Method for measuring angle of bone joint cut surface

Abstract

The present application relates to a method for measuring the angle of a bone joint cut surface on the basis of image analysis, comprising the steps of: (a) acquiring point cloud data by photographing a cut surface of a bone joint with a depth camera; (b) preprocessing the point cloud data; (c) setting a representative normal vector from the preprocessed data; and (d) deriving a cross-sectional angle of the cut surface from the representative normal vector and a normal vector of the front plane of the depth camera.

Description

Method for measuring the angle of the cross-section of a bone joint

[0001] The present invention relates to a method for measuring the angle of a bone joint cut surface, and more specifically, to a method for measuring the angle of a bone joint cut surface based on image analysis (e.g., vision).

[0002]

[0003] Among surgical techniques used in surgery or orthopedics, one involves amputating a patient's joint and replacing it with an artificial joint. This type of artificial joint replacement can be applied to various joints, including the hip joint, knee joint, and more.

[0004] Among these, partial knee arthroplasty refers to a surgery that replaces one of the medial joint, lateral joint, or patellar joint of the knee joint with an artificial joint. The lifespan of the artificial knee joint prosthesis after partial knee arthroplasty is closely related to the accuracy of the leg alignment angle, and according to previous related studies, the recommended error range is ±3° from the frontal plane. In order to extend the lifespan of the artificial knee joint, the alignment and angle of the leg axis, which can vary from patient to patient, should be learned through simulated surgery, so that more precise and highly skilled surgery can be performed in the actual surgery.

[0005] During the bone cutting process of the simulated surgery, a means is needed to assess whether the bone cutting was performed according to the surgical plan. After the simulated partial replacement artificial knee joint surgery, photographing the cut section with a 3D depth camera allows the angle of the cut surface to be calculated, which is expected to facilitate the assessment of the suitability of the artificial knee joint for attachment.

[0006] U.S. Patent Publication No. 15 / 192,733, which is the background technology of this application, relates to a system and method for measuring in vivo wear of an artificial knee joint.

[0007] The present invention aims to address the aforementioned problems of prior art and to provide a method for measuring the angle of a bone joint cut surface. Specifically, the present invention aims to provide a system for evaluating bone cut accuracy in simulated artificial knee joint replacement surgery based on image analysis (e.g., vision).

[0008] However, the technical tasks to be achieved by the embodiments of the present invention are not limited to the technical tasks described above, and other technical tasks may exist.

[0009] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention relates to a method for measuring an angle of a cross-section of a bone joint based on image analysis, comprising the steps of: (a) photographing a cross-section of a bone joint with a depth camera to obtain point cloud data; (b) preprocessing the point cloud data; (c) setting a representative normal vector from the preprocessed data; and (d) deriving a cross-sectional angle of the cross-section from the representative normal vector and the normal vector of the front plane of the depth camera.

[0010] According to one embodiment of the present invention, in the step (a), the point cloud data may include, but is not limited to, distance information between the depth camera and the bone joint cross-section, and position information on the bone joint cross-section.

[0011] According to one embodiment of the present invention, the step (b) may include, but is not limited to, the step of (b1) denoising the point cloud data, and the step of (b2) down sampling the point cloud data.

[0012] According to one embodiment of the present invention, the step (b1) may include, but is not limited to, the steps of: (b11) selecting a preset number of points adjacent to a first point randomly selected from the point cloud data as neighboring points of the first point; (b12) measuring an average (μ) and a standard deviation (σ) of distances (r) between the first point and the neighboring points; and (b13) deleting a second point spaced apart from the first point by a predetermined distance or more.

[0013] According to one embodiment of the present invention, the step (b2) may include, but is not limited to, the step of (b21) dividing the point cloud data into voxels; and the step of (b22) compressing the point cloud data within the voxels.

[0014] According to one embodiment of the present invention, the step (b2) may include, but is not limited to, performing a technique selected from the group consisting of a uniform sampling technique, a random sampling technique, a non-uniform sampling technique, and combinations thereof.

[0015] According to one embodiment of the present invention, the step (c) may include, but is not limited to, (c1) a step of estimating a normal vector of each point in the point cloud data; and (c2) a step of calculating a representative normal vector from among the estimated normal vectors based on average information of the normal vectors of each point.

[0016] According to one embodiment of the present invention, the step (d) may be to extract the angle of the cross-section through the following mathematical formula 1, but is not limited thereto:

[0017] [Mathematical Formula 1]

[0018] .

