CN113876420A - Path planning method, system, device and medium for planning surgical path - Google Patents
Path planning method, system, device and medium for planning surgical path Download PDFInfo
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Abstract
The present application relates to the field of robotics, and in particular, to a path planning method, system, device, and medium for planning a surgical path. The method comprises the following steps: determining a plurality of initial planning paths from a starting point of the three-dimensional model to an area to be operated according to the three-dimensional model of the object tissue of the object to be treated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model; and selecting a target planning path from the plurality of initial planning paths, wherein the selected target planning path comprises at least one preset spatial feature. The method can improve the accuracy of path planning.
Description
Technical Field
The present application relates to the field of robotics, and in particular, to a path planning method, system, device, and medium for planning a surgical path.
Background
With the development of modern medical technology, in order to obtain information about a physical lesion of a patient, biopsy samples may be taken from various tissues to perform a combing of a pathological workflow based on the taken biopsy samples.
In a conventional manner, when taking a biopsy sample, an operation path is usually directly planned based on a start position and an end position, and then the operation is performed.
However, for the position with a narrow end structure and a special shape, the above method cannot accurately plan the sampling path, and the accuracy of the planning of the sampling path is low.
Disclosure of Invention
In view of the foregoing, it is necessary to provide a path planning method, system, device and medium for planning a surgical path, which can improve the accuracy of sampling path planning.
A path planning method for planning a surgical path, the method comprising: determining a plurality of initial planning paths from a starting point of the three-dimensional model to an area to be operated according to the three-dimensional model of the object tissue of the object to be treated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model; selecting a target planned path from a plurality of initial planned paths; wherein the selected target planning path comprises at least one preset spatial feature.
A robotic system, the system comprising: a memory, a processor, and a robot execution end; the memory stores a computer program, and the processor realizes the method of any one of the above embodiments when executing the computer program, and determines a target planning path; the robot executing end is used for moving from an initial point of the object tissue of the object to be treated to the area to be operated according to the target planning path.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method of any of the above embodiments when the processor executes the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
In the above path planning method, system, device and medium for planning a surgical path, a plurality of initial planned paths from a starting point of a three-dimensional model to an area to be operated are determined according to the three-dimensional model of a target tissue of a subject to be treated, wherein the area to be operated is determined based on the tissue to be operated in the corresponding target tissue in the three-dimensional model, and then a target planned path is selected from the plurality of initial planned paths, wherein the selected target planned path includes at least one preset spatial feature. Therefore, a plurality of initial planning paths from the starting point to the region to be operated can be determined based on the three-dimensional model of the object tissue of the object to be treated, and then the target planning path is selected from the initial planning paths based on the preset spatial characteristics, so that the determination of the target path combines the spatial characteristics of all paths, and the accuracy of the determination of the target path can be improved.
Drawings
FIG. 1 is a diagram of an application scenario of a path planning method for planning a surgical path in one embodiment;
FIG. 2 is a schematic flow chart diagram of a path planning method for planning a surgical path in one embodiment;
FIG. 3 is a schematic illustration of a CT scan in one embodiment;
FIG. 4 is a schematic illustration of slice data in one embodiment;
FIG. 5 is a schematic illustration of nodule localization in one embodiment;
FIG. 6 is a schematic representation of a trachea segmentation in one embodiment;
FIG. 7 is a schematic diagram of a branch node in one embodiment;
FIG. 8 is a schematic diagram of an initial planned path in one embodiment;
FIG. 9 is a diagram of feature extraction criteria determination in one embodiment;
FIG. 10 is a diagram of target path determination in one embodiment;
FIG. 11 is a schematic illustration of a virtual path demonstration in one embodiment;
FIG. 12 is a diagram that illustrates feature scores for spatial features in one embodiment;
FIG. 13 is a diagram illustrating path curvature indicator calculation in one embodiment;
FIG. 14 is a diagram of a path distance metric in one embodiment;
FIG. 15 is a schematic illustration of biopsy distance determination in one embodiment;
FIG. 16 is a schematic diagram of a pulmonary nodule multipath planning system in one embodiment;
FIG. 17 is a schematic diagram of a path planning method for planning a surgical path in another embodiment;
FIG. 18 is a block diagram of a path planning device for planning a surgical path in one embodiment;
FIG. 19 is a schematic view of a robotic system in one embodiment;
FIG. 20 is a diagram illustrating an internal structure of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The path planning method for planning the surgical path provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The terminal 102 may include an interactive display terminal for interacting with the operation object and displaying the initial planned path and the target planned path. In particular, the server 104 may determine a plurality of initial planned paths from a starting point of the three-dimensional model to the region to be operated on from the three-dimensional model of the object tissue of the object to be treated. Wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model. The server 104 may then select a target planned path from the plurality of initial planned paths based on the preset spatial features. The interactive display terminal may include, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server, a server cluster formed by a plurality of servers, or the same computer device as the terminal.
In one embodiment, as shown in fig. 2, a path planning method for planning a surgical path is provided, which is exemplified by the application of the method to the server in fig. 1, and includes the following steps:
step S202, according to the three-dimensional model of the object tissue of the object to be treated, a plurality of initial planning paths from the starting point of the three-dimensional model to the region to be operated are determined.
The object to be treated may refer to a patient in a medical diagnosis scene or a medical treatment scene. The subject tissue may refer to tissue of a body part to be treated by the subject to be treated and may include, but is not limited to, tissue of a heart, lung, brain, etc.
