Cluster-based path planning method, surgical robot and related products

Abstract

The application discloses a clustering-based path planning method, a surgical robot and related products. The method comprises the following steps: acquiring a first three-dimensional CT image, the first three-dimensional CT image comprising a skin region of a target object and a first tissue of the target object; determining a terminal point of a target path from the first tissue; determining at least two first candidate points of the target path from the skin region; determining at least two first candidate paths based on the at least two first candidate points and the terminal point of the target path; clustering the at least two first candidate paths to obtain at least one first path set; determining a second path set from the at least one first path set, the number of first candidate paths in the second path set being greater than or equal to a second threshold value; and determining the target path based on the second path set. The method can improve the fault tolerance of the target path.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical image processing, in particular to a path planning method based on clustering, a surgical robot and related products. BACKGROUND

[0002] By performing computed tomography (CT) on a target object, a three-dimensional CT image of the target object can be obtained, and then a target point position in the target object can be located based on the three-dimensional CT image, and a path from a skin region of the target object to the target point of the target object can be planned.

[0003] The current common way is for a doctor to plan a path from a skin region of a target object to a target point of the target object based on experience.

[0004] Considering that there are infeasible regions including infeasible tissues in the target object, where the infeasible tissues are larger in damage to the target object when collided, such as blood vessels. Therefore, the path from the skin region of the target object to the target point of the target object should avoid the infeasible regions. However, the safety deviation of the path planned in this way is small, and thus the fault tolerance is low. Wherein, in the process of moving from the skin region of the target object to the target point according to the path determined based on this way, if the deviation between the actual path and the planned path is greater than or equal to the safety deviation, the actual path passes through the infeasible region, and if the deviation between the actual path and the planned path is less than the safety deviation, the actual path does not pass through the infeasible region. SUMMARY

[0005] The present application provides a path planning method based on clustering, a surgical robot and related products, wherein the related products include a path planning device based on clustering, an electronic device, and a computer readable storage medium, to improve the fault tolerance of the target path.

[0006] In a first aspect, a path planning method based on clustering is provided, the path planning method based on clustering comprising:

[0007] obtaining a first three-dimensional CT image, the first three-dimensional CT image comprising a skin region of a target object and a first tissue of the target object;

[0008] determining a terminal point of a target path from the first tissue;

[0009] determining at least two first candidate points of the target path from the skin region;

[0010] determining at least two first candidate paths based on the at least two first candidate points and the terminal point of the target path;

[0011] clustering the at least two first candidate paths to obtain at least one first path set, any two first candidate paths in a same first path set having a distance less than or equal to a first threshold, any two first candidate paths belonging to different first path sets having a distance greater than or equal to the first threshold;

[0012] determining a second path set from the at least one first path set, the second path set having a number of first candidate paths greater than or equal to a second threshold;

[0013] determining the target path based on the second path set.

[0014] In combination with any embodiment of the present application, the at least two first candidate points determined from the skin region include:

[0015] determining at least two second candidate paths based on at least two skin points in the skin region and an end point of the target path, an origin of a second candidate path in the at least two second candidate paths being the skin point in the at least two skin points, and an end of the second candidate path in the at least two second candidate paths being the end point of the target path;

[0016] determining at least two first normal vectors of the at least two skin points based on the first three-dimensional CT image;

[0017] determining at least two first angles between the second candidate path in the at least two second candidate paths and the corresponding first normal vector in the at least two first normal vectors;

[0018] in a case where a number of the first angles less than or equal to a third threshold in the at least two first angles is greater than or equal to 2, determining the at least two first candidate points based on the skin points in the at least two skin points corresponding to the first angles less than or equal to the third threshold;

[0019] in a case where the number of the first angles less than or equal to the third threshold in the at least two first angles is less than 2, determining the at least two first candidate points based on the skin points in the at least two skin points corresponding to the n smallest first angles in the at least two first angles, the n being an integer greater than or equal to 2.

[0020] In combination with any embodiment of the present application, before the at least two second candidate paths are determined based on the at least two skin points in the skin region and the end point of the target path, the path planning method based on clustering further includes:

[0021] determine a reference sphere based on the end point of the target path and a preset radius, wherein a center of the reference sphere is the end point of the target path, and a radius of the reference sphere is the preset radius;

[0022] determine at least two second candidate points based on the skin points in the skin region that are in the reference sphere;

[0023] determine the at least two skin points based on the at least two second candidate points.

[0024] In combination with any of the embodiments of the present application, the determining the at least two first candidate points based on the skin point in the at least two skin points corresponding to the first included angle less than or equal to the third threshold value comprises:

[0025] determine at least two third candidate paths based on the skin point in the at least two skin points corresponding to the first included angle less than or equal to the third threshold value and the end point of the target path, wherein a start point of the third candidate path in the at least two third candidate paths is the skin point in the at least two skin points corresponding to the first included angle less than or equal to the third threshold value, and an end point of the third candidate path in the at least two third candidate paths is the end point of the target path;

[0026] cluster the at least two third paths to obtain at least one third path set, wherein a distance between any two third candidate paths in a same third path set is less than or equal to a fourth threshold value, and a distance between any two third candidate paths respectively belonging to different third path sets is greater than or equal to the fourth threshold value;

[0027] determine a fourth path set from the at least one third path set, wherein a number of the third candidate paths in the fourth path set is greater than or equal to a fifth threshold value;

[0028] determine the at least two first candidate points based on the start points of the third candidate paths in the fourth path set.

[0029] In combination with any of the embodiments of the present application, before the determining the at least two first candidate paths based on the at least two first candidate points and the end point of the target path, the path planning method based on clustering further comprises:

[0030] determine a second tissue from the first three-dimensional CT image, wherein the second tissue is different from the first tissue;

[0031] the determining the at least two first candidate paths based on the at least two first candidate points and the end point of the target path comprises:

[0032] determining at least two fourth candidate paths based on the at least two first candidate points and the end point of the target path, wherein a start point of the fourth candidate path in the at least two fourth candidate paths is the candidate point in the at least two first candidate points, and an end point of the fourth candidate path in the at least two fourth candidate paths is the end point of the target path;

[0033] determining a minimum distance between the fourth candidate path in the at least two fourth candidate paths and the second tissue, to obtain at least two first distances;

[0034] in a case where the first distance greater than or equal to the sixth threshold value in the at least two first distances is greater than or equal to 2, obtaining the at least two first candidate paths based on the fourth candidate path in the at least two fourth candidate paths corresponding to the first distance greater than or equal to the sixth threshold value;

[0035] in a case where the first distance greater than or equal to the sixth threshold value in the at least two first distances is less than 2, obtaining the at least two first candidate paths based on s fourth candidate paths in the at least two fourth candidate paths with the maximum first distance, wherein s is an integer greater than or equal to 2.

[0036] In combination with any one of the embodiments of the present application, the determining the second tissue from the first three-dimensional CT image comprises:

[0037] determining at least one first enclosing region based on the at least two first candidate points, wherein the first enclosing region in the at least one first enclosing region comprises the at least two first candidate points;

[0038] taking the first enclosing region with the minimum area in the at least one first enclosing region as a second enclosing region;

[0039] determining a normal vector of the first candidate point in the second enclosing region based on the first three-dimensional CT image, to obtain at least one second normal vector;

[0040] determining a third tissue of the target object from the first three-dimensional CT image, wherein the third tissue is a tissue different from the first tissue;

[0041] determining the second tissue based on a point in the third tissue with a distance to the at least two second normal vectors less than or equal to a seventh threshold value.

