Upper anterior dental implant automatic positioning method based on alveolar bone segmentation

Abstract

An upper anterior dental implant automatic positioning method based on alveolar bone segmentation comprises the following steps: S1, inputting an original oral cavity CBCT image into a segmentation model to obtain a tooth segmentation result It and an alveolar bone segmentation result Ie; s2, generating an implant boundary according to the tooth segmentation result It and the alveolar bone segmentation result Ie, and obtaining an implant reasonable implantation range curved surface set Sp; s3, according to the implant reasonable planting range curved surface set Sp, performing slice-by-slice fitting to generate the optimal axial direction of the implant, and obtaining visual data Ip of the implant. According to the method, the upper anterior tooth alveolar bone segmentation aiming at the CBCT data is realized, the optimal implant position prediction for immediate implantation and the generation of the optimal implant image position are carried out, and the problems that the current upper anterior tooth immediate implantation operation depends on the subjective experience of doctors, the implantation is inaccurate and the time is consumed are solved.

Description

Technical Field

[0001] The invention belongs to the field of medical image processing, and in particular relates to an automatic positioning method for anterior maxillary dental implants based on alveolar bone segmentation. Background Art

[0002] The health of teeth plays a key role in oral function and appearance. In modern oral medical practice, immediate implantation is a cutting-edge oral restoration technology that can not only restore the function and beauty of missing teeth, but also reduce the waiting time of patients after tooth extraction, thereby providing a more convenient oral rehabilitation solution. It is widely used in implants in the upper anterior teeth area. However, immediate implant surgery is highly dependent on the doctor's subjective experience, and different doctors directly affect the effect of implant surgery. Therefore, it is crucial to realize the automatic selection of the optimal position of the implant and the generation of preoperative implant influences to improve the efficiency and accuracy of immediate implant surgery.

[0003] For example, the Chinese invention patent application with the invention name of "An assisted implantation method for rapidly generating virtual implant positions", publication number CN116269748A and publication date 2023.06.23, discloses an assisted implantation method for rapidly generating virtual implant positions, which imports oral CBCT and intraoral three-dimensional scanning data, performs three-dimensional reconstruction and data alignment, and uses three-dimensional surface data to generate implant positions. This method requires intraoral three-dimensional scanning data to assist oral CBCT, and both types of data are re-entered in three dimensions. Although the implant position is determined, there is no targeted use of constraints such as the thickness of a single alveolar bone wall to improve the segmentation accuracy of a single tooth position, and the constraint information of adjacent teeth on the implant position is not fully considered, and manual intervention is required, which cannot achieve an end-to-end method. The problem of data accuracy loss in three-dimensional reconstruction has not been solved, which affects the constraints of the proximal labial and proximal lingual bone walls of the alveolar bone, and affects the accuracy of the implant position and the efficiency of immediate implantation.

[0004] For another example, the Chinese invention patent application entitled "An oral implant design method, system and storage medium", with publication number CN116999195A and publication date 2023.11.07, discloses an oral implant design method, system and storage medium. The method uses the identified tooth crown center point to fit the arc parabola of the upper and lower jaw dentition, and then fits the optimal axial position of the implant at the edentulous position based on the tooth body center axis of the remaining tooth. Although this method can generate the axial position of the implant, since the axial position of the implant is determined by the tooth body axis of the adjacent teeth and the upper and lower jaw dentition, there is a large error in the determination of the implant axis due to the influence of the adjacent teeth. In addition, the bone wall constraint of the alveolar bone at the edentulous position is not considered, resulting in the determination of the axial position of the implant not taking into account the initial stability of the implant, which will cause deviations in the optimal position of the implant and affect the efficiency and accuracy of immediate implantation. Summary of the invention

[0005] In order to overcome the shortcomings of the existing technology, the present invention provides an automatic positioning method for maxillary anterior dental implants based on alveolar bone segmentation, which realizes the alveolar bone segmentation of the maxillary anterior teeth position based on CBCT data and predicts the optimal position of the implant for immediate implantation and generates the optimal image position of the implant, thereby solving the problems of current maxillary anterior dental immediate implantation surgery that relies on the doctor's subjective experience, is inaccurate and time-consuming.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] An automatic positioning method for anterior maxillary dental implant based on alveolar bone segmentation comprises the following steps:

[0008] S1. Input the original oral CBCT image into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results I e ;

[0009] S2. According to the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p ;

[0010] S3. Surface set S according to reasonable implant range p , generate the best axial direction of the implant by fitting each slice, and obtain the visualization data of the implant I p .