In one embodiment, the subject tissue may include at least a portion of lung tissue and at least a portion of bronchial tissue.
A three-dimensional model refers to a model of the tissue of an object. For example, it may refer to a three-dimensional model of the lung, a three-dimensional model of the heart, or a three-dimensional model of the whole body, etc.
In this embodiment, the medical staff may collect raw data of a target portion of the object to be treated, such as CT (computed tomography) data, by the scanning device, and transmit the collected raw data to the server, so that the server constructs a three-dimensional model of the object tissue of the object to be treated based on the collected raw data.
In one embodiment, the server constructs a three-dimensional model of the subject tissue of the subject to be treated based on the acquired raw data, which may include: acquiring slice data of a subject tissue of a subject to be treated; and carrying out three-dimensional reconstruction on the slice data to obtain a corresponding three-dimensional model.
As previously mentioned, the subject tissue may be lung tissue and bronchial tissue. The server may perform a thoracoabdominal volume scan on the patient via a pre-operative detection device, such as CT or MRI (Magnetic Resonance Imaging), to obtain one or more sets of lung slice data, i.e., slice images.
Figure 3 shows a schematic view of performing a thoracoabdominal volume scan in one embodiment. Wherein 301 is a measured object, 302 is a detection device, and the server may scan the measured object 301 through the detection device 302 to obtain slice data.
In this embodiment, the slice data obtained by the server may be as shown in fig. 4, that is, the obtained slice data may include a multi-layer slice image.
Further, the server may perform reconstruction of a three-dimensional model of the lung based on the obtained slice data to obtain a corresponding three-dimensional model. For example, the server may perform reconstruction of a three-dimensional model or the like by medical reconstruction software.
In this embodiment, the server, after determining the three-dimensional model of the object tissue of the object to be treated, may determine a plurality of initial planned paths in the three-dimensional model from a starting point of the three-dimensional model to the region to be operated.
In the present embodiment, the region to be operated on is determined based on the tissue to be operated on in the corresponding object tissue in the three-dimensional model. Wherein the operation includes an operation performed on a part or all of the subject tissue in order to remove a lesion or to alleviate a physical abnormality caused by the lesion, or an operation performed to detect whether a part or all of the subject tissue has a lesion. The tissue to be operated on is the target of the operation. The region to be operated is a model region corresponding to the tissue to be operated in the three-dimensional model. By way of example, the target tissue includes lung tissue and lungs of bronchial tissue, the three-dimensional model is a three-dimensional model of the lungs, the tissue to be operated on may be a pulmonary nodule, and the region to be operated on refers to a region determined in the three-dimensional model based on the pulmonary nodule.
In this embodiment, the starting point refers to a starting position for performing the path planning, and continuing to take the lung as an example, the starting point may refer to a position in the three-dimensional model corresponding to the position of the surgical flexible instrument when entering the bronchus of the lung.
In this embodiment, after determining the starting point of the three-dimensional model of the target tissue of the object to be treated and the region to be operated, the server may plan the initial path based on the three-dimensional model to obtain a plurality of initial planned paths for diagnosis and treatment. For example, when the diagnosis is biopsy sampling, the plurality of initial planned paths are determined as initial sampling paths for performing biopsy sampling.
In this embodiment, the number of initially planned paths and the path trajectory determined by the server may be different according to different location points and different area sizes of the area to be operated. The method may be determined according to an actual application scenario, and the method is not limited in this respect.
Step S204, selecting a target planning path from the plurality of initial planning paths.
Wherein the selected target planning path comprises at least one preset spatial feature.
In this embodiment, the spatial feature refers to a path feature of the initially planned path, and may include, but is not limited to, at least one of a path curvature, a path distance, and a biopsy region sampling distance.
In this embodiment, the server may extract the spatial features of each initial planned path based on a preset feature extraction standard, so as to obtain the spatial features corresponding to each initial planned path. If the feature extraction criterion is to extract a path curvature, the server may extract a path curvature from each initial planned path, and if the feature extraction criterion is to extract a path distance and a biopsy region sampling distance, the server may extract a path distance and a biopsy region sampling distance from each initial planned path, which may be specifically determined based on the needs of an actual application scenario, which is not limited herein.
In this embodiment, after the spatial features are extracted, the server determines a target planned path from the plurality of initial planned paths, that is, determines a target planned path for performing biopsy sampling, by comparing and determining the extracted spatial features and combining with a selection instruction of a medical staff.
In one embodiment, the server may control the surgical robot based on the determined target planned path such that the surgical robot performs the target action. For example, taking lung biopsy sampling as an example, after determining the target planned path, the server may obtain the biopsy sample by controlling the surgical robot such that the surgical robot performs biopsy sampling of a lung nodule location based on the target planned path.
In the path planning method for planning the surgical path, a plurality of initial planned paths from a starting point of the three-dimensional model to an area to be operated are determined according to the three-dimensional model of the object tissue of the object to be treated, wherein the area to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model, and then a target planned path is selected from the plurality of initial planned paths, wherein the selected target planned path comprises at least one preset spatial feature. Therefore, a plurality of initial planning paths from the starting point to the region to be operated can be determined based on the three-dimensional model of the object tissue of the object to be treated, and then the target planning path is selected from the initial planning paths based on the preset spatial characteristics, so that the determination of the target path combines the spatial characteristics of all paths, and the accuracy of the determination of the target path can be improved.
