corresponding partial schematic diagrams. Among them, the Z2 part points to Figure 25 which is the partial intraoral tissue structure concentrated at the position of the maxillary left central incisor in the oral three-dimensional model. The Z3 part is the partial intraoral tissue structure corresponding to the intraoral photo image at the position of the maxillary left central incisor. Referring to

[0315] Combined with Figure 21 and Figure 25 as shown, for example, if the maxillary left central incisor position in Z2 and Z3 is selected as the relative reference tooth position, the pixel size relationship can be established based on the size data in the same direction of this tooth position. For example, the length, width of its dental crown or the area of the labial surface of the dental crown can be used as the size data, so as to obtain the reference pixel size data corresponding to the intraoral photo image and the reference physical size data corresponding to the oral three-dimensional model.

[0316] Specifically, the unit of the reference pixel size data is pixel, or can be interpreted as the number of pixels. The unit of the reference physical size is millimeter, or interpreted as the length of the size data. In this way, the mapping relationship between the planar image and the three-dimensional image can be established, which is convenient for subsequent reconstruction of oral features.

[0317] Preferably, the at least one same direction includes the dental crown width direction. In this way, it can avoid the influence on the establishment of the pixel size relationship caused by the inconsistent length of the anterior and posterior dental crowns and the area of the labial surface of the dental crown due to special situations such as gingival recession, and use the relatively stable dental crown width as the data basis for establishing the pixel size relationship.

[0318] Preferably, the size mapping factor is the quotient of the reference physical size data and the reference pixel size data. In this way, the physical size represented by a single pixel in the intraoral photo image can be calculated, that is, the length value (unit: millimeter) corresponding to a single pixel in the oral three-dimensional model. Using such "scale" - like data can ensure the stability of the feature correspondence relationship during the reconstruction process.

[0319] Based on this, the present application provides a specific example based on the above-mentioned second embodiment, specifically refining the process of calculating the reference pixel size data and the reference physical size data. By selecting reference feature points at corresponding tooth positions on the intraoral image and the oral three-dimensional model, the establishment of the mapping relationship between the two is completed. As Figure 26 or Figure 27 shown, this specific example is basically the same as other steps in the above-mentioned second embodiment, but the following refined steps are provided for the step 434.

[0320] Step 81, determine the first reference feature point and the second reference feature point relative to the reference tooth position on the intraoral image, calculate and obtain the reference pixel size data according to the number of pixels between the first reference feature point and the second reference feature point.

[0321] Step 82, determine the third reference feature point and the fourth reference feature point relative to the reference tooth position on the oral three-dimensional model, calculate and obtain the reference physical size data according to the Euclidean distance between the third reference feature point and the fourth reference feature point.

[0322] Wherein, the first reference feature point and the second reference feature point are located on both sides of the long axis of the tooth body of the relative reference tooth position, and the distance between the first reference feature point and the incisal edge of the corresponding relative reference tooth crown is equal to the distance between the second reference feature point and the incisal edge of the corresponding relative reference tooth crown. The third reference feature point and the fourth reference feature point are located on both sides of the long axis of the tooth body of the relative reference tooth position, and the distance between the third reference feature point and the incisal edge of the corresponding relative reference tooth crown is equal to the distance between the fourth reference feature point and the incisal edge of the corresponding relative reference tooth crown.

[0323] Combined with Figure 25 shown, on the intraoral image, if the relative reference tooth position is determined to be the tooth position of the left maxillary central incisor, the first reference feature point and the second reference feature point can be, respectively, the points on the mesial boundary and the distal boundary located on both sides of the long axis 1121r of the left maxillary central incisor. If the relative reference tooth position is determined to be the tooth position of the right maxillary central incisor, the first reference feature point and the second reference feature point can be, respectively, the points on the mesial boundary and the distal boundary located on both sides of the long axis 1111s of the right maxillary central incisor. If the relative reference tooth positions are determined to be the tooth position of the left maxillary central incisor and the tooth position of the right maxillary central incisor, the first reference feature point and the second reference feature point can also be, respectively, the points on the distal boundary of the left maxillary central incisor tooth position and the distal boundary of the right maxillary central incisor tooth position located on both sides of the long axes of the two. In addition, the distances of the above two reference feature points from the incisal edge of the tooth crown are equal, which can ensure that they accurately reflect the width characteristics of the tooth crown.

