Automatic registration point sequence identification method, device and equipment in spinal operation

Abstract

The invention provides an automatic registration point sequence recognition method, device and equipment in a spinal operation and a computer readable storage medium. The method comprises the following steps: acquiring an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with nine small balls, namely seven steel balls and two aluminum balls; dividing the nine small balls into four groups according to positions through a clustering algorithm, and determining a central point of each group and a central point A of the nine small balls; after the group containing the three small balls is determined, the distances between the three small balls and the center point A are calculated respectively, and the serial numbers of the three small balls are determined according to the distances; determining the serial numbers of the two aluminum balls according to the density measurement and the distance between the aluminum balls and the central point A; according to the serial numbers of the two aluminum balls, the serial numbers of the other two small balls in the same group with the two aluminum balls are determined respectively; respectively calculating the distance between the last two small balls and the central point A, and determining the serial number of the last two small balls according to the distance. According to the embodiment of the invention, registration point sequence identification can be carried out quickly and accurately.

Description

Technical Field

[0001] The present 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 point sequence recognition during spinal surgery. Background Art

[0002] Traditional spinal intraoperative registration is that doctors manually mark key anatomical points on X-ray images, such as vertebral bodies, pedicles, and lamina, and use rulers or measuring tools to determine the relative positions and distances of these points. Doctors rely on anatomical landmarks, such as the edges of vertebral bodies, pedicles, and lamina, to identify and locate spinal structures.

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

[0004] Therefore, how to quickly and accurately identify the registration point sequence is a technical problem that those skilled in the art need to solve urgently. Summary of the invention

[0005] The embodiments of the present application provide a method, device, equipment and computer-readable storage medium for automatic registration point sequence recognition during spinal surgery, which can quickly and accurately perform registration point sequence recognition.

[0006] In a first aspect, an embodiment of the present application provides a method for automatically recognizing a point sequence during spinal surgery, comprising:

[0007] Obtain an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls;

[0008] Through the 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;

[0009] After determining the group containing three balls, calculate the distance between the three balls and the center point A respectively, and determine the sequence numbers of the three balls according to the distances;

[0010] Determine the serial numbers of the two aluminum balls based on the density measurement and the distance from the center point A;

[0011] According to the serial numbers of the two aluminum balls, determine the serial numbers of the other two small balls in the same group as the two aluminum balls;

[0012] Calculate the distance between the last two balls and the center point A respectively, and determine the sequence numbers of the last two balls according to the distance.

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

[0014] Get the position data of 9 balls;

[0015] After determining in advance that the data 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 predetermining that the group 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 4 groups of 4 center points;

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

[0019] Once all balls have been assigned to their nearest center point, calculate the average position of all 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] When the algorithm converges, the new center point of each group is the final center point of the 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 three balls, respectively calculate the distances between the three balls and the center point A, and determine the serial numbers of the three balls according to the distances, including:

[0024] Calculate the Euclidean distance between the three balls and the center point A respectively;

[0025] According to the distance of the three Euclidean distances, the serial numbers of the three balls are determined respectively: serial number 4, serial number 6 and serial number 7.

[0026] Optionally, the serial numbers of the two aluminum balls are determined based on the density measurement and the distance from the center point A, including:

[0027] Through density measurement, it is determined that the two aluminum balls are made of aluminum alloy;

[0028] Calculate the Euclidean distance between the two aluminum balls and the center point A respectively;

[0029] According to the distance of the two Euclidean distances, the serial numbers of the two aluminum balls are determined respectively: serial number 0 and serial number 3.

[0030] Optionally, according to the serial numbers of the two aluminum balls, the serial numbers of the other two small balls in the same group as the two aluminum balls are determined respectively, including:

[0031] Determine the serial number of another small ball in the same group as the serial number 0 aluminum ball: serial number 2;

[0032] Determine the serial number of the other small ball in the same group as the serial number 3 aluminum ball: serial number 1.

[0033] Optionally, the distances between the last two balls and the center point A are calculated respectively, and the serial numbers of the last two balls are determined according to the distances, including:

[0034] Calculate the Euclidean distance between the last two balls and the center point A respectively;

[0035] According to the distance of the two Euclidean distances, the serial numbers of the last two balls are determined respectively: serial number 5 and serial number 8.