In one embodiment, as mentioned above, the target tissue is lung tissue and bronchial tissue, and the region to be operated on may refer to a region determined based on a pulmonary nodule. The server may determine the three-dimensional model based on a cross-sectional image, a coronal image and a sagittal image of the lungs of the subject to be treated.
Specifically, the slice data acquired by the detection device may include a cross-sectional image, a coronal plane image, and a sagittal plane image of the lung, and the server may determine the three-dimensional model of the target tissue according to the cross-sectional image, the coronal plane image, and the sagittal plane image, and further determine a region to be operated of the target tissue, that is, a position region where a lung nodule is located.
Referring to fig. 5, the server may determine a region to be operated in the subject tissue of the subject to be treated, i.e., a region where a pulmonary nodule is located, based on three views consisting of a cross-sectional image, a coronal plane image, and a sagittal plane image, as indicated by a black dot in fig. 5.
In particular, the server may determine the location region of a lung nodule based on a cross-sectional image, a coronal image, and a sagittal image of the lung, in conjunction with medical imaging criteria.
In this embodiment, it can be determined based on medical imaging standards that in the CT image, the pixel values of different tissues are not consistent, the trachea inside the lung is filled with air, the air is partially pure black, and the pixel value of the nodule in the CT image is close to the trachea but the spatial structure is completely different from the trachea. The server may determine a pulmonary nodule based on the pixel values of the slice picture in the slice data and the spatial structure of the pulmonary trachea and determine the location region of the pulmonary nodule.
In one embodiment, the region to be operated on may include the coordinates of the center point of the pulmonary nodule and the nodule region range.
Wherein, the central point position is the central coordinate position of the pulmonary nodule. The nodule range may be used to indicate the size of pulmonary nodules.
With continued reference to fig. 5, the server may mark the region to be operated on in the three-dimensional model corresponding to the pulmonary nodule with a maximum circumscribed spherical surface 501, or may directly use the actual outer surface contour 502 of the corresponding nodule in the three-dimensional model to represent the region to be operated on.
Specifically, whether the lung nodule is marked by the maximum circumscribed spherical surface 501 or the actual outer surface contour 502, the central space coordinate, i.e., the center point coordinate, of the lung nodule and the maximum size range of the lung nodule in three axes, i.e., the nodule region range, need to be determined.
In this embodiment, the server may determine the maximum size ranges, i.e., ab, cd, and ef, of the corresponding lung nodules in the three-dimensional model on three coordinate axes according to the cross-sectional image, the coronal plane image, and the sagittal plane image, and then mark the region to be operated corresponding to the lung nodules by using the maximum circumscribed spherical surface 501 or the actual outer surface contour 502.
In this embodiment, the server may plan an initial planned path from the start point to the region to be operated, by using the coordinates of the center point as coordinates of an end point of path planning, that is, an end point of biopsy acquisition, and combining with the range of the nodule region.
In one embodiment, determining a plurality of initial planning paths from a starting point of the three-dimensional model to the region to be operated on based on the three-dimensional model of the object tissue of the object to be treated may include: determining a path channel and a path branch node in object tissue according to a three-dimensional model of the object tissue of an object to be treated; and determining an initial planning path from an initial point of the three-dimensional model to the region to be operated according to the path channel and the path branch node.
In this embodiment, after obtaining the three-dimensional model of the object tissue, the server may determine the path channel in the object tissue based on the three-dimensional model.
Specifically, continuing with the example of lung tissue and bronchial tissue, the pathway channels may be lung airways, and the server may determine each lung airway based on the three-dimensional model.
In this embodiment, referring to fig. 6, the server may perform the growth of the path channel by using an image segmentation algorithm, such as setting a seed point and a growth direction, to determine the path channel corresponding to the object tissue.
Further, the server may perform skeletonization and refinement processing on the path channel to determine a path center line of the path channel, and then determine a center point intersection point or a bifurcation point as a path branch node based on the obtained path center point. Specifically, when the target tissue is lung tissue and bronchial tissue, the path channel is a lung trachea, the path center line is a trachea center point, and the path branch node is a trachea branch node.
In this embodiment, the server may perform skeletonization and refinement processing by using multiple algorithms to obtain a path centerline and a path branch node, where the obtained path branch node may be as shown in fig. 7, and each black dot in fig. 7 represents a corresponding path branch node.
In one embodiment, the server may determine the path centerline and the path branch nodes through a voxel-based algorithm.
Specifically, the server may first extract a contour of voxel data segmented out of the path channel, which is labeled as 0, and mark the region non-edge data as 1, that is, mark the contour of the path channel as 0, and mark each voxel in the path channel as 1.
Then, the server can calculate a minimum distance value, such as a chamfer distance, of the distance profile for each voxel in the path channel, and obtain a distance field of the entire three-dimensional model, wherein the distance field reflects the profile information of the model to a certain extent. For example, the value on the contour is 0, and the distance difference between two adjacent voxels is greater than or equal to 1.
Further, the server may determine skeleton lines and branch nodes, i.e., a path centerline and path branch nodes, based on the distance field.
Further, the server can also perform filtering processing on the obtained skeleton line to obtain a smoother skeleton line.
