Method, device and equipment for automatic registration point sequence recognition during orthopedic surgical robot surgery

Abstract

This application provides a method, apparatus, device, and computer-readable storage medium for automatic registration point sequence recognition during orthopedic surgical robots. The method includes: acquiring an X-ray image from a two-dimensional calibrator; dividing nine small balls into four groups based on their positions using a clustering algorithm, and determining the center point A of each group and the center point A of the nine small balls; after determining the group containing three small balls, calculating the distance between each of the three small balls and the center point A, and determining the sequence number of these three small balls based on the distance; dividing the X-ray image into four quadrants based on the coordinates of the center point A, and determining the quadrant to which the group containing the three small balls belongs; calculating the relative positional relationship between the other small balls and the group containing the three small balls, grouping them, and determining their respective quadrants; calculating the distance between each small ball in each group and the center point A, and determining the sequence number within the group based on the distance. According to the embodiments of this application, registration point sequence recognition can be performed quickly and accurately.

Description

Technical Field

[0001] This application belongs to the field of point sequence recognition, and in particular relates to a method, device, equipment and computer-readable storage medium for automatic registration of point sequence recognition during orthopedic surgical robots. Background Technology

[0002] In traditional spinal surgery, registration involves the surgeon manually marking key anatomical points, such as vertebral bodies, pedicles, and laminae, on X-ray images and using rulers or measuring tools to determine the relative positions and distances of these points. Surgeons rely on anatomical landmarks, such as the edges of vertebral bodies, pedicles, and laminae, to identify and locate the structures of the spine.

[0003] However, manual marking by doctors, using rulers or measuring tools, can easily lead to low efficiency and accuracy.

[0004] Therefore, how to perform registration point sequence identification quickly and accurately is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] This application provides a method, apparatus, device, and computer-readable storage medium for automatic registration point sequence recognition during orthopedic surgical robots, which can perform registration point sequence recognition quickly and accurately.

[0006] In a first aspect, embodiments of this application provide a method for automatic registration point sequence recognition during orthopedic surgical robot surgery, including:

[0007] Acquire X-ray images of a two-dimensional calibrator; the two-dimensional calibrator is equipped with nine small steel balls;

[0008] Using a clustering algorithm, the nine balls are divided into four groups according to their positions, and the center point of each group and the center point A of the nine balls are determined.

[0009] After determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance.

[0010] Based on the coordinates of the center point A, the X-ray image is divided into 4 quadrants, and the quadrant containing the 3 small balls is determined.

[0011] Calculate the relative positions of the other balls to the group containing 3 balls, group them, and determine their respective quadrants;

[0012] Calculate the distance between each ball in each group and the center point A, and determine the group number based on the distance.

[0013] Optionally, using a clustering algorithm, the 9 balls are divided into 4 groups according to their positions, and the center point of each group and the center point A of the 9 balls are determined, including:

[0014] Obtain the position data of the 9 balls;

[0015] After determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls.

[0016] Optionally, after pre-determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls, including:

[0017] Initialize the four center points of the four groups;

[0018] For each ball, calculate its Euclidean distance to each center point and assign the ball to the group containing the nearest center point;

[0019] Once all the balls have been assigned to the nearest center point, calculate the average position of all the balls in each group, and this average position will become the new center point.

[0020] Repeat the above steps until the position of the center point no longer changes, or the preset number of iterations is reached;

[0021] Once the algorithm converges, the new center point of each group becomes the final center point of that group.

[0022] After obtaining the center point of each group, calculate the average position of all 9 balls. This position is the center point A of all the balls.

[0023] Optionally, after determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance, including:

[0024] Calculate the Euclidean distances between each of the three balls and the center point A;

[0025] Based on the distances between the three Euclidean distances, the three balls are numbered as follows: number 4, number 6, and number 7.

[0026] Optionally, based on the coordinates of the center point A, the X-ray image is divided into four quadrants, and the quadrant containing the three small balls is determined, including:

[0027] Determine the quadrant to which ball number 4 belongs based on its position coordinates.

[0028] Optionally, calculate the relative positional relationships between the other balls and the group containing 3 balls, group them, and determine their respective quadrants, including:

[0029] Calculate the relative positional relationship between the other balls and ball number 7, group them, and determine their respective quadrants.

