Intelligent surgical planning method and system for autogenous tooth transplantation

Abstract

The invention provides an intelligent operation planning method and system for autologous tooth transplantation, and relates to the technical field of image processing, and the method comprises the steps: obtaining a tooth CBCT image to be operated; through a 3D UX-Net network, each single tooth in the tooth CBCT image is segmented; the upper jaw and the lower jaw in the tooth CBCT image are segmented through a SegResNet network; according to the segmentation result of the single tooth and the segmentation result of the jaw bone, determining various feasible transplantation operation planning schemes for transplanting the alternative donor tooth to the acceptor tooth through a principal component analysis method; and according to the tooth crown overlapping rate, the tooth root overlapping rate and the bone grinding amount of the receptor teeth and the donor teeth in various transplantation planning schemes, determining an optimal transplantation planning scheme. Precise segmentation of a single tooth and upper and lower jawbones is realized; the bone grinding amount is effectively controlled, the matching between donor teeth and acceptor teeth is optimized, and the operation success rate and the postoperative comfort are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image processing, and in particular to an intelligent surgery planning method and system for autologous tooth transplantation. Background Art

[0002] Autogenous Tooth Transplantation (ATT) is a dental procedure that aims to transfer a patient's own teeth from their original location to another missing tooth location in the same patient's mouth. This surgical technique is often used to treat impacted teeth, malpositioned teeth, or ectopic erupted teeth, by transferring these teeth to other extraction sites or alveolar sockets in missing teeth areas to restore oral function and aesthetics.

[0003] The activity of the donor tooth's periodontal ligament is a key factor affecting the success of the transplant. The activity of periodontal ligament cells will significantly decrease as the tooth is exposed outside the mouth for a longer time. In order to maximize the activity of periodontal ligament cells, the recipient site needs to be shaped quickly and precisely during autologous tooth transplantation surgery to ensure that the donor tooth can be accurately embedded in the new position.

[0004] Currently, in the context of digital dental surgery, many studies have used computers to assist in ATT. Case-control reports have shown that this method effectively shortens the time that donor teeth are in vitro, facilitates postoperative healing of soft and hard tissues in the recipient area, and greatly reduces the risk of complications from multiple fittings using donor teeth.

[0005] However, existing technologies are still insufficient in terms of the accuracy of segmenting single teeth and upper and lower jaws; at the same time, they cannot effectively control the amount of bone grinding and optimize the match between donor teeth and recipient teeth, resulting in a low surgical success rate and patients are prone to postoperative discomfort, which in turn affects their comfort and satisfaction. Summary of the invention

[0006] In order to solve the technical problems that the prior art is still insufficient in the accuracy of segmenting single teeth and upper and lower jaws; at the same time, it is unable to effectively control the amount of bone grinding and optimize the matching between donor teeth and recipient teeth, resulting in a low surgical success rate and patients are prone to postoperative discomfort, which in turn affects the comfort and satisfaction of patients, the present invention provides an intelligent surgical planning method and system for autologous tooth transplantation.

[0007] The technical solution provided by the embodiment of the present invention is as follows:

[0008] First aspect:

[0009] An embodiment of the present invention provides an intelligent surgical planning method for autologous tooth transplantation, comprising:

[0010] S1: Acquire CBCT images of the tooth to be operated on;

[0011] S2: Segmenting each single tooth in the dental CBCT image through a 3D UX-Net network;

[0012] S3: segmenting the upper and lower jaws in the dental CBCT image through a SegResNet network;

[0013] S4: According to the segmentation results of the single tooth and the jaw bone, various feasible transplantation surgery planning schemes for transplanting the candidate donor tooth to the recipient tooth are determined by principal component analysis;

[0014] S5: Determine the best transplant surgery planning plan based on the crown overlap rate, root overlap rate and bone grinding amount of the recipient tooth and the donor tooth in the various transplant surgery planning plans.