[0011] Furthermore, in S1, the segmentation model includes a feature encoding network and a feature decoding network, the feature encoding network includes five convolution blocks and a downsampling operation. The feature decoding network includes four convolution blocks, an upsampling operation and a jump connection, and the original oral CBCT image is input into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results I e .

[0012] Preferably, the process of S1 is as follows:

[0013] Step 1.1: Input the original oral CBCT image into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F1, whose dimension size is

[0014] Step 1.2: Perform maximum pooling on the feature map F1 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F2, whose dimension size is

[0015] Step 1.3: Perform maximum pooling on the feature map F2 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F3, whose dimension size is

[0016] Step 1.4: Perform maximum pooling on the feature map F3 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F4, whose dimension size is

[0017] Step 1.5: Perform maximum pooling on the feature map F4 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F5, whose dimension size is

[0018] Step 1.6: Upsample the feature map F5, then concatenate it with the feature map F4 at the channel level and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the feature map F up1 , its dimension size is consistent with F4;

[0019] Step 1.7: The feature map F up1 Up-sampled, then concatenated with feature map F3 at the channel level and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up2 , its dimension size is consistent with F3;

[0020] Step 1.8: The feature map F up2Up-sample, then concatenate with feature map F2 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up3 , its dimension size is consistent with F2;

[0021] Step 1.9: The feature map F up3 Up-sample, then concatenate with feature map F1 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up4 , its dimension size is consistent with F1;

[0022] Step 1.10: transform the feature map F up4 Input to the convolution block with a convolution kernel size of 1×1×1 to obtain the tooth segmentation result I t And alveolar bone segmentation results I e .

[0023] Furthermore, in S2, according to the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p , the process is as follows:

[0024] Step 2.1: Tooth segmentation result I t And alveolar bone segmentation results I e According to the different values ​​of the elements in the array, it is divided into the left adjacent tooth point set D l , right adjacent tooth point set D r 、Tooth point set D t and alveolar point set D e ;

[0025] Step 2.2: Calculate the left adjacent tooth point set D l , right adjacent tooth point set D r and alveolar point set D e The normal vectors of each point set are obtained from their respective point sets, including the normal vectors of all points in each point set. First, a scalar field function f(x, y, z) is defined for each point set to calculate the normal vector of each point. The normal vector is calculated by solving for each point p i The gradient of the function f The calculation formula is:

[0026]

[0027] Among them, p i is a point in the point set, belonging to the point set D l , D r and D e . Left adjacent tooth point set D l , right adjacent tooth point set Dr and alveolar point set D e The normal vector sets are the left adjacent tooth normal vector set v l and the normal vector set v of the right adjacent tooth r And the alveolar bone normal vector set v e , each set contains the normal vectors of all points in the corresponding point set;

[0028] Step 2.3: According to the left adjacent tooth point set D l and the right adjacent tooth point set D r Extract the outer surface point set D of the left adjacent tooth lw , right adjacent tooth outer surface point set D rw and the highest point of the left adjacent tooth p l and the highest point p of the right adjacent tooth r , the left adjacent tooth outer surface point set D lw and the right adjacent tooth outer surface point set D rw According to the highest point p of the left adjacent tooth l and the highest point p of the right adjacent tooth r Divided into the right outer surface point set D of the left adjacent tooth lrw and the left outer surface point set D of the right adjacent tooth rlw ;

[0029] Step 2.4: Calculate the alveolar bone point set D e The major axis vector v el The long axis vector is obtained by calculating each point P from the alveolar point set i To the geometric centroid of the point set The long axis vector reflects the main extension direction of the alveolar bone in space and is the representative vector of the entire point set. The formula is as follows:

[0030]

[0031] Step 2.5: Set the tooth point set D t The highest point p on the z-axis in the three-dimensional array t As the highest point of the tooth hole, create a section B parallel to the y-axis e Contains the alveolar bone long axis vector v el and the highest point of the tooth hole p t . From section B e Set the alveolar bone point D e Divided into mesial alveolar point cluster D ef D eb ;

[0032] Step 2.6: Traverse the alveolar bone point set D on the mesial labial side using the y-axis slice ef D eb, connect the z-axis bottom endpoints of the two point sets in the same slice as the lower boundary points, and record all the points on each slice connection line as the lower boundary point set D d ;

