Auxiliary method and device for scraping in bone tumor focus and electronic equipment

Abstract

The invention provides a bone tumor lesion internal scraping auxiliary method and device and electronic equipment, and relates to the technical field of medical treatment, and the method comprises the steps: carrying out the image processing and surface reconstruction processing of a bone tumor CT image of a patient, and determining a bone tumor three-dimensional model; performing feature edge extraction processing on the bone tumor three-dimensional model by using included angles between the triangular patches, and determining a target feature edge set and a target non-feature edge set; performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set, determining a target bone tumor three-dimensional model, and determining coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as bone tumor position coordinates; and curette tip coordinates of the surgical curette are obtained, and path planning and path monitoring of bone tumor scraping are carried out according to the curette tip coordinates and the bone tumor position coordinates, so that the curette can accurately reach the bone tumor position. According to the invention, the bone tumor scraping accuracy can be obviously improved, so that bone tumor residues are reduced.

Description

Technical Field

[0001] The present invention relates to the field of medical technology, and in particular to a method, device and electronic equipment for assisting the scraping of bone tumor lesions. Background Art

[0002] Curettage of bone tumor is a surgical method commonly used to treat benign bone tumors or certain low-grade malignant bone tumors. It can completely remove tumor tissue while preserving the structure and function of the bone as much as possible. At present, relevant technologies have proposed that the thoroughness of bone tumor curettage can be verified in the following two ways. Option 1 is to evaluate the curettage area through visual inspection and palpation to observe whether there is obvious tumor residue, or touch the curettage area with your hand to determine whether there is abnormal hardness or lump; Option 2 is through intraoperative X-ray or CT scanning: real-time imaging, check whether there is any suspicious residue in the curettage area, but Option 1 mainly depends on the clinical experience of the surgeon and lacks quantitative standards, while Option 2 has high requirements for the operating room environment, and repeated CT scanning for bone tumor positioning will cause the patient to be exposed to additional radiation. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a method, device and electronic equipment for auxiliary scraping of bone tumor lesions, which can significantly improve the accuracy of bone tumor scraping and thus reduce bone tumor residues.

[0004] In a first aspect, an embodiment of the present invention provides an auxiliary method for scraping bone tumor lesions, the method comprising: performing image processing and surface reconstruction processing on a patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is composed of three vertices corresponding to each triangular facet in a triangular facet set connected together; using the angles between each triangular facet, performing feature edge extraction processing on the three-dimensional model of the bone tumor to determine a target feature edge set and a target non-feature edge set; performing contour enhancement processing on the three-dimensional model of the bone tumor based on the target non-feature edge set to determine a target bone tumor three-dimensional model, and determining the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as the bone tumor position coordinates; obtaining the scraper tip coordinates of a surgical scraper, and performing path planning and path monitoring for bone tumor scraping based on the scraper tip coordinates and the bone tumor position coordinates, so that the scraper can accurately reach the bone tumor position.

[0005] In one embodiment, the steps of performing image processing and surface reconstruction on the patient's bone tumor CT image to determine the three-dimensional model of the bone tumor include: performing image preprocessing on the patient's bone tumor CT image to determine primary image data, wherein the image preprocessing includes: denoising, correction and contrast enhancement; performing image segmentation on the primary image data using a preset threshold segmentation model and a regional growth model to extract the bone tumor structure from the primary image data to determine the secondary image data; extracting an isosurface from the secondary image data using a preset surface reconstruction algorithm, and using the isosurface to generate a three-dimensional model of the bone tumor.

[0006] In one embodiment, the steps of extracting feature edges of a three-dimensional model of a bone tumor using the angles between various triangular facets and determining a target feature edge set and a target non-feature edge set include: obtaining vertex coordinates corresponding to various triangular facets, and calculating normal vectors corresponding to various triangular facets according to the vertex coordinates; calculating normal vector angles between various adjacent triangular facets respectively, and determining the normal vector angles as dihedral angles corresponding to common edges between two adjacent triangular facets; performing first feature edge extraction processing on various dihedral angles respectively using a preset angle threshold, and determining the feature edge type to which the common edge corresponding to the dihedral angle belongs, wherein the feature edge type includes: a first dominant feature edge, a recessive feature edge, and a first non-feature edge; performing second feature edge extraction processing on the recessive feature edge using the edge perimeter information of two triangular facets corresponding to the recessive feature edge, and determining a secondary classification result of the recessive feature edge, wherein the secondary classification result includes: a second dominant feature edge and a second non-feature edge; determining the first dominant feature edge and the second dominant feature edge of each item as a target feature edge set, and determining the first non-feature edge and the second non-feature edge of each item as a target non-feature edge set.