[0030] Optionally, calculate the distance between each ball in each group and the center point A, and determine the group number based on the distance, including:

[0031] Calculate the Euclidean distance between each ball in each group and the center point A;

[0032] Based on the distance between the two Euclidean distances within each group, determine the serial numbers of the two balls in each group: serial number 0 and serial number 2; serial number 1 and serial number 3; serial number 5 and serial number 8.

[0033] Secondly, embodiments of this application provide an automatic registration point sequence recognition device for orthopedic surgical robots during surgery, comprising:

[0034] The image acquisition module is used to acquire X-ray images of the two-dimensional calibrator; the two-dimensional calibrator is equipped with 9 small steel balls;

[0035] The clustering algorithm module is used to divide the 9 balls into 4 groups according to their positions using a clustering algorithm, and to determine the center point of each group and the center point A of the 9 balls;

[0036] The sequence number determination module is used to calculate the distance between each of the three balls and the center point A after determining the group containing the three balls, and determine the sequence number of the three balls based on the distance; based on the coordinates of the center point A, the X-ray image is divided into four quadrants, and the quadrant to which the group containing the three balls belongs is determined; the relative positional relationship between the other balls and the group containing the three balls is calculated, and they are grouped and their respective quadrants are determined; the distance between each ball in each group and the center point A is calculated, and the sequence number within the group is determined based on the distance.

[0037] Thirdly, embodiments of this application provide an electronic device, which includes: a processor and a memory storing computer program instructions;

[0038] When the processor executes the computer program instructions, it implements an automatic registration point sequence recognition method for orthopedic surgical robots during surgery.

[0039] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement an automatic registration point sequence recognition method for orthopedic surgical robots during surgery.

[0040] The method, apparatus, device, and computer-readable storage medium for automatic registration point sequence recognition during orthopedic surgical robot surgery according to the embodiments of this application can perform registration point sequence recognition quickly and accurately.

[0041] The method for automatic registration point sequence recognition during surgery of this orthopedic surgical robot includes:

[0042] Acquire X-ray images of a two-dimensional calibrator; the two-dimensional calibrator is equipped with 9 small balls: 7 steel balls and 2 aluminum balls;

[0043] Using a clustering algorithm, the nine balls are divided into four groups according to their positions, and the center point of each group and the center point A of the nine balls are determined.

[0044] After determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance.

[0045] Based on the density measurement and the distance from the center point A, determine the serial number of the two aluminum balls;

[0046] Based on the serial numbers of the two aluminum balls, determine the serial numbers of the other two balls in the same group as the two aluminum balls;

[0047] Calculate the distances between the last two balls and the center point A, and determine the sequence number of the last two balls based on the distances. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a flowchart illustrating an embodiment of an orthopedic surgical robot's automatic registration point sequence recognition method during surgery.

[0050] Figure 2 This is a schematic diagram of the automatic registration point sequence recognition result provided in one embodiment of this application;

[0051] Figure 3 This is a schematic diagram of the structure of an intraoperative automatic registration point sequence recognition device for an orthopedic surgical robot provided in one embodiment of this application;

[0052] Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0053] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0054] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0055] To address the problems of the prior art, embodiments of this application provide a method, apparatus, device, and computer-readable storage medium for automatic intraoperative registration point sequence recognition of orthopedic surgical robots. The method for automatic intraoperative registration point sequence recognition of orthopedic surgical robots provided in this application embodiment is described below.

[0056] Figure 1 This illustration shows a flowchart of an automatic registration point sequence recognition method for orthopedic surgical robots provided in one embodiment of this application. Figure 1 As shown, the method for automatic registration point sequence recognition during orthopedic surgical robot operation includes:

[0057] S101. Acquire the X-ray image of the two-dimensional calibrator; wherein, the two-dimensional calibrator is equipped with 9 small steel balls;

[0058] S102. Using a clustering algorithm, the 9 balls are divided into 4 groups according to their positions, and the center point of each group and the center point A of the 9 balls are determined.

[0059] S103. After determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance.

[0060] S104. Based on the coordinates of the center point A, divide the X-ray image into 4 quadrants and determine the quadrant containing the 3 small balls.

