Automatic registration method and system for surgical navigation, electronic equipment and medium

Abstract

The invention discloses an automatic registration method and system for surgical navigation, electronic equipment and a medium, and aims to solve the problem that an automatic registration technology capable of effectively acquiring data is lacked for an operation, such as an esophageal endoscope, operated at a narrow closed part. The method comprises the following steps: generating an image center curve containing key morphological feature positions of the esophagus by using a medical image; in an intraoperative registration stage, collecting one-time complete lens entering and exiting trajectory data points, calculating the key morphological feature positions of the esophagus in the operation, performing fitting to generate an intraoperative trajectory center curve, and performing automatic registration by using two groups of curve point sets; therefore, the collection of the form of the internal organ such as the esophagus is realized, and a basis is provided for the navigation registration of the esophageal surgery.

Description

Technical Field

[0001] The present invention relates to the technical field of surgical navigation, and in particular to a surgical navigation automatic registration method, system, electronic equipment and medium. Background Art

[0002] The purpose of surgical navigation is to accurately match the patient's preoperative and intraoperative imaging data with the intraoperative anatomical structure, track the surgical instruments during surgery, and update their position in real time in the form of probes on the three-dimensional reconstructed data, so that the doctor can clearly see the position of the surgical instruments relative to the patient's anatomical structure, making the surgical operation faster, more accurate and safer. Registration is a key technology of the surgical navigation system. The accuracy and efficiency of registration largely determine the quality of the navigation system and the success or failure of the operation.

[0003] Most of the related technologies focus on navigation and registration for surgeries on areas with obvious and easily identifiable external features, such as the nose and eyes. However, for surgeries performed in narrow and closed areas, such as esophageal endoscopy, it is not convenient to extract the external features of organ tissues through endoscopic images, or to set feature markers before or during surgery. It is also difficult to use large dedicated equipment to collect data with rich geometric features. Therefore, there is still a lack of an automatic registration technology that can effectively collect data. Summary of the Invention

[0004] The purpose of the present invention is to solve the above problems and provide a surgical navigation automatic registration method, system, electronic equipment and medium to solve the problems in related technologies.

[0005] In order to solve the above problems, the present invention provides the following technical solutions: On the one hand, a surgical navigation automatic registration method, Obtain the patient's esophageal imaging data and construct a continuous spatial point set based on the center point of the esophageal cross section; Extract the starting point, end point and image feature points of the esophagus from the spatial point set and connect them to form an image center curve containing the key morphological feature positions of the esophagus; The image center curve is associated with the three-dimensional trajectory data of the esophageal endoscope's advancement and withdrawal, and a rough registration is performed to obtain the trajectory center curve; After obtaining the trajectory center curve, two refined registrations are performed to obtain the final spatial transformation matrix.

[0006] Furthermore, a surgical navigation automatic registration method also includes denoising the spatial point set before calculating the image feature points, which includes determining new points in the spatial point set by averaging the coordinates of all adjacent points in the window to construct the latest spatial point set.

[0007] Furthermore, the image feature point extraction includes determining curvature values ​​according to adjacent coordinates in the spatial point set, and arranging the curvature values ​​in descending order to obtain a curvature list; A threshold is set, and multiple spatial point subset intervals are obtained based on the curvature list and adjacent differences. The points that best match the preset esophageal empirical model are selected from the spatial point subset intervals as image feature points. Specifically, the following are included: A threshold is set and the adjacent differences of the indexes corresponding to the sorted curvature values ​​are calculated. When the adjacent differences are greater than the threshold, they are used as grouping boundaries. Multiple spatial point set subintervals are formed based on the difference values. The internal curvature of each subinterval is relatively stable and close to each other in space. The degree of conformity between the coordinate average point in each subinterval of the spatial point set and the preset esophageal empirical model is calculated, and the coordinate average point of the closest subinterval is selected as the image feature point.

[0008] Furthermore, the image center curve is associated with the three-dimensional trajectory data of the esophageal endoscope's advancement and withdrawal, and a rough registration is performed to obtain the trajectory center curve, including: The pose of the three-dimensional trajectory data including the advance and retract stages is obtained. According to the transformation relationship defined by the preset rotation matrices R1 and R2 and the translation vector T, the three-dimensional trajectory data is rotated to the image center curve coordinate system. The feature points of the trajectory data and the feature point set of the center curve are averagely translated to complete the coarse registration transformation and obtain the trajectory center curve.

