Model surface sharp bulge removal method and device, medical system and equipment

Abstract

The embodiment of the invention discloses a method and device for removing sharp protrusions on the surface of a model, a medical system and equipment. The method comprises the steps that multiple to-be-processed data points located in a model sharp corner area are screened from multiple data points of a to-be-processed brain nucleus model; for the plurality of to-be-processed data points, determining to-be-rejected data points from the plurality of to-be-processed data points based on relative position information between the current to-be-processed data point and a first brain nucleus model associated with the to-be-processed brain nucleus model; according to the method, the to-be-processed brain nucleus model is subjected to surface sharp protrusion removal processing on the basis of the to-be-removed data points, the target brain nucleus model is obtained, sharp spines in the brain nucleus model are reduced, the authenticity and attractiveness of the brain nucleus model are improved, connectivity in the nucleus is effectively protected through an internal sharp corner protection mechanism, and the reliability of the brain nucleus model is improved. The internal structure between the combined nucleus models is prevented from being damaged in the sharp corner removing process, and the authenticity of the brain nucleus model is further improved.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular, to a method, device, medical system and equipment for removing sharp protrusions on the surface of a model. Background Art

[0002] Constructing a three-dimensional model of brain nuclei that can truly reflect the brain nuclei of the human body has a profound impact on human brain research. At present, there are a large number of sharp protrusions on the surface of the constructed three-dimensional model of brain nuclei, and these sharp protrusions affect the visual beauty during model rendering. In order to eliminate the sharp protrusions on the three-dimensional model of brain nuclei, a smoothing algorithm is usually used to smooth the surface of the three-dimensional model of brain nuclei.

[0003] However, there are nerve fiber connection structures representing adjacent nuclear nerve pathways between different three-dimensional models of brain nuclei, and these connection structures also appear in the form of sharp protrusions in the three-dimensional model. Using the above-mentioned smoothing algorithm will eliminate all the sharp protrusions on the three-dimensional model of brain nuclei, resulting in the problem of over-smoothing of the three-dimensional model of brain nuclei. In this way, the smoothed three-dimensional model of brain nuclei does not conform to the real structure of the brain nuclei, and there is a technical problem of poor authenticity of the three-dimensional model of brain nuclei. Summary of the Invention

[0004] The present invention provides a method, device, medical system and equipment for removing sharp protrusions on the surface of a model, so as to reduce the sharp spikes in the model, improve the authenticity and beauty of the brain nuclei model, and effectively protect the connectivity inside the nuclei through an internal sharp corner protection mechanism, avoiding damaging the internal structure between the combined nuclei models during the process of removing sharp corners, and further improving the authenticity of the brain nuclei model.

[0005] In a first aspect, an embodiment of the present invention provides a method for removing sharp protrusions on the surface of a model, the method comprising:

[0006] Screening a plurality of data points to be processed located in the sharp corner area of the brain nuclei model to be processed from a plurality of data points of the brain nuclei model to be processed;

[0007] For the plurality of data points to be processed, determining data points to be removed from the plurality of data points to be processed based on the relative position information between the current data point to be processed and a first brain nuclei model associated with the brain nuclei model to be processed;

[0008] Performing surface sharp protrusion removal processing on the brain nuclei model to be processed based on the data points to be removed to obtain a target brain nuclei model.

[0009] In a second aspect, an embodiment of the present invention further provides a device for removing sharp protrusions on the surface of a model, the device comprising:

[0010] A data point screening module, configured to screen multiple data points to be processed located in the sharp corner area of the brain nucleus model to be processed from multiple data points of the brain nucleus model to be processed;

[0011] An eliminated data point determination module, configured to determine eliminated data points from the multiple data points to be processed based on the relative position information between the current data point to be processed and a first brain nucleus model associated with the brain nucleus model to be processed for the multiple data points to be processed;

[0012] A target model determination module, configured to perform surface sharp protrusion removal processing on the brain nucleus model to be processed based on the eliminated data points to obtain a target brain nucleus model.

[0013] In a third aspect, an embodiment of the present invention further provides an electronic device, which includes:

[0014] One or more processors;

[0015] A storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the model surface sharp protrusion removal method according to any one of the embodiments of the present invention.

[0016] In a fourth aspect, an embodiment of the present invention further provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the model surface sharp protrusion removal method according to any one of the embodiments of the present invention when executed by a computer processor.