[0042] In a second aspect, a clustering-based path planning device is provided, which comprises:

[0043] An acquisition unit is used to acquire a first three-dimensional CT image, wherein the first three-dimensional CT image includes the skin region of the target object and the first tissue of the target object;

[0044] A determining unit is used to determine the endpoint of the target path from the first organization;

[0045] The determining unit is further configured to determine at least two first candidate points of the target path from the skin region;

[0046] The determining unit is further configured to determine at least two first candidate paths based on the at least two first candidate points and the endpoint of the target path;

[0047] A clustering unit is used to cluster the at least two first paths to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same set of first paths is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different sets of first paths is greater than or equal to the first threshold.

[0048] The determining unit is further configured to determine a second path set from the at least one first path set, wherein the number of the first candidate paths in the second path set is greater than or equal to a second threshold.

[0049] The determining unit is further configured to determine the target path based on the second path set.

[0050] In conjunction with any embodiment of this application, the determining unit is further configured to:

[0051] Based on at least two skin points in the skin region and the endpoint of the target path, at least two second candidate paths are determined, wherein the starting point of the second candidate path is the skin point among the at least two skin points, and the endpoint of the second candidate path is the endpoint of the target path.

[0052] Based on the first three-dimensional CT image, at least two first normal vectors of the at least two skin points are determined;

[0053] Determine the angle between the second candidate path in the at least two second candidate paths and the corresponding first normal vector in the at least two first normal vectors to obtain at least two first angles;

[0054] If the number of the first included angles less than or equal to the third threshold among the at least two first included angles is greater than or equal to 2, the at least two first candidate points are determined based on the skin points among the at least two skin points corresponding to the first included angles less than or equal to the third threshold;

[0055] If the number of first angles less than or equal to the third threshold among the at least two first angles is less than 2, the at least two first candidate points are determined based on the skin points corresponding to the n smallest first angles among the at least two skin points, where n is an integer greater than or equal to 2.

[0056] In conjunction with any embodiment of this application, the determining unit is further configured to:

[0057] A reference sphere is determined based on the endpoint of the target path and a preset radius, wherein the center of the reference sphere is the endpoint of the target path and the radius of the reference sphere is the preset radius;

[0058] Based on the skin points in the skin region that are located within the reference sphere, at least two second candidate points are determined;

[0059] Based on the at least two second candidate points, the at least two skin points are determined.

[0060] In conjunction with any embodiment of this application, the determining unit is further configured to:

[0061] Based on the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the target path, at least two third candidate paths are determined. The starting point of the third candidate path among the at least two third candidate paths is the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the third candidate path among the at least two third candidate paths is the endpoint of the target path.

[0062] Clustering the at least two third paths yields at least one set of third paths. The distance between any two third candidate paths in the same set of third paths is less than or equal to a fourth threshold. The distance between any two third candidate paths belonging to different sets of third paths is greater than or equal to the fourth threshold.

[0063] A fourth path set is determined from the at least one third path set, wherein the number of the third candidate paths in the fourth path set is greater than or equal to a fifth threshold;

[0064] Based on the starting point of the third candidate path in the fourth path set, the at least two first candidate points are determined.

[0065] In conjunction with any embodiment of this application, the determining unit is further configured to:

[0066] A second tissue, which is different from the first tissue, is identified from the first 3D CT image;

[0067] Based on the at least two first candidate points and the endpoint of the target path, at least two fourth candidate paths are determined, wherein the starting point of the fourth candidate path is the candidate point among the at least two first candidate points, and the endpoint of the fourth candidate path is the endpoint of the target path.

[0068] Determine the minimum distance between the fourth candidate path (one of the at least two fourth candidate paths) and the second organization to obtain at least two first distances;

[0069] If the first distance among the at least two first distances is greater than or equal to the sixth threshold and is greater than or equal to 2, the at least two first candidate paths are obtained based on the fourth candidate paths among the at least two fourth candidate paths that correspond to the first distances greater than or equal to the sixth threshold;

[0070] If the first distance, which is greater than or equal to the sixth threshold, is less than 2 among the at least two first distances, the at least two first candidate paths are obtained based on the s fourth candidate paths with the largest first distance among the at least two fourth candidate paths, where s is an integer greater than or equal to 2.

[0071] In conjunction with any embodiment of this application, the determining unit is further configured to:

[0072] Based on the at least two first candidate points, at least one first enclosed region is determined, wherein the first enclosed region in the at least one first enclosed region includes the at least two first candidate points;

[0073] The first enclosed region with the smallest area among the at least one first enclosed region shall be the second enclosed region;

[0074] Based on the first three-dimensional CT image, the normal vector of the first candidate point in the second enclosed region is determined to obtain at least one second normal vector;

[0075] A third tissue of the target object is determined from the first three-dimensional CT image, wherein the third tissue is a tissue different from the first tissue;

[0076] The second organization is determined based on the points in the third organization whose distance to the at least two second normal vectors is less than or equal to the seventh threshold.

[0077] Thirdly, a surgical robot is provided, including a cluster-based path planning device as described in the second aspect. In this third aspect, the surgical robot can execute a cluster-based path planning method through the cluster-based path planning device, thereby improving the fault tolerance of the target path.

[0078] Fourthly, an electronic device is provided, comprising: a processor and a memory, the memory for storing computer program code, the computer program code including computer instructions, wherein, when the processor executes the computer instructions, the electronic device performs a method as described in the first aspect above and any possible implementation thereof.

[0079] Fifthly, another electronic device is provided, comprising: a processor, a transmitting device, an input device, an output device, and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein, when the processor executes the computer instructions, the electronic device performs a method as described in the first aspect above and any possible implementation thereof.

[0080] In a sixth aspect, a computer-readable storage medium is provided, wherein a computer program is stored therein, the computer program including program instructions that, when executed by a processor, cause the processor to perform a method as described in the first aspect above and any possible implementation thereof.

[0081] In a seventh aspect, a computer program product is provided, the computer program product comprising a computer program or instructions, wherein, when the computer program or instructions are executed on a computer, the computer performs the method described in the first aspect and any possible implementation thereof.

[0082] In this embodiment, the first three-dimensional CT image includes the skin region of the target object and the first tissue of the target object. After acquiring the first three-dimensional CT image, the planning device determines the endpoint of the target path from the first tissue and at least two first candidate points of the target path from the skin region. Then, based on the at least two first candidate points and the endpoint of the target path, at least two first candidate paths are determined. The at least two first candidate paths are clustered to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same first path set is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different first path sets is greater than or equal to the first threshold. A second path set is determined from the at least one set of first paths, wherein the number of first candidate paths in the second path set is greater than or equal to a second threshold. Based on the second path set, the target path is determined, which can improve the safety deviation of the target path and thus improve the fault tolerance of the target path. It should be understood that the deviation between the actual path and the target path can be the distance between the actual path and the target path. Attached Figure Description

[0083] To more clearly illustrate the technical solutions in the embodiments of this application or the background art, the accompanying drawings used in the embodiments of this application or the background art will be described below.