[0015] Second aspect:

[0016] An embodiment of the present invention provides an intelligent surgery planning system for autologous tooth transplantation, comprising:

[0017] processor;

[0018] A memory having computer-readable instructions stored therein, wherein when the computer-readable instructions are executed by the processor, the method for intelligent surgical planning of autologous tooth transplantation as described in the first aspect is implemented.

[0019] The third aspect:

[0020] An embodiment of the present invention provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for intelligent surgery planning for autologous tooth transplantation as described in the first aspect is implemented.

[0021] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0022] (1) In the present invention, the individual teeth in the dental CBCT image are segmented by the 3D UX-Net network, and the upper and lower jaws in the dental CBCT image are segmented by the SegResNet network, thereby achieving accurate segmentation of individual teeth and upper and lower jaws.

[0023] (2) In the present invention, various feasible transplantation surgery planning schemes for transplanting alternative donor teeth to recipient teeth are determined based on the segmentation results of a single tooth and the segmentation results of the jawbone through the principal component analysis method. The optimal transplantation surgery planning scheme is determined based on the crown overlap rate, root overlap rate and bone grinding amount between the recipient tooth and the donor tooth in various transplantation surgery planning schemes, and the bone grinding amount is effectively controlled to optimize the matching between the donor tooth and the recipient tooth, thereby improving the success rate of the operation and postoperative comfort, thereby improving the comfort and satisfaction of the patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 A schematic diagram of a process flow of an intelligent surgical planning method for autologous tooth transplantation provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of the structure of an intelligent surgery planning system for autologous tooth transplantation provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0028] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0029] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0030] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0031] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0032] Reference Manual Attached Figure 1 , showing a flow chart of an intelligent surgical planning method for autologous tooth transplantation provided by an embodiment of the present invention.

[0033] The embodiment of the present invention provides an intelligent surgery planning method for autologous tooth transplantation, which can be implemented by an intelligent surgery planning method device for autologous tooth transplantation, and the intelligent surgery planning method device for autologous tooth transplantation can be a terminal or a server. The processing flow of the intelligent surgery planning method for autologous tooth transplantation can include the following steps:

[0034] S1: Obtain a CBCT image of the tooth to be operated on.

[0035] S2: Segmentation of individual teeth in dental CBCT images using the 3D UX-Net network.

[0036] In a possible implementation, S2 specifically includes:

[0037] S201: Perform binary segmentation of teeth and background on the dental CBCT image through the V-Net network to determine the tooth area image.

[0038] Among them, V-Net is a deep learning model for medical image segmentation, especially suitable for 3D image segmentation tasks. It was proposed by Fausto Milletari et al. in 2016 and is designed to handle the automatic segmentation of organs and lesions in medical images.

[0039] In a possible implementation, the loss function in the V-Net network training process is specifically: Dice loss function.

[0040] The Dice loss function is specifically:

[0041]

[0042] Among them, L dice represents the Dice loss function, X t represents the set of gold standard voxels, Y p Represents the set of prediction voxels.

[0043] Specifically, in the regional positioning stage, the V-Net network is used to perform binary segmentation of teeth and background. In order to reduce the computational complexity and ensure that the network learns the global information of the key areas, the CBCT is randomly cropped with a fixed size and used as the input of the network in this stage to obtain a positioning frame containing all teeth. The volume data is then cropped according to the positioning frame information as the input for segmentation positioning.

[0044] In the present invention, V-Net is used to perform preliminary segmentation of teeth and background, reducing the computational burden of subsequent processing, which can significantly reduce the demand for computing resources and improve overall processing efficiency.

[0045] S202: Predict the center of mass of each single tooth through an encoder-decoder network based on the tooth region image.

[0046] S203: Segment the tooth region image according to the centroid of each single tooth to obtain each single tooth block.

[0047] Among them, the Encoder-Decoder Network is a neural network architecture widely used in fields such as image processing and natural language processing. It is mainly used to encode input data into a certain feature representation and then decode these feature representations into output data.