[0324] Correspondingly, in the three-dimensional oral model, it also includes the long axis 1121r' of the tooth body at the position of the left maxillary central incisor and the long axis 1111r' of the tooth body at the position of the right maxillary central incisor. Based on a technical solution similar to that of the first reference feature point and the second reference feature point, at least the above three selection schemes for the third reference feature point and the fourth reference feature point can be obtained.

[0325] Under the technical route of this specific example, such as Figure 24 and Figure 26 , the present application further provides a first specific example of the second embodiment. This first specific example mainly refines step 434, adds a tooth crown integrity judgment step 801 to the step, and refines step 802A executed after judging that the tooth crown is complete, as well as steps 811A and 812A belonging to step 81, so as to realize the determination of the relative reference tooth position and the calculation of the reference pixel size. It can be understood that for other steps except the refined steps, they will not be described in detail below. For example, step 40 may specifically include steps 41 and 42. Similarly, since steps 801 to 82 belong to step 434, the description of step 434 itself will be omitted below; since steps 811A and 812A belong to step 81, the description of step 81 itself will be omitted below. This first specific example specifically includes the following steps.

[0326] Step 40, obtain three-dimensional oral model data, and judge whether a specified intraoral tissue area in the three-dimensional oral model meets a preset integrity condition.

[0327] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the three-dimensional oral model data, and reconstruct the part representing the oral feature data on the three-dimensional oral model data to obtain oral structure information. The specific steps of step 43 include:

[0328] Step 430, obtain the corresponding intraoral photo image data.

[0329] Step 801, judge whether the left maxillary central incisor position and the right maxillary central incisor position in the intraoral photo image and the three-dimensional oral model both include complete tooth crowns.

[0330] If so, jump to step 802A to determine the left maxillary central incisor position and the right maxillary central incisor position corresponding to each other in the intraoral photo image and the three-dimensional oral model as the relative reference tooth positions.

[0331] Step 811A: Determine a first reference feature point at the distal boundary of the crown of the maxillary left central incisor tooth position in the intraoral image, and determine a second reference feature point at the distal boundary of the crown of the maxillary right central incisor tooth position in the intraoral image.

[0332] Step 812A: Calculate the number of pixels between the first reference feature point and the second reference feature point, and use half of the number of pixels as the reference pixel size data.

[0333] Step 82: Determine a third reference feature point and a fourth reference feature point relative to the reference tooth position on the oral three-dimensional model, calculate the Euclidean distance between the third reference feature point and the fourth reference feature point, and obtain the reference physical size data.

[0334] Step 435: According to the reference pixel size data and the reference physical size data, fit to obtain a size mapping factor to characterize the pixel size relationship.

[0335] In this way, the specific example of the second embodiment can cope with special situations such as missing teeth, adaptively change the strategy for establishing the pixel size relationship, and thus has a wider application scenario. It can be understood that the specific example of the above preferred embodiment uses the central incisor tooth position as a reference and can obtain more definite data. However, the present application does not exclude technical solutions that use other tooth positions for pixel size relationship fitting.

[0336] The method for judging whether the tooth crown is complete can be based on the fitting of the aforementioned convex hull contour. For example, judge whether the area or distribution of the convex hull contour is uniform, whether the convex hull contour can be fitted, etc., which will not be elaborated here.

[0337] When judging that the tooth crowns are all complete, the overall width of the crowns of the two maxillary central incisor tooth positions can be used as the basis for fitting the pixel size relationship, which has higher accuracy. As shown in Figure 25 , in the intraoral image, the pixel point 1121s at the distal boundary of the maxillary left central incisor can be determined as the first reference feature point, and the pixel point 1111s at the distal boundary of the maxillary right central incisor can be determined as the second reference feature point. Calculate the number of pixels between the above two pixel points, and directly use it as the reference pixel size data. Preferably, calculate half of it as the reference pixel size data, which can reflect the average pixel situation of the width of the central incisor.

