Bone tissue thickness determination method and device, electronic equipment and storage medium

Abstract

The invention discloses a bone tissue thickness determination method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-segmented craniofacial CBCT image, and segmenting a target bone tissue from the craniofacial CBCT image; smoothing the region where the target bone tissue is located, and if the target bone tissue has a cavity region, rendering and filling the cavity region of the target bone tissue as required; and measuring or calculating the thickness of a target position of the target bone tissue area, or obtaining a thickness chromatogram of the target bone tissue, so as to determine the thickness of the target bone tissue. According to the method, the target bone tissue can be segmented, the thickness of the target bone tissue can be measured or calculated, related diseases can be further predicted or prevented according to the thickness information, a prevention scheme is provided for orthodontic bone windowing and bone cracking in stomatology, and calculation indexes are provided for bone joint diseases and bones in clinical medicine.

Description

Technical Field

[0001] The present invention relates to the technical field of oral medicine, and in particular to a method, device, electronic device and storage medium for determining bone tissue thickness. Background Art

[0002] Segmentation of different skull regions currently focuses on archaeological, anatomical, or mechanical methods. Anatomical atlases typically focus on segmenting images of ex vivo tissues, while mechanical analysis focuses on the segmentation of specific regions. Regarding the segmentation of different skull tissues, taking temporal bone segmentation as an example, existing techniques include atlas-based methods and customized solutions (such as post-registration segmentation) for segmenting the facial nerve, auditory ossicles, and cochlea.

[0003] For example, the human skull visualization project at Ohio University in the United States used General Electric's light-speed ultra-slice CT scanner to scan a human skull. The thickness of each slice was 625μm, which segmented different tissue models of the brain, including bones, inner ears, paranasal sinuses, etc.

[0004] The craniofacial skeleton can affect facial symmetry, and severe bone problems can lead to migraines and hearing loss. For example, before placing maxillary posterior dental implants, doctors or technicians will assess the patient's existing bone mass. If bone is insufficient, surgery may be performed. The alveolar bone is also a key component of periodontal disease diagnosis and treatment. Therefore, accurately segmenting bone tissue and measuring its thickness and height to inform clinical diagnosis and treatment have become pressing technical challenges. Summary of the Invention

[0005] In view of the above problems, the present invention is proposed to provide a method, device, electronic device and storage medium for determining bone tissue thickness that overcome the above problems or at least partially solve the above problems.

[0006] According to one aspect of the present invention, a method for determining bone tissue thickness is provided, the method comprising:

[0007] Acquiring a craniofacial CBCT image to be segmented, and segmenting target bone tissue from the craniofacial CBCT image;

[0008] Smoothing the area where the target bone tissue is located, and if there is a cavity area in the target bone tissue, rendering and filling the cavity area of ​​the target bone tissue as needed;

[0009] The thickness of the target bone tissue is determined by measuring or calculating the thickness of the target position of the target bone tissue region, or obtaining a thickness chromatogram of the target bone tissue.

[0010] In some embodiments, segmenting the target bone tissue from the craniofacial CBCT image comprises:

[0011] analyzing and obtaining features of the craniofacial CBCT image;

[0012] setting a threshold range for the target bone tissue according to the features, and segmenting the target bone tissue using threshold segmentation method;

[0013] reconstructing the target bone tissue in three dimensions using a marching cubes algorithm.

[0014] In some embodiments, segmenting the target bone tissue region from the craniofacial CBCT image comprises:

[0015] selecting a plurality of representative craniofacial CBCT images, and labeling bone tissue regions in the images, the types of labels including at least one of the following: maxilla, parietal bone, temporal bone, mandible, sphenoid bone, nasal bone, ethmoid bone, lacrimal bone, palatine bone, vomer bone, or alveolar bone;

[0016] using the established neural network model to perform segmentation testing on the craniofacial CBCT image, and segmenting the desired bone tissue;

[0017] if the segmentation does not meet the predetermined standard, repeating the labeling and optimizing the neural network model until a termination condition is reached;

[0018] using the trained neural network model to perform automatic segmentation on the craniofacial CBCT image to be segmented.