[0009] Furthermore, after obtaining the trajectory center curve, two refined registrations are performed to obtain the final spatial transformation matrix including: The initial refined registration includes: obtaining the center curve of the trajectory data, and initially registering it with the image center curve based on the iterative closest point algorithm to obtain the spatial transformation relationship; Secondary refined registration involves re-segmenting the endoscopic trajectory data and performing high-precision scanning with a smaller step size to fit a more accurate image center curve. This is then initially registered with the image center curve using an iterative closest point algorithm to obtain a precise transformation relationship. When the registration is completed, the obtained spatial transformation matrix accurately maps the endoscope position acquired in real-time spatial pose to the corresponding medical image and pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery.

[0010] Furthermore, before associating the image center curve with the three-dimensional trajectory data of the esophageal endoscope, the three-dimensional trajectory data of the esophageal endoscope is also preprocessed; Preprocessing includes calculating the distance from the trajectory point to the starting point of the trajectory. When the distance of the latter point is less than the distance of the previous point, this point is used as the boundary, and the point after this point is the retreat stage, thereby dividing the trajectory point set into the advance trajectory point set and the retreat trajectory point set.

[0011] Furthermore, obtaining the pose data including the three-dimensional trajectory data of the mirror advancing and retracting stages includes The trajectory data is processed in the same way as the image center curve to obtain the trajectory starting point, curvature feature point, and trajectory end point, and the posture vector is obtained based on these three points; This pose is represented by a 3x3 rotation matrix and a 3x1 translation vector. R1 is the rotation matrix that rotates the 3D trajectory during the approach phase to the image center curve coordinate system, and R is the rotation matrix that rotates the 3D trajectory during the retreat phase to the image center curve coordinate system.

[0012] In a second aspect, an automatic registration system for esophageal surgery navigation trajectory and three-dimensional model includes: Image center curve extraction module: used to process the patient's esophageal image data to extract the image center curve; 3D trajectory data pose vector acquisition module: used to pre-process 3D trajectory data and obtain pose vectors; Coarse registration module: used to associate the image center curve with the pose vector of the 3D trajectory data, and obtain the trajectory center curve through the rotation matrix; Fine-tuning registration module: obtains the final spatial transformation matrix through fine-tuning of the trajectory center curve and the image center curve; Mapping module: Through the final spatial transformation matrix, the endoscope position acquired in real-time spatial posture is accurately mapped to the corresponding medical image and pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery.

[0013] In a third aspect, an electronic device includes a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor is characterized in that it enables the processor to perform an automatic registration method for surgical navigation.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is run on a computer, the computer is enabled to execute a surgical navigation automatic registration method.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention uses medical images to obtain a spatial point set through midline points, and calculates feature points to obtain an image center curve containing the key morphological feature positions of the esophagus.

[0016] (2) The present invention realizes the acquisition of the morphology of the internal organ such as the esophagus, providing a basis for the navigation registration of esophageal surgery; and allows certain point pairs to be lost during the acquisition process, and allows the setting of any device acquisition coordinate system, and can manually correct the specified feature point pairs and fine-tune the registration results.

[0017] (3) The present invention automatically aligns the image center curve of the key morphological feature position of the esophagus with the center curve of the three-dimensional trajectory data containing the advancement and withdrawal stages. This is the first of its kind in esophageal surgical navigation. The final spatial transformation matrix obtained in this way accurately maps the endoscope position acquired from the real-time spatial posture to the corresponding medical image and the pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery and realizing navigation of surgery in narrow and closed areas such as the esophagus. It can also be used in similar surgical navigation of other internal organs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which: Figure 1 is a flow chart of the present invention; Figure 2 It is the curve fitting diagram of the present invention; Figure 3 This is a registration effect diagram of the present invention; Figure 4 Schematic diagram of the electronic device of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the present invention clearer, the following Figures 1 to 4 The present invention is further described in detail. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] 1. Principle of the present invention

[0021] This paper proposes an automatic registration and navigation method for esophageal endoscopic surgery and medical imaging, combined with spatial pose acquisition technology (such as a magnetic navigation system). The main principle is to use medical images to generate an image center curve containing the locations of key morphological features of the esophagus. During the intraoperative registration phase, data points of a complete advancement and withdrawal trajectory are collected. The locations of key morphological features of the esophagus during the procedure are calculated, and the intraoperative trajectory center curve is fitted. Automatic registration is performed using these two sets of curve points.