[0338] Correspondingly, for step 82, it can also include steps corresponding to step 811A and step 812A. The specific step descriptions will not be elaborated here. As shown in Figure 25It can be known that in the oral three-dimensional model, the distal boundary space point 1121s' of the left maxillary central incisor can be determined as the third reference feature point, and the distal boundary space point 1111s' of the right maxillary central incisor can be determined as the fourth reference feature point, and the Euclidean distance between the above two pixel points is calculated.

[0339] Correspondingly, as Figure 24 and Figure 27 , the present application further provides a second specific example of a second embodiment. This second specific example provides a further judgment step 802B after judging that the tooth crown is incomplete, as well as steps 811B and 8112B belonging to step 81. It can be understood that for other steps except for the refinement steps, they will not be described in detail below. For example, step 40 may specifically include steps 41 and 42. Similarly, since steps 801 to 82 belong to step 434, the description of step 434 itself will be omitted below; since steps 811B and 812B belong to step 81, the description of step 81 itself will be omitted below. This second specific example specifically includes the following steps.

[0340] Step 40, obtain oral three-dimensional model data, and judge whether the specified intraoral tissue area in the oral three-dimensional model meets the preset integrity condition.

[0341] If not, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain oral structure information. The specific steps of step 43 include:

[0342] Step 430, obtain the corresponding intraoral photo image data.

[0343] Step 801, judge whether the tooth positions of the left maxillary central incisor and the right maxillary central incisor in the intraoral photo image and the oral three-dimensional model both include complete tooth crowns.

[0344] If not, (step 802B) and if only the tooth position of the left maxillary central incisor in the intraoral photo image and the oral three-dimensional model includes a complete tooth crown, then determine the tooth position of the left maxillary central incisor corresponding to each other in the intraoral photo image and the oral three-dimensional model as the relative reference tooth position.

[0345] Step 811B, determine the first reference feature point at the distal boundary of the tooth crown of the left maxillary central incisor tooth position in the intraoral photo image, and determine the second reference feature point at the mesial boundary of the tooth crown of the left maxillary central incisor tooth position in the intraoral photo image.

[0346] Step 812B: Calculate the number of pixels between the first reference feature point and the second reference feature point, and use this number of pixels as the reference pixel size data.

[0347] Step 82: Determine the third reference feature point and the fourth reference feature point relative to the reference tooth position on the three-dimensional oral model, calculate the Euclidean distance between the third reference feature point and the fourth reference feature point, and obtain the reference physical size data.

[0348] Step 435: Fit the size mapping factor based on the reference pixel size data and the reference physical size data to characterize the pixel size relationship.

[0349] In this specific example, the judgment of the crown loss of the maxillary left central incisor tooth position is also carried out, and it has a technical effect similar to that of the previous specific example. The two can also be combined with each other to form a complete specific example included in the second embodiment. In addition, although in the written description of the present application, it is based on the maxillary left central incisor tooth position including a complete crown, it can be understood that for different actual situations, the technical solutions provided by the present application can be implemented to achieve the corresponding technical effects.

[0350] In this case, in combination with Figure 25 As shown, in the intraoral image, the distal boundary pixel point 1121s of the maxillary left central incisor can be determined as the first reference feature point, and the mesial boundary pixel point Cs of the maxillary left central incisor can be determined as the second reference feature point, and calculate the number of pixels between the above two pixel points.

[0351] Correspondingly, for Step 82, it can also include steps corresponding to Step 811B and Step 812B. The specific step descriptions are not repeated here. In combination with Figure 25 As can be seen, in the three-dimensional oral model, the distal boundary spatial point 1121s' of the maxillary left central incisor can be determined as the third reference feature point, and the mesial boundary spatial point Cs' of the maxillary left central incisor can be determined as the fourth reference feature point, and calculate the Euclidean distance between the above two pixel points.