[0007] In one embodiment, the preset angle threshold includes: an explicit feature edge threshold and an implicit feature edge threshold. The preset angle threshold is used to perform first feature edge extraction processing on each dihedral angle respectively, and the step of determining the feature edge type to which the common edge corresponding to the dihedral angle belongs includes: when the dihedral angle is not less than the explicit feature edge threshold and is less than 180 degrees, the common edge corresponding to the dihedral angle is determined as the first explicit feature edge; when the dihedral angle is greater than the implicit feature edge threshold and is less than the explicit feature edge threshold, the common edge corresponding to the dihedral angle is determined as the implicit feature edge; when the dihedral angle is greater than 0 degrees and not greater than the implicit feature edge threshold, the common edge corresponding to the dihedral angle is determined as the first non-feature edge.

[0008] In one embodiment, the step of performing a second feature edge extraction process on the implicit feature edge using the side perimeter information of two triangular facets corresponding to the implicit feature edge, and determining a secondary classification result of the implicit feature edge includes: obtaining a first side perimeter and a second side perimeter of the two triangular facets corresponding to the implicit feature edge, wherein the first side perimeter is smaller than the second side perimeter; comparing the ratio of the second side perimeter to the first side perimeter with a preset side perimeter threshold to determine the side perimeter ratio; when the side perimeter ratio is greater than the preset side perimeter threshold, determining the implicit feature edge to be the second explicit feature edge; and when the side perimeter ratio is not greater than the preset side perimeter threshold, determining the implicit feature edge to be the second non-feature edge.

[0009] In one embodiment, the step of performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set to determine the target bone tumor three-dimensional model includes: merging two triangular facets corresponding to each target non-feature edge in the target non-feature edge set to enhance the contour of the bone tumor three-dimensional model and determine the target bone tumor three-dimensional model.

[0010] In one embodiment, the steps of obtaining the coordinates of the scraper tip of a surgical scraper, and performing path planning and path monitoring for bone tumor scraping according to the scraper tip coordinates and the bone tumor position coordinates include: obtaining a three-dimensional coordinate matrix of a positioning bracket at the end of the scraper and a deflection matrix from the end of the scraper to the scraper tip; determining the product of the three-dimensional coordinate matrix of the bracket and the deflection matrix as the scraper tip coordinates; planning a lesion edge point set according to the bone tumor position coordinates, and monitoring in real time the operating edge point set of the scraper tip coordinate movement during the bone tumor scraping process; when the operating edge point set coincides with the lesion edge point set, determining that the bone tumor scraping is completed.

[0011] In a second aspect, an embodiment of the present invention further provides a bone tumor lesion scraping auxiliary device, the device comprising: a model construction module, performing image processing and surface reconstruction processing on the patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is composed of three vertices corresponding to each triangular facet in a triangular facet set connected together; a feature edge extraction module, performing feature edge extraction processing on the bone tumor three-dimensional model using the angles between each triangular facet, and determining a target feature edge set and a target non-feature edge set; a bone tumor positioning module, performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set, determining a target bone tumor three-dimensional model, and determining the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as the bone tumor position coordinates; a scraping auxiliary module, obtaining the scraper tip coordinates of a surgical scraper, and performing path planning and path monitoring for bone tumor scraping based on the scraper tip coordinates and the bone tumor position coordinates, so that the scraper can accurately reach the bone tumor position.

[0012] In a third aspect, an embodiment of the present invention further provides an electronic device, comprising a processor and a memory, wherein the memory stores computer-executable instructions that can be executed by the processor, and the processor executes the computer-executable instructions to implement any one of the methods provided in the first aspect.

[0013] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement any one of the methods provided in the first aspect.