[0033] Step 2.7, according to the alveolar bone point set D e Extract the point set D of the alveolar bone outer surface ew , the alveolar bone outer surface point set D ew The mesial alveolar bone point set D ef and mesiolingual alveolar point cluster D eb Take the union and get the point set D of the outer surface of the alveolar bone on the mesial labial side. efw and the point set D on the outer surface of the alveolar bone on the near lingual side ebw ;

[0034] Step 2.8: Based on the right side outer surface point set D of the left adjacent tooth lrw , the point set D of the left outer surface of the right adjacent tooth rlw and the point set D on the outer surface of the alveolar bone on the mesial labial side efw The corresponding left adjacent tooth normal vector set v l , right adjacent tooth normal vector set v r 、Alveolar bone normal vector set v e , expand the point set by a certain distance in the direction of the normal vector, and obtain the point set D of the right outer surface of the left adjacent tooth after expansion lrwc , point set D of the left outer surface of the right adjacent tooth after expansion rlwc and the point set D on the outer surface of the mesial alveolar bone after expansion efwc , the coordinates of each point after movement P i The calculation formula is as follows:

[0035] P i ′=(x i +d·v xi ,y i +d·v yi ,z i +d·v zi )

[0036] Among them, P i =(x i ,y i ,z i ) represents the coordinates of the points in the point set, belonging to the point set D lrw , D rlw and D efw , Represents the normal vector corresponding to the point, which belongs to the normal vector set v l 、v r and v e , d is the distance moved;

[0037] Step 2.9, point set Dlrwc , D rlwc , D efwc , D eb and D d The points are combined into an open cuboid, and the space enclosed by the point set is the reasonable implant range of the implant, which is recorded as the surface set S of the reasonable implant range p .

[0038] In S3, according to the reasonable implant range surface set S p , the optimal axial direction of the implant is generated by slice-by-slice fitting, and the visualization data of the implant is obtained. The process is as follows:

[0039] Step 3.1: Traverse the expanded mesial alveolar bone outer surface point set D by slicing in the y-axis direction efwc and the point set D on the outer surface of the alveolar bone on the near lingual side ebw , use the least squares method to perform straight line fitting on the points of the current slice of the point set, and calculate the slope of the straight line corresponding to each slice, which is recorded as the slope k of the outer surface of the proximal labial side on each slice ef , intercept d ef and the slope k of the straight line on the outer surface of the near-lingual side eb , intercept d eb , the formula is as follows:

[0040]

[0041] Among them, x i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the y-axis slice, respectively, and n is the number of points in the slice;

[0042] Step 3.2: According to the slope k of the outer surface of the proximal labial side ef , intercept d ef and the slope k of the straight line on the outer surface of the near-lingual side eb , intercept d eb , get the slope k of the center line on the y-axis slice e is (k ef +k eb ) / 2, intercept d e for (d ef +d eb ) / 2, record the points that the center line on the y-axis slice passes through, and record it as the y-plane axial point set D ep ;

[0043] Step 3.3: Traverse the right outer surface point set D of the left adjacent tooth after expansion by slicing in the y-axis direction lrwc , point set D of the left outer surface of the right adjacent tooth after expansion rlwc, use the least squares method to fit the points of the current slice of the point set, and calculate the slope of the line corresponding to each slice, which is recorded as the slope k of the outer surface of the left adjacent tooth on the right side of each slice. tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , the formula is as follows:

[0044]

[0045] Among them, y i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the x-axis slice, respectively, and n is the number of points in the slice;

[0046] Step 3.4: According to the slope k of the straight line on the right side of the left adjacent tooth tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , get the slope k of the center line on the x-axis slice t is (k tl +k tr ) / 2, intercept d t for (d tl +d tr ) / 2, record the points that the center line on the x-axis slice passes through, and record them as the x-plane axial point set D tp ;

[0047] Step 3.5: Direct the y-plane axis to the point set D ep and the x-plane axial point set D tp Take the intersection and get the implant axial point set D p , project all points in the point set onto the y-axis plane, ignoring the y coordinates of all points, according to the x i and z i Coordinate fitting of a two-dimensional straight line to find the slope k xz , intercept d xz Similarly, project all points onto the x-axis plane, according to the y i and z i Coordinate fitting of a two-dimensional straight line to find the slope k yz , intercept d yz Finally, a straight line in three-dimensional space is obtained, which is recorded as the implant axial straight line L. The formula is as follows:

[0048] z=d xz +k xz x

[0049] z=d yz +kyz y

[0050] Step 3.6: Based on the implant axial line L and the lower boundary point set D d , calculate the intersection of the implant axis and the lower boundary, and record it as the implant starting point p ps The implant length d is extended from the starting point along the implant axis. p , get the implant endpoint p pe ;

[0051] Step 3.7: According to the implant starting point p ps and implant endpoint p pe As well as the implant width r, the points between the two points whose distance from the implant axial straight line L is less than the implant width r are marked as implants, and the visualization data I of the implant is obtained. p .