[0352] In other cases, the mesial boundary pixel point Cs of the maxillary left central incisor can also be interpreted as the mesial boundary pixel point of the maxillary right central incisor, and the mesial boundary spatial point Cs' of the maxillary left central incisor can also be interpreted as the mesial boundary spatial point of the maxillary right central incisor. Based on this, it is also possible to calculate the number of pixels between the distal boundary pixel point 1111s of the maxillary right central incisor and this mesial boundary pixel point, and the Euclidean distance between the distal boundary spatial point 1111s' of the maxillary right central incisor and this mesial boundary spatial point.

[0353] For any specific example or derivative technical solution in the second embodiment, further preferably, the distance from the first reference feature point to the gingival end of the corresponding incisor tooth position is three times the distance from the first reference feature point to the incisal end of the crown of the corresponding incisor tooth. In other words, the distance from the first reference feature point to the incisal end of the crown can be one-fourth of the crown length. Using the width value extracted in this way as the basis for establishing the pixel size relationship has stronger stability. Based on this, those skilled in the art can understand that the second reference feature point, the third reference feature point, and the fourth reference feature point can also be configured to have the above position characteristics.

[0354] Based on any of the above embodiments, as Figure 4 and Figure 28 shown, the third embodiment of a method for generating oral structure information provided by the present application refines step 43 and specifically provides steps 436 to 437. It can be understood that for other steps except the refined steps, they will not be described in detail below. For example, step 40 may specifically include steps 41 and 42. The third embodiment specifically includes the following steps.

[0355] Step 40, obtain oral three-dimensional model data, and determine whether a specified intraoral tissue area in the oral three-dimensional model meets a preset integrity condition.

[0356] If it does not meet the condition, jump to step 43, obtain the corresponding intraoral photo image data, extract the oral feature data corresponding to the intraoral tissue area in the intraoral photo image data, establish the pixel size relationship between the intraoral photo image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain the oral structure information. The specific steps of step 43 include:

[0357] Step 436, in the intraoral photo image data, determine at least two intraoral tissue feature points according to the oral feature data.

[0358] Step 437, according to the intraoral tissue feature points, at the feature missing position in the oral three-dimensional model, fit the tissue space feature points corresponding to the intraoral tissue feature points following the pixel size relationship, and form the part representing the oral feature data on the oral three-dimensional model data.

[0359] Based on this, at least when the oral feature data includes the height data of the vestibular sulcus, the width data of the dental arch, the width data of the labial frenum, or the protrusion amplitude data of the maxillofacial region, the most representative tissue space feature points can be reconstructed at the feature missing positions of the oral three-dimensional model. For cases where the oral feature data includes the dental arch curvature data, etc., multiple intraoral tissue feature points need to be determined to fit the overall curvature curve of the dental arch on the oral three-dimensional model.

[0360] In an application scenario, preferably, the intraoral tissue region includes the vestibular sulcus, the oral cavity feature data includes the vestibular sulcus height data, and the intraoral tissue feature points include the upper sulcus bottom feature point and the lower sulcus bottom feature point. In this way, the most typical feature points can be extracted for medical workers to refer to.

[0361] In this application scenario, continue as Figure 28 shown, step 437 above can further include the following steps.

[0362] Step 4371, calculate the distance between the upper sulcus bottom feature point and the midpoint of the gingival margin of the corresponding tooth position to obtain the upper sulcus bottom spacing parameter, and calculate the corresponding upper sulcus bottom mapping parameter according to the upper sulcus bottom spacing parameter and the size mapping factor representing the pixel size relationship.

[0363] Step 4372, according to the reference feature coordinates of the target tooth position corresponding to the upper sulcus bottom feature point and the upper sulcus bottom mapping parameter, fit to obtain the upper space feature point representing the position of the upper sulcus bottom.