In this embodiment, after obtaining the path centerline and the path branch node, the server may determine an initial planned path from the starting point to the to-be-operated area based on the obtained path centerline and the path branch node.
Specifically, referring to fig. 8, 801 is a region to be operated, and in the lung tissue, there may be a region where a pulmonary nodule is located, and 802 is a starting point, for example, there may be a location where a main airway of the lung tissue is located. In this embodiment, the server may determine an initial planned path from the starting point to the area to be operated according to the path centerline and the path branch node, that is, determine that there are 3 initial planned paths.
In one embodiment, each of the initial planned paths is smoothed.
Specifically, after the server determines the initial planned path, the server may perform multiple convolution operations on the initial planned path by using filter templates of different sizes to obtain a smooth curve, that is, each initial planned path after being smoothed.
In one embodiment, selecting a target planned path from a plurality of initial planned paths may include: sending the initial planning paths to a terminal for displaying; and selecting a target planning path from the plurality of initial planning paths based on a selection instruction fed back by the terminal.
In this embodiment, after the server obtains a plurality of initial planned paths, performs feature extraction on each initial planned path, and generates corresponding spatial features, the server may send the plurality of initial planned paths to a terminal for display, so that medical staff select/sort according to the spatial features to determine a target planned path. For example, referring to fig. 9, a healthcare worker may select his or her own preferences to make recommendations for a target planned path.
In this embodiment, referring to fig. 10, the server may determine a recommended path from the plurality of initially planned paths according to the preference of the medical staff's selection, and then present the determined recommended path to the medical staff through the terminal.
In this embodiment, referring to fig. 11, when the server displays the recommended path to the medical staff through the terminal, the recommended path may be displayed in a simulated manner, that is, the three-dimensional model, the path channel, and the recommended path to be constructed are displayed on the terminal through an external section view angle, and compared with an endoscopic view angle, the server may focus on the spatial turning amplitude and direction of the planned path more realistically from the external section view angle.
In this embodiment, with continued reference to fig. 10, the medical staff may determine to accept the recommended route, that is, determine that the recommended route is the target planned route, based on the recommended route displayed by the terminal, or may not accept the recommended route, that is, the server may determine the target planned route from the plurality of initial planned routes based on the selection instruction fed back by the terminal.
In one embodiment, the determining the target planned path from the plurality of initial planned paths based on the selection instruction fed back by the terminal includes at least one of: sequencing each initial planning path based on the space characteristics contained in the selection instruction fed back by the terminal to determine a target planning path; and determining the target planning path according to the initial planning path provided in the selection instruction fed back by the terminal, wherein the target planning path comprises preset spatial characteristics.
In this embodiment, referring to fig. 10, after the server sends the recommended path to the terminal and displays the recommended path to the medical care personnel, when the medical care personnel determines to accept the recommended path, the terminal may generate a feedback instruction and feed the feedback instruction back to the server. The recommended path may be a plurality of initial planned paths obtained. Or the recommended path comprises a plurality of initially planned paths and representations of respective spatial features, for example, describing the spatial features by color marks, words or the like for medical staff to select, and the like.
Specifically, the selection instruction fed back by the terminal is an instruction generated based on a prompt/option on a human-computer interaction interface displayed by the terminal, and at this time, the server may determine a certain recommended path as the target planning path based on the selection instruction. The server may then save the target planning path as a navigation path for controlling the robot to take biopsy samples and output.
In this embodiment, when the medical staff determines not to accept the recommended path of the server, the terminal may feed back a selection instruction based on the user instruction, that is, according to the selection of the medical staff on the plurality of initial planned paths, determine the target planned path from the plurality of initial planned paths.
In one embodiment, the server may re-determine the spatial features based on the selection instruction fed back by the terminal, and rank the spatial features in each initial planned path to determine the target planned path.
Specifically, when the user determines that the recommended path of the server is not accepted, the server may send an instruction of whether to reset the decision to the terminal, and when the terminal feeds back and determines the reset decision, the server sorts the spatial features in each initially planned path based on the spatial features (i.e., the preference for reselection) determined again by the medical staff, determines the recommended path again, and sends the recommended path to the terminal, so that the medical staff performs determination and selection processing.
Specifically, the server may obtain a feature score of each initial planned path corresponding to each spatial feature based on the spatial feature extracted from each initial planned path.
In this embodiment, each spatial feature of the initially planned path may be represented by a feature score, that is, for the same spatial feature, the feature scores corresponding to each initially planned path are different, as in fig. 12, for the path curvature, the feature scores of path 1, path 2, and path 3 are different.
In this embodiment, the server may normalize the feature score of each spatial feature to a value between 0 and 1, where the feature score corresponding to the path 1 is 0.65, the feature score corresponding to the path 2 is 1, and the feature score corresponding to the path 3 is 0.45, so as to improve the contrast and further improve the user experience.
In this embodiment, the server may send each initial planned path and the feature score of each corresponding spatial feature to the terminal, and display the result to the medical staff through the terminal.
Further, the medical staff may select the spatial feature based on the initial planned path and the feature score displayed by the terminal, for example, the medical staff may select a path curvature, a path distance, or a biopsy region sampling distance.
Further, the server may perform sequencing of the initial planned paths based on the spatial features selected by the medical staff, so that the server determines a recommended path from the plurality of initial planned paths and sends the recommended path to the terminal for display, and determines a target planned path based on a selection instruction of the medical staff.