[0061] S105. Calculate the relative positional relationship between the other balls and the group containing 3 balls, group them, and determine their respective quadrants;

[0062] S106. Calculate the distance between each ball in each group and the center point A, and determine the group number based on the distance.

[0063] Figure 2 This is a schematic diagram of the automatic registration point sequence recognition result provided in one embodiment of this application.

[0064] In one embodiment, a clustering algorithm is used to divide the nine balls into four groups according to their positions, and the center point of each group and the center point A of the nine balls are determined, including:

[0065] Obtain the position data of the 9 balls;

[0066] After determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls.

[0067] In one embodiment, after pre-determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls, including:

[0068] Initialize the four center points of the four groups;

[0069] For each ball, calculate its Euclidean distance to each center point and assign the ball to the group containing the nearest center point;

[0070] Once all the balls have been assigned to the nearest center point, calculate the average position of all the balls in each group, and this average position will become the new center point.

[0071] Repeat the above steps until the position of the center point no longer changes, or the preset number of iterations is reached;

[0072] Once the algorithm converges, the new center point of each group becomes the final center point of that group.

[0073] After obtaining the center point of each group, calculate the average position of all 9 balls. This position is the center point A of all the balls.

[0074] In one embodiment, after determining the group containing 3 balls, the distances between each of the 3 balls and the center point A are calculated, and the sequence number of the 3 balls is determined based on the distance, including:

[0075] Calculate the Euclidean distances between each of the three balls and the center point A;

[0076] Based on the distances between the three Euclidean distances, the three balls are numbered as follows: number 4, number 6, and number 7.

[0077] In one embodiment, based on the coordinates of the center point A, the X-ray image is divided into four quadrants, and the quadrant containing the three spheres is determined, including:

[0078] Determine the quadrant to which ball number 4 belongs based on its position coordinates.

[0079] In one embodiment, calculating the relative positional relationship between the other balls and the group containing 3 balls, grouping them, and determining their respective quadrants includes:

[0080] Calculate the relative positional relationship between the other balls and ball number 7, group them, and determine their respective quadrants.

[0081] In one embodiment, the distance between each ball in each group and the center point A is calculated, and the group number is determined based on the distance, including:

[0082] Calculate the Euclidean distance between each ball in each group and the center point A;

[0083] Based on the distance between the two Euclidean distances within each group, determine the serial numbers of the two balls in each group: serial number 0 and serial number 2; serial number 1 and serial number 3; serial number 5 and serial number 8.

[0084] Figure 3 This is a schematic diagram of the structure of an intraoperative automatic registration point sequence recognition device for an orthopedic surgical robot according to an embodiment of this application; the intraoperative automatic registration point sequence recognition device for the orthopedic surgical robot includes:

[0085] The image acquisition module 301 is used to acquire the X-ray image of the two-dimensional calibrator; wherein, the two-dimensional calibrator is provided with 9 small steel balls;

[0086] Clustering algorithm module 302 is used to divide the 9 balls into 4 groups according to their positions using a clustering algorithm, and to determine the center point of each group and the center point A of the 9 balls;

[0087] The sequence number determination module 303 is used to, after determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance; divide the X-ray image into 4 quadrants based on the coordinates of the center point A, and determine the quadrant to which the group containing 3 balls belongs; calculate the relative positional relationship between the other balls and the group containing 3 balls, group them, and determine their respective quadrants; calculate the distance between each ball in each group and the center point A, and determine the sequence number within the group based on the distance.

[0088] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application is shown.

[0089] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0090] Specifically, the processor 401 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0091] Memory 402 may include mass storage for data or instructions. For example, and not limitingly, memory 402 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, memory 402 may include removable or non-removable (or fixed) media. Where suitable, memory 402 may be internal or external to an electronic device. In a particular embodiment, memory 402 may be a non-volatile solid-state memory.

[0092] In one embodiment, memory 402 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0093] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any of the intraoperative automatic registration point sequence recognition methods for orthopedic surgical robots in the above embodiments.

[0094] In one example, the electronic device may also include a communication interface 403 and a bus 410. For example, Figure 4 As shown, the processor 401, memory 402, and communication interface 403 are connected through bus 410 and complete communication with each other.

