Three-dimensional intersection-to-union ratio analysis method for digital guide plate guided oral implantation precision

Abstract

The invention provides a three-dimensional intersection-to-union ratio analysis method for digital guide plate guided oral implanting precision, relates to the field of oral medicine, and solves the problems of non-visual precision evaluation, complex operation and large subjective error after a digital guide plate guided oral implanting operation. The method comprises the following steps: respectively acquiring a preoperative design position image and a postoperative actual position image of an implant of a patient before and after an oral tooth implantation operation of the patient; after the preoperative design position image and the postoperative actual position image are subjected to three-dimensional reconstruction respectively, a three-dimensional position relation graph of the designed implant and the actual implant is determined, and a three-dimensional intersection-to-union ratio value of the designed implant and the actual implant is calculated; according to the calculated three-dimensional intersection-to-union ratio value, the precision of the dental implant of the oral cavity of the patient is analyzed and evaluated; the evaluation index is the three-dimensional intersection-to-union ratio value between the preoperative design position and the postoperative actual position of the implant, so that the planting precision is accurately quantified, and objective quality control on the postoperative result is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of stomatology and is applied to the process of evaluating the accuracy of dental implants, and specifically to a three-dimensional intersection-combination analysis method for the accuracy of oral implants guided by digital guides. Background Art

[0002] In the field of modern stomatology, digital guide-guided dental implant surgery has become a widely used technical means. Compared with the traditional free-hand operation method, this technology significantly improves the accuracy and predictability of the surgery. Traditional dental implant surgery mainly relies on the surgeon's experience and technical level to determine the position, angle and depth of the implant. Due to the differences in the technical proficiency of each doctor, this often leads to a certain degree of uncertainty in the surgical results, affecting the final treatment effect.

[0003] With the introduction of digital guides, the ideal position of implants can be accurately planned through detailed preoperative three-dimensional imaging data (such as CT or CBCT scans). The surgical guides made based on these data provide clear guidance for drilling and implantation steps during the actual operation, greatly reducing the impact of human factors on the results. This method can not only improve the success rate of a single implant, but also improve the relative position relationship between multiple implants, which is especially beneficial for complex cases.

[0004] However, although the application of digital guides has significantly improved the quality of dental implant surgery, how to accurately evaluate postoperative accuracy remains a challenge in clinical practice. The current methods for evaluating the effect of dental implant surgery guided by digital guides mainly include comparing key indicators such as shoulder deviation, root deviation, depth deviation, and angle deviation between preoperative design and actual postoperative results. However, although some existing software provide built-in measurement tools, their operation is relatively complex and not intuitive enough, making it difficult for non-professionals to quickly master the skills. In addition, the selection of appropriate sections for analysis usually requires personal experience, a process that is highly subjective and prone to deviations in results due to different operators.

[0005] Therefore, it is particularly necessary to develop a more simple, easy-to-use and objective postoperative accuracy evaluation method. Such a new method must have the characteristics of digital and automated application, reduce the errors caused by human intervention, and provide a clear and concise display of evaluation results to help doctors and patients better understand the results of the surgery. Summary of the invention

[0006] Based on the current status of the background technology, the purpose of the present invention is to solve the problems of non-intuitive accuracy evaluation, complicated operation and large subjective error of digital guide-guided oral implant surgery, and therefore proposes a three-dimensional intersection-and-union analysis method for the accuracy of digital guide-guided oral implants. The evaluation index of the present invention is the three-dimensional intersection-and-union value between the preoperative design position and the actual postoperative position of the patient's implant. This method allows the implant accuracy to be accurately quantified, facilitates objective quality control of postoperative results, and promotes the progress and development of oral digitalization.

[0007] The present invention adopts the following technical solutions to achieve the purpose:

[0008] A three-dimensional intersection-and-union ratio analysis method for the accuracy of oral implants guided by a digital guide plate comprises: obtaining a preoperative design position image and a postoperative actual position image of the patient's implant respectively before and after the patient receives an oral implant surgery; after three-dimensionally reconstructing the preoperative design position image and the postoperative actual position image respectively, determining a three-dimensional position relationship diagram of the designed implant and the actual implant, and calculating a three-dimensional intersection-and-union ratio value of the designed implant and the actual implant; and analyzing and evaluating the accuracy of the patient's oral implant based on the calculated three-dimensional intersection-and-union ratio value.