[0048] In a possible implementation, the loss function in the encoder-decoder network training process is specifically: L1 loss function.

[0049] The L1 loss function is specifically:

[0050]

[0051] Among them, L1 is the L1 loss function, which represents the sum of the absolute values ​​of the differences between the predicted value and the true value, y i represents the true value of the voxel, represents the predicted value of the voxel, and N represents the total number of voxels.

[0052] Specifically, the output of the segmentation and localization stage contains a 3D vector and a binary segmentation mask. The 3D vector represents the vector that points from each voxel in the centroid offset map to the corresponding tooth centroid. The binary mask is used to eliminate the background voxels in the offset map. The peak in the tooth centroid density map is located using a fast search clustering method, thereby obtaining the tooth centroid.

[0053] In the present invention, the centroid is predicted by the encoder-decoder network, and 3DUX-Net is used in the subsequent stage for more refined segmentation, which can gradually improve the accuracy of segmentation. At the same time, the fast search clustering method can efficiently extract the centroid from the density map and reduce the calculation time.

[0054] S204: Perform semantic segmentation on the single tooth block through the 3D UX-Net network to determine each single tooth in the tooth CBCT image.

[0055] Among them, 3D UX-Net is a deep learning network specifically used for 3D medical image segmentation, which aims to improve segmentation accuracy and the ability to process complex 3D structures. It combines traditional convolutional neural networks (CNN) and advanced feature extraction technology, and is suitable for processing complex 3D medical images.

[0056] In a possible implementation, the loss function in the 3D UX-Net network training process is specifically: a binary cross entropy loss function.

[0057] The binary cross entropy loss function is specifically:

[0058]

[0059] Among them, L bce represents the binary cross entropy loss function, N represents the total number of voxels, y i represents the true value of the voxel, p(y i ) indicates that the prediction belongs to y i The probability value of .

[0060] Specifically, according to the segmentation positioning results, the image and centroid label map are cropped into multiple single tooth blocks as input, and the 3D UX-Net network is used for semantic segmentation. The framework consists of an encoder-decoder, and the long jump connection operation is combined to help the network better restore the positioning of spatial details. A maximum pooling layer and three fully connected layers are added at the end of the encoder to identify the FDI number of each patch, and finally the single tooth segmentation result is obtained.

[0061] In the present invention, 3D UX-Net combines long skip connections and encoder-decoder architecture, which can effectively restore spatial details and provide high-precision segmentation results. At the same time, through skip connections, 3D UX-Net can better retain detail information, prevent information loss, and improve the quality of segmentation results.

[0062] S3: Segmentation of the maxillary and mandibular bones in dental CBCT images using the SegResNet network.

[0063] Among them, the SegResNet network is an image segmentation model based on deep learning, which combines the advantages of convolutional neural networks and residual networks (ResNet) and is specifically used to handle image segmentation tasks. It is particularly effective in medical image segmentation and can accurately segment the target area.

[0064] In a possible implementation, the SegResNet network uses an encoder-decoder convolutional neural network, and the SegResNet network includes: an encoder and a decoder.

[0065] Each ResNet block in the encoder consists of two convolutional layers. GroupNormalization is used after each convolutional layer to perform normalization and a ReLU activation function is applied. The input is directly connected to the output of the convolutional layer through skip connections.

[0066] The decoder consists of 1x1x1 convolutional layers and uses 3D bilinear upsampling.

[0067] The SegResNet network outputs a 3-channel mask of the same size, representing the probability of each voxel belonging to the background, maxilla, and mandible, respectively.

[0068] In the present invention, the SegResNet network uses ResNet blocks in the encoder part, which contain jump connections and can effectively solve the gradient vanishing problem in deep networks. This enables the network to better train and learn complex features, thereby improving segmentation accuracy. At the same time, by adding the input directly to the output of the convolutional layer, the residual block retains more detailed information and avoids information loss that may occur in deep networks. This is particularly important for processing medical images with complex details, and can improve the accurate segmentation of the upper and lower jaws.