[0095] The communication interface 403 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0096] Bus 410 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 410 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0097] Furthermore, in conjunction with the intraoperative automatic registration point sequence recognition method for orthopedic surgical robots in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when executed by a processor, these computer program instructions implement any of the intraoperative automatic registration point sequence recognition methods for orthopedic surgical robots in the above embodiments.

[0098] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0099] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0100] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0101] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0102] The above description is only a specific embodiment of the present application. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A method for automatic registration point sequence recognition during orthopedic surgical robot surgery, characterized in that, include: Acquire X-ray images of a two-dimensional calibrator; the two-dimensional calibrator is equipped with nine small steel balls; Using a clustering algorithm, the nine balls are divided into four groups according to their positions, and the center point of each group and the center point A of the nine balls are determined. After determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A, and determine the sequence number of the 3 balls based on the distance. Based on the coordinates of the center point A, the X-ray image is divided into 4 quadrants, and the quadrant containing the 3 small balls is determined. Calculate the relative positions of the other balls to the group containing 3 balls, group them, and determine their respective quadrants; Calculate the distance between each ball in each group and the center point A, and determine the group number based on the distance.

2. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 1, characterized in that, Using a clustering algorithm, the 9 balls are divided into 4 groups according to their positions, and the center point of each group and the center point A of the 9 balls are determined, including: Obtain the position data of the 9 balls; After determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls.

3. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 2, characterized in that, After pre-determining that the balls will be divided into 4 groups, the K-means algorithm is used to determine the center point of each group and the center point A of the 9 balls, including: Initialize the four center points of the four groups; For each ball, calculate its Euclidean distance to each center point and assign the ball to the group containing the nearest center point; Once all the balls have been assigned to the nearest center point, calculate the average position of all the balls in each group, and this average position will become the new center point. Repeat the above steps until the position of the center point no longer changes, or the preset number of iterations is reached; Once the algorithm converges, the new center point of each group becomes the final center point of that group. After obtaining the center point of each group, calculate the average position of all 9 balls. This position is the center point A of all the balls.

4. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 3, characterized in that, After determining the group containing 3 balls, calculate the distance between each of the 3 balls and the center point A. Determine the sequence number of the 3 balls based on the distance, including: Calculate the Euclidean distances between each of the three balls and the center point A; Based on the distances between the three Euclidean distances, the three balls are numbered as follows: number 4, number 6, and number 7.

5. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 4, characterized in that, Based on the coordinates of the center point A, the X-ray image is divided into 4 quadrants, and the quadrant containing the 3 small balls is determined, including: Determine the quadrant to which ball number 4 belongs based on its position coordinates.

6. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 5, characterized in that, Calculate the relative positions of the other balls to the group containing 3 balls, group them, and determine their respective quadrants, including: Calculate the relative positional relationship between the other balls and ball number 7, group them, and determine their respective quadrants.

7. The method for automatic registration point sequence recognition during orthopedic surgical robot operation according to claim 6, characterized in that, Calculate the distance between each ball in each group and the center point A, and determine the group number based on the distance, including: Calculate the Euclidean distance between each ball in each group and the center point A; Based on the distance between the two Euclidean distances within each group, determine the serial numbers of the two balls in each group: serial number 0 and serial number 2; serial number 1 and serial number 3; serial number 5 and serial number 8.

8. An automatic registration point sequence recognition device for orthopedic surgical robots during surgery, characterized in that, The device includes: The image acquisition module is used to acquire X-ray images of the two-dimensional calibrator; the two-dimensional calibrator is equipped with 9 small steel balls; The clustering algorithm module is used to divide the 9 balls into 4 groups according to their positions using a clustering algorithm, and to determine the center point of each group and the center point A of the 9 balls; The sequence number determination module is used to calculate the distance between each of the three balls and the center point A after determining the group containing the three balls, and determine the sequence number of the three balls based on the distance; based on the coordinates of the center point A, the X-ray image is divided into four quadrants, and the quadrant to which the group containing the three balls belongs is determined; the relative positional relationship between the other balls and the group containing the three balls is calculated, and they are grouped and their respective quadrants are determined; the distance between each ball in each group and the center point A is calculated, and the sequence number within the group is determined based on the distance.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the intraoperative automatic registration point sequence recognition method for orthopedic surgical robots as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the intraoperative automatic registration point sequence recognition method for orthopedic surgical robots as described in any one of claims 1-7.

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