[0019] (In the above mathematical expression 1, n p is the representative normal vector, and n d is the normal vector of the front plane of the depth camera).

[0020] Meanwhile, the second aspect of the present invention relates to a method for measuring the accuracy of joint osteotomy, comprising: a step of cutting any joint at a first angle; a step of measuring an angle of a cut surface of the joint to extract a second angle; and a step of measuring an error between the first angle and the second angle; wherein the step of extracting the second angle is performed according to a method for measuring an angle of a cut surface of a joint according to the first aspect.

[0021] The above-described problem-solving methods are merely exemplary and should not be construed as limiting the present invention. In addition to the exemplary embodiments described above, additional embodiments may be included in the drawings and detailed description of the invention.

[0022] The method for measuring the angle of the osteotomy plane according to the present invention can contribute to evaluating the results of artificial knee joint partial replacement simulation surgery and improving the surgical skills of doctors by providing a comparative index with the surgical plan through calculation of the angle of the osteotomy plane.

[0023] In addition, the method for measuring the angle of the above-mentioned bone joint cut surface is performed by measuring the cut surface with a depth camera, and the angle of the cut surface can be measured with an error of about 1°.

[0024] In addition, the method for measuring the angle of the above-mentioned joint cut surface can be applied when measuring the cut surface of various joints as well as the patellar, medial, and lateral joints of the knee joint.

[0025] However, the effects that can be obtained from this center are not limited to the effects described above, and other effects may exist.

[0026] Figure 1 is a flowchart showing a method for measuring a cross-section of a bone joint according to one embodiment of the present invention.

[0027] Figure 2 is a flowchart showing a method for measuring a cross-section of a bone joint according to an embodiment of the present invention.

[0028] Figure 3 is a flowchart showing a method for measuring a cross-section of a bone joint according to an embodiment of the present invention.

[0029] Figure 4 is a flowchart showing a method for measuring a cross-section of a bone joint according to one embodiment of the present invention.

[0030] Figure 5 is a flowchart showing a method for measuring a cross-section of a bone joint according to one embodiment of the present invention.

[0031] Figure 6 is a schematic diagram showing a denoising step according to one implementation example of the present invention.

[0032] Fig. 7a is point cloud data according to an implementation example of the present invention, and Fig. 7b is denoised point cloud data.

[0033] Fig. 8a is denoised point cloud data according to an implementation example of the present invention, and Fig. 8b is downsampled point cloud data according to an implementation example of the present invention.

[0034] Figures 9a and 9b are drawings estimating the normal vector of point cloud data according to an implementation example of the present invention.

[0035] Fig. 10a is a photograph showing a cross-sectional measurement method according to an embodiment of the present invention, and Fig. 10b is a cross-sectional measurement result according to an embodiment of the present invention.

[0036] Figures 11a and 11b are photographs of a joint cutting jig.

[0037] Fig. 12 is a photograph showing a cross-sectional measurement method of a tibia according to one embodiment of the present invention.

[0038] Figure 13 is a photograph of the left (femur left) and right (femur right) sides of a cross-section according to one embodiment of the present invention.

[0039] Figure 14 summarizes the cross-sectional angles of the tibia, left femur, and right femur according to one embodiment of the present invention.

[0040] Figure 15 summarizes the cross-sectional angles of the tibia, left femur, and right femur according to one embodiment of the present invention.

[0041] Below, with reference to the attached drawings, an embodiment of the present invention is described in detail so that a person having ordinary knowledge in the technical field to which the present invention pertains can easily carry out the present invention.

[0042] However, the present invention can be implemented in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts irrelevant to the description have been omitted, and similar parts have been designated with similar drawing reference numerals throughout the specification.

[0043] Throughout this specification, when a part is said to be "connected" to another part, this includes not only cases where it is "directly connected" but also cases where it is "electrically connected" with another element in between.

[0044] Throughout this specification, when it is said that a member is located “on,” “above,” “upper,” “lower,” “lower” or “lower” another member, this includes not only cases where the member is in contact with the other member, but also cases where another member exists between the two members.

[0045] Throughout this specification, whenever a part is said to "include" a component, this means that it may include other components, but not to the exclusion of other components, unless otherwise specifically stated.

[0046] The terms "about," "substantially," and the like, as used herein, are used to mean at or near the numerical value when manufacturing and material tolerances inherent to the meanings referred to are presented, and are used to prevent unscrupulous infringers from unfairly exploiting disclosures that contain precise or absolute numerical values ​​to aid understanding of the present disclosure. Furthermore, throughout the present disclosure, the terms "step of ~" or "step of ~" do not mean "step for ~."