[0052] In the present invention, the segmentation results of teeth and alveolar bones are first obtained through the segmentation model. The segmentation results of teeth and alveolar bones automatically calculate the reasonable implantation range of the implant, and then the optimal axial position of the implant is selected slice by slice from two dimensions according to the reasonable implantation range, and finally the visualization data of the optimal position of the implant is generated.

[0053] The beneficial effects of the present invention are mainly manifested in: realizing the segmentation of alveolar bone of the upper anterior teeth position based on CBCT data and predicting the optimal position of the implant for immediate implantation and generating the optimal image position of the implant, solving the problems of current upper anterior teeth immediate implantation surgery that rely on the subjective experience of doctors, implantation is inaccurate and time-consuming. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a flow chart of an automatic positioning method for anterior maxillary dental implants based on alveolar bone segmentation;

[0055] Figure 2 This is a technical framework diagram of an automatic positioning method for anterior maxillary dental implants based on alveolar bone segmentation;

[0056] Figure 3 This is a sample image of the automatic positioning method for maxillary anterior dental implants based on alveolar bone segmentation. DETAILED DESCRIPTION

[0057] The present invention will be further described below in conjunction with the accompanying drawings.

[0058] Reference Figure 1 to Figure 3 , an automatic positioning method for anterior maxillary dental implant based on alveolar bone segmentation, comprising the following steps:

[0059] S1. Input the original oral CBCT image into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results Ie .

[0060] The segmentation model described in the present application includes a feature encoding network and a feature decoding network. The feature encoding network includes five convolution blocks and a downsampling operation. The feature decoding network includes four convolution blocks, an upsampling operation and a jump connection. The original oral CBCT image is input into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results I e ; The process of S1 is as follows:

[0061] Step 1.1: Input the original oral CBCT image into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F1, whose dimension size is

[0062] Step 1.2: Perform maximum pooling on the feature map F1 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F2, whose dimension size is

[0063] Step 1.3: Perform maximum pooling on the feature map F2 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F3, whose dimension size is

[0064] Step 1.4: Perform maximum pooling on the feature map F3 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F4, whose dimension size is

[0065] Step 1.5: Perform maximum pooling on the feature map F4 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F5, whose dimension size is

[0066] Step 1.6: Upsample the feature map F5, then concatenate it with the feature map F4 at the channel level and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the feature map F up1 , its dimension size is consistent with F4;

[0067] Step 1.7: The feature map F up1 Up-sampled, then concatenated with feature map F3 at the channel level and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up2 , its dimension size is consistent with F3;

[0068] Step 1.8: The feature map F up2Up-sample, then concatenate with feature map F2 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up3 , its dimension size is consistent with F2;

[0069] Step 1.9: The feature map F up3 Up-sample, then concatenate with feature map F1 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up4 , its dimension size is consistent with F1;

[0070] Step 1.10: transform the feature map F up4 Input to the convolution block with a convolution kernel size of 1×1×1 to obtain the tooth segmentation result I t And alveolar bone segmentation results I e .

[0071] Among them, the convolution block includes not only the convolution of the above-mentioned convolution kernel size, but also a normalization and a ReLU activation function.

[0072] S2. According to the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p .

[0073] like Figure 3 As shown, the present application describes the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p , the process is as follows:

[0074] Step 2.1: Tooth segmentation result I t And alveolar bone segmentation results I e According to the different values ​​of the elements in the array, it is divided into the left adjacent tooth point set D l , right adjacent tooth point set D r 、Tooth point set D t and alveolar point set D e ;

[0075] Step 2.2: Calculate the left adjacent tooth point set D l , right adjacent tooth point set D r and alveolar point set D e The normal vectors of each point set are obtained from their respective point sets, including the normal vectors of all points in each point set. First, a scalar field function f(x, y, z) is defined for each point set to calculate the normal vector of each point. The normal vector is calculated by solving for each point p iThe gradient of the function f The calculation formula is:

[0076]

[0077] Among them, p i is a point in the point set, belonging to the point set D l , D r and D e . Left adjacent tooth point set D l , right adjacent tooth point set D r and alveolar point set D e The normal vector sets are the left adjacent tooth normal vector set v l and the normal vector set v of the right adjacent tooth r And the alveolar bone normal vector set v e , each set contains the normal vectors of all points in the corresponding point set;