[0084] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with this application and, together with the specification, serve to explain the technical solutions of this application.

[0085] Figure 1 A flowchart illustrating a clustering-based path planning method provided in this application embodiment;

[0086] Figure 2 A flowchart illustrating another clustering-based path planning method provided in this application embodiment;

[0087] Figure 3 A flowchart illustrating yet another clustering-based path planning method provided in this application embodiment;

[0088] Figure 4 A schematic diagram of a cluster-based path planning device provided in an embodiment of this application;

[0089] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0090] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0091] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0092] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. It should be understood that in this application, "at least one" means one or more, "more" means two or more, and "at least two" means two or three or more.

[0093] The execution entity of the cluster-based path planning method in this application embodiment is a cluster-based path planning device (hereinafter referred to as the path planning device). The segmentation device can be any electronic device capable of executing the technical solutions disclosed in the method embodiments of this application. Optionally, the segmentation device can be one of the following: a computer or a server.

[0094] It should be understood that the method embodiments of this application can also be implemented by a processor executing computer program code. The embodiments of this application are described below with reference to the accompanying drawings. Please refer to... Figure 1 , Figure 1 This is a flowchart illustrating a clustering-based path planning method provided in an embodiment of this application.

[0095] 101. Acquire a first three-dimensional CT image, wherein the first three-dimensional CT image includes the skin region of the target object and the first tissue of the target object.

[0096] In this embodiment, the target object can be a human being. The first three-dimensional CT image is a three-dimensional image obtained by performing a CT scan on the target object. The first three-dimensional CT image includes multiple tissues of the target object, such as skin, kidneys, lungs, blood vessels, bones, etc.

[0097] In one implementation of acquiring a first three-dimensional CT image, the path planning device receives the first three-dimensional CT image input by a user through input components. These input components include: a keyboard, a mouse, a touchscreen, a touchpad, and an audio input device.

[0098] In another implementation of acquiring the first 3D CT image, the path planning device receives the first 3D CT image sent by the terminal. Optionally, the terminal can be any of the following: a mobile phone, a computer, a tablet computer, or a server.

[0099] 102. Determine the endpoint of the target path from the first organization.

[0100] In this embodiment, the target path is the path to be planned, and the endpoint of the target path is located within the first tissue. Optionally, the endpoint of the target path is a target point within the first tissue. In some schemes, the path planning device performs semantic segmentation on the first tissue in the first 3D CT image to obtain the semantics of the pixels in the first tissue. The endpoint of the target path is determined based on the semantics of the pixels in the first tissue. In other schemes, the path planning device obtains the position of the endpoint of the target path within the first tissue and determines the endpoint of the target path from the first tissue based on this position.

[0101] 103. Identify at least two first candidate points for the target path from the skin region.

[0102] In this embodiment, the first candidate point is a candidate point for the starting point of the target path. Since the starting point of the target path is located within the skin region, the path planning device determines at least two first candidate points of the target path from the skin region. In some embodiments, the path planning device receives a first instruction input by the user, wherein the first instruction is used to indicate the positions of at least two first candidate points in the skin region. Based on the positions of at least two first candidate points in the skin region, at least two first candidate points are determined from the skin region.

[0103] 104. Based on at least two first candidate points and the endpoint of the target path, determine at least two first candidate paths.

[0104] In this embodiment, the starting point of the first candidate path is the first candidate point, and the ending point of the first candidate path is the ending point of the target path. In one possible implementation, the path planning device determines a first candidate path based on one of at least two first candidate points and the ending point of the target path. Therefore, based on at least two first candidate points and the ending point of the target path, at least two first candidate paths can be determined.

[0105] Optionally, the first candidate path is a feasible path between the skin region of the target object and the endpoint of the target path. For example, if the target object includes blood vessels and the path cannot pass through the blood vessels, then the feasible path refers to a path that does not pass through the blood vessels.

[0106] Optionally, the first candidate path is a straight line, that is, the first candidate path is a straight line between the starting point and the ending point of the first candidate path.

[0107] 105. Cluster at least two first candidate paths to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same first path set is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different first path sets is greater than or equal to the first threshold.

[0108] In this embodiment, the path planning device clusters at least two first candidate paths based on the distance between them, obtaining at least one set of first paths. Specifically, any two first candidate paths within the same first path set have a small distance, while any two first candidate paths in different first path sets have a large distance. In this embodiment, the path planning device determines whether the distance between two first candidate paths is large or small based on a first threshold. Specifically, if the distance between two first candidate paths is less than or equal to the first threshold, the distance between the two first candidate paths is small; conversely, if the distance between two first candidate paths is greater than the first threshold, the distance between the two first candidate paths is large. Optionally, the distance between two first candidate paths is the minimum distance between them.

[0109] In some schemes, the path planning device clusters at least two first candidate paths based on one of the following clustering algorithms: density-based spatial clustering of applications with noise (DBSCAN) or K-means clustering.

[0110] 106. Determine a second path set from at least one first path set, wherein the number of first candidate paths in the second path set is greater than or equal to a second threshold.

[0111] Because the first candidate path is a feasible path between the skin region of the target object and the endpoint of the target path, the distance between any two first candidate paths in the first path set is small, so the connected region corresponding to the first path set has a high probability of being a feasible region. For example, if the target object includes blood vessels, and the path cannot pass through blood vessels, then the feasible region is the region that does not include blood vessels.

[0112] Optionally, the connected region corresponding to the first candidate path in the first path set can be determined by the following steps: determining at least one path region based on the first candidate path in the first path set, wherein the at least one path region includes the region traversed by the first candidate path in the first path set. The region with the smallest area among the at least one path region is taken as the connected region of the first path set.

[0113] A larger area of ​​the connected region corresponding to the first path set indicates a higher probability that the feasible region will have a larger area when moving from the skin region of the target object to the endpoint of the target path according to the first candidate path in the first path set. Therefore, when moving from the skin region of the target object to the endpoint of the target path according to the first candidate path in the first path set, even if the movement deviates from the first candidate path during the movement, the probability that the position after deviation is still within the feasible region is high. In other words, the probability that the actual path moving from the skin region of the target object to the endpoint of the target path is within the feasible region is high. In other words, the safety deviation of the target path is large, that is, the fault tolerance of the target path is high. Specifically, during the movement from the skin region of the target object to the target point according to the path determined in this way, if the deviation of the actual path from the planned path is greater than or equal to the safety deviation, the actual path passes through the infeasible region; if the deviation of the actual path from the planned path is less than the safety deviation, the actual path does not pass through the infeasible region. For example, if the feasible region is a region excluding blood vessels, then the infeasible region is a region including blood vessels. If the area of ​​the connected region corresponding to the first path set is large, then when moving from the skin region of the target object to the end point of the target path according to the first candidate path in the first path set, even if the actual path deviates significantly from the target path, the probability of the actual path passing through blood vessels will not be high, thus reducing the probability of blood vessels being collided with. This allows for a large deviation between the actual path and the target path.