[0017] The technical solution of the embodiment of the present invention screens multiple data points to be processed located in the sharp corner area of the brain nucleus model to be processed from multiple data points of the brain nucleus model to be processed, and further, for the multiple data points to be processed, based on the relative position information between the current data point to be processed and a first brain nucleus model associated with the brain nucleus model to be processed, determines eliminated data points from the multiple data points to be processed, so as to perform surface sharp protrusion removal processing on the brain nucleus model to be processed based on the eliminated data points to obtain a target brain nucleus model. The technical solution of this embodiment reduces the sharp spikes in the model by identifying the sharp corner feature points in the brain nucleus model and performing special processing on these points, improving the authenticity and aesthetics of the brain nucleus model; and by determining whether the surface normal of the sharp corner feature point intersects the surface of the combination of the brain nucleus models, it decides whether to retain the sharp corner feature point, and this internal sharp corner protection mechanism effectively protects the connectivity inside the nucleus, avoiding damage to the internal structure between the combined nucleus models during the sharp corner removal process, and further improving the authenticity of the brain nucleus model. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief introduction will be given below to the drawings required for describing the embodiments. Obviously, the introduced drawings are only the drawings of a part of the embodiments to be described in the present invention, rather than all the drawings. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic structural diagram of three-dimensional models of multiple brain nuclei related to the embodiments of the present invention;

[0020] Figure 2 It is a schematic flowchart of a method for removing sharp protrusions on the model surface provided by the embodiments of the present invention;

[0021] Figure 3 It is a schematic structural diagram of a brain nucleus model to be processed related to the embodiments of the present invention;

[0022] Figure 4 It is a schematic diagram of a brain nucleus model to be processed related to the embodiments of the present invention;

[0023] Figure 5 It is a schematic diagram of a brain nucleus model to be processed and a first brain nucleus model related to the embodiments of the present invention;

[0024] Figure 6 It is a schematic diagram of a target brain nucleus model related to the embodiments of the present invention;

[0025] Figure 7 It is a schematic diagram of another method for removing sharp protrusions on the model surface provided by the embodiments of the present invention;

[0026] Figure 8 It is a schematic diagram of a brain nucleus surface model to be repaired and a hole area related to the embodiments of the present invention;

[0027] Figure 9 It is a specific implementation flowchart of a method for removing sharp protrusions on the model surface provided by the embodiments of the present invention;

[0028] Figure 10 It is a schematic structural diagram of a device for removing sharp protrusions on the model surface provided by the embodiments of the present invention;

[0029] Figure 11 It is a schematic structural diagram of an electronic device provided by the embodiments of the present invention;

[0030] Figure 12 It is a schematic structural diagram of a medical system provided by the embodiments of the present invention. Detailed implementation manners

[0031] Next, in combination with the accompanying drawings and specific embodiments, the present application will be further described. It should be noted that, on the premise of no conflict, the following-described embodiments or technical features can be arbitrarily combined to form new embodiments.

[0032] Before introducing the technical solution, an exemplary description of the application scenario can be given first. The present technical solution can be applied to scenarios where it is necessary to perform surface sharp protrusion removal processing on the three-dimensional model of the brain nucleus.

[0033] Exemplarily, Deep Brain Stimulation (DBS) is an advanced treatment method for treating various neurological diseases. In DBS treatment, accurately predicting the electric field distribution around the stimulating electrode is crucial for optimizing the stimulation parameters and improving the treatment effect. In order to predict the electric field distribution around the stimulating electrode, it is usually necessary to construct a three-dimensional model of the brain nucleus to simulate the stimulation effect of the stimulating electrode in the three-dimensional model of the brain nucleus. It can be seen that constructing a three-dimensional model of the brain nucleus that can truly reflect the brain nucleus has a profound impact on studying the role of the stimulating electrode.

[0034] Currently, usually based on human brain images, such as brain CT, brain MRI and other data, multiple three-dimensional models of the brain nucleus including the structures of different brain nuclei in the human body can be constructed. For the structural schematic diagrams of multiple three-dimensional models of the brain nucleus, see Figure 1 , Figure 1 The rectangular frames in it indicate that there are a large number of sharp protrusions on the surface of the constructed three-dimensional model of the brain nucleus. These sharp protrusions do not conform to the real human nucleus structure and will also affect the visual beauty during model rendering.

[0035] In order to eliminate the sharp protrusions on the three-dimensional model of the brain nucleus, usually a smoothing algorithm can be used to smooth the surface of the three-dimensional model of the brain nucleus. However, the above-mentioned smoothing algorithm will smooth out the nerve fiber structures between different three-dimensional models of the brain nucleus, resulting in the technical problems of poor authenticity of the three-dimensional model of the brain nucleus and poor prediction accuracy of the electrode stimulation range. The purpose of the embodiments of the present invention is to automatically remove the sharp protrusions on the three-dimensional model of the brain nucleus and retain the sharp protrusions representing the nerve fiber structures on the three-dimensional model of the brain nucleus, so as to improve the authenticity of the three-dimensional model of the brain nucleus.

[0036] Embodiment 1

[0037] Figure 2The following is a schematic flowchart of a method for removing sharp protrusions on the surface of a model provided by an embodiment of the present invention. This embodiment is applicable to the situation where it is necessary to remove sharp protrusions on the surface of a three-dimensional model of a brain nucleus. This method can be executed by a device for removing sharp protrusions on the surface of a model, and this device can be implemented in the form of software and / or hardware. The hardware can be an electronic device, such as a mobile terminal, a PC, or a server, etc.