[0069] Furthermore, the output of SegResNet is a 3-channel mask of the same size as the input image, where each channel represents the probability of background, maxilla, and mandible, and can accurately classify each voxel as background, maxilla, or mandible, improving the fine-grainedness of segmentation. At the same time, the classification probability of each voxel is provided, which is helpful for subsequent processing steps, such as calculating the overlap rate, the amount of bone grinding, etc.

[0070] In a possible implementation, the loss function in the SegResNet network training process is specifically: a hybrid cross entropy loss function.

[0071] The hybrid cross entropy loss function is specifically:

[0072] L seg =L dice +μ1L bce

[0073] Among them, L seg represents the mixed cross entropy loss function, μ1 represents the weight of the balanced loss, L bce represents the binary cross entropy loss function, L dice Represents the Dice loss function.

[0074] In the present invention, Dice loss and binary cross entropy loss are combined. This combination can better handle the problem of category imbalance and improve segmentation accuracy.

[0075] Specifically, in the encoder part, each ResNet block contains two convolutional layers. Group Normalization is used after each convolutional layer for normalization, and the ReLU activation function is applied. In order to avoid the problem of gradient vanishing in deep networks, skip connections are added to add the input directly to the output of the convolutional layer to help the network learn the residual between input and output; in the decoder part, the number of features is reduced by 1x1x1 convolution, and 3D bilinear upsampling is used to increase the spatial dimension, gradually restoring the spatial size of the original image. The network outputs a 3-channel mask of the same size as the input, indicating the probability of each voxel belonging to the background, maxilla and mandible respectively.

[0076] In summary, the benefit of using the SegResNet network for maxillary and mandibular segmentation is that it combines residual learning and the encoder-decoder structure to accurately restore image details and generate high-resolution segmentation masks, thereby improving segmentation accuracy and fine-grained classification capabilities.

[0077] S4: Based on the segmentation results of a single tooth and the segmentation results of the jaw bone, various feasible transplantation surgery planning schemes for transplanting the alternative donor tooth to the recipient tooth are determined by principal component analysis.

[0078] Among them, Principal Component Analysis (PCA) is a commonly used data dimensionality reduction technology, which is used to extract the main features in the data and reduce the dimension of the data while retaining the main information of the original data as much as possible.

[0079] In one possible embodiment, the transplant surgery planning plan includes: a translation matrix for moving the center point of the donor tooth to the center point of the recipient tooth, a rotation matrix for transforming the long axis vector of the donor tooth to the axial vector of the recipient tooth, and a rotation matrix for transforming the initial direction of the donor tooth to the initial direction of the recipient tooth.

[0080] The calculation method of the translation matrix of the donor tooth center point to the recipient tooth center point specifically includes:

[0081] Define the global coordinate system.

[0082] Calculate the translation matrix of the donor tooth center point to the recipient tooth center point in the global coordinate system.

[0083] The calculation method of the rotation matrix of the long axis vector of the donor tooth to the axial vector of the recipient tooth specifically includes:

[0084] Calculate the covariance matrix of the decentralized original sample matrix and perform eigendecomposition.

[0085] The eigenvector corresponding to the maximum eigenvalue is selected as the tooth long axis vector.

[0086] Determine the upper and lower teeth and update the direction of the tooth long axis vector.

[0087] Taking the center point of the tooth as the rotation center, calculate the rotation matrix of the long axis vector of the donor tooth to the axial vector of the recipient tooth.

[0088] The calculation method of the rotation matrix from the initial orientation of the donor tooth to the initial orientation of the recipient tooth specifically includes:

[0089] The dental arch curves of the upper and lower teeth are fitted according to the tooth center points of the upper and lower teeth.

[0090] The initial orientation of the donor tooth was calculated based on morphology.

[0091] The initial orientation of the recipient tooth was calculated based on the tangent direction of the dental arch, and the rotation matrix from the initial orientation of the donor tooth to the initial orientation of the recipient tooth was calculated based on the long axis of the tooth.

