Method for automatically separating teeth from 3D scan data using tooth boundary curves, and computer-readable recording medium having recorded thereon a program for executing the method on a computer

Abstract

A method for automatically separating teeth from 3D scan data using a tooth boundary curve includes the steps of: detecting teeth in scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating tooth mapping data corresponding to a predetermined space based on the tooth scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate a tooth boundary curve; and mapping the tooth boundary curve onto the scan data.

Description

[Technical Field]

[0001] The present invention relates to an automatic tooth separation method for 3D scan data using tooth boundary curves, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer. More particularly, the present invention relates to an automatic tooth separation method for 3D scan data, which detects teeth from scan data through deep learning, performs mesh parameterization for each tooth, and generates tooth boundary curves within mapping data of each tooth through deep learning, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer. [Background technology]

[0002] In dentistry, there is a need for technology to separate teeth from a patient's 3D scan data for diagnosis, analysis, prosthetic fabrication, etc. In particular, digital orthodontics using oral scanners is becoming increasingly common in dentistry. In orthodontics, it is important to predict tooth alignment and occlusion and establish an appropriate plan, and for this, tooth separation is essential.

[0003] A commonly used method for tooth separation is as follows: First, tooth scan data is obtained using an oral scanner. Then, an operator manually specifies the tooth boundaries and specifies the plane to be used for tooth separation using the axis information and the tooth boundaries. Finally, the operator checks the separated surfaces between the teeth and corrects any necessary parts to complete the process. This process is then repeated for all teeth to obtain separated tooth data.

[0004] When workers do this manually, they must visually specify boundaries for 3D data on a 2D screen, which reduces accuracy and requires a high level of skill and effort from the worker.

[0005] Conventional tooth separation technology mainly uses a method of masking the plane corresponding to the tooth, which can lead to problems such as some areas of the tooth not being masked (Figures 1, 2, and 3), the mask not being formed within a single tooth and affecting adjacent teeth or gums (Figures 4 and 5), and the mask mistaking multiple teeth for a single tooth (Figure 6).

[0006] For example, the arrows in Figure 1 show that the upper region of the tooth is not masked; the arrows in Figure 2 show that a hole has been formed in the upper region of the tooth; and the arrows in Figure 3 show that the tooth boundary region is not masked. The dotted oval in Figure 4 shows that the mask has invaded adjacent teeth; and the dotted oval in Figure 5 shows that the mask has missed the tooth and invaded the gums. The arrows in Figure 6 show that the mask identified two teeth as one tooth, and also that the mask has missed the tooth and invaded the gums.

[0007] Furthermore, conventional tooth separation techniques are limited to techniques for separating entire teeth due to limitations in algorithms. In actual clinical practice, scan data of partial teeth, rather than entire teeth, is often used, and conventional tooth separation techniques have a problem in that it is difficult to separate teeth from scan data of partial teeth. Furthermore, to separate teeth from scan data of partial teeth using deep learning, an additional model trained specifically for partial tooth data is required, which may result in excessive memory usage and excessive slowdown. Summary of the Invention [Problem to be solved by the invention]

[0008] The object of the present invention is to provide a method for automatic tooth segmentation of 3D scan data, which is performed automatically by an artificial intelligence neural network for tooth detection, mesh parameterization, and tooth boundary generation.

[0009] Another object of the present invention is to provide a computer-readable recording medium having recorded thereon a program for causing a computer to execute the method for automatically separating teeth from 3D scan data. [Means for solving the problem]

[0010] In order to achieve the above-mentioned object of the present invention, one embodiment of a method for automatically separating teeth from 3D scan data using a tooth boundary curve comprises the steps of: detecting teeth in the scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating tooth mapping data corresponding to a predetermined space based on the tooth scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate a tooth boundary curve; and mapping the tooth boundary curve to the scan data.

[0011] The method for automatically separating teeth from 3D scan data using tooth boundary curves further includes converting the 3D scan data into a 2D image, which is input to the first artificial intelligence neural network.

[0012] The step of detecting teeth in the scan data includes the steps of: the first artificial intelligence neural network receiving the two-dimensional image and outputting an output two-dimensional image including two-dimensional tooth detection information; and converting the two-dimensional tooth detection information into three-dimensional tooth detection information. Detecting the tooth in the scan data includes generating a tooth bounding box that surrounds one tooth.

[0013] The step of extracting tooth scan data includes extracting the tooth scan data corresponding to one tooth using a tooth cutting box corresponding to the tooth bounding box.