[0009] Specifically, the method for obtaining the preoperative designed position image and the postoperative actual position image is as follows: after the patient undergoes oral implant surgery, the patient's postoperative CBCT image data is obtained, and the postoperative implant and jaw data of the patient are obtained after the postoperative CBCT image data is processed by dental digital design software; the implant design data of the corresponding patient is obtained, and the implant design data, postoperative implant and jaw data are imported into the oral digital software for fitting processing to obtain the preoperative designed position image and postoperative actual position image of the patient's implant.

[0010] Specifically, the fitted image type is displayed as a mid-axis section image type through oral digitization software; corresponding to the preoperative designed position image and the postoperative actual position image, multiple images of the implant are cut out and saved as the basis for three-dimensional reconstruction of the designed implant and the actual implant.

[0011] Furthermore, after obtaining the preoperative design position image and the postoperative actual position image of the patient's implant, the same image preprocessing process is performed respectively, including: extracting masks, extracting color curves, processing concave surfaces, and filling.

[0012] Specifically, for all image files in the acquired preoperative design position image and postoperative actual position image, each image file is cropped and scaled, and then the mask of the preset area is extracted by file indexing, and the mask is applied to the cropped and scaled image and saved, thereby completing the extraction of the mask;

[0013] When extracting color curves, the color space of all image files is converted from RGB to HSV, the HSV thresholds of multiple color categories are set, and color masks are generated; the index value is selected by file indexing, and convolution kernels of different sizes are selected according to the index value. Dilation and erosion operations are performed on the mask of the preset color to obtain a binary image; after the binary image is refined to extract the skeleton, the connected areas are marked for the remaining color areas, and the bounding box attributes of the corresponding areas of the implants are extracted; finally, the multiple color masks are merged, and the corresponding areas of each color are retained to complete the extraction of color curves in the image;

[0014] For the binary mask corresponding to the binary image, the concave area in the binary mask is processed, and the external concave is filled by dilating the binary mask and detecting its contour.

[0015] Furthermore, the same three-dimensional reconstruction process is performed on the preoperative design position image and the postoperative actual position image, including: loop processing of image data sets, image reading and matching, geometric transformation operations, and storage of reconstruction results; wherein, the two types of images are formed into a rotated image structure through the geometric transformation operation.

[0016] Specifically, after image preprocessing is performed on the preoperative design position image and the postoperative actual position image, two corresponding data sets are formed, each of which includes multiple images that have been processed in a loop; a target array is set, and the target array is used to store 3D data, and each slice in the 3D data corresponds to each processed image in the data set; images are loaded in a preset order, and the loaded images are converted into grayscale mode and then cropped so that the cropped images match the width of the target array;

[0017] During three-dimensional reconstruction, the rotation method of the 3D data stored in the target array is determined by defining the idx value; when the index value is less than the preset value, the coordinates of each row of pixels in the corresponding image are transformed by sine and cosine rotation according to the specific angle, thereby simulating the rotation of the third quadrant; after determining the rotation center and the image half-width, the rotation angle is calculated and the x and y coordinate positions of the data are updated; when the index value is greater than or equal to the preset value, the corresponding direction is rotated directly according to the angle offset; the updated data is assigned to the target array according to the new coordinates to form a rotated image structure; the two reconstruction results corresponding to the preoperative design position image and the postoperative actual position image are saved separately.

[0018] Furthermore, after the three-dimensional reconstruction is completed, the three-dimensional intersection-and-union ratio value of the designed implant and the actual implant is calculated based on the three-dimensional reconstruction result, and the three-dimensional position relationship diagram of the designed implant and the actual implant is presented through rendering; for the calculated three-dimensional intersection-and-union ratio value, the larger the value, the higher the accuracy of the patient's oral dental implant.

[0019] Specifically, when calculating the 3D intersection-and-union ratio value, firstly, the 3D data corresponding to the designed implant and the actual implant in the 3D reconstruction result are converted into binary masks respectively, and the intersection area and the union area of ​​the two binary masks are calculated in turn, and the intersection set prime number and the union set prime number are obtained correspondingly. Then, the 3D intersection-and-union ratio value is the result of dividing the intersection set prime number by the union set prime number.