[0014] The embodiments of the present invention bring the following beneficial effects:

[0015] An embodiment of the present invention provides an auxiliary method, device and electronic device for scraping bone tumor lesions. The method performs image processing and surface reconstruction processing on a patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, and uses the angles between various triangular facets to perform feature edge extraction processing on the three-dimensional model of the bone tumor to determine a target feature edge set and a target non-feature edge set. The three-dimensional model of the bone tumor is then subjected to contour enhancement processing based on the target non-feature edge set to determine a target three-dimensional model of the bone tumor, and the coordinates corresponding to the target feature edge set in the target three-dimensional model of the bone tumor are determined as the bone tumor position coordinates. Finally, the scraper tip coordinates of a surgical scraper are obtained, and path planning and path monitoring for bone tumor scraping are performed based on the scraper tip coordinates and the bone tumor position coordinates, so that the scraper accurately reaches the bone tumor position. The embodiment of the present invention can avoid repeated CT scanning for bone tumor positioning, reduce the patient's radiation dose, and significantly improve the accuracy of bone tumor scraping, thereby reducing bone tumor residues.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0017] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 A schematic diagram of a flow chart of a bone tumor lesion scraping auxiliary method provided by an embodiment of the present invention;

[0020] Figure 2 A schematic diagram of the structure of a three-dimensional model of a bone tumor provided by an embodiment of the present invention;

[0021] Figure 3 A schematic diagram of the structure of a bone tumor lesion scraping auxiliary device provided by an embodiment of the present invention;

[0022] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described in combination with the embodiments below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. 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.

[0024] Currently, curettage of bone tumor is a surgical method commonly used to treat benign bone tumors or certain low-grade malignant bone tumors. The operation generally uses general anesthesia or regional anesthesia (such as epidural anesthesia or spinal anesthesia). Under the guidance of imaging, a suitable incision site is selected to cut the skin, subcutaneous tissue and muscle to expose the diseased bone area. A surgical curette is used to carefully scrape off the tumor tissue to ensure that it is scraped cleanly without leaving any tumor residue.

[0025] Bone tumor scraping surgery can completely remove tumor tissue while preserving the structure and function of the bone as much as possible. Verifying whether the bone tumor scraping is thorough is a key step in bone tumor scraping surgery, which aims to remove all tumor tissue as much as possible and reduce the risk of recurrence. Currently, surgeons generally use two methods to judge during surgery: 1. Evaluate the scraped area through visual inspection and palpation to check whether there is obvious tumor residue, or touch the scraped area with your hand to determine whether there is abnormal hardness or lump. This scheme depends on the surgeon's clinical experience and has no quantitative standards; 2. Intraoperative X-ray or CT scanning, by real-time imaging, check whether there is any suspicious residue in the scraped area, but this scheme has high requirements on the operating room environment and there is an additional radiation dose. Based on this, the bone tumor lesion scraping auxiliary method provided by the present invention can avoid repeated CT scanning for bone tumor positioning, reduce the patient's radiation dose, and significantly improve the accuracy of bone tumor scraping, thereby reducing bone tumor residue.

[0026] See also Figure 1 The flowchart of a method for assisting the scraping of bone tumor lesions is shown in FIG. 1 , and the method mainly includes the following steps S102 to S108:

[0027] Step S102, performing image processing and surface reconstruction processing on the patient's bone tumor CT image to determine the three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is composed of three vertices corresponding to each triangular facet in a triangular facet set, and the image processing includes image preprocessing and graphic segmentation processing on the CT image, that is, the three-dimensional model mesh is composed of many small triangular facets, and the vertices of these triangular facets are the points that constitute the mesh, each triangular facet is defined by three vertices, and each vertex has three coordinate values ​​(x, y, z) in the three-dimensional space, corresponding to the normal vector (nx, ny, nz) of the plane.

[0028] In one embodiment, a surface reconstruction algorithm, such as the Marching Cubes algorithm, may be used to extract isosurfaces from an image to determine a three-dimensional model of a bone tumor, wherein the CT values ​​(ie, CT ray attenuation coefficients) of the isosurfaces are the same.

[0029] Step S104, using the angles between the various triangular facets, the feature edge extraction process is performed on the three-dimensional model of the bone tumor to determine the target feature edge set and the target non-feature edge set. In one embodiment, the smaller the dihedral angle between two triangular facets, the smoother the two adjacent triangles. When the dihedral angle is 0°, it means that the two triangles are coplanar. Conversely, the larger the dihedral angle is, when it exceeds the preset dihedral angle threshold, an edge line will appear, and the edge line (the overlapping edge between the two triangular facets) can be regarded as a feature edge.

[0030] Step S106, based on the target non-feature edge set, the contour enhancement processing is performed on the bone tumor three-dimensional model, the target bone tumor three-dimensional model is determined, and the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model are determined as the bone tumor position coordinates. In one embodiment, since there are many redundant situations in the initial bone tumor three-dimensional model, for example, small triangles are parallel and the angle is too small, therefore, the contour enhancement processing refers to retaining only the overall structure of the triangle where the feature edge is located, so that the contour of the model is clearer, thereby reducing the amount of calculation for subsequent intraoperative calculations.