[0114] The probability of colliding with a blood vessel is low because the actual path deviates from the target path.

[0115] Furthermore, since the number of first candidate paths in the first path set is related to the area of ​​the connected region corresponding to the first path set, a larger number of first candidate paths in the first path set suggests a higher probability that the area of ​​the connected region corresponding to the first path set is large. In this embodiment, the path planning device uses a second threshold to determine whether the number of first candidate paths in the first path set is large or small. Specifically, if the number of first candidate paths in the first path set is greater than or equal to the second threshold, it indicates that the number of first candidate paths in the first path set is large; conversely, if the number of first candidate paths in the first path set is less than the second threshold, it indicates that the number of first candidate paths in the first path set is small. Therefore, the path planning device determines a second path set from at least one first path set, wherein the number of first candidate paths in the second path set is greater than or equal to the second threshold.

[0116] 107. Determine the target path based on the second path set.

[0117] In one possible implementation, the path planning device randomly selects a first candidate path from the second set of paths as the target path. In another possible implementation, the path planning device determines the center of the starting point of the first candidate path in the second set of paths, and selects the starting point closest to that center from the starting points of the first candidate paths in the second set of paths as the target starting point. The first candidate path between the target starting point and the end point of the target path is then determined as the target path.

[0118] exist Figure 1 In the clustering-based path planning method shown, the first 3D CT image includes the skin region of the target object and the first tissue of the target object. After acquiring the first 3D CT image, the planning device determines the endpoint of the target path from the first tissue and at least two first candidate points of the target path from the skin region. Then, based on the at least two first candidate points and the endpoint of the target path, at least two first candidate paths are determined. The at least two first candidate paths are clustered to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same first path set is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different first path sets is greater than or equal to the first threshold. A second path set is determined from the at least one set of first paths, wherein the number of first candidate paths in the second path set is greater than or equal to a second threshold. Based on the second path set, the target path is determined, which can improve the safety deviation of the target path and thus improve the fault tolerance of the target path. It should be understood that the deviation between the actual path and the target path can be the distance between the actual path and the target path. Optionally, the deviation between the actual path and the target path is the minimum distance between the actual path and the target path.

[0119] As an optional implementation, the path planning device performs the following steps during step 103:

[0120] 201. Based on at least two skin points in the skin region and the endpoint of the target path, determine at least two second candidate paths, wherein the starting point of the second candidate path among the at least two second candidate paths is a skin point among the at least two skin points, and the endpoint of the second candidate path among the at least two second candidate paths is the endpoint of the target path.

[0121] In this embodiment, points in the skin region are called skin points. At least two skin points in the skin region can be all skin points in the skin region or only a portion of the skin points. The second candidate path corresponds one-to-one with a skin point among the at least two skin points. For example, the at least two skin points include skin point p1 and skin point p2, and the at least two second candidate paths include second candidate path t1 and second candidate path t2, wherein the starting point of second candidate path t1 is skin point p1, and the starting point of second candidate path t2 is skin point p2.

[0122] In one possible implementation, before executing step 201, the path planning device determines at least two skin points in the skin region by performing the following steps: determining a reference sphere based on the endpoint of the target path and a preset radius, wherein the center of the reference sphere is the endpoint of the target path and the radius of the reference sphere is the preset radius; determining at least two second candidate points based on skin points in the skin region that are within the reference sphere; and determining at least two skin points based on the at least two second candidate points.

[0123] In this implementation, since the center of the reference sphere is the endpoint of the target path and the radius of the reference sphere is a preset radius, the distance from a skin point within the reference sphere in the skin region to the endpoint of the target path is less than or equal to the preset radius. Therefore, after determining at least two second candidate points based on skin points within the reference sphere in the skin region, and then determining at least two skin points based on these at least two second candidate points, the length of at least two second candidate paths determined based on these at least two skin points can be less than or equal to the preset radius.

[0124] Optionally, if the number of skin points within the reference sphere in the skin region is greater than or equal to 2, the skin points within the reference sphere in the skin region are selected as at least two second candidate points. If the number of skin points within the reference sphere in the skin region is less than 2, at least two second candidate points are determined based on the skin points within the reference sphere in the skin region and the skin point in the skin region closest to the reference sphere.

[0125] 202. Based on the first three-dimensional CT image, determine at least two first normal vectors for at least two skin points.

[0126] In this embodiment, the first normal vector is the normal vector of one of at least two skin points. Optionally, based on the three-dimensional information of pixels in the first three-dimensional CT image in the image coordinate system of the first three-dimensional CT image, the normal vector of the skin point among at least two skin points in the image coordinate system can be determined, in which case the first normal vector is a vector in the image coordinate system of the first three-dimensional CT image.

[0127] 203. Determine the angle between the second candidate path in at least two second candidate paths and the corresponding first normal vector in at least two first normal vectors, and obtain at least two first angles.

[0128] In this embodiment, the second candidate path corresponds to the first normal vector of its starting point. For example, at least two candidate paths include second candidate path t1 and second candidate path t2, and at least two first normal vectors include first normal vector v1 and first normal vector v2, where first normal vector t1 is the normal vector of the starting point of second candidate path t1, and first normal vector t2 is the normal vector of the starting point of second candidate path t2. Therefore, second candidate path t1 corresponds to first normal vector v1, and second candidate path t2 corresponds to first normal vector v2.

[0129] The first included angle is the angle between the second candidate path and the corresponding first normal vector. Since there are at least two second candidate paths, there are also at least two first included angles.

[0130] 204. If the number of first angles less than or equal to the third threshold among at least two first angles is greater than or equal to 2, at least two first candidate points are determined based on the skin points corresponding to the first angles less than or equal to the third threshold among at least two skin points.

[0131] For example, at least two second candidate paths include second candidate path t1, second candidate path t2, and second candidate path t3, wherein the starting point of second candidate path t1 is skin point p1, the starting point of second candidate path t2 is skin point p2, and the starting point of second candidate path t3 is skin point p3. The first angle between the normal vector of second candidate path t1 and skin point p1 is the first angle j1, the first angle between the normal vector of second candidate path t2 and skin point p2 is the first angle j2, and the first angle between the normal vector of second candidate path t3 and skin point p3 is the first angle j3. The skin point corresponding to the first angle j1 is skin point p1, the skin point corresponding to the first angle j2 is skin point p2, and the skin point corresponding to the first angle j3 is skin point p3. If both the first angle j1 and the first angle j2 are less than or equal to a third threshold, then the path planning device determines at least two first candidate points based on skin points p1 and p2.

[0132] 205. If the number of first angles less than or equal to the third threshold among at least two first angles is less than 2, determine at least two first candidate points based on the skin points corresponding to the n smallest first angles among at least two skin points, where n is an integer greater than or equal to 2.

[0133] When moving from the skin region of the target object into the body of the target object along the target path and towards the target object's endpoint, a smaller angle between the normal vector of the target path and the starting point of the target path within the skin region is more beneficial in reducing the deviation between the actual path moving towards the target object's endpoint and the target path. Therefore, when the first angle is small, determining the first candidate point based on the skin point corresponding to the first angle is beneficial in reducing the deviation between the actual path moving towards the target object's endpoint and the target path when subsequently determining the first candidate path based on the first candidate point and the target path based on the first candidate path.