[0020] For the 3D data of the designed implant and the actual implant, the corresponding volume data files of the 3D reconstruction results are obtained; the two volume data files are spliced ​​along the width axis to form spliced ​​volume data, and the two volume data files are added element by element to form superimposed volume data; the spliced ​​volume data and the superimposed volume data are reserved for calling during rendering;

[0021] Finally, while presenting the 3D intersection-to-union ratio value to the outside, two rendering scenes are created to display the stitched volume data and the superimposed volume data respectively; the rendering effect of the rendering scene is controlled by using the transfer function, and the rendering scene is presented to the outside synchronously.

[0022] The present invention also provides a computer system, including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the aforementioned three-dimensional intersection-and-parallel analysis method for guiding oral implant accuracy using a digital guide.

[0023] In summary, due to the adoption of this technical solution, the beneficial effects of the present invention are as follows:

[0024] The present invention can accurately quantify the difference between the preoperative design position of the patient's implant and the actual postoperative position, providing clinicians with a more intuitive and easy-to-understand evaluation standard. Specifically, the present invention uses the three-dimensional intersection-to-union ratio as an evaluation indicator, which not only simplifies the process of manually selecting specific sections for comparison, but also avoids the result deviation caused by differences in subjective judgments of different operators. This method makes the entire evaluation process more automated and standardized, improving work efficiency while ensuring the consistency and reliability of the results.

[0025] The evaluation system brought about by the application of the method of the present invention has broad application prospects. It can not only serve as an important tool for internal quality control in hospitals, helping medical institutions to better monitor and manage surgical results and promote the overall improvement of medical service levels; it also helps to promote the entire industry to move towards a higher level of digital transformation. With the adoption of this set of evaluation standards, greater breakthroughs are expected to be made in improving the success rate of dental implant surgery in the future. It can be seen that the present invention achieves a more accurate and objective quantitative evaluation of the results of implant surgery by performing three-dimensional intersection and comparison analysis on the accuracy of oral implants under the guidance of digital guides, laying the foundation for further exploration and development of more efficient and accurate oral medical technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall steps of the method of the present invention;

[0027] Figure 2 Schematic diagram of the patient's implant.

[0028] Figure 3 is a schematic diagram of the patient's jaw;

[0029] Figure 4 A schematic diagram for comparing the preoperative designed position image and the postoperative actual position image;

[0030] Figure 5 It is the three-dimensional position relationship diagram between the designed implant and the actual implant;

[0031] Figure 6 It is a schematic diagram of the numerical calculation results of the three-dimensional intersection-to-union ratio in the method of the present invention;

[0032] Figure 7 Schematic diagram of the image preprocessing process in the method of the present invention;

[0033] Figure 8 Schematic diagram of the process of three-dimensional reconstruction of images in the method of the present invention;

[0034] Fig. 9 It is a schematic diagram of the process of external rendering and three-dimensional intersection-over-union ratio calculation in the method of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the parts of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0036] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] Example 1

[0038] A three-dimensional intersection and parallel analysis method for the accuracy of oral implantation guided by digital guides. Figure 1 The overall process of the method is shown, which can be viewed simultaneously. The specific steps of the method are described as follows:

[0039] S1. After the patient receives oral implant surgery, obtain the patient's postoperative image data, such as postoperative CBCT images, and put them into dental digital design software such as EXOCAD, adjust the image threshold range, delete additional hard tissue, obtain the patient's postoperative implant and jaw data and save them; implants can refer to Figure 2 For the jawbone, see Figure 3 's hint.

[0040] S2. Import the implant and jaw data extracted by EXOCAD software, together with the corresponding patient's implant design data (corresponding to 3Shape software as .3oxz file), into oral digital software such as 3Shape for fitting, and obtain the preoperative design position image and postoperative actual position image of the patient's implant, such as Figure 4 shown.

[0041] S3. In the 3Shape software, the fitted image type is displayed as a mid-axis section image type; corresponding to the preoperative design position image and the postoperative actual position image, all 180 section images of the implant are cut out and saved to the specified path.