[0078] Step 2.3: According to the left adjacent tooth point set D l and the right adjacent tooth point set D r Extract the outer surface point set D of the left adjacent tooth lw , right adjacent tooth outer surface point set D rw and the highest point of the left adjacent tooth p l and the highest point p of the right adjacent tooth r , the left adjacent tooth outer surface point set D lw and the right adjacent tooth outer surface point set D rw According to the highest point p of the left adjacent tooth l and the highest point p of the right adjacent tooth r Divided into the right outer surface point set D of the left adjacent tooth lrw and the left outer surface point set D of the right adjacent tooth rlw ;

[0079] Step 2.4: Calculate the alveolar bone point set D e The major axis vector v el The long axis vector is obtained by calculating each point P from the alveolar point set i To the geometric centroid of the point set The long axis vector reflects the main extension direction of the alveolar bone in space and is the representative vector of the entire point set. The formula is as follows:

[0080]

[0081] Step 2.5: Set the tooth point set D t The highest point p on the z-axis in the three-dimensional array t As the highest point of the tooth hole, create a section B parallel to the y-axis e Contains the alveolar bone long axis vector v el and the highest point of the tooth hole p t, from section B e Set the alveolar bone point D e Divided into mesial alveolar point cluster D ef D eb ;

[0082] Step 2.6: Traverse the alveolar bone point set D on the mesial labial side using the y-axis slice ef D eb , connect the z-axis bottom endpoints of the two point sets in the same slice as the lower boundary points, and record all the points on each slice connection line as the lower boundary point set D d ;

[0083] Step 2.7: Based on the alveolar bone point set D e Extract the point set D of the alveolar bone outer surface ew , the alveolar bone outer surface point set D ew The mesial alveolar bone point set D ef and mesiolingual alveolar point set D eb Take the union and get the point set D of the outer surface of the alveolar bone on the mesial labial side. efw and the point set D on the outer surface of the alveolar bone on the near lingual side ebw ;

[0084] Step 2.8: Based on the right side outer surface point set D of the left adjacent tooth lrw , the point set D of the left outer surface of the right adjacent tooth rlw and the point set D on the outer surface of the alveolar bone on the mesial labial side efw The corresponding left adjacent tooth normal vector set v l , right adjacent tooth normal vector set v r 、Alveolar bone normal vector set v e , expand the point set by a certain distance in the direction of the normal vector, and obtain the point set D of the right outer surface of the left adjacent tooth after expansion lrwc , point set D of the left outer surface of the right adjacent tooth after expansion lrwc and the point set D on the outer surface of the mesial alveolar bone after expansion efwc , the coordinates of each point after movement P i The calculation formula is as follows:

[0085] P i ′=(x i +d·v xi ,y i +d·v yi ,z i +d·v zi )

[0086] Among them, P i =(x i ,y i ,z i) represents the coordinates of the points in the point set, belonging to the point set D lrw , D rlw and D efw , Represents the normal vector corresponding to the point, which belongs to the normal vector set v l 、v r and v e , d is the distance moved;

[0087] Step 2.9, point set D lrwc , D rlwc , D efwc , D eb and D d The points are combined into an open cuboid, and the space enclosed by the point set is the reasonable implant range of the implant, which is recorded as the surface set S of the reasonable implant range p ;

[0088] S3. Surface set S according to reasonable implant range p , generate the best axial direction of the implant by fitting each slice, and obtain the visualization data of the implant I p .

[0089] The surface set S according to the reasonable implant range of the implant described in this application p , the optimal axial direction of the implant is generated by slice-by-slice fitting, and the visualization data of the implant is obtained. The process is as follows:

[0090] Step 3.1: Traverse the expanded mesial alveolar bone outer surface point set D by slicing in the y-axis direction efwc and the point set D on the outer surface of the alveolar bone on the near lingual side ebw , use the least squares method to perform straight line fitting on the points of the current slice of the point set, and calculate the slope of the straight line corresponding to each slice, which is recorded as the slope k of the outer surface of the proximal labial side on each slice ef , intercept d ef and the slope k of the straight line on the outer surface of the near-lingual side eb , intercept d eb , the formula is as follows:

[0091]

[0092] Among them, x i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the y-axis slice, respectively, and n is the number of points in the slice;