[0092] In the present invention, the center point, long axis vector and initial direction of the donor tooth and the recipient tooth are accurately aligned by translation matrix and rotation matrix, thereby improving the accuracy of transplantation. At the same time, it ensures that the geometric shape and direction of the donor tooth match the recipient tooth, reduces the need for postoperative adjustment, and improves the surgical effect.

[0093] Furthermore, by considering the center point movement, long axis direction and initial direction, the surgical plan is fully optimized to ensure that the donor tooth can be transplanted in the best position and direction of the recipient tooth.

[0094] S5: Determine the best transplant surgery plan based on the crown overlap rate, root overlap rate and bone grinding amount of the recipient tooth and donor tooth in various transplant surgery planning plans.

[0095] In a possible implementation, S5 specifically includes:

[0096] S501: Construct optimization function:

[0097] F operation =(1-R c )*w1+(1-R r )*w2+(G b / C)*w3

[0098] Among them, F operation represents the optimization function, R c represents the overlap ratio between the recipient tooth and the donor tooth crown, R r represents the root overlap ratio, G b represents the amount of bone grinding, C represents a constant, ω1 represents the weight of the crown overlap rate, ω2 represents the weight of the root overlap rate, and ω3 represents the weight of the amount of bone grinding.

[0099] S502: Calculate the optimization function value of each transplant surgery planning scheme.

[0100] S503: The transplant surgery planning scheme with the smallest optimization function value is used as the best transplant surgery planning scheme.

[0101] In the present invention, the optimization function quantifies the crown overlap rate, root overlap rate and bone grinding amount into a comprehensive index, providing a systematic method to evaluate the comprehensive effect of each surgical plan. At the same time, the plan with the smallest optimization function value is taken as the best plan, which can objectively select the transplant surgery plan that best meets the requirements, thereby improving the success rate and effect of the operation.

[0102] Furthermore, the optimization process can identify and avoid potential problems, such as excessive bone resection or poor crown overlap, improving the safety and expected results of the procedure.

[0103] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0104] (1) In the present invention, the individual teeth in the dental CBCT image are segmented by the 3D UX-Net network, and the upper and lower jaws in the dental CBCT image are segmented by the SegResNet network, thereby achieving accurate segmentation of individual teeth and upper and lower jaws.

[0105] (2) In the present invention, various feasible transplantation surgery planning schemes for transplanting alternative donor teeth to recipient teeth are determined based on the segmentation results of a single tooth and the segmentation results of the jawbone through the principal component analysis method. The optimal transplantation surgery planning scheme is determined based on the crown overlap rate, root overlap rate and bone grinding amount between the recipient tooth and the donor tooth in various transplantation surgery planning schemes, and the bone grinding amount is effectively controlled to optimize the matching between the donor tooth and the recipient tooth, thereby improving the success rate of the operation and postoperative comfort, thereby improving the comfort and satisfaction of the patients.

[0106] Reference Manual Attached Figure 2 , showing a schematic structural diagram of an intelligent surgical planning system for autologous tooth transplantation provided by the present invention.

[0107] The present invention further provides an intelligent surgery planning system 20 for autologous tooth transplantation, which is applied to the above-mentioned intelligent surgery planning method for autologous tooth transplantation, and comprises:

[0108] Processor 201;

[0109] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the method for intelligent surgery planning of autologous tooth transplantation as described in the method embodiment is implemented.

[0110] The autologous tooth transplantation intelligent surgery planning system 20 provided by the present invention can execute the above-mentioned autologous tooth transplantation intelligent surgery planning method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0111] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0112] (1) In the present invention, the individual teeth in the dental CBCT image are segmented by the 3D UX-Net network, and the upper and lower jaws in the dental CBCT image are segmented by the SegResNet network, thereby achieving accurate segmentation of individual teeth and upper and lower jaws.