[0364] Step 4373, calculate the distance between the lower sulcus bottom feature point and the midpoint of the gingival margin of the corresponding tooth position to obtain the lower sulcus bottom spacing parameter, and calculate the corresponding lower sulcus bottom mapping parameter according to the lower sulcus bottom spacing parameter and the size mapping factor.

[0365] Step 4374, according to the reference feature coordinates of the target tooth position corresponding to the lower sulcus bottom feature point and the lower sulcus bottom mapping parameter, fit to obtain the lower space feature point representing the position of the lower sulcus bottom.

[0366] In this way, the midpoint of the gingival margin, which is also a reference point in the oral three-dimensional model, can be used to fit the mapping relationship between the upper and lower sulcus bottom feature points, maintaining the consistency between the intraoral image and the oral three-dimensional model. Based on the above steps, combined with Figure 6 shown, at least the first vestibular sulcus bottom coordinate point 13A can be fitted as the upper space feature point on the oral three-dimensional model.

[0367] Of course, the present application is not limited to the above application scenario. In another application scenario, preferably, the intraoral tissue region includes the vestibular sulcus, or the root eminence, or both the vestibular sulcus and the root eminence. The oral cavity feature data includes the dental arch width data, and the intraoral tissue feature points include the left sulcus bottom feature point and the right sulcus bottom feature point. In this application scenario, the establishment of the mapping relationship and the fitting of the feature points are similar to the previous application scenario, which will not be elaborated here.

[0368] Of course, the embodiments provided in this application are not limited to fitting only some spatial feature points in the oral three-dimensional model. Of course, all spatial feature points can also be fitted, or even a spatial distribution curve or a tissue spatial distribution surface can be fitted. Based on this, in a specific example of the third embodiment above, steps 436 and 437 can be further configured as the following steps 436' and 437'.

[0369] Step 436', in the intraoral image, determine all intraoral tissue feature points according to the oral feature data.

[0370] Step 437', according to the intraoral tissue feature points, at the feature missing positions in the oral three-dimensional model, fit tissue spatial feature points corresponding to the intraoral tissue feature points following the pixel size relationship, fit a tissue spatial distribution curve or a tissue spatial distribution surface according to the tissue spatial feature points, and use this as the part representing the oral feature data on the oral three-dimensional model data.

[0371] Similarly, as shown in Figure 6 It is possible to apply the above preferred technical solutions to achieve the fitting of the first tissue spatial distribution curve 13a or the second tissue spatial distribution curve 13b, or the fitting of the first tissue spatial distribution surface Sa. In particular, for the fitting of the tissue spatial distribution curve and the tissue spatial distribution surface, or for the coordinate values of the tissue spatial feature points in the second coordinate axis Ry direction (or, the direction perpendicular to the paper surface), they can be determined according to the gray value, brightness value of the corresponding points in the intraoral image or the actual depth on the side view image of the intraoral image.

[0372] As Figure 29 shown, another embodiment of this application provides an oral structure information generation method. The application program or instructions corresponding to this method can be carried on the above storage medium and / or the above oral structure information generation system 300 to achieve the technical effect of oral structure information generation. The oral structure information generation method can specifically include the following steps.

[0373] Step 91, obtain oral three-dimensional model data and corresponding intraoral image data.

[0374] Step 92, extract oral feature data corresponding to the target intraoral tissue area in the intraoral image data, establish the pixel size relationship between the intraoral image data and the oral three-dimensional model data, and reconstruct the part representing the oral feature data on the oral three-dimensional model data to obtain oral structure information.

[0375] The above embodiments, compared with this application in Figure 4 , Figure 5 or Figure 19For the implementation manners provided herein, as well as other embodiments or specific examples obtained by combining steps and appending them, the step of judging the integrity of the oral three-dimensional model is cancelled, and the intraoral image data is directly used for feature extraction, and the extracted features are reconstructed on the oral three-dimensional model, thereby avoiding the processing of complex point cloud data on the oral three-dimensional model, and being able to display more intuitive feature points, curves or surfaces on the oral three-dimensional model.