[0031] Step S108, obtaining the coordinates of the surgical curette tip, and performing path planning and path monitoring for bone tumor scraping based on the coordinates of the curette tip and the bone tumor position coordinates, so that the curette accurately reaches the bone tumor position. In one embodiment, since the surgical curette enters the bone after opening a hole on the bone surface to perform a bone tumor operation, the inside of the bone cannot be observed with the naked eye, and therefore it can be judged whether the bone tumor has been scraped cleanly by the characteristic edge position and the coordinates of the curette tip.

[0032] The above-mentioned bone tumor lesion scraping auxiliary method provided by the embodiment of the present invention can avoid repeated CT scanning for bone tumor positioning, reduce the patient's radiation dose, and significantly improve the accuracy of bone tumor scraping, thereby reducing bone tumor residues.

[0033] In one embodiment, before determining the three-dimensional model of the bone tumor, it is necessary to first segment the bone tumor and the femur model, scan the target object using a CT scanner, and obtain a series of cross-sectional images (i.e., slice images). These images are obtained layer by layer according to a preset slice spacing. These cross-sectional images are used as CT images, and the CT images are subjected to image preprocessing and graphic segmentation processing to extract the bone tumor structure, and then the three-dimensional model of the bone tumor is constructed by a preset surface reconstruction algorithm, specifically, including the following (A) to (B):

[0034] (A) Specifically, the patient's bone tumor CT image is subjected to image preprocessing to determine primary image data, wherein the image preprocessing includes: denoising processing, correction processing and contrast enhancement processing. The denoising processing is used to denoise the acquired CT image to reduce noise interference in the image. The correction processing is used to perform geometric correction and intensity correction to ensure the accuracy and consistency of the image. The contrast enhancement processing is used to improve the visibility of image details through contrast enhancement technology.

[0035] (B) The primary image data is segmented by a preset threshold segmentation model and a region growing model to extract the bone tumor structure from the primary image data, determine the secondary image data, extract the isosurface from the secondary image data by a preset surface reconstruction algorithm, and generate a three-dimensional model of the bone tumor using the isosurface, wherein the preset threshold segmentation model is used to divide the image into foreground and background according to pixel values, and the region growing model is used to start from a seed point and gradually classify pixels similar to the seed point into the same region.

[0036] The embodiment of the present invention also provides an implementation method for determining a target bone tumor three-dimensional model by extracting characteristic edges of the bone tumor. For details, see (1) to (4) below:

[0037] (1) Obtain the vertex coordinates corresponding to each triangular face, and calculate the normal vector corresponding to each triangular face according to the vertex coordinates. After determining the normal vector, calculate the normal vector angles between each adjacent triangular face, and determine the normal vector angle as the dihedral angle corresponding to the common edge between two adjacent triangular face. In one embodiment, there is a common edge e i+1 The normal vector of one of the adjacent triangles is n i , the normal vector of the other patch is n i+1 , the angle between their normal vectors It is the common edge e of the triangle patch i+1 The dihedral angle is:

[0038]

[0039] (2) Using a preset angle threshold, perform first feature edge extraction processing on each dihedral angle respectively to determine the feature edge type to which the common edge corresponding to the dihedral angle belongs, wherein the feature edge type includes: a first explicit feature edge, an implicit feature edge, and a first non-feature edge, and the preset angle threshold includes: an explicit feature edge threshold and an implicit feature edge threshold. Specifically, the first feature edge extraction determination method includes the following (a) to (c):

[0040] (a) When the dihedral angle is not less than the dominant feature edge threshold and less than 180 degrees (i.e., ), the common edge corresponding to the dihedral angle is determined as the first dominant characteristic edge, wherein the first dominant characteristic edge is a characteristic edge with sharp edges and corners;

[0041] (b) When the dihedral angle is greater than the implicit feature edge threshold and is less than the dominant feature edge threshold (i.e., ), the common edge corresponding to the dihedral angle is determined as the implicit characteristic edge, wherein the implicit characteristic edge is the characteristic edge with a smaller dihedral angle corresponding to the transition surface, and the implicit characteristic edge is between the characteristic edge and the non-characteristic edge, and its specific classification needs to be determined through secondary determination;

[0042] (c) When the dihedral angle is greater than 0 degrees and not greater than the implicit feature edge threshold (i.e., ), the common edge corresponding to the dihedral angle is determined as the first non-feature edge.