[0093] Step 3.2: According to the slope k of the outer surface of the proximal labial side ef , intercept d ef and the slope k of the straight line on the outer surface of the near-lingual side eb , intercept d eb, get the slope k of the center line on the y-axis slice e is (k ef +k eb ) / 2, intercept d e for (d ef +d eb ) / 2, record the points that the center line on the y-axis slice passes through, and record it as the y-plane axial point set D ep ;

[0094] Step 3.3: Traverse the right outer surface point set D of the left adjacent tooth after expansion by slicing in the y-axis direction lrwc , point set D of the left outer surface of the right adjacent tooth after expansion rlwc , use the least squares method to fit the points of the current slice of the point set, and calculate the slope of the line corresponding to each slice, which is recorded as the slope k of the outer surface of the left adjacent tooth on the right side of each slice. tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , the formula is as follows:

[0095]

[0096] Among them, y i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the x-axis slice, respectively, and n is the number of points in the slice;

[0097] Step 3.4: According to the slope k of the straight line on the right side of the left adjacent tooth tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , get the slope k of the center line on the x-axis slice t is (k tl +k tr ) / 2, intercept d t for (d tl +d tr ) / 2, record the points that the center line on the x-axis slice passes through, and record them as the x-plane axial point set D tp ;

[0098] Step 3.5: Direct the y-plane axis to the point set D ep and the x-plane axial point set D tp Take the intersection and get the implant axial point set D p , project all points in the point set onto the y-axis plane, ignoring the y coordinates of all points, according to the x i and z i Coordinate fitting of a two-dimensional straight line to find the slope kxz , intercept d xz Similarly, project all points onto the x-axis plane, according to the y i and z i Coordinate fitting of a two-dimensional straight line to find the slope k yz , intercept d yz Finally, a straight line in three-dimensional space is obtained, which is recorded as the implant axial straight line L. The formula is as follows:

[0099] z=d xz +k xz x

[0100] z=d yz +k yz y

[0101] Step 3.6: Based on the implant axial line L and the lower boundary point set D d , calculate the intersection of the implant axis and the lower boundary, and record it as the implant starting point p ps , extending the implant length d from the starting point along the implant axis p , get the implant endpoint p pe ;

[0102] Step 3.7: According to the implant starting point p ps and implant endpoint p pe As well as the implant width r, the points between the two points whose distance from the implant axial straight line L is less than the implant width r are marked as implants, and the visualization data I of the implant is obtained. p .

[0103] It should be noted that the example of the method for automatically positioning anterior dental implants based on alveolar bone segmentation of the present application is shown in FIG. Figure 3 shown.

[0104] It should be noted that in this application, C is the number of channels, D is the depth of the image, H is the height of the image, W is the width of the image, and the subscript of the letter represents the serial number, which is used to distinguish the dimensions of different feature maps.

[0105] This application obtains the segmentation results of teeth and alveolar bones through segmentation models. The reasonable implantation area of ​​an implant is adaptively determined using the adjacent tooth data and the alveolar bone data of the missing teeth. Then, according to the reasonable implantation range, the optimal axial position of the implant is selected slice by slice from two dimensions, and finally the visualization data of the optimal position of the implant is generated.

[0106] The contents described in the embodiments of this specification are merely enumerations of implementation forms of the inventive concept and are for illustrative purposes only. The protection scope of the present invention should not be considered to be limited to the specific forms described in this embodiment, and the protection scope of the present invention also extends to equivalent technical means that can be thought of by ordinary technicians in this field based on the inventive concept.

Claims

1. A method for automatic positioning of anterior dental implants based on alveolar bone segmentation, characterized in that: The method comprises the following steps: S1. Input the original oral CBCT image into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results I e ; S2. According to the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p ; S3. Surface set S according to reasonable implant range p , generate the best axial direction of the implant by fitting each slice, and obtain the visualization data of the implant I p .

2. The method for automatic positioning of anterior dental implants based on alveolar bone segmentation according to claim 1, characterized in that: In S1, the segmentation model includes a feature encoding network and a feature decoding network, the feature encoding network includes five convolution blocks and downsampling operations, and the feature decoding network includes four convolution blocks, upsampling operations and jump connections. The original oral CBCT image is input into the segmentation model to obtain the tooth segmentation result I t And alveolar bone segmentation results I e .