In one embodiment, with continued reference to fig. 10, when the healthcare worker determines not to reset the decision, i.e., not to re-determine the spatial feature, at this time, the terminal may present the plurality of initial planned paths to the healthcare worker, and generate a selection instruction based on a selection of the healthcare worker, so that the server may determine, based on the selection instruction, that an initial planned path selected by the healthcare worker from the presented plurality of initial planned paths is a target planned path.
As previously described, the spatial features may include at least one of path curvature, path distance, and biopsy region sampling distance.
In one embodiment, the spatial features may include path curvature.
In this embodiment, selecting the target planned path from the plurality of initial planned paths may include: extracting curvature extreme points of each initial planning path; and determining a target planning path from the plurality of initial planning paths by analyzing the curvature extreme points of the initial planning paths.
In this embodiment, referring to fig. 13, each initially planned path may include a plurality of broken line segments, that is, may include a plurality of transitions, for example, n turns, where one turn may be understood as one curved line segment, and then the initially planned path may include n curved line segments.
In this embodiment, the server may determine a fitting equation set corresponding to the initial planned path based on each curve segment of the initial planned path, and then obtain the curvature extreme point corresponding to the initial planned path based on the fitting equation set.
In this embodiment, for each initial planned path, the server may determine the curvature extreme point corresponding to each initial planned path by constructing a fitting equation set.
Further, the server may determine the curvature of each curve segment in the initial planned path according to the determined curvature extreme point of the initial planned path, for example, the curvature of each curve segment may be represented as V1, V2, V3 … Vn, respectively.
In this embodiment, after determining the curvature of each curve segment corresponding to each curve segment, the server may calculate the curvature of the curve of the initially planned path by the following formula (1).
In the present embodiment, the server traverses each initial planned path, and may obtain the curvature of the curve of each initial planned path, which is respectively labeled as S1, S2, and S3 … Sm.
Further, the server may calculate the curvature score of each initial planned path by the following formula (2), which may be a numerical value between 0 and 1.
Wherein S ismin(SminNot equal to 0) is the minimum of a plurality of curve curvatures, MiDenotes a path curvature index, SiThe curve curvature of the smooth curve corresponds to the ith initially planned path.
In this embodiment, the server may define that the initial planned path with the curvature score of 1 is optimal, and the worse the initial planned path closer to 0 is, the server may determine, based on the curvature of the curve, the target planned path in the initial planned path, for example, the server determines that the initial planned path with the curvature score closest to 1 is the target planned path.
In one embodiment, the spatial features may include path distances.
In this embodiment, selecting the target planned path from the plurality of initial planned paths may include: calculating each path distance of each single initial planning path by using each path branch node in each initial planning path; and determining initial planned paths meeting preset path distance conditions as target planned paths, wherein the path distance conditions are set according to evaluation of path distances of the initial planned paths.
Specifically, for each initial planned path, the server may determine node distances between branch nodes of adjacent paths in the initial planned path, and then determine a total distance of the initial planned path based on the determined node distances, that is, determine path distances of single initial planned paths.
In this embodiment, after determining the total distance corresponding to each initial planned path, the server may determine the target planned path from the plurality of initial planned paths according to each total distance of each initial planned path.
In this embodiment, after determining each initial planned path, the server may determine each path branch node included in each initial planned path based on the determined path branch node of the path channel of the target site. For example, referring to fig. 14, the server may determine that the path branch node corresponding to the initial planned path includes nodes b1, b2, b3, b4, b5, b6, b7, b 8.
In this embodiment, the path branch node of each initially planned path may also include a start point and an end point, that is, a start point and an area to be operated.
Further, for the initial planned path shown in fig. 14, the server may sequentially calculate node distances of a start point and a node b1, a node b1 and a node b2, a node b2 and a node b3, a node b3 and a node b4, a node b4 and a node b5, a node b5 and a node b6, a node b6 and a node b7, a node b7 and a node b8, and a node b8 and a termination point (to-be-operated area), and obtain a total distance of the initial planned path based on the respective node distances.
Specifically, the server may sequentially calculate euclidean distances between every two adjacent path branch nodes of the initial planned path, and add the euclidean distances to obtain a total distance of each initial planned path. It should be understood by those skilled in the art that this is only an example, and in other embodiments, the server may also directly perform an integral operation on the initial planned path to obtain the total distance corresponding to the initial planned path.
Further, the server may determine a distance score for each initial planned path based on the determined total distance for each initial planned path.
Specifically, the total distance of each initial planned path is denoted as L1, L2, and L3 … Ln, respectively, and the server can calculate the distance score of each initial planned path by the following formula (3).
Wherein L ismin(LminNot equal to 0) is the minimum of the total distances of the plurality of initially planned paths, PiDistance score, L, for the ith initially planned pathiFor the ith initial planThe total distance of the path.
In this embodiment, the preset path distance condition is used to evaluate the path distance of each initially planned path, and the preset path distance condition may be a distance score optimal condition, for example, the server may define that an initially planned path with a distance score of 1 is optimal, and the initially planned path with a distance score closer to 0 is worse, after the server obtains the distance score corresponding to each initially planned path, the initially planned path with a path score closest to 1 may be determined to be the target planned path. It should be understood by those skilled in the art that the present disclosure is only illustrative, and in other embodiments, the preset path distance condition may be other conditions, the path distance is shortest, and the like, which is not limited in the present disclosure.