[0014] The tooth cutting box includes the tooth bounding box and is larger than the tooth bounding box. The step of detecting the teeth in the scan data includes mapping the center points of the tooth bounding boxes onto the tooth surfaces to generate tooth feature points.

[0015] The step of extracting tooth scan data includes extracting the tooth scan data corresponding to one tooth using a tooth cut area formed based on the tooth feature points. The method for automatically separating teeth from 3D scan data using tooth boundary curves further includes converting the tooth scan data into curvature data representing a curvature value of each point in the tooth scan data.

[0016] The curvature data represents the minimum curvature value of each of the points. The method for automatically separating teeth from 3D scan data using the tooth boundary curve further includes a step of inverting the gradation of the curvature data so that a large minimum curvature value of each point is represented in white, and a small minimum curvature value of each point is represented in black.

[0017] The step of generating the tooth mapping data includes mapping the curvature data of the tooth scan data into the predetermined space to generate the tooth mapping data. The inputs of the second artificial intelligence neural network are the mapping data and the tooth bounding box, and the output of the second artificial intelligence neural network is the tooth boundary curve.

[0018] Generating the tooth boundary curve includes rotating the tooth bounding box to generate a diamond outline. Furthermore, generating the tooth boundary curve includes shifting the position of the vertex of the diamond contour within the mapping data by the curvature value to generate a pole point.

[0019] Furthermore, the step of generating the tooth boundary curve includes the step of generating an octagonal contour that passes through the extreme points based on the extreme points. Furthermore, generating the tooth boundary curve includes displacing the octagonal contour within the mapping data by the curvature value to generate the boundary curve.

[0020] The first artificial intelligence neural network receives the three-dimensional scan data and outputs three-dimensional tooth detection information. A program for causing a computer to execute the method for automatically separating teeth from 3D scan data using the tooth boundary curve is recorded on a computer-readable recording medium. [Effects of the Invention]

[0021] The method for automatically separating teeth from 3D scan data using tooth boundary curves according to the present invention can reduce the effort required to separate teeth from scan data by automatically performing the process through an AI neural network for tooth detection, mesh parameterization, and an AI neural network for tooth boundary generation.

[0022] The tooth boundary curve, not the tooth surface, is accurately generated using the tooth detection AI neural network, the mesh parameterization, and the tooth boundary generation AI neural network, which prevents problems such as partial tooth regions not being masked, incorrect regions that are not teeth being determined as teeth, masks not being formed within a single tooth and thus invading adjacent teeth or gums, and multiple teeth being determined as a single tooth, thereby improving the accuracy of the tooth separation.

[0023] In addition, the artificial intelligence neural network for tooth boundary generation can generate the tooth boundary curve that separates the teeth and gums at once by modifying the initially input contour data, thereby improving the accuracy of the tooth separation.

[0024] Furthermore, even if scan data for only a portion of the teeth, rather than the entire set of teeth, is input, the tooth detection artificial intelligence neural network can accurately determine the area of ​​each tooth and generate boundary curves for each tooth-specific scan data corresponding to the tooth area. This allows tooth separation to be performed using the same model, regardless of whether the scan data represents the entire set of teeth or only a portion of the teeth. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation from 3D scan data. [Figure 2] FIG. 2 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation of 3D scan data. [Figure 3] FIG. 3 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation of 3D scan data. [Figure 4] FIG. 4 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation of 3D scan data. [Figure 5] FIG. 5 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation of 3D scan data. [Figure 6] FIG. 6 is a diagram showing an example of a tooth separation error that occurs in conventional automatic tooth separation of 3D scan data. [Figure 7] FIG. 7 is a simplified flowchart illustrating a method for automatically separating teeth from 3D scan data using tooth boundary curves according to an embodiment of the present invention. [Figure 8] FIG. 8 is a flowchart showing in detail the automatic tooth separation method of FIG. [Figure 9] FIG. 9 is a conceptual diagram showing the tooth detection process of FIG. [Figure 10] FIG. 10 is a diagram showing an example of input for the mesh imaging of FIG. [Figure 11] FIG. 11 is a diagram showing an example of the mesh imaging output of FIG. [Figure 12] FIG. 12 is a diagram showing an example of an output of the first artificial intelligence neural network of FIG. [Figure 13] FIG. 13 is a diagram showing an example of an output of the first artificial intelligence neural network of FIG. [Figure 14] FIG. 14 is a conceptual diagram illustrating the parameterization process of FIG. [Figure 15] FIG. 15 is a diagram illustrating the curvature data used in the parameterization process of FIG. [Figure 16] FIG. 16 is a diagram showing tooth scan data generated by extracting a portion of the mesh in FIG. [Figure 17] FIG. 17 is a diagram showing tooth mapping data generated by parameterizing the curvature data of the tooth scan data shown in FIG. [Figure 18] FIG. 18 is a conceptual diagram showing the boundary curve generation process of FIG. [Figure 19] FIG. 19 is a conceptual diagram specifically illustrating the operation of the second artificial intelligence neural network of FIG. [Figure 20] FIG. 20 shows the final tooth segmentation result produced by the boundary 3D mapping of FIG. [Figure 21] FIG. 21 is a detailed flowchart illustrating a method for automatically separating teeth from 3D scan data using tooth boundary curves according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0026] Specific structural or functional descriptions of the embodiments of the present invention set forth herein are merely exemplary for purposes of describing the embodiments of the present invention, and the example embodiments of the present invention may be embodied in various forms and should not be construed as being limited to the embodiments set forth herein.