[0047] Throughout this specification, the term "combination thereof" included in the expressions in the Makushi format means one or more mixtures or combinations selected from the group consisting of the components described in the expressions in the Makushi format, and means including one or more selected from the group consisting of said components.

[0048] Throughout this specification, references to “A and / or B” mean “A or B, or A and B.”

[0049] Hereinafter, a method for measuring the angle of a cross-section of a bone joint will be described in detail with reference to implementation examples, examples, and drawings. However, the present invention is not limited to these implementation examples, examples, and drawings.

[0050] As a technical means for achieving the above-mentioned technical task, the first aspect of the present invention relates to a method for measuring an angle of a cross-section of a bone joint based on image analysis, comprising the steps of: (a) photographing a cross-section of a bone joint with a depth camera to obtain point cloud data; (b) preprocessing the point cloud data; (c) setting a representative normal vector from the preprocessed data; and (d) deriving a cross-sectional angle of the cross-section from the representative normal vector and the normal vector of the front plane of the depth camera.

[0051] Some surgical or orthopedic techniques involve amputating a portion of a bone (joint) and replacing it with an artificial joint. However, if the discrepancy between the angle of the cut surface of the joint and the leg alignment angle increases, the lifespan of the artificial joint can be shortened. To overcome this problem, it is necessary to accurately measure the angle of the cut surface of the joint and the leg alignment angle. This center provides a method for measuring the cut surface of a joint.

[0052] The leg alignment angle according to this invention refers to the angle formed by the line connecting the center of the knee joint to the center of the femoral head and the line connecting the center of the ankle.

[0053] FIGS. 1 to 5 are flowcharts illustrating a method for measuring a cross-section of a joint according to an embodiment of the present invention. In this regard, FIG. 1 illustrates a method for measuring the angle of an entire cross-section of a joint, FIGS. 2 to 4 illustrate a step for preprocessing a point cloud, and FIG. 5 illustrates a step for setting a representative normal vector from preprocessed data.

[0054] First, a cross-section of the bone joint is captured using a depth camera to obtain point cloud data (S100).

[0055] According to one embodiment of the present invention, in the step (a), the point cloud data may include, but is not limited to, distance information between the depth camera and the bone joint cross-section, and position information on the bone joint cross-section.

[0056] When a cross-section of a bone joint is captured with a depth camera, point cloud data for the cross-section of the bone joint can be obtained through information about the distance between any point on the cross-section of the bone joint and the depth camera, and information about the location on the cross-section of the bone joint.

[0057] Next, the point cloud data is preprocessed (S200).

[0058] According to one embodiment of the present invention, the step (b) may include, but is not limited to, the steps of (b1) denoising the point cloud data, and (b2) down sampling the point cloud data. Referring to Fig. 2, the point cloud data obtained in the step (a) may be denoised, and then down sampled to remove outliers (noise) from the point cloud data and compress the number of data. Through this, the time reduction effect can be obtained during the calculation process of the normal vector described later.

[0059] According to one embodiment of the present invention, the step (b1) may include, but is not limited to, the step of (b11) selecting a preset number of points adjacent to a first point randomly selected from the point cloud data as neighboring points of the first point; (b12) measuring an average (μ) and a standard deviation (σ) of distances (r) between the first point and the neighboring points; and (b13) deleting a second point spaced apart from the first point by a predetermined distance or more (S210).

[0060] Referring to FIG. 3, the step (S210) of denoising the point cloud data may include, but is not limited to, the step (S211) of selecting a preset number of points adjacent to a first point randomly selected from the point cloud data as neighboring points of the first point; the step (S212) of measuring the average (μ) and standard deviation (σ) of distances (r) between the first point and the neighboring points; and the step (S213) of deleting a second point spaced apart from the first point by a predetermined distance or more.

[0061] Denoising according to the present invention means removing outliers referred to as noise within data, and is specifically a process of removing outliers that may occur in the process of receiving point cloud data of a bone cross-section using a depth camera.

[0062] The process for calculating the above outliers is as follows. First, by setting an arbitrary point within the point cloud data as the first point, a sphere with a radius of r and centered around the first point can be set. Here, the first point refers to an arbitrary point within the point cloud, and is intended to distinguish it from points (noise) to be deleted within the point cloud data.

[0063] Next, by setting k points closest to the first point within the spherical range, the average distance (Mean k-nearest neighbors distance) between the first point and the k neighboring points can be calculated.