[0019] In some embodiments, segmenting the target bone tissue from the craniofacial CBCT image further comprises:

[0020] establishing a standard craniofacial CBCT data template, and labeling bone tissue information of various types in the data template;

[0021] measuring or calculating a probability value of the degree of overlap between the region segmented from the craniofacial CBCT image and the corresponding bone tissue region of the data template, to determine the bone tissue to which the segmented region belongs, thereby verifying the bone tissue.

[0022] In some embodiments, the target bone tissue includes the maxilla, and the method further comprises:

[0023] identifying or locating the cortical bone of the maxilla;

[0024] obtaining the thickness of the cortical bone by measurement or calculation.

[0025] In some embodiments, the method further comprises:

[0026] According to historical data, a statistical analysis is performed on the relationship between the thickness of the cortical bone and the bone fenestration or bone fracture, and a threshold range of the thickness of the cortical bone is determined, in which the probability of bone fenestration or bone fracture is less than a preset probability.

[0027] In some embodiments, the method further comprises:

[0028] When the thickness of the cortical bone segmented from the craniofacial CBCT image to be segmented is outside the threshold range, an alarm is issued.

[0029] According to another aspect of the present application, a bone tissue thickness determination device is provided, which comprises:

[0030] A segmentation module is adapted to obtain a craniofacial CBCT image to be segmented, and segment a target bone tissue from the craniofacial CBCT image.

[0031] A processing module is adapted to perform smoothing processing on a region where the target bone tissue is located, and render and fill a cavity region of the target bone tissue as needed if the target bone tissue has a cavity region.

[0032] A determination module is adapted to measure or calculate the thickness of a target position of the target bone tissue region, or obtain a thickness spectrum of the target bone tissue, so as to determine the thickness of the target bone tissue.

[0033] According to another aspect of the present application, an electronic device is provided, which comprises a processor, and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the bone tissue thickness determination method according to any of the above.

[0034] According to another aspect of the present application, a computer readable storage medium is provided, wherein the computer readable storage medium stores one or more programs, which when executed by a processor, implement the bone tissue thickness determination method according to any of the above.

[0035] As can be seen from the above, according to the technical solutions disclosed in the present application, the corresponding bone tissue can be effectively segmented, and the thickness and height information of the bone tissue can be measured, which provides a reference for clinical disease diagnosis and treatment, prevents related diseases, and improves the efficiency and accuracy of segmentation and measurement, and provides a prevention scheme for orthodontic bone fenestration and bone fracture in oral medicine, and provides a calculation index for bone joint diseases and bones in clinical medicine.

[0036] The above description is only a summary of the technical solutions of the present application. In order to enable a more thorough understanding of the technical means of the present application, the present application can be implemented according to the content of the specification, and in order to enable the above and other purposes, characteristics and advantages of the present application to be more apparent and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0037] Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The accompanying drawings are included to provide a description of the preferred embodiments and are not meant to limit the present application. Furthermore, the same reference numerals in the drawings indicate the same or similar components. In the drawings:

[0038] Figure 1 A flowchart of a bone tissue thickness determination method according to an embodiment of the present application is shown;

[0039] Figure 2 A thickness analysis diagram of the maxillary cortical bone according to an embodiment of the present application is shown;

[0040] Figure 3 A two-dimensional sectional view of the thickness of the posterior cortical bone in the posterior tooth area of the maxilla according to an embodiment of the present application is shown;

[0041] Figure 4 A structural diagram of a bone tissue thickness determination device according to an embodiment of the present application is shown;

[0042] Figure 5 A structural diagram of an electronic device according to an embodiment of the present application is shown. DETAILED DESCRIPTION

[0043] Exemplary embodiments of the present application will be described below in greater detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present application can be more thoroughly understood and the scope of the present application can be accurately conveyed to those skilled in the art.

[0044] Figure 1 A flowchart of a bone tissue thickness determination method according to an embodiment of the present application is shown, including the following steps:

[0045] Step S110, acquiring a craniofacial CBCT image to be segmented, and segmenting a target bone tissue from the craniofacial CBCT image;

[0046] Step S120, performing smoothing processing on the region where the target bone tissue is located, and rendering and filling the cavity region of the target bone tissue as needed if the target bone tissue has a cavity region;

[0047] In step S130, the thickness of the target position of the target bone tissue region is measured or calculated, or a thickness spectrum of the target bone tissue is obtained, so as to determine the thickness of the target bone tissue.