[0042] S4. Perform three-dimensional reconstruction on the images saved in the specified path to determine the three-dimensional position relationship diagram between the designed implant and the actual implant. Figure 5 ; Calculate the 3D intersection ratio between the designed implant and the actual implant, see Figure 6 ; Based on the calculated three-dimensional intersection ratio value, the accuracy of the patient's oral dental implant can be objectively and quantitatively analyzed and evaluated.

[0043] Example 2

[0044] Based on Example 1, this example introduces in detail the preprocessing process of the image stored in the specified path in step S4 of the method. Figure 7 As shown in the figure, this embodiment implements the relevant functions through the Python programming environment and related codes, and the sub-steps are as follows:

[0045] S4-1-1, Image preprocessing:

[0046] Process all image files in the image directory in the specified path, read each image in order, and perform cropping and scaling. Select the tag value by file indexing, pass it into the extract_colored_curves function, and extract the mask of the specific area; apply the mask to the cropped and scaled image and save it.

[0047] S4-1-2. Extract color curve:

[0048] This is the core of the extract_colored_curves function code. This embodiment mainly extracts green, brown and dark blue curves from the image to achieve corresponding functional effects.

[0049] First, convert the image from RGB to HSV color space to more easily define the color range; then set the HSV thresholds for green, brown, and dark blue, and generate a color mask. Based on the tag value, select convolution kernels of different sizes to perform dilation and erosion operations on the brown mask to remove small noise areas and fill small holes; then use skeletonize to refine the binary image to extract the skeleton. The mask_green part targets the green area, marks the connected area, and extracts the bounding box attributes of the specific area. Finally, merge the masks to retain each color area.

[0050] S4-1-3, processing concave surface:

[0051] Process the concave areas in the binary mask and fill the outer concave areas by dilating the mask and detecting contours.

[0052] S4-1-4, Filling:

[0053] This step is used to actually fill the empty space between 1 tags in a given list.

[0054] Example 3

[0055] Based on any of the above embodiments, this embodiment provides a detailed introduction to the three-dimensional reconstruction process of the image stored in the specified path in step S4 of the method. Figure 8 As shown in the figure, this embodiment implements the relevant functions through the Python programming environment and related codes, and the sub-steps are as follows:

[0056] S4-2-1, loop processing data set:

[0057] After image preprocessing of the preoperative design position images and postoperative actual position images, two corresponding data sets are formed, represented by base_path_1 and base_path_2, each of which includes multiple images that have been processed in a loop. Set the target array data_total to store 3D data, with a shape of (667, 681, 681), and each slice corresponds to each processed image in the data set.

[0058] S4-2-2, Image reading and processing:

[0059] Load the images in order according to the file name and convert them to grayscale mode, that is, (cv2.imread(...,0)). Crop the image to a size of 681 in width, that is, (data[:,0:681]), making sure it matches the width of the target array data_total.

[0060] S4-2-3, Geometric transformation processing:

[0061] The processing logic is divided into two parts, and the rotation method is determined by defining the idx value. The idx value is used to track the index of the current image file, that is, the index value when traversing each image file. This embodiment uses the idx value to control the rotation angle of the image. For the 180 cross-sectional images intercepted in step S3, the first 90 images and the last 90 images are rotated differently. In addition, after idx is converted into an angle (through math.radians(idx) function or math.radians(idx-90) function), it is used to calculate subsequent sine and cosine values ​​to realize the rotation of the image, as follows:

[0062] When the idx value is less than 90, the coordinates of each row of pixels in the corresponding image are transformed by sine and cosine rotation according to the angle to simulate the rotation of the third quadrant. After determining the rotation center e and the image half-width q, the rotation angle is calculated using sin_1_degree and cos_1_degree to update the x and y coordinate positions of the data.

[0063] When the idx value is greater than or equal to 90, the corresponding direction is rotated directly according to the angle offset, and the angle starts from idx-90. The updated data is assigned to the target array according to the new coordinates to form a rotated image structure.

[0064] S4-2-4. Save the results:

[0065] When idx == 0, save the target array data_total to the save_path_1 dataset, otherwise save it to the save_path_2 dataset, and generate two .npy files.