[0113] (2) In the present invention, various feasible transplantation surgery planning schemes for transplanting alternative donor teeth to recipient teeth are determined based on the segmentation results of a single tooth and the segmentation results of the jawbone through the principal component analysis method. The optimal transplantation surgery planning scheme is determined based on the crown overlap rate, root overlap rate and bone grinding amount between the recipient tooth and the donor tooth in various transplantation surgery planning schemes, and the bone grinding amount is effectively controlled to optimize the matching between the donor tooth and the recipient tooth, thereby improving the success rate of the operation and postoperative comfort, thereby improving the comfort and satisfaction of the patients.

[0114] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0115] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct RAMbus RAM (DR RAM).

[0116] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0117] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0118] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0119] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0120] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0121] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0122] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0125] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0126] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the method for intelligent surgery planning for autologous tooth transplantation as described in the method embodiment is implemented.

[0127] A computer-readable storage medium provided by the present invention can implement the steps and effects of the autologous tooth transplantation intelligent surgery planning method of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0128] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0129] (1) In the present invention, the individual teeth in the dental CBCT image are segmented by the 3D UX-Net network, and the upper and lower jaws in the dental CBCT image are segmented by the SegResNet network, thereby achieving accurate segmentation of individual teeth and upper and lower jaws.

[0130] (2) In the present invention, various feasible transplantation surgery planning schemes for transplanting alternative donor teeth to recipient teeth are determined based on the segmentation results of a single tooth and the segmentation results of the jawbone through the principal component analysis method. The optimal transplantation surgery planning scheme is determined based on the crown overlap rate, root overlap rate and bone grinding amount between the recipient tooth and the donor tooth in various transplantation surgery planning schemes, and the bone grinding amount is effectively controlled to optimize the matching between the donor tooth and the recipient tooth, thereby improving the success rate of the operation and postoperative comfort, thereby improving the comfort and satisfaction of the patients.

[0131] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0132] There are a few points to note:

[0133] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0134] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0135] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0136] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An intelligent surgical planning method for autologous tooth transplantation, characterized in that: include: S1: Acquire CBCT images of the tooth to be operated on; S2: Segmenting each single tooth in the dental CBCT image through a 3D UX-Net network; S3: segmenting the upper and lower jaws in the dental CBCT image through a SegResNet network; S4: According to the segmentation results of the single tooth and the jaw bone, various feasible transplantation surgery planning schemes for transplanting the candidate donor tooth to the recipient tooth are determined by principal component analysis; S5: Determine the best transplant surgery planning plan based on the crown overlap rate, root overlap rate and bone grinding amount of the recipient tooth and the donor tooth in the various transplant surgery planning plans.

2. The method for intelligent surgery planning of autologous tooth transplantation according to claim 1, characterized in that: The S2 specifically includes: S201: performing binary segmentation of teeth and background on the tooth CBCT image through a V-Net network to determine a tooth region image; S202: predicting the centroid of each single tooth through an encoder-decoder network according to the tooth region image; S203: Segmenting the tooth region image according to the centroid of each single tooth to obtain each single tooth block; S204: Perform semantic segmentation on the single tooth block through a 3D UX-Net network to determine each single tooth in the tooth CBCT image.

3. The method for intelligent surgery planning of autologous tooth transplantation according to claim 2, characterized in that: The loss function in the V-Net network training process is specifically: Dice loss function; The Dice loss function is specifically: Among them, L dice represents the Dice loss function, X t represents the set of gold standard voxels, Y p Represents the set of prediction voxels.

4. The method for intelligent surgery planning for autologous tooth transplantation according to claim 2, characterized in that: The loss function in the encoder-decoder network training process is specifically: L1 loss function; The L1 loss function is specifically: Among them, L1 is the L1 loss function, which represents the sum of the absolute values ​​of the differences between the predicted value and the true value, y i represents the true value of the voxel, represents the predicted value of the voxel, and N represents the total number of voxels.