[0022] This method can map the esophageal endoscope pose to medical images. For image data and endoscope trajectory acquisition, this method supports: (1) allowing certain point pairs to be lost during acquisition, (2) setting an arbitrary device acquisition coordinate system, and (3) manually correcting specified feature point pairs and fine-tuning the registration results.

[0023] 2.1 Medical Image Processing

[0024] 2.1.1 As Figure 1 As shown in the figure, using the patient's esophageal imaging (such as CT) scan data, the pixel coordinates of the esophageal area in the cross-sectional image sequence are averaged to determine the esophageal center of each cross-sectional area, and the Z-axis coordinate information is combined to construct a continuous spatial point set. , i is the slice number of the CT scan.

[0025] Determination of the esophageal center in each cross section: On the 2D CT cross section, for the mask image ,in Indicates that the pixel belongs to the esophagus area. If it does not belong to the esophagus, the center point of the esophagus The coordinate mean of all pixel points in the esophageal area can be calculated using formula (1-1) and formula (1-2): (1-1) (1-2) 2.1.2 In order to improve the data quality, the point set is subjected to sliding window average filtering to reduce the influence of noise. , consider a window For all adjacent points within , use formula (1-3) to calculate their coordinate mean: (1-3) in, p l Indicates that the sequence number in window W is l point.

[0026] 2.1.3 Based on the spatial point set, extract the image center curve and its key morphological feature locations, including the esophageal starting and ending points, and a feature point with significant curvature change located near the third esophageal stenosis. The image center curve is obtained based on the curvature value, starting and ending points, and feature points.

[0027] The selection of curvature feature points follows the following steps: (1) For a point set of the form Three consecutive points , the curvature value can be estimated using formula (1-4) : , , (1-4) in, are the coordinate vectors of the three points, There are two points P i+1 and P i The straight-line distance between them.

[0028] (2) The curvature values ​​of all points Sort and get an ordered curvature list ,in Indicates the sorted Large curvature.

[0029] (3) Set a threshold , calculate the adjacent differences of the curvature corresponding index after sorting .if , then it is believed that There is a spatial discontinuity between adjacent points of the same curvature value, which is used as the grouping boundary. According to the difference value grouping, multiple point set subintervals are formed. The internal curvature of each subinterval is relatively stable and spatially close.

[0030] (4) For each subinterval, calculate the degree of conformity between its coordinate average point and the preset empirical model (e.g., the location of the third esophageal stenosis). Select the coordinate average point of the subinterval that is closest to the empirical model as the image feature point.

[0031] 2.2 Endoscopic trajectory analysis and registration preparation

[0032] 2.2.1 Preprocess the three-dimensional trajectory data of the endoscope's advancement and retreat obtained by the spatial posture acquisition device. The data is in the form of The point set of . Distance to starting point Calculated using formula (1-5): (1-5) in, is the XYZ coordinate of the point numbered m, It is the XYZ coordinate of the point numbered m+i.

[0033] Set the threshold for consecutive decreases , if the conditions are met: , then it is believed that The mirror withdrawal phase begins, and the trajectory point set is divided accordingly.

[0034] 2.2.2 Associate the preprocessed trajectory data with the image center curve in the 3D reconstruction model and perform rough registration first: use formula (1-3) to filter valid trajectory points for the two segments of trajectory data, apply similar data smoothing methods, and use formula (1-4) to find the feature points corresponding to the two segments of trajectory through similar steps and average them.

[0035] For a set of basic feature point pairs, that is, the starting point , curvature feature points ,end , forming the pose vector as shown in formula (1-6): , , , (1-6) in, is a three-dimensional vector, is a vector length, Calculates the dot product of two vectors.

[0036] 2.2.3 In three-dimensional space, the rough transformation of the endoscope trajectory to the model centerline can be achieved by the rotation matrix , and translation vectors Define the transformation relationship. For the model pose , , and trajectory description of endoscope posture , , , Describe Rotate parallel to The transformation of Describe Rotate parallel to Transformation, defining the basic operation of rotation As shown in formula (1-7). Describes the endoscope trajectory after the first two rotation transformations The feature point set in the model to the feature point set in the center line (starting point , curvature feature points ,end and more specified corresponding points). Finally, the coarse registration transformation is calculated by formula group (1-8):

[0037] in,

[0038]

[0039]

[0040]

[0041] (1-7)

[0042]

[0043]

[0044]

[0045] (1-8) 3 Central Curve Fitting and Refined Registration

[0046] 2.3.1 In coarse registration Based on the above, the trajectory point set is transformed to develop along the -z direction, and then a step-by-step refinement strategy is used to fit the image center curve in actual operation. First, based on formula (1-3), the trajectory data is preliminarily scanned along the -z direction with a large step size to obtain a rough center curve. ; Then, the iterative closest point (ICP) algorithm, that is, formula (1-9), is used to perform the initial registration of the coarse fitting center curve with the image center curve extracted from the medical image to obtain the preliminary spatial transformation relationship .