[0043] (3) Using the edge perimeter information of the two triangular facets corresponding to the implicit feature edge, perform a second feature edge extraction process on the implicit feature edge to determine a secondary classification result of the implicit feature edge, wherein the secondary classification result includes: a second explicit feature edge and a second non-feature edge. Specifically, obtain a first edge perimeter and a second edge perimeter of the two triangular facets corresponding to the implicit feature edge, compare the ratio of the second edge perimeter to the first edge perimeter with a preset edge perimeter threshold, determine the edge perimeter ratio, and perform a second feature edge extraction process using the edge perimeter ratio, wherein the first edge perimeter C i Less than the second side perimeter C i+1 , C i+1 and C i is the perimeter of the adjacent sides. The larger of the two values ​​is Ci+1, and the smaller is Ci. Therefore, their ratio λ=C i+1 / C i Greater than or equal to 1. Specifically, the second feature edge extraction determination method includes the following (d) to (e):

[0044] (d) When the edge perimeter ratio is greater than a preset edge perimeter threshold, that is, λ>α, the implicit feature edge is determined to be the second dominant feature edge, where α is the edge perimeter threshold, and the edge perimeter threshold is the average of the perimeter ratios of two adjacent triangles of each edge in the model;

[0045] (e) When the edge perimeter ratio is not greater than a preset edge perimeter threshold, that is, λ<=α, the implicit feature edge is determined to be the second non-feature edge.

[0046] (4) See Figure 2 A structural schematic diagram of a three-dimensional model of a bone tumor is shown, in which each first dominant feature edge and each second dominant feature edge are determined as a target feature edge set, and each first non-feature edge and each second non-feature edge are determined as a target non-feature edge set. After the target non-feature edge set is determined, the two triangular facets corresponding to each target non-feature edge in the target non-feature edge set are merged to enhance the contour of the three-dimensional model of the bone tumor and determine the target three-dimensional model of the bone tumor.

[0047] In one embodiment, after obtaining the target bone tumor three-dimensional model, the coordinates of the target feature edge set in the target bone tumor three-dimensional model can be used to plan and monitor the movement path of the surgical curette. Specifically, the three-dimensional coordinate matrix of the positioning bracket at the end of the curette can be obtained. and the deflection matrix from the end of the curette to the tip of the curette Among them, since the curette is a rigid structure, the deflection matrix from the end of the curette to the tip of the curette is fixed, and the product of the three-dimensional coordinate matrix of the bracket and the deflection matrix is ​​determined as the coordinate of the tip of the curette, that is, the coordinate of the tip of the curette is:

[0048]

[0049] Then, the lesion edge point set (i.e., the planned path) is planned according to the bone tumor position coordinates, and the operating edge point set of the movement of the scraper tip coordinates during the bone tumor scraping process is monitored in real time. Finally, when the operating edge point set coincides with the lesion edge point set, it is determined that the bone tumor scraping is completed.

[0050] Furthermore, let the bone tumor position coordinate planning lesion edge point set be U1, and the operation edge point set of the curette tip coordinate movement be U2. Real-time monitoring and calculation of U Δ , where U Δ =U1-U2, and display U Δ The mesh model prompts the surgeon to reach the unoperated area and operate until U Δ Completes the operation when the set is empty.

[0051] In summary, the present invention can avoid repeated CT scanning for bone tumor positioning, reduce the patient's radiation dose, and significantly improve the accuracy of bone tumor scraping, thereby reducing bone tumor residues.

[0052] For the bone tumor lesion scraping auxiliary method provided in the above embodiment, the present invention provides a bone tumor lesion scraping auxiliary device, see Figure 3 The schematic diagram of the structure of a bone tumor lesion scraping auxiliary device is shown, and the device includes the following parts:

[0053] The model building module 302 performs image processing and surface reconstruction on the patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is formed by connecting three vertices corresponding to each triangular face in a triangular face set;

[0054] The feature edge extraction module 304 performs feature edge extraction processing on the three-dimensional model of the bone tumor by using the angles between the various triangular facets to determine a target feature edge set and a target non-feature edge set;

[0055] The bone tumor positioning module 306 performs contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set, determines the target bone tumor three-dimensional model, and determines the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as the bone tumor position coordinates;

[0056] The scraping auxiliary module 308 obtains the coordinates of the tip of the surgical scraper, and performs path planning and path monitoring for bone tumor scraping according to the coordinates of the tip of the scraper and the coordinates of the bone tumor position, so that the scraper can accurately reach the bone tumor position.