[0036] In a second aspect, an embodiment of the present application provides a device for automatically recognizing a point sequence during spinal surgery, comprising:

[0037] An image acquisition module is used to acquire an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls;

[0038] The clustering algorithm module is used to divide the 9 balls into 4 groups according to their positions through the clustering algorithm, and determine the center point of each group and the center point A of the 9 balls;

[0039] The serial number determination module is used to calculate the distance between the three small balls and the center point A respectively after determining the group containing the three small balls, and determine the serial numbers of the three small balls according to the size of the distance; determine the serial numbers of the two aluminum balls according to the density measurement and the distance between the three small balls and the center point A; determine the serial numbers of the other two small balls in the same group with the two aluminum balls according to the serial numbers of the two aluminum balls; calculate the distance between the last two small balls and the center point A respectively, and determine the serial numbers of the last two small balls according to the size of the distance.

[0040] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising: a processor and a memory storing computer program instructions;

[0041] When the processor executes the computer program instructions, a method for automatically recognizing the sequence of registration points during spinal surgery is implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a method for automatic registration point sequence recognition during spinal surgery.

[0043] The method, device, equipment and computer-readable storage medium for automatic registration point sequence recognition during spinal surgery according to the embodiments of the present application can perform registration point sequence recognition quickly and accurately.

[0044] The method for automatically registering point sequence during spinal surgery includes:

[0045] Obtain an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls;

[0046] Through the 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;

[0047] After determining the group containing three balls, calculate the distance between the three balls and the center point A respectively, and determine the sequence numbers of the three balls according to the distances;

[0048] Determine the serial numbers of the two aluminum balls based on the density measurement and the distance from the center point A;

[0049] According to the serial numbers of the two aluminum balls, determine the serial numbers of the other two small balls in the same group as the two aluminum balls;

[0050] Calculate the distance between the last two balls and the center point A respectively, and determine the sequence numbers of the last two balls according to the distance. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] 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 application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0052] Figure 1 It is a flowchart of a method for automatically recognizing point sequence during spinal surgery provided by an embodiment of the present application;

[0053] Figure 2 is a schematic diagram of automatic registration point sequence recognition results provided by an embodiment of the present application;

[0054] Figure 3 It is a structural schematic diagram of a device for automatically registering point sequence during spinal surgery provided by an embodiment of the present application;

[0055] Figure 4 It is a schematic diagram of the structure of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0056] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0057] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0058] In order to solve the problems of the prior art, the present invention provides a method, device, apparatus and computer-readable storage medium for automatically recognizing point sequence during spinal surgery. The method for automatically recognizing point sequence during spinal surgery provided by the present invention is first introduced below.

[0059] Figure 1 FIG. 1 is a flow chart of a method for automatically recognizing the sequence of registration points during spinal surgery provided by an embodiment of the present application. Figure 1 As shown, the method for automatic registration point sequence recognition during spinal surgery includes:

[0060] S101, obtaining an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls;

[0061] S102, using a clustering algorithm, dividing the 9 balls into 4 groups according to their positions, and determining the center point of each group and the center point A of the 9 balls;

[0062] S103, after determining the group containing three balls, respectively calculate the distances between the three balls and the center point A, and determine the serial numbers of the three balls according to the distances;

[0063] S104, determining the serial numbers of the two aluminum balls according to the density measurement and the distance from the center point A;

[0064] S105, according to the serial numbers of the two aluminum balls, respectively determine the serial numbers of the other two small balls in the same group as the two aluminum balls;

[0065] S106, respectively calculating the distances between the last two small balls and the center point A, and determining the serial numbers of the last two small balls according to the distances.

[0066] Figure 2 This is a schematic diagram of the automatic registration point sequence recognition result provided by an embodiment of the present application.

[0067] In one embodiment, the 9 balls are divided into 4 groups according to their positions by a clustering algorithm, and the center point of each group and the center point A of the 9 balls are determined, including:

[0068] Get the position data of 9 balls;

[0069] After determining in advance that the data 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.