[0047] (1-9) 2.3.2 Further refine the registration process, re-segment the endoscopic trajectory data, and perform high-precision scanning with a smaller step size, such as Figure 2 As shown, fitting a more accurate image center curve Finally, apply (1-9) again for fine registration until the preset registration accuracy is achieved, and obtain the fine registration transformation .

[0048] 2.4 Registration Completion and Application

[0049] like Figure 3 As shown, after the registration is completed, the resulting spatial transformation matrix The endoscope position acquired from the real-time spatial posture can be accurately mapped to the corresponding medical image and the pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery.

[0050] In one embodiment of the present invention, an automatic registration system for esophageal surgery navigation trajectory and three-dimensional model includes: Image center curve extraction module: used to process the patient's esophageal image data to extract the image center curve; used to perform the aforementioned 2.1 medical image processing to extract the image center curve; 3D trajectory data pose vector acquisition module: used to preprocess 3D trajectory data and obtain pose vectors; used to execute the 2.2.1 and 2.2.2 processes in the aforementioned 2.2 Endoscopic trajectory analysis and registration preparation.

[0051] Coarse registration module: used to associate the image center curve and the pose vector of the 3D trajectory data, and obtain the trajectory center curve through the rotation matrix; used to execute the 2.2.3 process in the aforementioned 2.2 Endoscopic trajectory analysis and registration preparation.

[0052] Fine-tuning registration module: obtains the final spatial transformation matrix through fine-tuning of the trajectory center curve and the image center curve; it is used to execute the aforementioned 2.3 center curve fitting and fine-tuning registration process.

[0053] Mapping module: Through the final spatial transformation matrix, the endoscope position acquired in real-time spatial posture is accurately mapped to the corresponding medical image and pre-established 3D model, thereby realizing real-time navigation of esophageal endoscopic surgery; it is used to execute the aforementioned 2.4 registration completion and application process.

[0054] In one embodiment of the present invention, Figure 4 As shown, an electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the steps of a surgical navigation automatic registration method are implemented.

[0055] The electronic device may be a desktop computer, a laptop, a PDA, a cloud server, or other electronic device. The electronic device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the figures are merely examples of electronic devices and do not limit the scope of the electronic device. The electronic device may include more, fewer, or different components than shown.

[0056] The processor can be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0057] Memory can be an internal storage unit of an electronic device, such as its hard drive or memory. It can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Memory can also include both internal storage units and external storage devices. Memory is used to store computer programs and other programs and data required by the electronic device.

[0058] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a portion of code, and the module, program segment or a portion of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or action, or can be implemented using a combination of dedicated hardware and computer instructions.

[0059] In addition, the functional modules in the various embodiments of the present invention may be integrated together to form an independent part, or each module may exist independently, or two or more modules may be integrated to form an independent part.

[0060] If the functions are implemented as software modules and sold or used as standalone products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product, stored in a storage medium, includes instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. It should be noted that, in this document, relational terms such as first and second, etc., are used solely to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between these entities or operations. Furthermore, the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. Without further limitation, the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, article, or device comprising the elements.

[0061] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Various modifications and variations are readily apparent to those skilled in the art. Any modifications, equivalent substitutions, improvements, and the like made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention. It should be noted that similar reference numerals and letters denote similar items in the following figures. Therefore, once an item is defined in one figure, it need not be further defined or explained in subsequent figures.

[0062] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A surgical navigation automatic registration method, characterized in that: Obtain the patient's esophageal imaging data and construct a continuous spatial point set based on the center point of the esophageal cross section; Extract the starting point, end point and image feature points of the esophagus from the spatial point set and connect them to form an image center curve containing the key morphological feature positions of the esophagus; The image center curve is associated with the three-dimensional trajectory data of the esophageal endoscope's advancement and withdrawal, and a rough registration is performed to obtain the trajectory center curve; After obtaining the trajectory center curve, two refined registrations are performed to obtain the final spatial transformation matrix.