[0057] The above-mentioned bone tumor lesion scraping auxiliary device provided in the embodiment of the present application can avoid repeated CT scanning for bone tumor positioning, reduce the patient's radiation dose, and significantly improve the accuracy of bone tumor scraping, thereby reducing bone tumor residues.

[0058] In one embodiment, when performing image processing and surface reconstruction processing on the patient's bone tumor CT image to determine the three-dimensional model of the bone tumor, the above-mentioned model construction module 302 is also used to: perform image preprocessing on the patient's bone tumor CT image to determine the primary image data, wherein the image preprocessing includes: denoising processing, correction processing and contrast enhancement processing; perform image segmentation processing on the primary image data through a preset threshold segmentation model and a regional growth model to extract the bone tumor structure from the primary image data and determine the secondary image data; extract the isosurface from the secondary image data through a preset surface reconstruction algorithm, and use the isosurface to generate the three-dimensional model of the bone tumor.

[0059] In one embodiment, when performing feature edge extraction processing on the three-dimensional model of bone tumor using the angles between various triangular facets to determine the step of target feature edge set and target non-feature edge set, the feature edge extraction module 304 is further used to: obtain the vertex coordinates corresponding to each triangular facet, and calculate the normal vector corresponding to each triangular facet according to the vertex coordinates; respectively calculate the normal vector angles between each adjacent triangular facet, and determine the normal vector angle as the dihedral angle corresponding to the common edge between two adjacent triangular facets; and respectively perform a first feature extraction on each dihedral angle using a preset angle threshold. The feature edge extraction process is performed to determine the feature edge type to which the common edge corresponding to the dihedral angle belongs, wherein the feature edge types include: a first explicit feature edge, an implicit feature edge and a first non-feature edge; a second feature edge extraction process is performed on the implicit feature edge using the edge perimeter information of the two triangular facets corresponding to the implicit feature edge to determine the secondary classification result of the implicit feature edge, wherein the secondary classification result includes: a second explicit feature edge and a second non-feature edge; each item of the first explicit feature edge and the second explicit feature edge is determined as a target feature edge set, and each item of the first non-feature edge and the second non-feature edge is determined as a target non-feature edge set.

[0060] In one embodiment, the preset angle threshold includes: an explicit feature edge threshold and an implicit feature edge threshold. When using the preset angle threshold to perform the first feature edge extraction processing on each dihedral angle respectively, and determining the feature edge type to which the common edge corresponding to the dihedral angle belongs, the above-mentioned feature edge extraction module 304 is also used to: when the dihedral angle is not less than the explicit feature edge threshold and is less than 180 degrees, determine the common edge corresponding to the dihedral angle as the first explicit feature edge; when the dihedral angle is greater than the implicit feature edge threshold and is less than the explicit feature edge threshold, determine the common edge corresponding to the dihedral angle as the implicit feature edge; when the dihedral angle is greater than 0 degrees and not greater than the implicit feature edge threshold, determine the common edge corresponding to the dihedral angle as the first non-feature edge.

[0061] In one embodiment, when performing the step of performing a second feature edge extraction process on the implicit feature edge using the side perimeter information of two triangular facets corresponding to the implicit feature edge to determine the secondary classification result of the implicit feature edge, the feature edge extraction module 304 is also used to: obtain the first side perimeter and the second side perimeter of the two triangular facets corresponding to the implicit feature edge, wherein the first side perimeter is smaller than the second side perimeter; compare the ratio of the second side perimeter to the first side perimeter with a preset side perimeter threshold to determine the side perimeter ratio; when the side perimeter ratio is greater than the preset side perimeter threshold, determine the implicit feature edge as the second explicit feature edge; when the side perimeter ratio is not greater than the preset side perimeter threshold, determine the implicit feature edge as the second non-feature edge.

[0062] In one embodiment, when performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set to determine the target bone tumor three-dimensional model, the above-mentioned bone tumor positioning module 306 is also used to: merge the two triangular facets corresponding to each target non-feature edge in the target non-feature edge set to enhance the contour of the bone tumor three-dimensional model and determine the target bone tumor three-dimensional model.