[0070] In one embodiment, after it is predetermined that the data is to be divided into 4 groups, the center point of each group and the center point A of the 9 balls are determined using the K-means algorithm, including:

[0071] Initialize 4 groups of 4 center points;

[0072] For each ball, calculate its Euclidean distance to each center point and assign the ball to the group with the closest center point;

[0073] Once all balls have been assigned to their nearest center point, calculate the average position of all balls in each group, and this average position will become the new center point;

[0074] Repeat the above steps until the position of the center point no longer changes or the preset number of iterations is reached;

[0075] When the algorithm converges, the new center point of each group is the final center point of the group;

[0076] 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.

[0077] In one embodiment, after determining the group containing three balls, respectively calculating the distances between the three balls and the center point A, and determining the serial numbers of the three balls according to the distances, including:

[0078] Calculate the Euclidean distance between the three balls and the center point A respectively;

[0079] According to the distance of the three Euclidean distances, the serial numbers of the three balls are determined respectively: serial number 4, serial number 6 and serial number 7.

[0080] In one embodiment, the serial numbers of the two aluminum balls are determined based on the density measurement and the distance from the center point A, including:

[0081] Through density measurement, it is determined that the two aluminum balls are made of aluminum alloy;

[0082] Calculate the Euclidean distance between the two aluminum balls and the center point A respectively;

[0083] According to the distance of the two Euclidean distances, the serial numbers of the two aluminum balls are determined respectively: serial number 0 and serial number 3.

[0084] In one embodiment, according to the serial numbers of the two aluminum balls, the serial numbers of the other two small balls in the same group as the two aluminum balls are determined respectively, including:

[0085] Determine the serial number of another small ball in the same group as the serial number 0 aluminum ball: serial number 2;

[0086] Determine the serial number of the other small ball in the same group as the serial number 3 aluminum ball: serial number 1.

[0087] In one embodiment, the distances between the last two balls and the center point A are calculated respectively, and the serial numbers of the last two balls are determined according to the distances, including:

[0088] Calculate the Euclidean distance between the last two balls and the center point A respectively;

[0089] According to the distance of the two Euclidean distances, the serial numbers of the last two balls are determined respectively: serial number 5 and serial number 8.

[0090] Figure 3 : is a schematic diagram of a structure of a device for automatically registering points during spinal surgery provided by an embodiment of the present application; the device for automatically registering points during spinal surgery comprises:

[0091] The image acquisition module 301 is used to acquire an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls;

[0092] The clustering algorithm module 302 is used to divide the 9 balls into 4 groups according to their positions by using a clustering algorithm, and determine the center point of each group and the center point A of the 9 balls;

[0093] The serial number determination module 303 is used to calculate the distance between the three small balls and the center point A respectively after determining the group containing the three small balls, and determine the serial numbers of the three small balls according to the size of the distance; determine the serial numbers of the two aluminum balls according to the density measurement and the distance between the three small balls and the center point A; determine the serial numbers of the other two small balls in the same group with the two aluminum balls according to the serial numbers of the two aluminum balls; calculate the distance between the last two small balls and the center point A respectively, and determine the serial numbers of the last two small balls according to the size of the distance.

[0094] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown.

[0095] The electronic device may include a processor 401 and a memory 402 storing computer program instructions.

[0096] Specifically, the processor 401 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0097] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, memory 402 may include a removable or non-removable (or fixed) medium. In appropriate cases, memory 402 may be inside or outside of an electronic device. In a particular embodiment, memory 402 may be a non-volatile solid-state memory.

[0098] In one embodiment, the memory 402 may be a 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 a flash memory, or a combination of two or more of these.

[0099] The processor 401 reads and executes the computer program instructions stored in the memory 402 to implement any one of the methods for automatically recognizing the point sequence during spinal surgery in the above embodiments.

[0100] In one example, the electronic device may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401, the memory 402, and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0101] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0102] Bus 410 includes hardware, software or both, and the parts of electronic equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0103] In addition, in combination with the method for automatically registering points during spinal surgery in the above embodiments, the present application embodiment can provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any one of the methods for automatically registering points during spinal surgery in the above embodiments is implemented.