[0048] Through the embodiment, the segmentation and thickness measurement of the bone tissue are realized, a reference is provided for the clinical disease diagnosis and treatment, so that the related diseases can be prevented, and the efficiency and accuracy of the segmentation and measurement are effectively improved, a prevention scheme is provided for the orthodontic bone windowing and bone cracking in oral medicine, and a calculation index is provided for the bone joint diseases and bones in clinical medicine.

[0049] In some embodiments, the step S110 of segmenting the target bone tissue from the craniofacial CBCT image comprises:

[0050] The features of the craniofacial CBCT image are analyzed and obtained;

[0051] The threshold range of the target bone tissue is set according to the features, and the target bone tissue is segmented by using a threshold segmentation method;

[0052] The target bone tissue is three-dimensionally reconstructed by using a marching cubes algorithm.

[0053] It should be noted that the threshold segmentation method is a region-based image segmentation technique, and the principle is to divide the image pixel points into several categories. Image threshold segmentation is a traditional and most commonly used image segmentation method. Because it is simple to implement, has small amount of calculation, and stable performance, it becomes the most basic and most widely used segmentation technique in image segmentation. It is especially suitable for images in which the target and background occupy different gray level ranges. It not only greatly compresses the data amount, but also greatly simplifies the analysis and processing steps, and therefore in many cases, it is a necessary image preprocessing process before image analysis, feature extraction and pattern recognition. The purpose of image thresholding is to divide the pixel set according to the gray level, and each subset obtained forms a region corresponding to the real scene, and each region has consistent properties, while adjacent regions do not have such consistent properties. Such division can be achieved by selecting one or more thresholds from the gray level.

[0054] The main idea of the marching cubes algorithm is to approximate the isosurface in a three-dimensional discrete data field by linear interpolation, which is also a relatively common three-dimensional reconstruction algorithm, and will not be described here.

[0055] In some embodiments, the step S110 of segmenting the target bone tissue region from the craniofacial CBCT image can also adopt the following steps:

[0056] selecting a plurality of representative craniofacial CBCT images, labeling bone tissue regions in the images, the types of labels including at least one of the following: jaw bone, parietal bone, temporal bone, jaw bone, sphenoid bone, nasal bone, ethmoid bone, lacrimal bone, palatine bone, vomer bone, or alveolar bone;

[0057] segmenting the craniofacial CBCT images using the established neural network model to segment the desired bone tissue;

[0058] If the segmentation does not meet the predetermined standard, repeat the labeling and optimize the neural network model until the termination condition is reached.

[0059] Using the trained neural network model to automatically segment the craniofacial CBCT images to be segmented.

[0060] The neural network model described above can be a U-net model or an RCNN model, and other models for image recognition are also within the scope of the present embodiment.

[0061] In some embodiments, segmenting the target bone tissue from the craniofacial CBCT image further comprises:

[0062] Establishing a standard craniofacial CBCT data template, and labeling the bone tissue information of each type in the data template;

[0063] Measuring or calculating the probability value of the overlap between the region segmented from the craniofacial CBCT image and the corresponding bone tissue region of the data template, to determine the bone tissue to which the segmented region belongs, thereby verifying the segmentation result of the bone tissue.

[0064] In some embodiments, the target bone tissue includes the maxilla, and the method further comprises:

[0065] Identifying or locating the cortical bone of the maxilla;

[0066] Measuring or calculating the thickness of the cortical bone.

[0067] According to this embodiment, after segmenting the maxilla region, a smoothing operation is performed on it in the morphological operation to remove part of the noise, and then the thickness of the cortical bone is calculated. The calculation result of the thickness of the cortical bone is as shown in Figure 2

[0068] In the orthodontic process, it often causes the maxilla to appear bone windowing and bone cracking. This embodiment uses CBCT image information to segment the cortical bone on the labial side of the tooth in advance and calculate its thickness, which can be used to prevent bone cracking or bone windowing to reduce the pain phenomenon caused by orthodontics and effectively reduce the probability of bone cracking or bone windowing. ​

[0069] Further, the thickness information described above can also be subjected to multi-dimensional analysis. After analysis and calculation, a two-dimensional cross-section of the thickness of the posterior cortical bone in the posterior tooth region of the maxilla is shown in FIG. 8. Figure 3

[0070] In some embodiments, the method further comprises:

[0071] According to historical data, the relationship between the thickness of the cortical bone and bone fenestration or bone fracture is statistically analyzed to determine a threshold range of the thickness of the cortical bone. Within the threshold range, the probability of bone fenestration or bone fracture is less than a preset probability.