[0027] The present invention can be modified in various ways and can have various forms, and specific embodiments are shown by way of example in the drawings and will be described in detail herein, but it should be understood that this is not to limit the invention to the particular forms disclosed, but rather to include all modifications, equivalents, and alternatives falling within the spirit and scope of the invention.

[0028] Terms such as "first" and "second" are used to describe various components, but the components should not be limited by these terms. These terms are used only to distinguish one component from another. For example, a first component can be referred to as a second component, and similarly, a second component can be referred to as a first component, without departing from the scope of the present invention.

[0029] When a component is said to be "coupled" or "connected" to another component, it should be understood that the component may be directly coupled or connected to the other component, but there may also be other components in between. On the other hand, when a component is said to be "directly coupled" or "directly connected" to another component, it should be understood that there are no other components in between. Other expressions describing the relationship between components, such as "between" and "immediately between," or "adjacent to" and "directly adjacent to," should be interpreted similarly.

[0030] The terms used in this application are merely used to describe specific embodiments and are not intended to limit the present invention. The singular expressions include the plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and should be understood not to preclude the presence or possibility of addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0031] Unless otherwise defined, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and should not be interpreted as having an idealized or overly formal meaning unless expressly defined in this application.

[0032] However, when an embodiment can be implemented differently, the functions or operations specified in a particular block may occur differently from the sequence specified in the flowchart. For example, two consecutive blocks may actually occur substantially simultaneously, or the blocks may be reversed depending on the functions or operations involved.

[0033] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. The same components in the drawings are designated by the same reference numerals, and duplicated descriptions of the same components will be omitted.

[0034] 7 is a flowchart illustrating a method for automatically separating teeth from 3D scan data using tooth boundary curves according to an embodiment of the present invention, and FIG. 8 is a flowchart illustrating the method for automatically separating teeth in FIG. 7 in detail.

[0035] As shown in Figures 7 and 8, the present invention uses mesh parameterization and deep learning to fully automatically segment individual teeth from 3D scan data. For example, the scan data may consist of a polygon mesh, and therefore the scan data may also be referred to as a mesh.

[0036] The method for automatic tooth segmentation from 3D scan data according to this embodiment uses a first artificial intelligence neural network (AI1) for tooth detection and a second artificial intelligence neural network (AI2) for mesh parameterization and tooth boundary generation to fully automatically segment individual teeth from the scan data.

[0037] The method for automatically separating teeth from 3D scan data according to this embodiment includes a tooth detection step of detecting teeth from an image of 3D oral cavity scan data, a step of parameterizing a mesh of a certain area based on the teeth detected in the tooth detection step, and a step of generating a boundary curve for the scan data by generating a boundary from the parameterized image.

[0038] Generally, a deep learning network can only accept input of data of a certain size (shaped data). The scan data is represented as a 3D polygon mesh, and there is a problem that the number of mesh points, edges, and cells varies depending on the data. Therefore, in order to improve the accuracy of a deep learning network that receives the scan data as input, a process of converting the scan data into shaped data is required. In the present invention, mesh parameterization can be used to convert the scan data into shaped data.

[0039] The mesh parameterization refers to one-to-one mapping of points constituting a 3D mesh to another predetermined space. To map from a space in which a mesh exists to the other predetermined space (parameter space), first, the boundary of the space in which the mesh exists is mapped to the boundary of the parameter space. The internal points excluding the boundary of the space in which the mesh exists are mapped to the interior of the boundary of the parameter space, and a method can be used in which the topology of the mesh is maintained as much as possible using an energy function.