[0134] In this embodiment, the path planning device uses a third threshold as a basis to determine whether the first included angle is large or small. Specifically, if the first included angle is less than or equal to the third threshold, it indicates that the first included angle is small; conversely, if the first included angle is greater than the third threshold, it indicates that the first included angle is large. Therefore, the path planning device determines at least two first candidate points by executing step 204 or step 205.

[0135] In some schemes, the path planning device achieves step 204, "determining at least two first candidate points based on the skin point corresponding to the first angle less than or equal to a third threshold among at least two skin points," by performing the following steps: Based on the skin point corresponding to the first angle less than or equal to the third threshold among at least two skin points and the endpoint of the target path, at least two third candidate paths are determined, wherein the starting point of the third candidate path among the at least two third candidate paths is the skin point corresponding to the first angle less than or equal to the third threshold among at least two skin points, and the endpoint of the third candidate path among the at least two third candidate paths is the endpoint of the target path. The at least two third paths are clustered to obtain at least one set of third paths, wherein the distance between any two third candidate paths within the same third path set is less than or equal to a fourth threshold, and the distance between any two third candidate paths belonging to different third path sets is greater than or equal to the fourth threshold. A fourth path set is determined from the at least one set of third paths, wherein the number of third candidate paths in the fourth path set is greater than or equal to a fifth threshold. At least two first candidate points are determined based on the starting points of the third candidate paths in the fourth path set.

[0136] In this scheme, because the distance between any two third candidate paths in the third path set is small, the probability that the connected region corresponding to the third path set is a feasible region is high. Furthermore, the large area of ​​the connected region corresponding to the third path set indicates a high probability that the feasible region has a large area when moving from the skin region of the target object to the endpoint of the target path according to the third candidate path in the third path set. Therefore, when using the starting point of the third candidate path in the third path set as the starting point of the target path, even if the actual starting point deviates from the starting point of the target path during actual movement, the probability that the actual path moving towards the endpoint of the target path will still be within the feasible region is high. In other words, selecting the starting point of the target path from the starting points of the third candidate paths in the third path set reduces the probability of the actual path entering an infeasible region due to starting point deviation, thereby increasing the safety deviation of the target path and thus improving the fault tolerance of the target path.

[0137] Furthermore, since the number of third candidate paths in the third path set is related to the area of ​​the connected region corresponding to the third path set, a larger number of third candidate paths in the third path set increases the probability that the area of ​​the connected region corresponding to the third path set is large. In this embodiment, the path planning device uses a fifth threshold to determine whether the number of third candidate paths in the third path set is large or small. Specifically, if the number of third candidate paths in the third path set is greater than or equal to the fifth threshold, it indicates that the number of third candidate paths in the third path set is large; conversely, if the number of third candidate paths in the third path set is less than the fifth threshold, it indicates that the number of third candidate paths in the third path set is small. Therefore, the path planning device determines a fourth path set from at least one third path set, wherein the number of third candidate paths in the fourth path set is greater than or equal to the fifth threshold. Then, based on the starting points of the third candidate paths in the fourth path set, at least two first candidate points are determined, which can improve the fault tolerance rate of the target path subsequently determined based on at least two first candidate points.

[0138] As an optional implementation, before performing step 104, the path planning device further determines a second tissue from the first three-dimensional CT image, wherein the second tissue is different from the first tissue. Optionally, the second tissue is a tissue that the target path cannot traverse, for example, the second tissue is a blood vessel in the infeasible region mentioned in the previous example.

[0139] After determining the second organization, the path planning device performs the following steps during step 104: Based on at least two first candidate points and the endpoint of the target path, at least two fourth candidate paths are determined, wherein the starting point of the fourth candidate path among the at least two fourth candidate paths is a candidate point among the at least two first candidate points, and the endpoint of the fourth candidate path among the at least two fourth candidate paths is the endpoint of the target path. The minimum distance between the fourth candidate path among the at least two fourth candidate paths and the second organization is determined, resulting in at least two first distances. If the first distance among the at least two first distances is greater than or equal to a sixth threshold and is greater than or equal to 2, at least two first candidate paths are obtained based on the fourth candidate paths among the at least two fourth candidate paths corresponding to the first distances greater than or equal to the sixth threshold. If the first distance among the at least two first distances is greater than or equal to the sixth threshold and is greater than 2, at least two first candidate paths are obtained based on the s fourth candidate paths with the largest first distances among the at least two fourth candidate paths, where s is an integer greater than or equal to 2. This reduces the probability that at least two first candidate paths will pass through the second tissue, thereby reducing the probability of collision with the second tissue when determining the target path based on at least two first candidate paths and moving from the skin region of the target object towards the end of the target path according to the target path.

[0140] In some schemes, the path planning device achieves "determining a second tissue from a first 3D CT image" by performing the following steps: Based on at least two first candidate points, at least one first enclosing region is determined, wherein the first enclosing region within the at least one first enclosing region includes at least two first candidate points. The first enclosing region with the smallest area among the at least one first enclosing region is designated as a second enclosing region. Based on the first 3D CT image, the normal vectors of the first candidate points in the second enclosing region are determined to obtain at least one second normal vector. A third tissue of the target object is determined from the first 3D CT image, wherein the third tissue is a tissue different from the first tissue. Optionally, the third tissue is a non-passable tissue. It should be understood that, in the embodiments of this application, a non-passable tissue refers to a tissue that the target path cannot pass through, such as the blood vessels mentioned above, or it could be an organ other than the first tissue, such as the lung if the first tissue is the lung, and the kidney if the non-passable tissue is the kidney. The second tissue is determined based on the portion of the third tissue whose distance to at least two second normal vectors is less than or equal to a seventh threshold.

[0141] In this scheme, the first enclosing region is the region that includes at least two first candidate points, that is, the first enclosing region includes at least two first candidate points. Optionally, the bounding box mentioned in this embodiment refers to an axis-aligned bounding box (AABB). The second enclosing region is the region with the smallest area among at least the first enclosing regions, that is, the second enclosing region is the smallest enclosing region of at least two first candidate points.

[0142] Because the angle between the direction of entry into the target object and the normal vector of the path's starting point is small when moving from the skin region into the target object's body, and the angle between the actual path and the normal vector of the starting point is small when moving towards the end point of the target path within the target object's body, the probability of a point in the unpassable tissue with a larger distance from the normal vector of the path's starting point colliding with the actual path is low. Based on this, when it is necessary to determine the distance between the fourth candidate path and the unpassable tissue among at least two fourth candidate paths, after determining the point in the unpassable tissue with a smaller distance from the normal vector of the path's starting point, the distance between that point and the fourth candidate path among at least two fourth candidate paths can be determined. This reduces the amount of data processing and improves processing efficiency.