[0063] In one embodiment, when obtaining the scraper tip coordinates of a surgical scraper and performing path planning and path monitoring for bone tumor scraping based on the scraper tip coordinates and the bone tumor position coordinates, the scraping auxiliary module 308 is also used to: obtain the three-dimensional coordinate matrix of the bracket of the positioning bracket at the end of the scraper and the deflection matrix from the end of the scraper to the scraper tip; determine the product of the three-dimensional coordinate matrix of the bracket and the deflection matrix as the scraper tip coordinates; plan the lesion edge point set based on the bone tumor position coordinates, and monitor the operating edge point set of the scraper tip coordinate movement during the bone tumor scraping process in real time; when the operating edge point set coincides with the lesion edge point set, it is determined that the bone tumor scraping is completed.

[0064] The device provided in the embodiment of the present invention has the same implementation principle and technical effects as those of the aforementioned method embodiment. For the sake of brief description, for matters not mentioned in the device embodiment, reference may be made to the corresponding contents in the aforementioned method embodiment.

[0065] An embodiment of the present invention provides an electronic device. Specifically, the electronic device includes a processor and a storage device. The storage device stores a computer program, and when the computer program is executed by the processor, it executes the method described in any one of the above-mentioned implementation methods.

[0066] Figure 4 A structural diagram of an electronic device provided in an embodiment of the present invention, the electronic device 100 includes: a processor 40, a memory 41, a bus 42 and a communication interface 43, wherein the processor 40, the communication interface 43 and the memory 41 are connected via the bus 42; the processor 40 is used to execute an executable module stored in the memory 41, such as a computer program.

[0067] The memory 41 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 43 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used.

[0068] The bus 42 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0069] Among them, the memory 41 is used to store programs, and the processor 40 executes the program after receiving the execution instruction. The method executed by the device for flow process definition disclosed in any embodiment of the above-mentioned embodiment of the present invention can be applied to the processor 40 or implemented by the processor 40.

[0070] The processor 40 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 40. The above processor 40 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly embodied as a hardware decoding processor to execute, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 41, and the processor 40 reads the information in the memory 41 and completes the steps of the above method in combination with its hardware.

[0071] The computer program product of the readable storage medium provided in the embodiment of the present invention includes a computer-readable storage medium storing program code, and the instructions included in the program code can be used to execute the methods described in the previous method embodiments. The specific implementation can be referred to the previous method embodiments, which will not be repeated here.

[0072] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, electronic device, or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0073] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-described embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-described embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for auxiliary scraping of bone tumor lesions, characterized in that: The method comprises: Performing image processing and surface reconstruction on the patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is composed of three vertices corresponding to each triangular facet in a triangular facet set; Using the angles between the triangular facets, the three-dimensional model of the bone tumor is subjected to feature edge extraction processing to determine a target feature edge set and a target non-feature edge set; Performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set to determine the target bone tumor three-dimensional model, and determining the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as the bone tumor position coordinates; The coordinates of the tip of the surgical curette are obtained, and path planning and path monitoring for bone tumor scraping are performed according to the coordinates of the tip of the curette and the coordinates of the bone tumor position, so that the curette accurately reaches the bone tumor position.

2. The method for auxiliary scraping of bone tumor lesions according to claim 1, characterized in that: The step of performing image processing and surface reconstruction processing on the patient's bone tumor CT image to determine the three-dimensional model of the bone tumor includes: Performing image preprocessing on the bone tumor CT image of the patient to determine primary image data, wherein the image preprocessing includes: noise removal processing, correction processing and contrast enhancement processing; Performing image segmentation processing on the primary image data by using a preset threshold segmentation model and a region growing model to extract the bone tumor structure from the primary image data and determine the secondary image data; An isosurface is extracted from the secondary image data by a preset surface reconstruction algorithm, and the three-dimensional model of the bone tumor is generated using the isosurface.

3. The method for auxiliary scraping of bone tumor lesions according to claim 1, characterized in that: The step of performing feature edge extraction processing on the three-dimensional model of the bone tumor by using the angles between the triangular facets to determine a target feature edge set and a target non-feature edge set comprises: Obtaining the vertex coordinates corresponding to each of the triangular face patches, and calculating the normal vector corresponding to each of the triangular face patches according to the vertex coordinates; Calculating respectively the normal vector angles between each adjacent triangular facet, and determining the normal vector angle as the dihedral angle corresponding to the common edge between two adjacent triangular facets; Using a preset angle threshold, performing first characteristic edge extraction processing on each of the dihedral angles, respectively, to determine the characteristic edge type to which the common edge corresponding to the dihedral angle belongs, wherein the characteristic edge type includes: a first explicit characteristic edge, an implicit characteristic edge, and a first non-characteristic edge; Using the perimeter information of the two triangular facets corresponding to the implicit feature edge, performing a second feature edge extraction process on the implicit feature edge to determine a secondary classification result of the implicit feature edge, wherein the secondary classification result includes: a second explicit feature edge and a second non-feature edge; Each of the first dominant feature edges and the second dominant feature edges is determined as the target feature edge set, and each of the first non-feature edges and the second non-feature edges is determined as the target non-feature edge set.