[0064] By repeating the above process for all points within the point cloud, the distances from each point to its k nearest neighbors can be measured, and then the mean μ and standard deviation σ of the mean k-nearest neighbor distances for all points can be calculated. Points further than μ±ασ are identified as outliers and removed. Here, α and k can vary depending on the characteristics of the cross-section from which data is to be acquired.

[0065] The above α and k may be positive integers, but are not limited thereto. For example, the above α may be a number greater than 0 and less than or equal to 4, and k may be from 1 to 10000, but are not limited thereto.

[0066] In addition, at a point further away than μ±ασ, the index to be compared with μ±ασ is not the distance between an arbitrary point (the first point) and the second point, but the Mean K-nearest neighbors distance calculated from the first point. In other words, a point further away than μ±ασ by the Mean k-nearest neighbors distance calculated from the first point can be determined as the second point, and the second point can be determined as an outlier and removed.

[0067] Fig. 6 is a schematic diagram illustrating a denoising step according to an embodiment of the present invention. Referring to Fig. 6, k points can be set based on a reference point (first point), and points outside the circle of Fig. 6 can be determined as outliers (noise) and removed.

[0068] Fig. 7a is point cloud data according to an implementation example of the present invention, and Fig. 7b is denoised point cloud data. In this case, Figs. 7a and 7b are examples of point cloud data including noise and denoised point cloud data, and since the bone joint cross-section is inclined at a predetermined angle, the point cloud obtained by photographing the bone joint cross-section with a depth camera may be inclined at a predetermined angle, unlike Figs. 7a and 7b, and thus, the denoised point cloud data of the bone joint cross-section may have a different shape from Fig. 7b.

[0069] Referring to FIGS. 7a and 7b, since the point cloud data may include a large amount of noise, a denoising step may be performed to remove such noise.

[0070] According to one embodiment of the present invention, the step (b2) may include, but is not limited to, the step of (b21) dividing the point cloud data into voxels; and the step of (b22) compressing the point cloud data within the voxels.

[0071] A voxel according to the present invention is a pixel having a volume, and can be defined in the shape of a hexahedron within three-dimensional coordinates.

[0072] When the above point cloud data is divided into three-dimensional voxels, a single voxel may contain multiple data points. In this case, by compressing the data contained within the voxel using the average value, the number of data points contained within the point cloud data can be compressed to the number of voxels, thereby reducing the overall amount of data.

[0073] Fig. 8a is denoised point cloud data according to an implementation example of the present invention, and Fig. 8b is downsampled point cloud data according to an implementation example of the present invention.

[0074] According to one embodiment of the present invention, the step (b2) may include, but is not limited to, performing a technique selected from the group consisting of a uniform sampling technique, a random sampling technique, a non-uniform sampling technique, and combinations thereof.

[0075] Preferably, the step (b2) may be performed by a uniform sampling technique.

[0076] Next, a representative normal vector is set from the above preprocessed data (S300).

[0077] According to one embodiment of the present invention, the step (c) may include, but is not limited to, the steps of (c1) estimating a normal vector of each point in the point cloud data; and (c2) calculating a representative normal vector from among the estimated normal vectors based on average information of the normal vectors of each point. The point cloud data from which the normal vector is extracted in the steps (c1) and (c2) may be point cloud data preprocessed through the step (b).

[0078] The above step (c) and the step (d) described later can be combined and called a cross-section angle calculation algorithm.

[0079] In the point cloud data of the cross-section of the bone joint acquired and preprocessed through the above depth camera, the normal vector of each point is estimated, and the average normal vector of all points is calculated to set it as the representative normal vector of the cross-section, and the cross-section angle can be derived from this. Specifically, the point cloud data set X of the cross-section of the bone joint can be defined as follows.

[0080] X = {x1, x2, x3, ... x n} (n is the number of data in the point cloud data)

[0081] Among the points belonging to the above X, x i Tangent plane Tp(x) for (1 ≤ i ≤ n) i ) is the unit normal vector n i and center a i can be expressed as . Also, any point p∈R 3 In the tangent plane Tp(x i ) Distance to disp i (p) is, (pa i )n i can be expressed as . At this time, the tangent plane Tp(x i ) unit normal vector n i and center ai is a point x within the set X i It can be determined by considering the number of neighboring points (k), and the set of these can be Nbhd(x i ) can be defined, and the center and normal vectors of the tangent plane are {disp i (p)=0} This Nbhd(x i ) can be calculated to be the best fitting plane.