[0143] In this embodiment, after determining the second enclosing region, the path planning device determines the normal vector of the first candidate point in the second enclosing region based on the first 3D CT image, obtaining at least one second normal vector. Then, it determines the third tissue of the target object from the first 3D CT image, and determines the second tissue based on points in the third tissue whose distance to at least two second normal vectors is less than or equal to a seventh threshold. This reduces data processing volume and improves processing efficiency when subsequently determining the distance between the fourth candidate path and the second tissue in at least two fourth candidate paths. The seventh threshold is the basis for determining whether the distance from a point in the third tissue to the second normal vector is small or large. The third tissue is a region in the first 3D CT image, and points in the third tissue can be understood as pixels in the third tissue. The distance from a point in the third tissue to at least two second normal vectors can be determined by the following steps: determining the minimum distance from a point in the third tissue to each of the at least two second normal vectors, obtaining at least two third distances. The minimum value of the at least two third distances is taken as the distance from a point in the third tissue to at least two second normal vectors.

[0144] The following example uses the lung as the first tissue and the target point in the lung as the endpoint of the target path to explain the clustering-based path planning method. Please refer to [link / reference needed]. Figure 2 , Figure 2 This is a flowchart illustrating another clustering-based path planning method provided in an embodiment of this application. Figure 2As shown, the path planning method comprises two phases: Phase 1, which involves simplifying the grid, and Phase 2, which involves path planning. Specifically, in Phase 1, a first 3D CT image is input into the path planning device. This image includes the lungs of the target object, and the region corresponding to the lungs is labeled. Optionally, the region corresponding to the lungs can be determined by segmenting the first 3D CT image, and then labeled. Next, a selection process is performed based on the path length. Specifically, a reference sphere is determined based on the target point in the lung and a preset radius, where the center of the reference sphere is the target point in the lung, and the radius is the preset radius. At least two skin points are then determined from the first 3D CT image. Finally, a selection process is performed based on the angle between the path and a first normal vector. Specifically, the skin point corresponding to a first angle less than or equal to a third threshold is determined from the at least two skin points based on the first angle between the path and at least two first normal vectors. Next, clustering is used for screening, specifically determining at least two first candidate points based on skin points corresponding to a first angle less than or equal to a third threshold. Specifically, based on the skin points corresponding to the first angle less than or equal to the third threshold and the endpoint of the target path, at least two third candidate paths are determined. These at least two third paths are clustered to obtain at least one set of third paths. A fourth path set is determined from this set. Based on the starting points of the third candidate paths in the fourth path set, at least two first candidate points are determined. Then, bounding box screening is used, specifically, the first bounding region with the smallest area within at least one first bounding region is designated as the second bounding region. Based on the first 3D CT image, the normal vectors of the first candidate points within the second bounding region are determined, resulting in at least one second normal vector. The third tissue of the target object is determined from the first 3D CT image. Based on points within the third tissue whose distance to at least two second normal vectors is less than or equal to a seventh threshold, the second tissue is determined. Finally, based on at least two first candidate points and the endpoint of the target path, at least two fourth candidate paths are determined. At this point, the first stage of the process is complete.

[0145] In Phase Two, at least two fourth candidate paths determined in Phase One are first acquired. Collision detection is then performed. Specifically, the minimum distance between the fourth candidate path and the second organization is determined, resulting in at least two first distances. If the first distance greater than or equal to a sixth threshold is greater than or equal to 2, at least two fifth candidate paths are obtained based on the fourth candidate paths corresponding to the first distance greater than or equal to the sixth threshold. If the first distance less than or equal to the sixth threshold is less than 2, at least two fifth candidate paths are obtained based on the s fourth candidate paths with the largest first distances. Then, a selection process is performed based on the angle between the path and the third normal vector, resulting in at least two first candidate paths, where the third normal vector is the normal vector of the starting point of the at least two fifth candidate paths. Specifically, based on the second angle between the path and the at least two third normal vectors, paths corresponding to the second angle less than or equal to the third threshold are selected from the starting points of the at least two fifth candidate paths as at least two first candidate paths, where the second angle is the angle between the third normal vector and the fifth candidate path. Finally, based on clustering screening, a second path set is obtained. Specifically, at least two first candidate paths are clustered to obtain at least one first path set. At least one second path set is determined from the at least one first path set. Finally, the target path is determined through collision detection. Specifically, for each second path set in the at least one second path set, the center of the paths in the set is determined, resulting in at least one center path for the at least one second path set. Then, the minimum distance between the at least one center path and the second path is determined, resulting in at least two second distances. From the at least two second distances, a target distance less than or equal to a sixth threshold is determined, and the center path corresponding to the target distance is determined as the target path.

[0146] Please see Figure 3 , Figure 3 This is a flowchart illustrating another clustering-based path planning method provided in an embodiment of this application. Figure 3In this process, the image input to the path planning device is a first 3D CT image, which includes the endpoint of the target path. Optionally, the coordinates of the endpoint of the target path in the first 3D CT image are (499, 250, 332). After inputting the first 3D CT image to the path planning device, the path planning device can output a result image, which includes the starting point of the target path. Optionally, the number of starting points of the target path is four, that is, the target path can be determined based on any one of the four starting points and the endpoint of the target path. The coordinates of the four starting points of the target path in the result image are (462.18, 415.86, 332.08), (492.18, 416.67, 332.88), (501.18, 183.31, 361.05), and (454.18, 175.26, 348.17).

[0147] Those skilled in the art will understand that, in the above-described method of the specific implementation, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0148] If the technical solution of this application involves personal information, the product using this technical solution has clearly informed the user of the personal information processing rules and obtained the user's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using this technical solution has obtained the user's separate consent before processing the sensitive personal information, and also meets the requirement of "express consent". For example, at personal information collection devices such as cameras, clear and prominent signs are set up to inform users that they have entered the scope of personal information collection and that personal information will be collected. If an individual voluntarily enters the collection scope, it is deemed that they have agreed to the collection of their personal information; or on the personal information processing device, while using clear signs / information to inform users of the personal information processing rules, authorization is obtained from the individual through pop-up information or by asking the individual to upload their personal information; wherein, personal information processing may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the types of personal information processed.

[0149] The methods of the embodiments of this application have been described in detail above, and the apparatus of the embodiments of this application is provided below.

[0150] Please see Figure 4 , Figure 4 This is a schematic diagram of a cluster-based path planning device provided in an embodiment of this application. The cluster-based path planning device 1 includes: an acquisition unit 11, a determination unit 12, and a clustering unit 13, wherein:

[0151] Acquisition unit 11 is used to acquire a first three-dimensional CT image, the first three-dimensional CT image including the skin region of the target object and the first tissue of the target object;

[0152] Determining unit 12 is used to determine the endpoint of the target path from the first organization;

[0153] The determining unit 12 is further configured to determine at least two first candidate points of the target path from the skin region;

[0154] The determining unit 12 is further configured to determine at least two first candidate paths based on the at least two first candidate points and the endpoint of the target path;

[0155] Clustering unit 13 is used to cluster the at least two first paths to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same set of first paths is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different sets of first paths is greater than or equal to the first threshold.

[0156] The determining unit 12 is further configured to determine a second path set from the at least one first path set, wherein the number of the first candidate paths in the second path set is greater than or equal to a second threshold.

[0157] The determining unit 12 is further configured to determine the target path based on the second path set.

[0158] In conjunction with any embodiment of this application, the determining unit 12 is further configured to:

[0159] Based on at least two skin points in the skin region and the endpoint of the target path, at least two second candidate paths are determined, wherein the starting point of the second candidate path is the skin point among the at least two skin points, and the endpoint of the second candidate path is the endpoint of the target path.