[0040] The mesh parameterization allows the scan data to be mapped into two-dimensional space, thereby properly forming the shaped data for use in the deep learning.

[0041] The mesh parameterization can improve the accuracy of the deep learning network of the second artificial intelligence neural network (AI2) that generates tooth boundary curves. As shown in FIG. 8, the method for automatically separating teeth from 3D scan data using a tooth boundary curve according to this embodiment includes the steps of: detecting teeth in the scan data using a first artificial intelligence neural network (S200); extracting tooth scan data from the scan data based on the tooth detection result (S300); generating tooth mapping data corresponding to a predetermined space based on the tooth scan data (S400); inputting the tooth mapping data into a second artificial intelligence neural network to generate a tooth boundary curve (S500); and mapping the tooth boundary curve to the scan data (S600).

[0042] For example, the method for automatically separating teeth from 3D scan data using tooth boundary curves further includes converting the 3D scan data into a 2D image (S100). The method for automatically separating teeth from 3D scan data according to the present embodiment is performed by a computing device.

[0043] Fig. 9 is a conceptual diagram showing the tooth detection process of Fig. 7. Fig. 10 is a diagram showing an example of input for mesh imaging of Fig. 9. Fig. 11 is a diagram showing an example of output for mesh imaging (S200) of Fig. 9. Fig. 12 is a diagram showing an example of output of the first artificial intelligence neural network (AI1) of Fig. 9. Fig. 13 is a diagram showing an example of output of the first artificial intelligence neural network (AI1) of Fig. 9.

[0044] 7 to 13, the tooth detection process includes converting the tooth scan data into a two-dimensional image (S100), which is also called mesh imaging.

[0045] In this embodiment, the input of the first artificial intelligence neural network (AI1) is a two-dimensional image, so the method includes converting the three-dimensional scan data into the two-dimensional image.

[0046] For example, the two-dimensional image is RGB grayscale data including red, green, and blue tones. For example, the two-dimensional image is black and white grayscale data. For example, the two-dimensional image is a depth map including depth information. The depth map is generated based on any one of the maximum value, minimum value, standard deviation, and average value within the two-dimensional coordinates of the 3D scan data.

[0047] The step of converting the scan data into the two-dimensional image includes a principal axis normalization step, which involves setting a spatial orientation through principal axis analysis, by analyzing the principal axes formed by points in the scan data and determining first, second, and third principal axes that are perpendicular to each other.

[0048] The longest axis among the first, second, and third principal axes extracted by the principal axis analysis is determined to be the left-right direction of the U-shape of the tooth. The shortest axis among the first, second, and third principal axes is determined to be the up-down direction of the U-shape. The second longest axis among the first, second, and third principal axes is determined to be the front-back direction of the U-shape.

[0049] The principal axis normalization step aligns the scan data to maximize visibility of the occlusal surfaces of the teeth. The step of detecting the teeth in the scan data includes the step of the first artificial intelligence neural network (AI1) receiving the two-dimensional image and outputting an output two-dimensional image including two-dimensional tooth detection information (S220), and the step of converting the two-dimensional tooth detection information into three-dimensional tooth detection information (S240).

[0050] Here, the tooth detection information is a tooth bounding box that surrounds each tooth, and tooth feature points that represent the center points of the surfaces of each tooth.

[0051] That is, the first artificial intelligence neural network (AI1) receives the two-dimensional image of FIG. 11, generates a two-dimensional bounding box (2D Point) (S220), and converts the two-dimensional bounding box (2D Point) into three dimensions (S240) to generate the three-dimensional bounding box (3D Point) in FIG. 12.

[0052] Alternatively, the first artificial intelligence neural network (AI1) receives the two-dimensional image of FIG. 11, generates two-dimensional tooth feature points (2D Points) (S220), and converts the two-dimensional tooth feature points (2D Points) into three-dimensional ones (S240) to generate three-dimensional tooth feature points (3D Points) in FIG. 13.

[0053] As described above, the tooth feature points can be automatically generated through the first artificial intelligence neural network (AI1), or can be generated using the bounding box automatically generated through the first artificial intelligence neural network (AI1). For example, the tooth feature points can be generated by mapping the center points of the tooth bounding boxes onto the tooth surface.

[0054] If the scan data contains 14 teeth, the first artificial intelligence neural network (AI1) will generate 14 bounding boxes, the size of which varies depending on the tooth. Similarly, if the scan data contains 14 teeth, the first artificial intelligence neural network (AI1) will generate 14 feature points.