In one embodiment, the spatial characteristic may comprise a biopsy region sampling distance.
In this embodiment, selecting the target planned path from the plurality of initial planned paths may include: determining a biopsy region sampling distance according to each tail end path branch node of each initial planning path; a target planned path is determined from the plurality of initial planned paths based on the respective biopsy region sampling distances.
In this embodiment, the server may determine, based on each initial planned path, each end path branch node corresponding to each initial planned path, and determine each biopsy region sampling distance from each end path branch node to the region to be operated.
Specifically, referring to fig. 15, for the initial planned path 151, the corresponding end path branch node is point a, for the initial planned path 152, the corresponding end path branch node is point B, and the to-be-operated region (i.e., the biopsy region), for example, the central point of the pulmonary nodule is point O, the server may calculate the distance between the end path branch node of each initial planned path and the to-be-operated region, respectively, to obtain the sampling distance of each biopsy region. I.e. the biopsy region sampling distance d1 between AOs and the biopsy region sampling distance d2 between BO.
Further, the server may determine a biopsy range score for each initial planned path based on each biopsy region sampling distance.
In this embodiment, the initial planned path may be n, the biopsy region sampling distances obtained by the server may be respectively represented as d1, d2, and d3 … dn, and the server may calculate the biopsy range score of each initial planned path by the following formula (4) based on the biopsy region sampling distances d1, d2, and d3 … dn.
Wherein D isiRepresenting the biopsy Range score, dmaxRepresenting the maximum of a plurality of biopsy region sampling distances, diThe biopsy region sampling distance representing the ith initial planned path.
In this embodiment, the server may define that the initial planned path with the biopsy range score of 1 is optimal, and the initial planned path with the biopsy range score closer to 0 is worse, that is, the server may determine the target planned path of the initial planned path with the area range score closest to 1 according to the biopsy range score of each initial planned path.
In one embodiment, a pulmonary nodule multipath planning system is also provided, which is described in detail below in conjunction with fig. 16.
In this embodiment, the pulmonary nodule multi-path planning system may include a path calculation unit, a structural feature extraction unit, an interaction unit, an evaluation unit, and a virtual navigation unit.
In this embodiment, the path calculating unit is configured to calculate a path that needs to be traveled from the starting point to the region to be operated, that is, calculate a path that needs to be traveled from the main trachea of the lung to the center point of the target nodule.
The structural feature extraction unit is used for extracting corresponding spatial features, such as path curvature, path distance, biopsy region sampling distance and the like, from one or more initial planning paths obtained by the path calculation unit.
The interaction unit provides the medical staff with the path decision with different characteristic priority for selection, and then the medical staff can autonomously decide whether to accept or manually select after the conclusion of the evaluation unit is obtained.
And the evaluation unit performs characteristic evaluation on all initial planned paths according to the decision selected by the interaction unit and gives an optimal path, namely a recommended path.
The virtual navigation unit is used in the interaction unit, and can demonstrate the advancing condition of the catheter tip in the path channel from head to tail along the path by adopting a virtual endoscopic or virtual global visual angle, such as the advancing condition in the lung bronchus, so as to help medical staff visually and intuitively know the form of the selected path.
In this embodiment, the interaction unit and the virtual navigation unit are located at an interface interaction layer, i.e., a top layer, and the decision information of the medical care personnel can be directly obtained. The middle layer is an evaluation unit which can receive the top layer information and the bottom layer calculation information, evaluate the comprehensive score and output the result to the top layer. The path calculation unit and the structural feature extraction unit belong to a calculation bottom layer and complete a specific algorithm function.
In this embodiment, the pulmonary nodule multi-path planning system may determine the target planning path based on the path planning method shown in fig. 17, which is described in detail below with reference to fig. 17.
Specifically, the server may perform chest and abdomen in vivo scanning on the object to be measured in a preoperative manner or the like to obtain a set of slice images.
The server may then perform a three-dimensional reconstruction of the lungs as data input for airway and nodule segmentation.
Further, the image segmentation algorithm can be used for extracting the outline of each level of lung trachea from the lung three-dimensional model, and the spatial position and the outline size of a nodule in the lung model can be positioned by combining with the medical imaging knowledge.
Further, according to the obtained contours of all levels of tracheas, corresponding center lines and intersection point information of all levels of branch airways are obtained through a skeletonization algorithm, and the center lines of the tracheas and branch nodes of the tracheas are obtained through recording.
Further, the main airway gallery protrusion can be set as a starting point, the nodule center is set as a terminal point, and a path communicating the two points is calculated according to a path planning algorithm, namely, an initial planning path is determined.
Further, the distance of each level of branch nodes passing through can be extracted according to the output initial planned path, and curve curvatures and the like of upper and lower adjacent two levels of curve segments in each initial planned path are calculated to extract path characteristics.
Further, an interaction one is performed, wherein the evaluation criteria are selected by the medical staff within the alternative decision, i.e. the feature extraction criteria are determined.
And further, evaluating and sequencing each initial planning path according to the determined feature extraction standard, and determining a recommended alternative planning path.
Further, the medical staff can demonstrate the scene of the robot catheter tip after entering the pulmonary airway through a virtual endoscope or a global visual angle or a simultaneous display mode according to the alternative planning path which is selected by the medical staff and is desired to be demonstrated from the starting point to the end point.