[0160] Based on the first three-dimensional CT image, at least two first normal vectors of the at least two skin points are determined;

[0161] Determine the angle between the second candidate path in the at least two second candidate paths and the corresponding first normal vector in the at least two first normal vectors to obtain at least two first angles;

[0162] If the number of the first included angles less than or equal to the third threshold among the at least two first included angles is greater than or equal to 2, the at least two first candidate points are determined based on the skin points among the at least two skin points corresponding to the first included angles less than or equal to the third threshold;

[0163] If the number of first angles less than or equal to the third threshold among the at least two first angles is less than 2, the at least two first candidate points are determined based on the skin points corresponding to the n smallest first angles among the at least two skin points, where n is an integer greater than or equal to 2.

[0164] In conjunction with any embodiment of this application, the determining unit 12 is further configured to:

[0165] A reference sphere is determined based on the endpoint of the target path and a preset radius, wherein the center of the reference sphere is the endpoint of the target path and the radius of the reference sphere is the preset radius;

[0166] Based on the skin points in the skin region that are located within the reference sphere, at least two second candidate points are determined;

[0167] Based on the at least two second candidate points, the at least two skin points are determined.

[0168] In conjunction with any embodiment of this application, the determining unit 12 is further configured to:

[0169] Based on the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the target path, at least two third candidate paths are determined. The starting point of the third candidate path among the at least two third candidate paths is the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the third candidate path among the at least two third candidate paths is the endpoint of the target path.

[0170] Clustering the at least two third paths yields at least one set of third paths. The distance between any two third candidate paths in the same set of third paths is less than or equal to a fourth threshold. The distance between any two third candidate paths belonging to different sets of third paths is greater than or equal to the fourth threshold.

[0171] A fourth path set is determined from the at least one third path set, wherein the number of the third candidate paths in the fourth path set is greater than or equal to a fifth threshold;

[0172] Based on the starting point of the third candidate path in the fourth path set, the at least two first candidate points are determined.

[0173] In conjunction with any embodiment of this application, the determining unit 12 is further configured to:

[0174] A second tissue, which is different from the first tissue, is identified from the first 3D CT image;

[0175] Based on the at least two first candidate points and the endpoint of the target path, at least two fourth candidate paths are determined, wherein the starting point of the fourth candidate path is the candidate point among the at least two first candidate points, and the endpoint of the fourth candidate path is the endpoint of the target path.

[0176] Determine the minimum distance between the fourth candidate path (one of the at least two fourth candidate paths) and the second organization to obtain at least two first distances;

[0177] If the first distance among the at least two first distances is greater than or equal to the sixth threshold and is greater than or equal to 2, the at least two first candidate paths are obtained based on the fourth candidate paths among the at least two fourth candidate paths that correspond to the first distances greater than or equal to the sixth threshold;

[0178] If the first distance, which is greater than or equal to the sixth threshold, is less than 2 among the at least two first distances, the at least two first candidate paths are obtained based on the s fourth candidate paths with the largest first distance among the at least two fourth candidate paths, where s is an integer greater than or equal to 2.

[0179] In conjunction with any embodiment of this application, the determining unit 12 is further configured to:

[0180] Based on the at least two first candidate points, at least one first enclosed region is determined, wherein the first enclosed region in the at least one first enclosed region includes the at least two first candidate points;

[0181] The first enclosed region with the smallest area among the at least one first enclosed region shall be the second enclosed region;

[0182] Based on the first three-dimensional CT image, the normal vector of the first candidate point in the second enclosed region is determined to obtain at least one second normal vector;

[0183] A third tissue of the target object is determined from the first three-dimensional CT image, wherein the third tissue is a tissue different from the first tissue;

[0184] The second organization is determined based on the points in the third organization whose distance to the at least two second normal vectors is less than or equal to the seventh threshold.

[0185] In this embodiment, the first three-dimensional CT image includes the skin region of the target object and the first tissue of the target object. After acquiring the first three-dimensional CT image, the clustering-based path planning device determines the endpoint of the target path from the first tissue and at least two first candidate points of the target path from the skin region. Then, based on the at least two first candidate points and the endpoint of the target path, at least two first candidate paths are determined. The at least two first candidate paths are clustered to obtain at least one set of first paths, wherein the distance between any two first candidate paths in the same first path set is less than or equal to a first threshold, and the distance between any two first candidate paths belonging to different first path sets is greater than or equal to the first threshold. A second path set is determined from the at least one set of first paths, wherein the number of first candidate paths in the second path set is greater than or equal to a second threshold. Based on the second path set, the target path is determined, which can improve the safety deviation of the target path and thus improve the fault tolerance of the target path. It should be understood that the deviation between the actual path and the target path can be the distance between the actual path and the target path. Optionally, the deviation between the actual path and the target path is the minimum distance between the actual path and the target path.

[0186] In some embodiments, the functions or modules of the apparatus provided in this application can be used to perform the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0187] Figure 5 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this application. The electronic device 2 includes a processor 21 and a memory 22. Optionally, the electronic device 2 also includes an input device 23 and an output device 24. The processor 21, memory 22, input device 23, and output device 24 are coupled together via connectors, which include various interfaces, transmission lines, or buses, etc., and are not limited in this embodiment. It should be understood that in the various embodiments of this application, coupling refers to mutual connection in a specific way, including direct connection or indirect connection through other devices, such as through various interfaces, transmission lines, buses, etc.

[0188] The processor 21 can be one or more graphics processing units (GPUs). If the processor 21 is a GPU, the GPU can be a single-core GPU or a multi-core GPU. Optionally, the processor 21 can be a processor group composed of multiple GPUs, with the multiple processors coupled to each other via one or more buses. Optionally, the processor can also be other types of processors, etc., which are not limited in this embodiment.

[0189] The memory 22 can be used to store computer program instructions, as well as various types of computer program code, including program code for executing the present application. Optionally, the memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), or compact disc read-only memory (CD-ROM), which is used for related instructions and data.

[0190] Input device 23 is used to input data and / or signals, and output device 24 is used to output data and / or signals. Input device 23 and output device 24 can be independent devices or an integrated device.

[0191] It is understood that in this embodiment of the application, the memory 22 can be used not only to store related instructions, but also to store related data. This embodiment of the application does not limit the specific data stored in the memory.

[0192] Understandable Figure 5 This is merely a simplified design of an electronic device. In practical applications, the electronic device may also include other necessary components, including, but not limited to, any number of input / output devices, processors, memories, etc., and all electronic devices that can implement the embodiments of this application are within the protection scope of this application.

[0193] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0194] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will also readily understand that the various embodiments of this application have different focuses, and for the sake of convenience and brevity, the same or similar parts may not be repeated in different embodiments. Therefore, parts not described or not described in detail in one embodiment can be referred to the descriptions in other embodiments.

[0195] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0196] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0197] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0198] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital versatile discs (DVDs)), or semiconductor media (e.g., solid-state disks (SSDs)).