[0066] In this embodiment, through the above steps, two reconstruction results corresponding to the preoperative designed position image and the postoperative actual position image are saved respectively.

[0067] Example 3

[0068] Based on any of the above embodiments, this embodiment provides a detailed introduction to the numerical calculation of the three-dimensional intersection-and-union ratio and the three-dimensional position relationship diagram in step S4 of the method. Fig. 9 As shown in the figure, this embodiment implements the relevant functions through the Python programming environment and related codes, and the sub-steps are as follows:

[0069] S4-3-1. IoU coefficient calculation:

[0070] In the python programming environment, the 3D intersection over union ratio (IoU coefficient) between two volume data is calculated by the iou_coefficient function. First, the 3D data corresponding to the designed implant and the actual implant in the 3D reconstruction result are converted into binary masks, which are Boolean arrays of uint8 type.

[0071] Use the np.logical_and function to calculate the intersection area of ​​the two masks; use the np.logical_or function to calculate the union area of ​​the two masks; after obtaining the intersection set prime number and the union set prime number, the IoU coefficient is the result of dividing the intersection set prime number by the union set prime number.

[0072] S4-3-2. Loading and processing 3D data:

[0073] Two volume data files are loaded. This embodiment takes total1_new.npy and total2_new.npy as examples.

[0074] The two volume data files are spliced ​​along the width axis (horizontally) to form spliced ​​volume data data_cat; the two volume data files are added element by element to form superimposed volume data data_total_3, which is used to display the overlapping area. The spliced ​​volume data and superimposed volume data are reserved for calling when rendering.

[0075] S4-3-3, display the three-dimensional intersection over union (IoU) results:

[0076] After calling the iou_coefficient function to calculate the IoU coefficient of the two masks, the result is printed out for external presentation. Figure 6 .

[0077] S4-3-4, Mayavi volume rendering:

[0078] Use the Mayavi library to create two rendering scenes, fig1 is used to display the stitched volume data data_cat, and fig2 is used to display the overlapping volume data data_total_3; vol1 and vol2 are used to represent the volume data of these two scenes respectively.

[0079] Set the opacity transfer function (_otf) and color transfer function (_ctf) to control the rendering effect, and gradually change the volume data from black (transparent) to white (fully visible). Finally, use the mlab.show() function to display the Mayavi scene window and present the rendered scene to the outside world synchronously. Figure 5 .

Claims

1. A three-dimensional intersection-combination analysis method for the accuracy of oral implantation guided by a digital guide plate, characterized by: Before and after the patient receives oral dental implant surgery, the preoperative designed position image and postoperative actual position image of the patient's implant are obtained respectively; after the preoperative designed position image and the postoperative actual position image are three-dimensionally reconstructed, the three-dimensional position relationship diagram of the designed implant and the actual implant is determined, and the three-dimensional intersection-and-union ratio value of the designed implant and the actual implant is calculated; based on the calculated three-dimensional intersection-and-union ratio value, the accuracy of the patient's oral dental implant is analyzed and evaluated.

2. The three-dimensional intersection-over-union analysis method according to claim 1, characterized in that: The method for obtaining the preoperative designed position image and the postoperative actual position image is as follows: after the patient undergoes oral implant surgery, the patient's postoperative CBCT image data is obtained, and the postoperative implant and jaw data of the patient are obtained after the postoperative CBCT image data is processed by dental digital design software; the implant design data of the corresponding patient is obtained, and the implant design data, postoperative implant and jaw data are imported into the oral digital software for fitting processing to obtain the preoperative designed position image and postoperative actual position image of the patient's implant.

3. The three-dimensional intersection-over-union analysis method according to claim 2, characterized in that: The fitted image type is displayed as a mid-axis section image type through oral digitization software; corresponding to the preoperative designed position image and the postoperative actual position image, multiple images of the implant are cut out and saved as the basis for three-dimensional reconstruction of the designed implant and the actual implant.

4. The three-dimensional intersection-over-union analysis method according to claim 1, characterized in that: After obtaining the preoperative design position image and the postoperative actual position image of the patient's implant, the same image preprocessing process is performed respectively, including: extracting masks, extracting color curves, processing concave surfaces, and filling.