Further, an interaction two is performed to ask the medical staff whether to accept the best result calculated by the system, namely the recommended alternative planning path, and when the medical staff determines to change the decision, namely to re-determine the extraction standard, the medical staff jumps back to the interaction one, re-determines the extraction standard and re-processes.
It should be understood that, although the steps in the flowcharts of fig. 2, 10 and 17 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2, 10, and 17 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performing the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternatingly with other steps or at least some of the sub-steps or stages of other steps.
In one embodiment, as shown in fig. 18, there is provided a path planning apparatus for planning a surgical path, comprising: an initial planned path determination module 181 and a target planned path determination module 182, wherein:
an initial planned path determining module 181, configured to determine, according to a three-dimensional model of a subject tissue of a subject to be treated, a plurality of initial planned paths from a start point of the three-dimensional model to a region to be operated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model.
A target planned path selection module 182, configured to select a target planned path from the plurality of initial planned paths; wherein the selected target planning path comprises at least one preset spatial feature.
In one embodiment, the target planning path determining module 182 may include:
and the sending submodule is used for sending the initial planning paths to the terminal for displaying.
And the first target planning path determining submodule is used for selecting a target planning path from the plurality of initial planning paths based on a selection instruction fed back by the terminal.
In one embodiment, the first target planning path determining sub-module may include at least one of the following:
and the first target planning path determining unit is used for sequencing each initial planning path based on the spatial characteristics contained in the selection instruction fed back by the terminal so as to determine the target planning path.
And the second target planning path determining unit is used for determining the target planning path according to the initial planning path provided in the selection instruction fed back by the terminal, wherein the target planning path comprises preset spatial characteristics.
In one embodiment, the spatial features may include at least one of path curvature, path distance, and biopsy region sampling distance.
In one embodiment, the spatial features include path curvature.
In this embodiment, the target planning path determining module 182 may include:
and the curvature extreme point extraction submodule is used for extracting the curvature extreme points of each initial planning path.
And the second target planned path determining submodule is used for determining the target planned path from the initial planned paths by analyzing the curvature extreme points of the initial planned paths.
In one embodiment, the spatial features may include path distances.
In this embodiment, the target planning path determining module 182 may include:
and the path distance determining submodule is used for calculating the path distances of the single initial planning paths by utilizing the path branch nodes in each initial planning path.
And the third target planned path determining submodule is used for determining the initial planned path meeting the preset path distance condition as the target planned path.
In one embodiment, the spatial characteristic may comprise a biopsy region sampling distance.
In this embodiment, the target planning path determining module 182 may include:
and the sampling distance determining submodule is used for determining the sampling distance of the biopsy region according to each tail end path branch node of each initial planning path.
And the fourth target planning path determining submodule is used for determining a target planning path from the plurality of initial planning paths based on the sampling distance of each biopsy region.
In one embodiment, the initial planned path determining module 181 may include:
and the path channel and path branch node determining submodule is used for determining the path channel and the path branch node in the object tissue according to the three-dimensional model of the object tissue of the object to be treated.
And the initial planning path determining submodule is used for determining an initial planning path from an initial point of the three-dimensional model to the to-be-operated area according to the path channel and the path branch node.
In one embodiment, the path channel is generated by setting seed points and the growth direction of the channel based on a three-dimensional model; and the path branch nodes are determined according to the obtained bifurcation points of the path center lines after the determined path channels are subjected to skeletonization and thinning treatment.
In one embodiment, each of the initial planned paths is smoothed.
In one embodiment, the object tissue may include at least a portion of lung tissue and at least a portion of bronchial tissue, and the region to be manipulated may include a region of the three-dimensional model determined based on a lung nodule.
In one embodiment, the region to be manipulated is marked by the maximum circumscribed sphere of a pulmonary nodule or the actual outer surface contour of a pulmonary nodule.
In one embodiment, the region to be operated on is determined based on a cross-sectional image, a coronal plane image, and a sagittal plane image of the lungs of the subject to be treated.
In one embodiment, the region to be operated on may include the coordinates of the center point of the pulmonary nodule and the nodule region range.
For specific definition of the path planning apparatus for planning the surgical path, reference may be made to the above definition of the path planning method for planning the surgical path, and details are not described here. The modules in the path planning device for planning the surgical path may be implemented in whole or in part by software, hardware, and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, as shown in fig. 19, there is provided a robot system, which may include: memory 191, processor 192, and robot execution end 193.
In this embodiment, the memory 191 stores a computer program, and the processor 192, when executing the computer program, implements the path planning method for planning a surgical path according to any of the above embodiments to determine a target planned path. The robot effector 193 is used to move from an initial point of the subject tissue of the subject to be treated to the region to be operated according to the target planned path.
In one embodiment, the system may further include a robot end, such as a robot trolley 194, the robot trolley 194 having a robot arm carried thereon, and the robot actuation tip 193 being mounted to the robot arm.
Specifically, the robot trolley 194 may be provided with a plurality of robots, each robot being provided with a different robot actuating tip 193 such as a scalpel, a forceps, an endoscope, etc., and each robot actuating tip 193 being used to perform a different target motion.
In one embodiment, the robot system may further include: an image display end, such as a trolley 195. The image trolley 195 is used for displaying a target planned path and displaying a movement track of the robot execution end 193.