[0104] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present application.

[0105] 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, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. Programs or code segments can be stored in machine-readable media, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable media" 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, optical fiber media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0106] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. 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 embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0107] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (system) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.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 can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0108] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity 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 protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for automatic registration point sequence recognition during spinal surgery, characterized in that: include: Obtain an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls; Through the 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; After determining the group containing three balls, calculate the distance between the three balls and the center point A respectively, and determine the sequence numbers of the three balls according to the distances; Determine the serial numbers of the two aluminum balls based on the density measurement and the distance from the center point A; According to the serial numbers of the two aluminum balls, determine the serial numbers of the other two small balls in the same group as the two aluminum balls; Calculate the distance between the last two balls and the center point A respectively, and determine the sequence numbers of the last two balls according to the distance.

2. The method for automatic registration point sequence recognition during spinal surgery according to claim 1, characterized in that: Through the 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: Get the position data of 9 balls; After determining in advance that the data 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 spinal surgery according to claim 2, characterized in that: After predetermining that the data 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 4 groups of 4 center points; For each ball, calculate its Euclidean distance to each center point and assign the ball to the group with the closest center point; Once all balls have been assigned to their nearest center point, calculate the average position of all 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; When the algorithm converges, the new center point of each group is the final center point of the 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 spinal surgery according to claim 3, characterized in that: After determining the group containing three balls, calculate the distance between the three balls and the center point A respectively, and determine the sequence numbers of the three balls according to the distance, including: Calculate the Euclidean distance between the three balls and the center point A respectively; According to the distance of the three Euclidean distances, the serial numbers of the three balls are determined respectively: serial number 4, serial number 6 and serial number 7.

5. The method for automatic registration point sequence recognition during spinal surgery according to claim 4, characterized in that: Based on the density measurement and the distance from the center point A, determine the serial numbers of the two aluminum balls, including: Through density measurement, it is determined that the two aluminum balls are made of aluminum alloy; Calculate the Euclidean distance between the two aluminum balls and the center point A respectively; According to the distance of the two Euclidean distances, the serial numbers of the two aluminum balls are determined respectively: serial number 0 and serial number 3.

6. The method for automatic registration point sequence recognition during spinal surgery according to claim 5, characterized in that: According to the serial numbers of the two aluminum balls, the serial numbers of the other two small balls in the same group as the two aluminum balls are determined respectively, including: Determine the serial number of another small ball in the same group as the serial number 0 aluminum ball: serial number 2; Determine the serial number of the other small ball in the same group as the serial number 3 aluminum ball: serial number 1.

7. The method for automatic registration point sequence recognition during spinal surgery according to claim 6, characterized in that: Calculate the distance between the last two balls and the center point A respectively, and determine the sequence numbers of the last two balls according to the distance, including: Calculate the Euclidean distance between the last two balls and the center point A respectively; According to the distance of the two Euclidean distances, the serial numbers of the last two balls are determined respectively: serial number 5 and serial number 8.

8. A device for automatically recognizing point sequence during spinal surgery, characterized in that: The device comprises: An image acquisition module is used to acquire an X-ray image of a two-dimensional calibrator; wherein the two-dimensional calibrator is provided with 9 small balls: 7 steel balls and 2 aluminum balls; The clustering algorithm module is used to divide the 9 balls into 4 groups according to their positions through the clustering algorithm, and determine the center point of each group and the center point A of the 9 balls; The serial number determination module is used to calculate the distance between the three small balls and the center point A respectively after determining the group containing the three small balls, and determine the serial numbers of the three small balls according to the size of the distance; determine the serial numbers of the two aluminum balls according to the density measurement and the distance between the three small balls and the center point A; determine the serial numbers of the other two small balls in the same group with the two aluminum balls according to the serial numbers of the two aluminum balls; calculate the distance between the last two small balls and the center point A respectively, and determine the serial numbers of the last two small balls according to the size of the distance.

9. An electronic device, characterized in that: The electronic device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for automatic registration point sequence recognition during spinal surgery as described in any one of claims 1-7 is implemented.

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 method for automatic registration point sequence recognition during spinal surgery as described in any one of claims 1-7.

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