[0072] The threshold range is a threshold of the cortical bone within a normal range, i.e., a reasonable and effective interval. The cortical bone within the interval range is normal cortical bone, and the risk of bone fenestration or bone fracture is low, such as less than 5%.

[0073] Of course, some local cortical bone characteristics, such as the distance between the teeth and the maxillary sinus, are analyzed to predict the probability of disease occurrence, making early prevention or treatment possible.

[0074] Taking the cortical bone thickness information of the alveolar bone in orthodontics as an example, when the cortical bone thickness information is less than 0.5 mm, it is defined as having a high risk of treatment. When the measured cortical bone thickness of the alveolar bone is 0.5-2 mm, it is considered to have a moderate risk of treatment. When the measured cortical bone thickness of the alveolar bone is greater than 2 mm, it is considered to have a low risk of treatment. When the cortical bone thickness of the alveolar bone is 0 or the area is already empty, it is defined as having a high risk of bone fenestration and bone fracture treatment.

[0075] In some embodiments, the method further comprises:

[0076] When the thickness of the cortical bone segmented from the craniofacial CBCT image to be segmented is outside the threshold range, an alarm is issued.

[0077] In order to realize disease risk warning, the embodiment of the application also realizes the function of automatic warning. When the thickness of the cortical bone is abnormal and the probability of disease is higher than the preset value, a warning is issued to remind the doctor or patient to pay attention and take preventive measures in advance.

[0078] According to another aspect of the application, referring to FIG. 4, a bone tissue thickness determination device is provided. The device 400 comprises: Figure 4 A segmentation module 410 is adapted to obtain a craniofacial CBCT image to be segmented, and segment a target bone tissue from the craniofacial CBCT image.

[0079]

[0080] ​​The processing module 420 is adapted to perform smoothing processing on the region where the target bone tissue is located, and render to fill the cavity region of the target bone tissue as needed if the target bone tissue has a cavity region.

[0081] The determining module 430 is adapted to measure or calculate the thickness of the target position of the target bone tissue region, or obtain a thickness spectrum of the target bone tissue, so as to determine the thickness of the target bone tissue.

[0082] The bone tissue thickness determination device provided by the above embodiments realizes segmentation and thickness measurement of bone tissue, provides a reference for clinical disease diagnosis and treatment, so as to prevent related diseases, and effectively improves the efficiency and accuracy of segmentation and measurement, and provides a prevention scheme for orthodontic bone windowing and bone cracking in oral medicine, and provides a calculation index for bone and joint diseases and bones in clinical medicine.

[0083] In some embodiments, the segmentation module 410 is further adapted to:

[0084] analyze and obtain the features of the craniofacial CBCT image;

[0085] set a threshold range of the target bone tissue according to the features, and segment the target bone tissue by using a threshold segmentation method;

[0086] perform three-dimensional reconstruction on the target bone tissue by using a marching cubes algorithm.

[0087] In some embodiments, the segmentation module 410 is further adapted to:

[0088] select a plurality of representative craniofacial CBCT images, and label the bone tissue regions in the images, the types of the labels including at least one of the following: jaw bone, parietal bone, temporal bone, jaw bone, sphenoid bone, nasal bone, ethmoid bone, lacrimal bone, palatine bone, vomer bone, or alveolar bone;

[0089] perform segmentation testing on the craniofacial CBCT image by using the established neural network model, and segment the required bone tissue;

[0090] if the segmentation does not meet the predetermined standard, repeat the labeling and optimize the neural network model until a termination condition is reached;

[0091] perform automatic segmentation on the craniofacial CBCT image to be segmented by using the trained neural network model.

[0092] In some embodiments, the segmentation module 410 is further adapted to:

[0093] establish a standard craniofacial CBCT data template, and label the bone tissue information of each type in the data template;

[0094] The probability value of the coincidence degree of the region where the segmented bone tissue from the craniofacial CBCT image is located and the region of the corresponding bone tissue of the data template is measured or calculated to determine the bone tissue to which the segmented region belongs, so that the bone tissue is verified.