In one embodiment, the robotic system may further include a ventilator, CT machine, or the like for providing assistance in performing clinical procedures.
In one embodiment, the robotic system may further include a sterile table 196 for holding a patient, i.e., the subject to be treated as described above.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 20. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for storing data such as the three-dimensional model and the initial planning path. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a path planning method for planning a surgical path.
Those skilled in the art will appreciate that the architecture shown in fig. 20 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, there is provided a computer device comprising a memory storing a computer program and a processor implementing the following steps when the processor executes the computer program: determining a plurality of initial planning paths from a starting point of the three-dimensional model to an area to be operated according to the three-dimensional model of the object tissue of the object to be treated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model; and extracting the spatial features of the initial planning paths, and determining a target planning path from the plurality of initial planning paths based on the extracted spatial features.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of: determining a plurality of initial planning paths from a starting point of the three-dimensional model to an area to be operated according to the three-dimensional model of the object tissue of the object to be treated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model; and extracting the spatial features of the initial planning paths, and determining a target planning path from the plurality of initial planning paths based on the extracted spatial features.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (18)
1. A path planning method for planning a surgical path, the method comprising:
determining a plurality of initial planning paths from a starting point of a three-dimensional model to an area to be operated according to the three-dimensional model of the object tissue of the object to be treated; wherein the region to be operated is determined based on the tissue to be operated in the corresponding object tissue in the three-dimensional model;
selecting a target planned path from the plurality of initial planned paths; wherein the selected target planning path comprises at least one preset spatial feature.
2. The method of claim 1, wherein said selecting a target planned path from the plurality of initial planned paths comprises:
sending the initial planning paths to a terminal for display;
and selecting a target planning path from the plurality of initial planning paths based on a selection instruction fed back by the terminal.
3. The method according to claim 2, wherein the selecting a target planned path from the plurality of initial planned paths based on the selection instruction fed back by the terminal includes at least one of:
sequencing each initial planning path based on the space characteristics contained in the selection instruction fed back by the terminal to determine a target planning path; and
and determining the target planning path according to the initial planning path provided in the selection instruction fed back by the terminal, wherein the target planning path comprises preset spatial characteristics.
4. The method of claim 1, wherein the spatial features include at least one of path curvature, path distance, and biopsy region sampling distance.
5. The method of claim 1, wherein the spatial feature comprises a path curvature; the selecting a target planned path from the plurality of initial planned paths comprises:
extracting curvature extreme points of each initial planning path;
and determining a target planning path from the plurality of initial planning paths by analyzing the curvature extreme points of the initial planning paths.
6. The method of claim 1, wherein the spatial features comprise path distances; the selecting a target planned path from the plurality of initial planned paths comprises:
calculating each path distance of each single initial planning path by using each path branch node in each initial planning path;
and determining an initial planned path meeting a preset path distance condition as a target planned path, wherein the path distance condition is set according to evaluation of path distances of the initial planned paths.
7. The method of claim 1, wherein the spatial feature comprises a biopsy region sampling distance; the selecting a target planned path from the plurality of initial planned paths comprises:
determining the biopsy region sampling distance according to each end path branch node of each initial planning path;
a target planning path is determined from the plurality of initial planning paths based on each of the biopsy region sampling distances.
8. The method according to claim 1, wherein determining a plurality of initial planned paths from a starting point of the three-dimensional model to the region to be operated on from the three-dimensional model of the subject tissue of the subject to be treated comprises:
determining a path channel and a path branch node in object tissue of an object to be treated according to a three-dimensional model of the object tissue;
and determining an initial planning path from an initial point of the three-dimensional model to the to-be-operated area according to the path channel and the path branch node.
9. The method of claim 8, wherein the path channel is generated by setting seed points and a channel growth direction based on the three-dimensional model; the path branch node is determined according to a branch point of the obtained path central line after the determined path channel is subjected to skeletonization and thinning processing.
10. The method of claim 1, wherein each of the initially planned paths is smoothed.
11. The method of claim 1, wherein the object tissue comprises at least a portion of lung tissue and at least a portion of bronchial tissue, and the region to be operated on comprises a region of the three-dimensional model determined based on a pulmonary nodule.
12. The method of claim 11, wherein the region to be manipulated is marked by a maximum circumscribing sphere of the pulmonary nodule or an actual outer surface contour of the pulmonary nodule.
13. The method according to claim 1, characterized in that the three-dimensional model is determined based on a cross-sectional image, a coronal image and a sagittal image of the lungs of the subject to be treated.
14. The method of claim 1, wherein the area to be operated comprises: the coordinates of the center point of the pulmonary nodule and the region of the nodule.
15. A robotic system, characterized in that the system comprises: a memory, a processor, and a robot execution end;
the memory stores a computer program, and the processor implements the method of any one of claims 1-14 when executing the computer program to determine a target planned path;
the robot executing end is used for moving from an initial point of the object tissue of the object to be treated to the area to be operated according to the target planning path.
16. The system of claim 15, further comprising a robot arm end loaded with a robot arm on which the robot effector tip is mounted.
17. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the method of any of claims 1-14.
18. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 14.
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| WO2023245830A1 (en) * | 2022-06-22 | 2023-12-28 | 苏州景昱医疗器械有限公司 | Path planning apparatus and method, surgical system, and computer-readable storage medium |
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