[0055] The first artificial intelligence neural network (AI1) detects individual teeth by learning bounding boxes surrounding teeth using a one-stage object detector. Here, the region of interest (ROI) of the bounding box input as training data to the first artificial intelligence neural network (AI1) can be set to a size that fits the tooth exactly. If the size of the ROI is too small or too large, the tooth detection performance will be reduced.

[0056] Fig. 14 is a conceptual diagram showing the parameterization process of Fig. 7. Fig. 15 is a diagram showing curvature data used in the parameterization process of Fig. 7. Fig. 16 is a diagram showing tooth scan data generated by extracting a mesh portion of Fig. 14. Fig. 17 is a diagram showing tooth mapping data generated by parameterizing the curvature data of the tooth scan data of Fig. 14. As shown in Figures 7 to 14, the parameterization process includes a step of extracting tooth scan data from the scan data based on the tooth detection result (S300), and a step of generating tooth mapping data corresponding to a predetermined space based on the tooth scan data (S400).

[0057] For example, if a tooth bounding box surrounding one tooth is generated in the step of detecting the tooth in the scan data, the step of extracting tooth scan data may extract the tooth scan data corresponding to one tooth using a tooth cutting box corresponding to the tooth bounding box. Here, the tooth cutting box includes the tooth bounding box and is larger than the tooth bounding box. As shown in FIG. 16, it can be seen that the tooth cutting box is larger than the tooth bounding box. The tooth bounding box is an area that closely corresponds to the boundary of the tooth in order to detect the tooth. The tooth cutting box is an area for generating the boundary curve after cutting the scan data into tooth units. Therefore, the tooth cutting box does not necessarily need to be identical to the tooth bounding box. It is preferable that the tooth cutting box includes the bounding box and is slightly larger than the tooth bounding box.

[0058] For example, if a tooth feature point indicating the surface center point of one tooth is generated in the step of detecting the teeth in the scan data, the step of extracting tooth scan data may extract the tooth scan data corresponding to one tooth using a tooth cut area formed based on the tooth feature point, for example, the tooth cut area is a spherical area centered on the tooth feature point.

[0059] The parameterization process includes converting the dental scan data into curvature data representing a curvature value for each point in the dental scan data. 15 shows curvature data indicating the curvature value of each point in the scan data. For example, the curvature data may indicate the maximum curvature value, the minimum curvature value, the Gaussian curvature value, and the mean curvature value. In FIG. 15, the curvature data indicates the minimum curvature value of each point.

[0060] The curvature value of the upper surface of the tooth is relatively constant. In contrast, the curvature value varies greatly at the boundary between the teeth. Therefore, the curvature value accurately represents the area where the tooth and gum meet, and when a tooth boundary curve is generated using the curvature value, the accuracy is high.

[0061] In conventional curvature data, the larger the curvature value, the blacker it is, and the smaller the curvature value, the whiter it is. In this embodiment, a further step may be performed in which the gradation of the curvature data is inverted so that a large minimum curvature value at each point is represented by white, and a small minimum curvature value at each point is represented by black. Figure 15 shows a diagram in which the gradation of the curvature data is inverted, where areas with large minimum curvature values ​​are represented by white, and areas with small minimum curvature values ​​are represented by black. For example, if the gradation of the curvature data is not inverted, a large minimum curvature value would be represented by black, and a small minimum curvature value would be represented by white, as opposed to Figure 15.

[0062] That is, if the curvature data has a value that does not invert the minimum curvature value, meaningful areas (areas with large minimum curvature values) such as areas where teeth and gums meet will appear black. However, in this embodiment, the grayscale inversion causes meaningful areas (areas with large minimum curvature values) such as areas where teeth and gums meet to appear white.

[0063] For example, if there is a hole in the scan data and no point exists in the corresponding area, so no curvature value exists, conventional curvature data will display the area without a curvature value in black. If the curvature data is not inverted, meaningful areas (e.g., areas where the teeth and gums touch) will appear in black, and in this case, areas without a curvature value will also be displayed in black, resulting in the problem of the area without a curvature value being mistaken for a meaningful area. In contrast, if the curvature data is inverted, meaningful areas (e.g., areas where the teeth and gums touch) will appear in white, and in this case, the hole area without a curvature value will not be recognized as a meaningful area, thereby eliminating the misidentification caused by areas without a curvature value.