4. The method for auxiliary scraping of bone tumor lesions according to claim 3, characterized in that: The preset angle threshold includes: an explicit feature edge threshold and an implicit feature edge threshold. The step of using the preset angle threshold to perform first feature edge extraction processing on each dihedral angle to determine the feature edge type to which the common edge corresponding to the dihedral angle belongs includes: When the dihedral angle is not less than the dominant feature edge threshold and is less than 180 degrees, determining the common edge corresponding to the dihedral angle as the first dominant feature edge; When the dihedral angle is greater than the implicit feature edge threshold and less than the explicit feature edge threshold, the common edge corresponding to the dihedral angle is determined as the implicit feature edge; When the dihedral angle is greater than 0 degrees and not greater than the implicit feature edge threshold, the common edge corresponding to the dihedral angle is determined as the first non-feature edge.

5. The method for auxiliary scraping of bone tumor lesions according to claim 3, characterized in that: The step of performing a second feature edge extraction process on the implicit feature edge by using the edge perimeter information of the two triangular facets corresponding to the implicit feature edge to determine the secondary classification result of the implicit feature edge comprises: Obtaining a first side perimeter and a second side perimeter of two triangular facets corresponding to the implicit feature edge, wherein the first side perimeter is smaller than the second side perimeter; Comparing the ratio of the second side perimeter to the first side perimeter with a preset side perimeter threshold to determine a side perimeter ratio; When the edge perimeter ratio is greater than a preset edge perimeter threshold, determining the implicit feature edge as the second explicit feature edge; When the edge perimeter ratio is not greater than a preset edge perimeter threshold, the implicit feature edge is determined to be the second non-feature edge.

6. The method for auxiliary scraping of bone tumor lesions according to claim 1, characterized in that: The step of performing contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set to determine the target bone tumor three-dimensional model includes: The two triangular facets corresponding to each target non-feature edge in the target non-feature edge set are merged to enhance the contour of the bone tumor three-dimensional model and determine the target bone tumor three-dimensional model.

7. The method for auxiliary scraping of bone tumor lesions according to claim 1, characterized in that: The step of obtaining the coordinates of the tip of the surgical curette and performing path planning and path monitoring for bone tumor scraping according to the coordinates of the tip of the surgical curette and the coordinates of the bone tumor position includes: Acquire a three-dimensional coordinate matrix of a positioning bracket at the end of the curette and a deflection matrix from the end of the curette to the tip of the curette; The product of the three-dimensional coordinate matrix of the bracket and the deflection matrix is ​​determined as the coordinate of the tip of the curette; Planning the lesion edge point set according to the bone tumor position coordinates, and monitoring the operation edge point set of the movement of the scraper tip coordinates during the bone tumor scraping process in real time; When the operation edge point set coincides with the lesion edge point set, it is determined that the bone tumor scraping is completed.

8. A bone tumor lesion scraping auxiliary device, characterized in that: The device comprises: A model building module performs image processing and surface reconstruction on the patient's bone tumor CT image to determine a three-dimensional model of the bone tumor, wherein the three-dimensional model of the bone tumor is a three-dimensional mesh model, and the three-dimensional mesh model is composed of three vertices corresponding to each triangular facet in a triangular facet set; A feature edge extraction module performs feature edge extraction processing on the three-dimensional model of the bone tumor by using the angles between the triangular facets to determine a target feature edge set and a target non-feature edge set; A bone tumor positioning module performs contour enhancement processing on the bone tumor three-dimensional model based on the target non-feature edge set, determines the target bone tumor three-dimensional model, and determines the coordinates corresponding to the target feature edge set in the target bone tumor three-dimensional model as the bone tumor position coordinates; The scraping auxiliary module obtains the scraper tip coordinates of the surgical scraper, and performs path planning and path monitoring for bone tumor scraping according to the scraper tip coordinates and the bone tumor position coordinates, so that the scraper can accurately reach the bone tumor position.

9. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the method according to any one of claims 1 to 7.

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