[0082] In this regard, a i is Nbhd(x i ) is the center of the normal vector, and the normal vector can be calculated through principal component analysis. For calculating the normal vector, Nbhd(x i ) can be defined as follows.

[0083] .

[0084] Eigenvalue λ of the above covariance matrix i 1 , λ i 2 , and λ i 3 (However, λ i 1 ≥ λ i 2 ≥ λ i 3 ), for λ i 3 The corresponding eigenvector v i 3 or -v i 3 is the normal vector n i becomes. At this time, depending on the choice of sign, the direction of the plane n i can be determined and needs to be set to be the same plane as the surrounding planes.

[0085] Next, through the above-described normal vector inference process, the normal vectors of all points in the point cloud data are calculated, and the average of the entire normal vectors is calculated to set the normal vector np of the entire joint cross-section. As will be described later, the angle of the joint cross-section can be calculated through operations between the normal vector np and other vectors.

[0086] Next, the cross-sectional angle of the cross-section is derived from the representative normal vector and the normal vector of the front plane of the depth camera (S400).

[0087] According to one embodiment of the present invention, the step (d) may be to extract the angle of the cross-section through the following mathematical formula 1, but is not limited thereto:

[0088] [Mathematical Formula 1]

[0089] .

[0090] (In the above mathematical expression 1, n p is the representative normal vector, and n d is the normal vector of the front plane of the depth camera).

[0091] In the above mathematical expression 1, θ can be greater than 0 and less than π (rad), i.e., 0 < θ < π (rad).

[0092] When the depth camera captures a cross-section of a bone joint, a vector may be formed in a direction perpendicular to the photographing plane (front plane). Accordingly, by taking the inner product of the normal vector of the front plane of the depth camera and the representative normal vector of the cross-section of the bone joint, the angle between the normal vector of the front plane of the depth camera and the representative normal vector of the cross-section of the bone joint can be extracted.

[0093] FIG. 9A and FIG. 9B are drawings estimating the normal vector of point cloud data according to one implementation example of the present invention. Specifically, FIG. 9B is an enlarged view of a portion of FIG. 9A, and the yellow lines in FIG. 9A and FIG. 9B represent vectors obtained as representative vectors of a cross-section by averaging the x, y, and z values ​​of vectors obtained at all points. That is, in the case of the yellow line, the size of the vector is displayed by scaling up the unit vector so that it can be checked at a glance on the drawing as a representative vector of a cross-section, and in actual calculations, it is calculated as the size of the unit vector.

[0094] Referring to FIGS. 9a and 9b, when a cross-section of a bone joint, for example, a cross-section of a knee joint, is captured with a depth camera, point cloud data is acquired in the form of a collection of countless points, and the point cloud data can be compressed by preprocessing through denoising and downsampling. When a normal vector is extracted from the preprocessed point cloud data, it can be expressed as in FIGS. 9a and 9b.

[0095] That is, by measuring the angle of the bone joint cut surface using the method described above, the error between the angle when cutting the bone joint and the angle of the cut surface can be measured.

[0096] Meanwhile, the second aspect of the present invention relates to a method for measuring the accuracy of joint osteotomy, comprising: a step of cutting any joint at a first angle; a step of measuring an angle of a cut surface of the joint to extract a second angle; and a step of measuring an error between the first angle and the second angle; wherein the step of extracting the second angle is performed according to a method for measuring an angle of a cut surface of a joint according to the first aspect.

[0097] The method for measuring the accuracy of joint arthroplasty according to this invention is to measure the error between the angle at which the joint is cut (the first angle) and the angle of the joint cut surface (the second angle). Since this error is related to the lifespan of the artificial joint, it is necessary to minimize this error as much as possible.

[0098] According to one embodiment of the present invention, the error may be 2 degrees or less, but is not limited thereto. Preferably, the error may be 1 degree or less.

[0099] Based on the situation where the cross-section of the above-mentioned joint and the camera plane are parallel, the error between the first angle and the second angle may be approximately 1 degree.

[0100] The present invention will be described in more detail through the following examples; however, the following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0101] [Example]

[0102] The kneecap was fixed to a jig and cut. The angle of the jig was then adjusted so that the axis of the kneecap was perpendicular to the plane of the depth camera, and the cut surface was photographed.

[0103] Next, the point cloud data acquired from the depth camera was denoised and uniformly sampled, and the angle of the photographed knee joint cross-section was calculated using a cross-sectional angle calculation algorithm. Then, the error between the cutting angle of the knee joint and the angle of the knee joint cross-section was measured through the difference between the angle of the knee joint cross-section and the angle of the knee joint cross-section.