[0095] In some embodiments, the target bone tissue includes the maxilla, and the device 400 is further adapted to:

[0096] Identifying or locating the cortical bone of the maxilla;

[0097] The thickness of the cortical bone is measured or calculated.

[0098] In some embodiments, the device 400 is further adapted to:

[0099] According to historical data, the relationship between the thickness of the cortical bone and bone fenestration or bone fracture is statistically analyzed to determine a threshold range of the thickness of the cortical bone, and when the thickness of the cortical bone is within the threshold range, the probability of bone fenestration or bone fracture is less than a preset probability.

[0100] In some embodiments, the device 400 is further adapted to:

[0101] When the thickness of the cortical bone segmented from the craniofacial CBCT image to be segmented is outside the threshold range, an alarm is issued.

[0102] It should be noted that the specific embodiments of the above-mentioned devices can be implemented with reference to the specific embodiments of the corresponding methods described above, and will not be described here.

[0103] It should be noted that:

[0104] The algorithms and displays provided herein are not inherently related to any particular computer, virtual apparatus, or other apparatus. Various general-purpose systems can be used with these teachings, based on the description as set forth above. The structure required to construct such systems is apparent from the above description. Moreover, the present application is not intended to be limited to any particular programming language. It will be appreciated that there are many programming languages that can be used to implement the teachings described herein, and any such programming language can be used in connection with the various aspects of the present application.

[0105] In the specification provided herein, a large number of specific details are described. However, it can be understood that the embodiments of the present application can be practiced without these specific details. In some examples, well-known methods, structures and techniques are not described in detail in order not to obscure the understanding of the present specification.

[0106] Similarly, it is to be understood that the embodiments of the present application can be alternately grouped together in a single embodiment, figure, or description thereof for the purpose of brevity and understanding in one or more of the various inventive aspects. However, it is not intended that the disclosed approach be construed as reflecting an intention that the claimed application requires more features than are explicitly recited in each claim.

[0107] Those skilled in the art will appreciate that the modules in the apparatuses in the embodiments can be adapted and arranged in one or more apparatuses other than the embodiments. The modules or units or components in the embodiments can be combined into one module or unit or component, and further can be divided into more sub-modules or sub-units or sub-components. Any combination of all the features disclosed in the specification (including the accompanying claims, abstract and drawings), and any method or apparatus otherwise disclosed herein, can be made unless the contrary is explicitly stated or is clear from the context. Each feature disclosed in the specification (including the accompanying claims, abstract and drawings), can be replaced by alternative features supporting the same, equivalent or similar purpose unless the contrary is explicitly stated or is clear from the context.

[0108] Further, those skilled in the art will appreciate that a combination of features of different embodiments can mean within the scope of the application and form a different embodiment.

[0109] The various component embodiments of the present application can be implemented in hardware, or as software modules running in one or more processors, or in combinations thereof. Those skilled in the art will appreciate that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the bone tissue thickness determination apparatus according to the embodiments of the present application. The present application can also be implemented as a device or apparatus program (e.g., a computer program and a computer program product) for performing part or all of the methods described herein. Such a program implementing the present application can be stored on a computer readable medium, or can have one or more signals. Such a signal can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0110] The embodiments of the present application provide a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the bone tissue thickness determination method in any method embodiment described above.

[0111] The embodiments of the present application provide a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the bone tissue thickness determination method in any method embodiment described above.Figure 5 The structure of the electronic device is shown in the schematic diagram of the embodiment of the present application, and the specific implementation of the electronic device is not limited in the embodiment of the present application.

[0112] As shown in the figure, the electronic device can include a processor 502, a communications interface 504, a memory 506, and a communications bus 508. Figure 5

[0113] The processor 502, the communications interface 504, and the memory 506 can communicate with each other through the communications bus 508. The communications interface 504 is configured to communicate with network elements such as clients or other servers. The processor 502 is configured to execute the program 510, and specifically can execute the related steps in the above-described bone tissue thickness determination method embodiment for the electronic device.

[0114] Specifically, the program 510 can include program code including computer operation instructions.

[0115] The processor 502 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the onboard image processing board can be the same type of processor, such as one or more CPUs; or can be different types of processors, such as one or more CPUs and one or more ASICs.