3. The method for automatic positioning of anterior dental implants based on alveolar bone segmentation according to claim 2, characterized in that: The process of S1 is as follows: Step 1.1: Input the original oral CBCT image into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F1, whose dimension size is Step 1.2: Perform maximum pooling on the feature map F1 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F2, whose dimension size is Step 1.3: Perform maximum pooling on the feature map F2 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F3, whose dimension size is Step 1.4: Perform maximum pooling on the feature map F3 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F4, whose dimension size is Step 1.5: Perform maximum pooling on the feature map F4 and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the output feature map F5, whose dimension size is Step 1.6: Upsample the feature map F5, then concatenate it with the feature map F4 at the channel level and input it into a convolution block with a convolution kernel size of 3×3×3 to obtain the feature map F up1 , its dimension size is consistent with F4; Step 1.7: The feature map F up1 Up-sampled, then concatenated with feature map F3 at the channel level and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up2 , its dimension size is consistent with F3; Step 1.8: The feature map F up2 Up-sample, then concatenate with feature map F2 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up3 , its dimension size is consistent with F2; Step 1.9: The feature map F up3 Up-sample, then concatenate with feature map F1 at the channel level, and input into a convolution block with a convolution kernel size of 3×3×3 to obtain feature map F up4 , its dimension size is consistent with F1; Step 1.10: transform the feature map F up4 Input to the convolution block with a convolution kernel size of 1×1×1 to obtain the tooth segmentation result I t And alveolar bone segmentation results I e .

4. The method for automatic positioning of anterior dental implants based on alveolar bone segmentation according to any one of claims 1 to 3, characterized in that: In S2, according to the tooth segmentation result I t and alveolar bone segmentation results I e , generate the implant boundary and obtain the surface set S of the reasonable implant range p , the process is as follows: Step 2.1: Tooth segmentation result I t And alveolar bone segmentation results I e According to the different values ​​of the elements in the array, it is divided into the left adjacent tooth point set D l , right adjacent tooth point set D r 、Tooth point set D t and alveolar point set D e ; Step 2.2: Calculate the left adjacent tooth point set D l , right adjacent tooth point set D r and alveolar point set D e The normal vectors of each point set are obtained from their respective point sets, including the normal vectors of all points in each point set. First, a scalar field function f(x, y, z) is defined for each point set to calculate the normal vector of each point. The normal vector is calculated by solving for each point p i The gradient of the function f The calculation formula is: Among them, p i is a point in the point set, belonging to the point set D l , D r and D e , the left adjacent tooth point set D l , right adjacent tooth point set D r and alveolar point set D e The normal vector sets are the left adjacent tooth normal vector set v l and the normal vector set v of the right adjacent tooth r And the alveolar bone normal vector set v e , each set contains the normal vectors of all points in the corresponding point set; Step 2.3: According to the left adjacent tooth point set D l and the right adjacent tooth point set D r Extract the outer surface point set D of the left adjacent tooth lw , right adjacent tooth outer surface point set D rw and the highest point of the left adjacent tooth p l and the highest point p of the right adjacent tooth r , the left adjacent tooth outer surface point set D lw and the right adjacent tooth outer surface point set D rw According to the highest point p of the left adjacent tooth l and the highest point p of the right adjacent tooth r Divided into the right outer surface point set D of the left adjacent tooth lrw and the left outer surface point set D of the right adjacent tooth rlw ; Step 2.4: Calculate the alveolar bone point set D e The major axis vector v el The long axis vector is obtained by calculating each point P from the alveolar point set i To the geometric centroid of the point set The long axis vector reflects the main extension direction of the alveolar bone in space and is the representative vector of the entire point set. The formula is as follows: Step 2.5: Set the tooth point set D t The highest point p on the z-axis in the three-dimensional array t As the highest point of the tooth hole, create a section B parallel to the y-axis e Contains the alveolar bone long axis vector v el and the highest point of the tooth hole p t , from section B e Set the alveolar bone point D e Divided into mesial alveolar point cluster D ef D eb ; Step 2.6: Traverse the alveolar bone point set D on the mesial labial side using the y-axis slice ef D eb , connect the z-axis bottom endpoints of the two point sets in the same slice as the lower boundary points, and record all the points on each slice connection line as the lower boundary point set D d ; Step 2.7, according to the alveolar bone point set D e Extract the point set D of the alveolar bone outer surface ew , the alveolar bone outer surface point set D ew The mesial alveolar bone point set D ef and mesiolingual alveolar point cluster D eb Take the union and get the point set D of the outer surface of the alveolar bone on the mesial labial side. efw and the point set D on the outer surface of the alveolar bone on the near lingual side ebw ; Step 2.8: Based on the right side outer surface point set D of the left adjacent tooth lrw , the point set D of the left outer surface of the right adjacent tooth rlw and the point set D on the outer surface of the alveolar bone on the mesial labial side efw The corresponding left adjacent tooth normal vector set v l , right adjacent tooth normal vector set v r 、Alveolar bone normal vector set v e , expand the point set by a certain distance in the direction of the normal vector, and obtain the point set D of the right outer surface of the left adjacent tooth after expansion lrwc , point set D of the left outer surface of the right adjacent tooth after expansion rlwc and the point set D on the outer surface of the mesial alveolar bone after expansion efwc , the coordinates of each point after movement P i ′ The calculation formula is as follows: P i ′ =(x i +d·v xi ,y i +d·v yi ,z i +d·v zi ) Among them, P i =(x i ,y i ,z i ) represents the coordinates of the points in the point set, belonging to the point set D lrw , D rlw and D efw , Represents the normal vector corresponding to the point, which belongs to the normal vector set v l 、v r and v e , d is the distance moved; Step 2.9, point set D lrwc , D rlwc , D efwc , D eb and D d The points are combined into an open cuboid, and the space enclosed by the point set is the reasonable implant range of the implant, which is recorded as the surface set S of the reasonable implant range p .