[0064] The tooth mapping data generating step (S400) involves mapping the curvature data of the tooth scan data corresponding to each tooth onto the predetermined space to generate the tooth mapping data. Figure 17 shows an example of the tooth mapping data. The tooth mapping data can be understood as the curvature data of tooth scan data corresponding to one tooth being mapped onto the predetermined space and converted into two-dimensional data.

[0065] For example, the predetermined space is a square. Since the tooth bounding box and the tooth cutting box have different shapes and sizes depending on the tooth, the shape and size of the tooth scan data also vary depending on the tooth. In contrast, the shape and size of the predetermined space can be set to be constant regardless of the tooth. In this case, the shape and size of the tooth mapping data are constant regardless of the tooth.

[0066] The tooth mapping data is preferably set to the same size since it is the input for the second artificial intelligence neural network (AI2). Fig. 18 is a conceptual diagram showing the boundary curve generation process of Fig. 7. Fig. 19 is a conceptual diagram specifically showing the operation of the second artificial intelligence neural network of Fig. 18. Fig. 20 is a diagram showing the final tooth segmentation result generated by the boundary 3D mapping of Fig. 18.

[0067] As shown in Figures 7 to 20, the inputs of the second artificial intelligence neural network (AI2) are the mapping data and the tooth bounding box, and the output of the second artificial intelligence neural network (AI2) is the tooth boundary curve.

[0068] Here, the tooth bounding box is used as initial contour information (initial box definition in FIG. 19) in the second artificial intelligence neural network (AI2). For example, the step of generating the tooth boundary curve (S500) may include rotating the tooth bounding box to generate a diamond contour, for example, rotating the tooth bounding box by 45 degrees to generate the diamond contour.

[0069] For example, the step of generating the tooth boundary curve (S500) may further include a step of generating an extreme point by deforming the vertex position of the diamond contour within the mapping data, which may be moved depending on the position where the curvature value is small or large within the mapping data.

[0070] For example, the step of generating the tooth boundary curve (S500) further includes the step of generating an octagonal contour passing through the pole points based on the pole points. For example, the step of generating the tooth boundary curve (S500) further includes the step of moving the octagonal contour within the mapping data by the curvature value to generate the boundary curve (object shape).

[0071] The tooth boundary curve, which is the output of the second artificial intelligence neural network (AI2), is three-dimensionally mapped onto the scan data (S600) to complete the tooth segmentation. Figure 20 shows the final tooth segmentation result.

[0072] According to this embodiment, the process is performed automatically through an artificial intelligence neural network for tooth detection (AI1) and an artificial intelligence neural network for mesh parameterization and tooth boundary generation (AI2), thereby reducing the effort required to separate teeth from scan data. The tooth detection AI neural network (AI1), the mesh parameterization, and the tooth boundary generation AI neural network (AI2) are used to accurately generate tooth boundary curves, not tooth surfaces, which can prevent problems such as partial tooth regions not being masked, incorrect regions that are not teeth being determined as teeth, masks not being formed within a single tooth and thus invading adjacent teeth or gums, and multiple teeth being determined as a single tooth, thereby improving the accuracy of tooth separation.

[0073] In addition, the artificial intelligence neural network for tooth boundary generation (AI2) can generate the tooth boundary curve that separates the teeth and gums at once by modifying the initially input contour data, thereby improving the accuracy of the tooth separation.

[0074] In addition, even if scan data for partial teeth rather than the entire set of teeth is input, the tooth detection artificial intelligence neural network (AI1) can accurately determine the area of ​​each tooth and generate boundary curves for each tooth scan data corresponding to the tooth area. Therefore, tooth separation can be performed using the same model (AI1, AI2) regardless of whether the scan data represents the entire set of teeth or partial teeth.

[0075] FIG. 21 is a detailed flowchart illustrating a method for automatically separating teeth from 3D scan data using tooth boundary curves according to an embodiment of the present invention. The automatic tooth separation method according to this embodiment is the same as the automatic tooth separation method of Figures 1 to 20 except for the input of the first artificial intelligence neural network, so the same components are given the same reference numerals and redundant explanations will be omitted.

[0076] As shown in FIG. 21 , the method for automatically separating teeth from 3D scan data using a tooth boundary curve according to this embodiment includes the steps of: detecting teeth in the scan data using a first artificial intelligence neural network (S250); extracting tooth scan data from the scan data based on the tooth detection result (S300); generating tooth mapping data corresponding to a predetermined space based on the tooth scan data (S400); inputting the tooth mapping data into a second artificial intelligence neural network to generate a tooth boundary curve (S500); and mapping the tooth boundary curve to the scan data (S600).