[0376] Those skilled in the art can understand that each step in this implementation manner can be alternatively implemented with the descriptions or limitations of steps in other implementation manners, embodiments or specific examples provided above. In particular, for the extraction of oral feature data, the establishment of pixel size relationships, and the reconstruction of the part representing oral feature data, etc., this application will not be elaborated herein.

[0377] Based on any of the above implementation manners, the oral structure information generated by this application can be interpreted as an oral three-dimensional model and its data containing the "part representing oral feature data", or can also be interpreted as only referring to the "part representing oral feature data" (that is, at least one of the feature points, curves or surfaces).

[0378] Of course, in some implementation manners, the oral structure information can also be interpreted as a kind of data, preferably oral feature data mapped through pixel size relationships. Specifically, it can be the value of the height of the vestibular sulcus on the oral three-dimensional model, the value of the width of the dental arch on the oral three-dimensional model, the value of the width of the labial frenum on the oral three-dimensional model, the value of the curvature of the dental arch on the oral three-dimensional model, or the value of the prominence / arc of the maxillofacial region on the oral three-dimensional model, etc.

[0379] It should be noted that although a label of a first direction D1 is given in some of the specification drawings, it does not limit that a technical solution of setting different first directions for different tooth positions cannot be adopted in the corresponding implementation manners. In particular, in the specification drawings Figure 6 、 Figure 15 、 Figure 17 、 Figure 25 、 the shown first direction D1 is only taken as an example, and can be interpreted as an example of the first direction of the central incisors when the oral three-dimensional model is in the forward viewing angle direction. For the first direction of the oral three-dimensional model in other viewing angle directions or for other tooth positions, it can be deduced by combining Figure 12 and Figure 13 .

[0380] In addition, between multiple embodiments corresponding to a certain implementation manner, overall combinations or combinations of partial steps can be carried out. For example Figure 4The three embodiments corresponding to the provided embodiments and their specific examples can be combined; multiple embodiments corresponding to a certain embodiment can be combined into another embodiment or yet another embodiment. For example Figure 4 the provided embodiment can be combined with Figure 5 another provided embodiment as a whole. Another example is Figure 4 and / or Figure 5 the provided embodiment can be combined with Figure 19 yet another provided embodiment as a whole, setting higher integrity condition judgment requirements. Another example is that the first to third embodiments corresponding to the provided embodiment can be combined with Figure 4 yet another provided embodiment, so as to enrich the content of this yet another embodiment and achieve the corresponding effects. Figure 19

[0381] In summary, for the method for generating oral structure information provided in this application, by analyzing the relative positional relationship between tooth position features and model boundary features, and when it is determined that the oral three-dimensional model does not meet the preset conditions, the corresponding intraoral photo image data is called to complete the features, so as to obtain complete oral structure information based on the oral three-dimensional model data. In this way, the requirements for extracting the oral three-dimensional model are reduced, and on the basis of being sufficient to obtain high-precision oral structure features, the steps and operation logics are simplified; the compounding process between the intraoral photo image data and the oral three-dimensional model data mainly reconstructs the features on the oral three-dimensional model according to the established pixel size relationship. The output oral structure information is based on three-dimensional data, with strong intuitiveness and accuracy, which is convenient for medical workers to conduct further analysis and also convenient for patients or other relevant personnel to consult.

[0382] It should be understood that although this specification is described according to embodiments, not every embodiment only contains an independent technical solution. This narrative way of the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0383] The series of detailed descriptions listed above are only specific descriptions of the feasible embodiments of this application, and they are not used to limit the protection scope of this application. Any equivalent embodiments or changes made without departing from the technical spirit of this application should be included in the protection scope of this application.​

Claims

1. A method for generating oral structure information, characterized in that, Including: Obtaining first data information for establishing an oral three-dimensional model; When the first data information does not meet the conditions for establishing an oral three-dimensional model, obtaining intraoral image data corresponding to the first data information, and supplementing the missing oral features in the oral three-dimensional model according to the intraoral image data to obtain the oral structure information.