5. The method for automatic positioning of anterior dental implants based on alveolar bone segmentation according to any one of claims 1 to 3, characterized in that: In S3, according to the reasonable implant range surface set S p , the optimal axial direction of the implant is generated by slice-by-slice fitting, and the visualization data of the implant is obtained. The process is as follows: Step 3.1: Traverse the expanded mesial alveolar bone outer surface point set D by slicing in the y-axis direction efwc and the point set D on the outer surface of the alveolar bone on the near lingual side ebw , use the least squares method to perform straight line fitting on the points of the current slice of the point set, and calculate the slope of the straight line corresponding to each slice, which is recorded as the slope k of the outer surface of the proximal labial side on each slice ef , intercept d ef and the slope k of the outer surface of the mesiolingual side eb , intercept d eb , the formula is as follows: Among them, x i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the y-axis slice, respectively, and n is the number of points in the slice; Step 3.2: According to the slope k of the outer surface of the proximal labial side ef , intercept d ef and the slope k of the outer surface of the mesiolingual side eb , intercept d eb , get the slope k of the center line on the y-axis slice e is (k ef +k eb ) / 2, intercept d e for (d ef +d eb ) / 2, record the points that the center line on the y-axis slice passes through, and record it as the y-plane axial point set D ep ; Step 3.3: Traverse the right outer surface point set D of the left adjacent tooth after expansion by slicing in the y-axis direction lrwc , point set D of the left outer surface of the right adjacent tooth after expansion rlwc , use the least squares method to fit the points of the current slice of the point set, and calculate the slope of the line corresponding to each slice, which is recorded as the slope k of the outer surface of the left adjacent tooth on the right side of each slice. tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , the formula is as follows: Among them, y i and z i denote the horizontal (x) and vertical (z) coordinates of each point in the x-axis slice, respectively, and n is the number of points in the slice; Step 3.4: According to the slope k of the straight line on the right side of the left adjacent tooth tl , intercept d tl and the slope k of the straight line on the left side of the right adjacent tooth tr , intercept d tr , get the slope k of the center line on the x-axis slice t is (k tl +k tr ) / 2, intercept d t for (d tl +d tr ) / 2, record the points that the center line on the x-axis slice passes through, and record them as the x-plane axial point set D tp ; Step 3.5: Direct the y-plane axis to the point set D ep and the x-plane axial point set D tp Take the intersection and get the implant axial point set D p , project all points in the point set onto the y-axis plane, ignoring the y coordinates of all points, according to the x i and z i Coordinate fitting of a two-dimensional straight line to find the slope k xz , intercept d xz Similarly, project all points onto the x-axis plane, according to the y i and z i Coordinate fitting of a two-dimensional straight line to find the slope k yz , intercept d yz Finally, a straight line in three-dimensional space is obtained, which is recorded as the implant axial straight line L. The formula is as follows: z=d xz +k xz x z=d yz +k yz y Step 3.6: Based on the implant axial line L and the lower boundary point set D d , calculate the intersection of the implant axis and the lower boundary, and record it as the implant starting point p ps , extending the implant length d from the starting point along the implant axis p , get the implant endpoint p pe ; Step 3.7: According to the implant starting point p ps and implant endpoint p pe As well as the implant width r, the points between the two points whose distance from the implant axial straight line L is less than the implant width r are marked as implants, and the visualization data I of the implant is obtained. p .

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