[0199] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A cluster-based path planning method, characterized in that, The cluster-based path planning method comprises: obtaining a first three-dimensional CT image, the first three-dimensional CT image comprising a skin region of a target object, a first tissue of the target object; determining a terminal point of a target path from the first tissue; determining at least two first candidate points of the target path from the skin region; The method comprises: determining at least two second candidate paths based on at least two skin points in the skin region and the terminal point of the target path, the starting point of the second candidate path in the at least two second candidate paths being the skin point in the at least two skin points, the terminal point of the second candidate path in the at least two second candidate paths being the terminal point of the target path; determining at least two first normal vectors of the at least two skin points based on the first three-dimensional CT image; determining at least two first angles between the second candidate path in the at least two second candidate paths and the corresponding first normal vector in the at least two first normal vectors; in the case that the number of the first angles less than or equal to a third threshold in the at least two first angles is greater than or equal to 2, determining the at least two first candidate points based on the skin points in the at least two skin points corresponding to the first angles less than or equal to the third threshold; in the case that the number of the first angles less than or equal to the third threshold in the at least two first angles is less than 2, determining the at least two first candidate points based on the skin points in the at least two skin points corresponding to the n smallest first angles in the at least two first angles, the n being an integer greater than or equal to 2; determining at least two first candidate paths based on the at least two first candidate points and the terminal point of the target path; clustering the at least two first candidate paths to obtain at least one first path set, the distance between any two first candidate paths in the same first path set being less than or equal to a first threshold, the distance between any two first candidate paths belonging to different first path sets being greater than or equal to the first threshold; determining a second path set from the at least one first path set, the number of the first candidate paths in the second path set being greater than or equal to a second threshold; determining the target path based on the second path set.

2. The cluster-based path planning method of claim 1, wherein, Before determining at least two second candidate paths based on at least two skin points in the skin region and the terminal point of the target path, the cluster-based path planning method further comprises: determining a reference sphere based on the terminal point of the target path and a preset radius, the center of the reference sphere being the terminal point of the target path, the radius of the reference sphere being the preset radius; determining at least two second candidate points based on the skin points in the skin region within the reference sphere; determining the at least two skin points based on the at least two second candidate points.

3. The cluster-based path planning method according to claim 1 or 2, characterized in that, The determination of the at least two first candidate points based on the skin points corresponding to the first angle that is less than or equal to the third threshold among the at least two skin points includes: Based on the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the target path, at least two third candidate paths are determined. The starting point of the third candidate path among the at least two third candidate paths is the skin point corresponding to the first angle between the at least two skin points and the third threshold, and the endpoint of the third candidate path among the at least two third candidate paths is the endpoint of the target path. Clustering the at least two third paths yields at least one set of third paths. The distance between any two third candidate paths in the same set of third paths is less than or equal to a fourth threshold. The distance between any two third candidate paths belonging to different sets of third paths is greater than or equal to the fourth threshold. A fourth path set is determined from the at least one third path set, wherein the number of the third candidate paths in the fourth path set is greater than or equal to a fifth threshold; Based on the starting point of the third candidate path in the fourth path set, the at least two first candidate points are determined.

4. The cluster-based path planning method according to claim 1 or 2, characterized in that, Before determining at least two first candidate paths based on the at least two first candidate points and the endpoint of the target path, the clustering-based path planning method further includes: A second tissue, which is different from the first tissue, is identified from the first 3D CT image; The determination of at least two first candidate paths based on the at least two first candidate points and the endpoint of the target path includes: Based on the at least two first candidate points and the endpoint of the target path, at least two fourth candidate paths are determined, wherein the starting point of the fourth candidate path is the candidate point among the at least two first candidate points, and the endpoint of the fourth candidate path is the endpoint of the target path. Determine the minimum distance between the fourth candidate path (one of the at least two fourth candidate paths) and the second organization to obtain at least two first distances; If the first distance among the at least two first distances is greater than or equal to the sixth threshold and is greater than or equal to 2, the at least two first candidate paths are obtained based on the fourth candidate paths among the at least two fourth candidate paths that correspond to the first distances greater than or equal to the sixth threshold; If the first distance, which is greater than or equal to the sixth threshold, is less than 2 among the at least two first distances, the at least two first candidate paths are obtained based on the s fourth candidate paths with the largest first distance among the at least two fourth candidate paths, where s is an integer greater than or equal to 2.

5. The cluster-based path planning method of claim 4, wherein, Determining the second tissue from the first three-dimensional CT image includes: determine at least one first enclosing region based on the at least two first candidate points, the first enclosing region of the at least one first enclosing region comprising the at least two first candidate points; determine a second enclosing region from the first enclosing region with the smallest area of the at least one first enclosing region; determine at least one second normal vector based on the first three-dimensional CT image and the first candidate point in the second enclosing region; determine a third tissue of the target object from the first three-dimensional CT image, the third tissue being different from the first tissue; determine the second tissue based on a point in the third tissue that is determined to be less than or equal to a seventh threshold value from the at least two second normal vectors.

6. A cluster-based path planning device, characterized by comprising: The clustering-based path planning device comprises: an acquisition unit configured to acquire a first three-dimensional CT image, the first three-dimensional CT image comprising a skin region of a target object and a first tissue of the target object; a determination unit configured to determine a terminal point of a target path from the first tissue; the determination unit is further configured to determine at least two first candidate points of the target path from the skin region; the determination unit is further configured to determine at least two second candidate paths based on at least two skin points in the skin region and the terminal point of the target path, a starting point of the second candidate path of the at least two second candidate paths being the skin point of the at least two skin points, and a terminal point of the second candidate path of the at least two second candidate paths being the terminal point of the target path; determine at least two first normal vectors of the at least two skin points based on the first three-dimensional CT image; determine at least two first angles by determining an included angle between the second candidate path of the at least two second candidate paths and the corresponding first normal vector of the at least two first normal vectors; in a case where a number of the first angles less than or equal to a third threshold value in the at least two first angles is greater than or equal to 2, determine the at least two first candidate points based on the skin points of the at least two skin points corresponding to the first angles less than or equal to the third threshold value; in a case where the number of the first angles less than or equal to the third threshold value in the at least two first angles is less than 2, determine the at least two first candidate points based on the skin points of the at least two skin points corresponding to the n smallest first angles of the at least two first angles, the n being an integer greater than or equal to 2; the determination unit is further configured to determine at least two first candidate paths based on the at least two first candidate points and the terminal point of the target path; a clustering unit configured to cluster the at least two first paths to obtain at least one first path set, a distance between any two first candidate paths in a same first path set being less than or equal to a first threshold value, and a distance between any two first candidate paths belonging to different first path sets being greater than or equal to the first threshold value; The determining unit is further configured to determine a second path set from the at least one first path set, a number of the first candidate paths in the second path set being greater than or equal to a second threshold value; The determining unit is further configured to determine the target path based on the second path set.

7. A surgical robot, characterised in that, The surgical robot comprises the clustering-based path planning device according to claim 6.

8. An electronic device, comprising: Comprise: A processor and a memory, the memory being configured to store computer program code, the computer program code comprising computer instructions, in a case where the processor executes the computer instructions, the electronic device executes the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program comprises program instructions, in a case where the program instructions are executed by a processor, the processor executes the method according to any one of claims 1 to 5.

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