5. The method for intelligent surgery planning for autologous tooth transplantation according to claim 2, characterized in that: The loss function in the 3D UX-Net network training process is specifically: a binary cross entropy loss function; The binary cross entropy loss function is specifically: Among them, L bce represents the binary cross entropy loss function, N represents the total number of voxels, y i represents the true value of the voxel, p(y i ) indicates that the prediction belongs to y i The probability value of .

6. The method for intelligent surgery planning of autologous tooth transplantation according to claim 1, characterized in that: The SegResNet network adopts an encoder-decoder convolutional neural network, and the SegResNet network includes: an encoder and a decoder; Each ResNet block in the encoder includes two convolutional layers; each convolutional layer is normalized using GroupNormalization and a ReLU activation function is applied; the input is directly connected to the output of the convolutional layer through a skip connection; The decoder includes a 1x1x1 convolutional layer, and the decoder adopts 3D bilinear upsampling; The SegResNet network outputs 3-channel masks of the same size, representing the probability of each voxel belonging to the background, maxilla, and mandible, respectively.

7. The method for intelligent surgery planning of autologous tooth transplantation according to claim 6, characterized in that: The loss function in the SegResNet network training process is specifically: a hybrid cross entropy loss function; the hybrid cross entropy loss function is specifically: L seg =L dice +μ1L bce Among them, L seg represents the mixed cross entropy loss function, μ1 represents the weight of the balanced loss, L bce represents the binary cross entropy loss function, L dice Represents the Dice loss function.

8. The method for intelligent surgery planning of autologous tooth transplantation according to claim 1, characterized in that: The transplant surgery planning scheme includes: a translation matrix for moving the center point of the donor tooth to the center point of the recipient tooth, a rotation matrix for transforming the long axis vector of the donor tooth to the axial vector of the recipient tooth, and a rotation matrix for transforming the initial direction of the donor tooth to the initial direction of the recipient tooth; The calculation method of the translation matrix of the donor tooth center point moving to the recipient tooth center point specifically includes: Define the global coordinate system; Calculate the translation matrix of the donor tooth center point to the recipient tooth center point in the global coordinate system; The calculation method of the rotation matrix of the donor tooth long axis vector transformed to the recipient tooth axial vector specifically includes: Calculate the covariance matrix of the decentralized original sample matrix and perform eigendecomposition; The eigenvector corresponding to the maximum eigenvalue is selected as the tooth long axis vector; Determine the upper and lower teeth, and update the direction of the tooth long axis vector; Taking the center point of the tooth as the rotation center, calculate the rotation matrix of the long axis vector of the donor tooth to the axial vector of the recipient tooth; The calculation method of the rotation matrix from the initial direction of the donor tooth to the initial direction of the recipient tooth specifically includes: Fit the dental arch curves of the upper and lower teeth according to the tooth center points of the upper and lower teeth; The initial orientation of the donor tooth was calculated based on morphology; The initial orientation of the recipient tooth was calculated based on the tangent direction of the dental arch, and the rotation matrix from the initial orientation of the donor tooth to the initial orientation of the recipient tooth was calculated based on the long axis of the tooth.

9. The method for intelligent surgery planning of autologous tooth transplantation according to claim 1, characterized in that: The S5 specifically includes: S501: Construct optimization function: F operation =(1-R c )*w1+(1-R r )*w2+(G b / C)*w3 Among them, F operation represents the optimization function, R c represents the overlap ratio between the recipient tooth and the donor tooth crown, R r Indicates the root overlap ratio, G b represents the amount of bone grinding, C represents a constant, ω1 represents the weight of the crown overlap rate, ω2 represents the weight of the root overlap rate, and ω3 represents the weight of the amount of bone grinding; S502: Calculating the optimization function value of each transplant surgery planning scheme; S503: The transplant surgery planning scheme with the smallest optimization function value is used as the optimal transplant surgery planning scheme.

10. An intelligent surgical planning system for autologous tooth transplantation, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for intelligent surgical planning of autologous tooth transplantation as claimed in any one of claims 1 to 9 is implemented.

Images