[0116] The memory 506 is configured to store the program 510. The memory 506 can include a high-speed RAM memory, and can also include a non-volatile memory such as at least one disk memory.

[0117] The program 510 can specifically be used to cause the processor 502 to perform the operations corresponding to the above-described bone tissue thickness determination method embodiment.

[0118] ​It should be noted that the above-mentioned embodiments illustrate rather than limit the application, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The application can be implemented by means of both hardware and software, and any combination thereof. In a unitary claim, several devices or means can be listed, comprising means which can be implemented by one and the same hardware item. The use of the word "a" or "an" does not exclude the presence of a plurality of such elements, nor does it imply that a single element is to be used.

Claims

1. A method for determining bone tissue thickness, the method comprising: obtaining a craniofacial CBCT image to be segmented, and segmenting a target bone tissue from the craniofacial CBCT image; performing smoothing processing on a region where the target bone tissue is located, and rendering and filling a cavity region of the target bone tissue as needed if the target bone tissue has the cavity region; measuring or calculating a thickness of a target position of the target bone tissue region, or obtaining a thickness profile of the target bone tissue, so as to determine the thickness of the target bone tissue.

2. The method of claim 1, wherein, Segmenting the target bone tissue from the craniofacial CBCT image comprises: analyzing and obtaining features of the craniofacial CBCT image; setting a threshold range of the target bone tissue according to the features, and segmenting the target bone tissue by using a threshold segmentation method; performing three-dimensional reconstruction on the target bone tissue by using a marching cubes algorithm.

3. The method of claim 1, wherein, Segmenting the target bone tissue region from the craniofacial CBCT image comprises: selecting a plurality of representative craniofacial CBCT images, and labeling bone tissue regions in the images, the types of the bone tissue regions including at least one of the following: a jaw bone, a parietal bone, a temporal bone, a maxilla bone, a sphenoid bone, a nasal bone, an ethmoid bone, a lacrimal bone, a palatine bone, a vomer bone, or an alveolar bone; performing segmentation testing on the craniofacial CBCT image by using a trained neural network model, and segmenting the needed bone tissue; if the segmentation does not meet a predetermined standard, repeating the labeling and optimizing the neural network model until a termination condition is reached; performing automatic segmentation on a craniofacial CBCT image to be segmented by using the trained neural network model.

4. The method according to claim 2 or 3, characterized in that, Segmenting the target bone tissue from the craniofacial CBCT image further comprises: establishing a standard craniofacial CBCT data template, and labeling bone tissue information of each type in the data template; measuring or calculating a probability value of a coincidence degree of a region where a segmented bone tissue is located and a corresponding bone tissue region of the data template, so as to determine a bone tissue to which the segmented region belongs, and thereby verifying the bone tissue.

5. The method of claim 1, wherein, The target bone tissue includes a maxilla bone, and the method further comprises: identifying or locating cortical bone of the maxilla bone; obtaining a thickness of the cortical bone by measurement or calculation.

6. The method of claim 5, wherein, The method further comprises: statistically analyzing a relationship between the thickness of the cortical bone and bone fenestration or bone fracture according to historical data, determining a threshold range of the thickness of the cortical bone, and determining that a probability of the bone fenestration or the bone fracture is less than a preset probability when the thickness of the cortical bone is within the threshold range.

7. The method of claim 6, wherein, The method further comprises: issuing an alarm when the thickness of the cortical bone segmented from the craniofacial CBCT image to be segmented is outside the threshold range. 8.A device for determining bone tissue thickness, the device comprising: a segmentation module adapted to obtain a craniofacial CBCT image to be segmented, and segment a target bone tissue from the craniofacial CBCT image; a processing module adapted to perform smoothing processing on a region where the target bone tissue is located, and render and fill a cavity region of the target bone tissue as needed if the target bone tissue has the cavity region; a determining module adapted to measure or calculate a thickness of a target position of the target bone tissue region, or to obtain a thickness profile of the target bone tissue, thereby determining the thickness of the target bone tissue.

9. An electronic device comprising: a processor; and a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the bone tissue thickness determining method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores one or more programs, which when executed by a processor, implement the bone tissue thickness determining method according to any one of claims 1-7.

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