2. The method for generating oral structure information according to claim 1, wherein The conditions for establishing an oral three-dimensional model include at least one of the following: The position between the tooth position and the model boundary in the first data information satisfies a first preset position relationship, and the first preset position relationship is used to confirm the data information of the part corresponding to the model boundary in the first data information; The position between the corresponding model boundaries in the first data information satisfies a second preset position relationship, and the second preset position relationship is used to confirm the data information of the part corresponding to the model boundary in the first data information.

3. The method for generating oral structure information according to claim 2, wherein The situation that the first data information does not meet the conditions for establishing an oral three-dimensional model includes at least one of the following: Determining the corresponding model boundary in the preset direction of the tooth position, and the distance between the tooth position and the corresponding model boundary is less than a first criterion value; Determining the corresponding upper boundary and lower boundary in the preset direction of the tooth position, and the distance between the upper boundary and the lower boundary is less than a second criterion value.

4. The method for generating oral structure information according to claim 2 or 3, characterized in that, Including at least one of the following: Obtaining the convex hull contour of the tooth position and determining the corresponding model boundary according to the center of the convex hull contour; Obtaining the gingival margin information of the tooth position and determining the corresponding model boundary according to the midpoint of the gingival margin.

5. The method for generating oral structure information according to claim 1 or 2 or 3, including: Obtaining the intraoral tissue area of interest in the first data information, Obtaining the feature data corresponding to the intraoral tissue area; When the feature data does not meet the conditions for establishing an oral three-dimensional model, obtaining intraoral image data including at least the intraoral tissue area.

6. The method for generating oral structure information according to claim 5, wherein, Including at least one of the following: The specified intraoral tissue area includes the vestibular sulcus, and the corresponding feature data includes the vestibular sulcus height data, which is determined according to the model boundary points of the incisor tooth position in the extension direction of the tooth long axis, The specified intraoral tissue area includes the vestibular sulcus, and the corresponding feature data includes the dental arch width data, which is determined according to the model boundary points of the molar tooth position in the extension direction of the tooth long axis, The specified intraoral tissue area includes the root eminence, and the corresponding feature data includes the dental arch width data, which is determined according to the model boundary points of the molar tooth position in the extension direction of the tooth long axis, The specified intraoral tissue area includes the labial frenum, and the corresponding feature data includes the labial frenum width data, which is determined according to the model boundary points of the central incisor tooth position in the extension direction of the tooth long axis.

7. The method for generating oral structure information according to claim 1, wherein, Including: Determining the oral feature data of the area of interest in the intraoral image data according to the neural network model, and the area of interest corresponds to the area lacking oral features in the oral three-dimensional model, Determining the missing oral features in the oral three-dimensional model according to the oral feature data of the area of interest.

8. The method for generating oral structure information according to claim 1, wherein Including: Registering the intraoral image data and the oral three-dimensional model according to the size data of the teeth at the mutually corresponding tooth positions in the intraoral image and the oral three-dimensional model.

9. A method for generating oral structure information, characterized in that, Including: Obtain the first data information and the intraoral image data corresponding to the first data information, where the first data information is used to establish an oral three-dimensional model. Based on the intraoral image data, supplement the missing oral features in the oral three-dimensional model data to obtain the oral structure information.

10. An oral structure information generation system, comprising a processor, a memory, and a communication bus, characterized in that, The processor and the memory complete communication with each other through the communication bus. The memory is used to store application programs. The processor is configured to, when executing the application program stored on the memory, implement the steps of the oral structure information generation method according to any one of claims 1-9.

11. A storage medium, on which an application program is stored, characterized in that, When the application program is executed, the steps of the oral structure information generation method according to any one of claims 1-9 are implemented.

12. An oral instrument, characterized in that, The oral device is constructed according to the oral structure information, and the oral structure information is generated according to the oral structure information generation method according to any one of claims 1-9.

13. The oral instrument according to claim 12, characterized in that, The oral device is used to train orofacial muscle function and / or to treat mouth breathing.

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