[0077] The method for automatically separating teeth from 3D scan data according to the present embodiment is performed by a computing device. In the automatic tooth separation method of Figures 1 to 20, the input of the first artificial intelligence neural network (AI1) is a two-dimensional image, but in this embodiment, the input of the first artificial intelligence neural network (AI1) is three-dimensional scan data.

[0078] In this embodiment, the first artificial intelligence neural network (AI1) receives the 3D scan data and outputs 3D tooth detection information, which may be tooth bounding boxes or tooth feature points.

[0079] According to this embodiment, the effort required to separate teeth from scan data can be reduced by automatically performing the process using an artificial intelligence neural network for tooth detection (AI1) and an artificial intelligence neural network for mesh parameterization and tooth boundary generation (AI2).

[0080] The tooth detection AI neural network (AI1), the mesh parameterization, and the tooth boundary generation AI neural network (AI2) are used to accurately generate tooth boundary curves, not tooth surfaces, which can prevent problems such as partial tooth regions not being masked, incorrect regions that are not teeth being determined as teeth, masks not being formed within a single tooth and thus invading adjacent teeth or gums, and multiple teeth being determined as a single tooth, thereby improving the accuracy of tooth separation.

[0081] In addition, the artificial intelligence neural network for tooth boundary generation (AI2) can generate the tooth boundary curve that separates the teeth and gums at once by modifying the initially input contour data, thereby improving the accuracy of the tooth separation.

[0082] In addition, even if scan data for partial teeth rather than the entire set of teeth is input, the tooth detection artificial intelligence neural network (AI1) can accurately determine the area of ​​each tooth and generate boundary curves for each tooth scan data corresponding to the tooth area. Therefore, tooth separation can be performed using the same model (AI1, AI2) regardless of whether the scan data represents the entire set of teeth or partial teeth.

[0083] According to one embodiment of the present invention, a computer-readable recording medium is provided having recorded thereon a program for executing the method for automatically separating teeth from 3D scan data using tooth boundary curves according to the embodiment. The method can be written as a computer-executable program and implemented on a general-purpose digital computer that runs the program using the computer-readable medium. Furthermore, data structures used in the method can be recorded on the computer-readable medium by various means. The computer-readable medium may include program instructions, data files, data structures, and the like, singly or in combination. The program instructions recorded on the medium may be those specifically designed and constructed for the present invention, or those well known and available to those skilled in the art of computer software. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes; optical media such as CD-ROMs and DVDs; magneto-optical media such as floptical disks; and hardware devices specifically configured to store and execute program instructions, such as ROMs, RAMs, and flash memories. Examples of program instructions include not only machine language code, such as that produced by a compiler, but also high-level language code, which can be executed by a computer using an interpreter, for example. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of this invention. The method for automatically separating teeth from 3D scan data using tooth boundary curves can also be implemented in the form of a computer program or application that is stored in a recording medium and executed by a computer.

[0084] [Industrial Applicability] The present invention relates to a method for automatically separating teeth from 3D scan data using tooth boundary curves, and a computer-readable recording medium having a program recorded thereon for executing the method on a computer, which can reduce the effort required for separating teeth and improve accuracy.

[0085] While the present invention has been described above with reference to preferred embodiments, those skilled in the art will appreciate that various modifications and variations can be made to the present invention without departing from the spirit and scope of the invention as set forth in the claims below.

Claims

1. detecting teeth in the scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating dental mapping data corresponding to a predetermined space based on the dental scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate tooth boundary curves; mapping the tooth boundary curve onto the scan data; and generating the tooth mapping data by one-to-one mapping points constituting a 3D mesh of the tooth scan data onto a predetermined other space, mapping a boundary of the tooth scan data onto a boundary of the predetermined other space, and mapping points inside the boundary of the tooth scan data onto the inside of the boundary of the predetermined other space.

2. further comprising converting the three-dimensional scan data into a two-dimensional image; The two-dimensional image is input to the first artificial intelligence neural network.

2. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 1.

3. The step of detecting teeth in the scan data includes: the first artificial intelligence neural network receiving the two-dimensional image and outputting an output two-dimensional image including two-dimensional tooth detection information; and converting the two-dimensional tooth detection information into three-dimensional tooth detection information.

3. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 2.

4. The step of detecting the teeth in the scan data includes generating a tooth bounding box that surrounds one tooth.

2. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 1.

5. The step of extracting tooth scan data may include extracting the tooth scan data corresponding to one tooth using a tooth cutting box corresponding to the tooth bounding box.

5. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 4.