[0104] Fig. 10a is a photograph of a cross-sectional measurement method according to an embodiment of the present invention, Fig. 10b is a cross-sectional measurement result according to an embodiment of the present invention, and Figs. 11a and 11b are photographs of a joint cutting jig. In this regard, the gray dots in Fig. 10b represent data constituting a point cloud, and the red dots represent a region of interest (ROI) in the point cloud.

[0105] Fig. 12 is a photograph showing a cross-sectional measurement method of a tibia according to an embodiment of the present invention, and Fig. 13 is a photograph showing the left (femur left) and right (femur right) cross-sections of a femur according to an embodiment of the present invention. In addition, Fig. 14 summarizes the cross-sectional angles of the tibia, the left femur, and the right femur according to an embodiment of the present invention, and Fig. 15 summarizes the cross-sectional angles of the tibia, the left femur, and the right femur according to an embodiment of the present invention.

[0106] Referring to FIGS. 12 to 15, when the actual tibia, left femur, and right femur are cut to have a cross-sectional angle of 1.1°, when measured using the method of FIG. 12, the error and RMSE (Root mean square error) can be measured as shown in Table 1 below, and it can be confirmed that the error range is 0.4° to 0.6°, which is very precisely measured.

[0107] [Table 1]

[0108]

[0109] (In Table 1, the units of actual angle, average measurement value, measurement error, and RMSE are °)

[0110] The above description of the present invention is for illustrative purposes only, and those skilled in the art will readily appreciate that the present invention can be readily modified into other specific forms without altering the technical spirit or essential characteristics of the present invention. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single entity may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined manner.

[0111] The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

Claims

1. A method for measuring the angle of a bone joint cross-section based on image analysis, (a) A step of acquiring point cloud data by photographing a cross-section of a bone joint with a depth camera; (b) a step of preprocessing the above point cloud data; (c) a step of setting a representative normal vector from the above preprocessed data; and (d) a step of deriving a cross-sectional angle of the cross-section from the representative normal vector and the normal vector of the front plane of the depth camera; A method for measuring the angle of a bone joint cut surface, including:

2. In paragraph 1, In step (a) above, A method for measuring an angle of a bone joint cut plane, wherein the point cloud data includes distance information between the depth camera and the bone joint cut plane and position information on the bone joint cut plane.

3. In paragraph 1, Step (b) above, (b1) a step of denoising the above point cloud data, and (b2) a step of down sampling the above point cloud data; A method for measuring the angle of a bone joint cut surface, comprising:

4. In paragraph 3, The above step (b1) is, (b11) For a first point randomly selected from the point cloud data, a step of selecting a preset number of points adjacent to the first point as neighboring points of the first point; (b12) a step of measuring the average (μ) and standard deviation (σ) of the distances (r) between the first point and the neighboring points; and (b13) A step of deleting a second point spaced apart from the first point by a predetermined distance or more; which includes, A method for measuring the angle of a bone joint cut surface.

5. In paragraph 3, Step (b2) above, (b21) a step of dividing the above point cloud data into voxels; and (b22) A step of compressing point cloud data within the above voxel; A method for measuring the angle of a bone joint cut surface, comprising:

6. In paragraph 3, Step (b2) above, A method for measuring an angle of a cross-section of a bone joint, characterized in that it performs a technique selected from the group consisting of a uniform sampling technique, a random sampling technique, a non-uniform sampling technique, and combinations thereof.

7. In paragraph 1, Step (c) above, (c1) a step of estimating a normal vector of each point in the above point cloud data; and (c2) A step of calculating a representative normal vector among the estimated normal vectors based on the average information of the normal vectors of each of the above points; A method for measuring the angle of a bone joint cut surface, comprising:

8. In paragraph 1, Step (d) above, A method for measuring the angle of a cross-section of a bone joint, wherein the cross-sectional angle (θ) of the cross-section is derived through the following mathematical formula 1: [Mathematical Formula 1] ; (In the above mathematical formula 1, n p is the representative normal vector, and n d is the normal vector of the front plane of the depth camera).

9. Step of cutting any bone joint at the first angle; A step of extracting a second angle by measuring the angle of the cross-section of the above-mentioned bone joint; and A step of measuring an error between the first angle and the second angle; Including, The step of extracting the second angle is performed according to the method of measuring the angle of the bone joint cut surface according to the first clause. A method for measuring the accuracy of joint arthroplasty.

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