5. The three-dimensional intersection-over-union analysis method according to claim 4, characterized in that: For all image files in the acquired preoperative design position image and postoperative actual position image, each image file is cropped and scaled, and then the mask of the preset area is extracted by file indexing, and the mask is applied to the cropped and scaled image and saved, thereby completing the extraction of the mask; When extracting color curves, the color space of all image files is converted from RGB to HSV, the HSV thresholds of multiple color categories are set, and color masks are generated; the index value is selected by file indexing, and convolution kernels of different sizes are selected according to the index value. Dilation and erosion operations are performed on the mask of the preset color to obtain a binary image; after the binary image is refined to extract the skeleton, the connected areas are marked for the remaining color areas, and the bounding box attributes of the corresponding areas of the implants are extracted; finally, the multiple color masks are merged, and the corresponding areas of each color are retained to complete the extraction of color curves in the image; For the binary mask corresponding to the binary image, the concave area in the binary mask is processed, and the external concave is filled by dilating the binary mask and detecting its contour.

6. The three-dimensional intersection-over-union analysis method according to claim 1, characterized in that: The same three-dimensional reconstruction process is performed on the preoperative design position image and the postoperative actual position image, including: loop processing of image data sets, image reading and matching, geometric transformation operations, and storage of reconstruction results; wherein, the two types of images are formed into a rotated image structure through the geometric transformation operation.

7. The three-dimensional intersection-over-union analysis method according to claim 6, characterized in that: After image preprocessing of the preoperative designed position images and the postoperative actual position images, two corresponding data sets are formed, each of which includes multiple images that have been cyclically processed; Set the target array and use it to store 3D data. Each slice in the 3D data corresponds to each processed image in the data set. Load the images in the preset order, convert the loaded images to grayscale mode, and then crop them so that the cropped images match the width of the target array. During 3D reconstruction, the rotation method of the 3D data stored in the target array is determined by defining the idx value; when the index value is less than the preset value, the coordinates of each row of pixels in the corresponding image are transformed by sine and cosine rotation according to the specific angle, thereby simulating the rotation of the third quadrant; after determining the rotation center and the half-width of the image, the rotation angle is calculated and the x and y coordinate positions of the data are updated; When the index value is greater than or equal to the preset value, the corresponding direction is rotated directly according to the angle offset; the updated data is assigned to the target array according to the new coordinates to form a rotated image structure; the two reconstruction results corresponding to the preoperative design position image and the postoperative actual position image are saved separately.

8. The three-dimensional intersection-over-union analysis method according to claim 1, characterized in that: After the three-dimensional reconstruction is completed, the three-dimensional intersection-and-union ratio value of the designed implant and the actual implant is calculated based on the three-dimensional reconstruction results, and the three-dimensional position relationship diagram of the designed implant and the actual implant is presented through rendering; for the calculated three-dimensional intersection-and-union ratio value, the larger the value, the higher the accuracy of the patient's oral dental implant.

9. The three-dimensional intersection-over-union analysis method according to claim 8, characterized in that: When calculating the 3D intersection-union ratio value, firstly, the 3D data corresponding to the designed implant and the actual implant in the 3D reconstruction result are converted into binary masks respectively, and the intersection area and the union area of ​​the two binary masks are calculated in turn, and the intersection set prime number and the union set prime number are obtained accordingly. Then, the 3D intersection-union ratio value is the result of dividing the intersection set prime number by the union set prime number. For the 3D data of the designed implant and the actual implant, the corresponding volume data files of the 3D reconstruction results are obtained; the two volume data files are spliced ​​along the width axis to form spliced ​​volume data, and the two volume data files are added element by element to form superimposed volume data; the spliced ​​volume data and the superimposed volume data are reserved for calling during rendering; Finally, while presenting the 3D intersection-to-union ratio value to the outside, two rendering scenes are created to display the stitched volume data and the superimposed volume data respectively; the rendering effect of the rendering scene is controlled by using the transfer function, and the rendering scene is presented to the outside synchronously.

10. A computer system comprising a memory, a processor, and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the three-dimensional intersection-and-combination analysis method for digital guide-guided oral implant accuracy as described in any one of claims 1 to 9.

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