6. The tooth cutting box includes the tooth bounding box and is larger than the tooth bounding box. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 5.

7. and detecting the teeth in the scan data by mapping center points of the tooth bounding boxes onto tooth surfaces to generate tooth feature points.

5. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 4.

8. the step of extracting the tooth scan data includes extracting the tooth scan data corresponding to one tooth using a tooth cut area formed based on the tooth feature points. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 7.

9. further comprising converting the dental scan data into curvature data representing a curvature value for each point in the dental scan data.

5. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 4.

10. The curvature data represents the minimum curvature value of each point. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 9.

11. 11. The method of claim 10, further comprising the step of inverting the gradation of the curvature data so that a large minimum curvature value of each point is represented in white and a small minimum curvature value of each point is represented in black.

12. the step of generating the tooth mapping data comprises mapping the curvature data of the tooth scan data into the predetermined space to generate the tooth mapping data. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 9.

13. an input of the second artificial intelligence neural network is the mapping data and the tooth bounding box, and an output of the second artificial intelligence neural network is the tooth boundary curve; A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 12.

14. The step of generating the tooth boundary curve includes a step of rotating the tooth bounding box to generate a diamond outline. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 13.

15. The step of generating the tooth boundary curve further includes a step of moving a position of a vertex of the diamond contour within the mapping data by the curvature value to generate a pole point. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 14.

16. The step of generating the tooth boundary curve further includes a step of generating an octagonal contour passing through the extreme points based on the extreme points. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 15.

17. The step of generating the tooth boundary curve further includes a step of moving the octagonal contour within the mapping data by the curvature value to generate the boundary curve. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 16.

18. The first artificial intelligence neural network receives the three-dimensional scan data and outputs three-dimensional tooth detection information.

2. A method for automatically separating teeth from three-dimensional scan data using the tooth boundary curve according to claim 1.

19. A computer-readable recording medium having a program recorded thereon for causing a computer to execute the method of any one of claims 1 to 18.

20. A method for detecting teeth in scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating dental mapping data corresponding to a predetermined space based on the dental scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate tooth boundary curves; mapping the tooth boundary curve onto the scan data; Detecting the tooth in the scan data includes generating a tooth bounding box that surrounds one tooth; The step of extracting tooth scan data includes extracting the tooth scan data corresponding to one tooth using a tooth cutting box corresponding to the tooth bounding box; The method for automatically separating teeth from 3D scan data using tooth boundary curves, wherein the tooth cutting box includes the tooth bounding box and is larger than the tooth bounding box.

21. A method for detecting teeth in scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating dental mapping data corresponding to a predetermined space based on the dental scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate tooth boundary curves; mapping the tooth boundary curve onto the scan data; Detecting the tooth in the scan data includes generating a tooth bounding box that surrounds one tooth; Detecting the teeth in the scan data includes mapping center points of the tooth bounding boxes onto tooth surfaces to generate tooth feature points; The method for automatically separating teeth from 3D scan data using tooth boundary curves is characterized in that the step of extracting tooth scan data comprises extracting the tooth scan data corresponding to one tooth using a tooth cut area formed based on the tooth feature points.

22. A method for detecting teeth in scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating dental mapping data corresponding to a predetermined space based on the dental scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate tooth boundary curves; mapping the tooth boundary curve onto the scan data; Detecting the tooth in the scan data includes generating a tooth bounding box that surrounds one tooth; converting the dental scan data into curvature data representing a curvature value for each point in the dental scan data; the curvature data represents the minimum curvature value of each of the points; 10. The method for automatically separating teeth from 3D scan data using tooth boundary curves, further comprising: inverting the gradation of the curvature data so that a large minimum curvature value of each point is represented in white, and a small minimum curvature value of each point is represented in black.

23. A method for detecting teeth in scan data using a first artificial intelligence neural network; extracting tooth scan data from the scan data based on the tooth detection result; generating dental mapping data corresponding to a predetermined space based on the dental scan data; inputting the tooth mapping data into a second artificial intelligence neural network to generate tooth boundary curves; mapping the tooth boundary curve onto the scan data; Detecting the tooth in the scan data includes generating a tooth bounding box that surrounds one tooth; converting the dental scan data into curvature data representing a curvature value for each point in the dental scan data; the step of generating the tooth mapping data includes mapping the curvature data of the tooth scan data into the predetermined space to generate the tooth mapping data; an input of the second artificial intelligence neural network is the mapping data and the tooth bounding box, and an output of the second artificial intelligence neural network is the tooth boundary curve.

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