2. The method according to claim 1, characterized in that Before calculating the image feature points, the spatial point set is also denoised, which includes determining new points in the spatial point set by taking the coordinate mean of all adjacent points in the window to construct the latest spatial point set.

3. The method according to claim 1, characterized in that Image feature point extraction includes determining curvature values ​​based on adjacent coordinates in the spatial point set, and arranging the curvature values ​​in descending order to obtain a curvature list; A threshold is set, and multiple spatial point subset intervals are obtained based on the curvature list and adjacent differences. The points that best match the preset esophageal empirical model are selected from the spatial point subset intervals as image feature points. Specifically include: A threshold is set and the adjacent differences of the indexes corresponding to the sorted curvature values ​​are calculated. When the adjacent differences are greater than the threshold, they are used as grouping boundaries. Multiple spatial point set subintervals are formed based on the difference values. The internal curvature of each subinterval is relatively stable and close to each other in space. The degree of conformity between the coordinate average point in each subinterval of the spatial point set and the preset esophageal empirical model is calculated, and the coordinate average point of the closest subinterval is selected as the image feature point.

4. The method according to claim 1, wherein The image center curve is associated with the three-dimensional trajectory data of the esophageal endoscope's advancement and withdrawal, and the trajectory center curve is obtained by coarse registration, including: The pose of the three-dimensional trajectory data including the advance and retract stages is obtained. According to the transformation relationship defined by the preset rotation matrices R1 and R2 and the translation vector T, the three-dimensional trajectory data is rotated to the image center curve coordinate system. The feature points of the trajectory data and the feature point set of the center curve are averagely translated to complete the coarse registration transformation and obtain the trajectory center curve.

5. The method according to claim 4, characterized in that After obtaining the trajectory center curve, two refined registrations are performed to obtain the final spatial transformation matrix including: The initial refined registration includes: obtaining the center curve of the trajectory data, and initially registering it with the image center curve based on the iterative closest point algorithm to obtain the spatial transformation relationship; Secondary refined registration involves re-segmenting the endoscopic trajectory data and performing high-precision scanning with a smaller step size to fit a more accurate image center curve. This is then initially registered with the image center curve using an iterative closest point algorithm to obtain a precise transformation relationship. When the registration is completed, the obtained spatial transformation matrix accurately maps the endoscope position acquired in real-time spatial pose to the corresponding medical image and pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery.

6. The method according to claim 4, characterized in that Before associating the image center curve with the three-dimensional trajectory data of the esophageal endoscope, the three-dimensional trajectory data of the esophageal endoscope is also preprocessed; Preprocessing includes calculating the distance from the trajectory point to the starting point of the trajectory. When the distance of the latter point is less than the distance of the previous point, this point is used as the boundary, and the point after this point is the retreat stage, thereby dividing the trajectory point set into the advance trajectory point set and the retreat trajectory point set.

7. The method according to claim 2 or 4, characterized in that Obtaining the pose data including the three-dimensional trajectory data of the mirror entry and retraction phases includes The trajectory data is processed in the same way as the image center curve to obtain the trajectory starting point, curvature feature point, and trajectory end point, and the posture vector is obtained based on these three points.

8. An automatic registration system for esophageal surgery navigation trajectory and three-dimensional model, characterized by: Used to implement the automatic registration method for surgical navigation according to any one of claims 1 to 7; It includes: Image center curve extraction module: used to process the patient's esophageal image data to extract the image center curve; 3D trajectory data pose vector acquisition module: used to pre-process 3D trajectory data and obtain pose vectors; Coarse registration module: used to associate the image center curve with the pose vector of the 3D trajectory data, and obtain the trajectory center curve through the rotation matrix; Fine-tuning registration module: obtains the final spatial transformation matrix through fine-tuning of the trajectory center curve and the image center curve; Mapping module: Through the final spatial transformation matrix, the endoscope position acquired in real-time spatial posture is accurately mapped to the corresponding medical image and pre-established three-dimensional model, thereby realizing real-time navigation of esophageal endoscopic surgery.

9. An electronic device comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, The processor is enabled to execute the surgical navigation automatic registration method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that The readable storage medium stores a computer program, and when the computer program is run on a computer, the computer is enabled to execute the surgical navigation automatic registration method according to any one of claims 1 to 7.

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