[0038] As Figure 2 shown, the method for removing sharp protrusions on the surface of the model includes:

[0039] S110. Screen a plurality of data points to be processed located in the sharp-corner area of the model from a plurality of data points of the brain nucleus model to be processed.

[0040] Among them, the brain nucleus model to be processed is a three-dimensional model of a brain nucleus that is about to have its sharp protrusions on the surface removed. For a schematic structural diagram of the brain nucleus model to be processed, see Figure 3 . The brain nucleus model to be processed can be a point cloud three-dimensional model composed of a large number of data points. In addition, the brain nucleus model to be processed can also be a three-dimensional surface model composed of a large number of mesh patches, where each mesh patch includes at least three vertices, and these vertices can also be called data points. The sharp-corner area of the model refers to the area of sharp protrusions on the surface of the brain nucleus model to be processed. The data points to be processed are a plurality of data points located in the sharp-corner area of the model.

[0041] In this embodiment, the brain nucleus model to be processed is composed of a large number of data points, and each data point has corresponding three-dimensional coordinate information. For the brain nucleus model to be processed, the data points in the sharp-corner area are more dense. Based on this, one or more data points to be processed located in the sharp-corner area of the model can be screened from a plurality of data points of the brain nucleus model to be processed according to the data point density information within a certain spatial range. Exemplarily, for a schematic diagram of the brain nucleus model to be processed, see Figure 4 , Figure 4 (a) The data points within the rectangular frame and Figure 4 (b) The data points within the circular ring are more densely distributed than the data points at other positions, indicating that this may be the sharp-corner area of the brain nucleus model to be processed.

[0042] Optionally, the specific implementation methods for screening a plurality of data points to be processed located in the sharp-corner area of the model from a plurality of data points of the brain nucleus model to be processed can at least include the following two:

[0043] The first method: Divide the brain nucleus model to be processed into at least one area to be processed with consistent spatial ranges; determine the model sharp-corner area from the brain nucleus model to be processed based on the data point distribution density information of the at least one area to be processed; determine multiple data points located within the model sharp-corner area as the data points to be processed.

[0044] Among them, the area to be processed refers to a part of the area belonging to the brain nucleus model to be processed. The data point distribution density information is the data point quantity information within the area to be processed.

[0045] In this embodiment, a certain spatial range can be preset in advance, which is called the preset spatial range. For example, the preset spatial range can be a spherical spatial range with a radius of R, or a cubic spatial range with a side length of A, etc. Divide the brain nucleus model to be processed into multiple areas to be processed with the size of the preset spatial range; further, for each area to be processed, count the number of data points within the area to be processed, and use this data point quantity as the corresponding data point distribution density information. Thus, the area to be processed with a data point distribution density information greater than or equal to the preset density threshold can be determined as the model sharp-corner area; based on this, some or all of the data points included in the model sharp-corner area can be used as the data points to be processed.

[0046] The second method: For multiple data points in the brain nucleus model to be processed, determine the adjacent data points of the current data point based on the spatial position information of the current data point and the preset spatial neighborhood range; determine multiple data points to be processed located in the model sharp-corner area based on the total number of adjacent data points and the preset quantity threshold.

[0047] Among them, the spatial position information is the three-dimensional position coordinates of the current data point. The adjacent data points refer to one or more data points located within the preset spatial neighborhood range of the current data point. The preset quantity threshold is a preset data point quantity threshold used to determine whether a certain data point is a data point to be processed.

[0048] In this embodiment, the processing procedure for each data point in the brain nucleus model to be processed is the same. Here, any one of the data points is taken as the current data point, and an exemplary description is given taking the current data point as an example. According to the spatial position information of the current data point and the three-dimensional position coordinates of each data point, it can be determined which data points are included within the preset spatial neighborhood range of the current data point, and thus these data points can be determined as the adjacent data points of the current data point. Further, the total number of adjacent data points can be counted, and based on the numerical relationship between the total number of adjacent data points and the preset number threshold, it can be determined whether the current data point is a data point to be processed located in the sharp corner area of the model. Optionally, a data point with the total number of adjacent data points greater than or equal to the preset number threshold is determined as a data point to be processed located in the sharp corner area of the model. Each data point in the brain nucleus model to be processed can be processed in the same way, and finally, multiple data points to be processed located in the sharp corner area of the model can be determined.

[0049] S120. For multiple data points to be processed, based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed, determine the data points to be excluded from the multiple data points to be processed.

[0050] Among them, the current data point to be processed refers to the data point to be processed that is currently being processed. The surface normal refers to the normal of the mesh surface to which the current data point to be processed belongs. The first brain nucleus model refers to the brain nucleus model that is spatially adjacent to the brain nucleus model to be processed. It can be understood that, from a physiological perspective, the brain nucleus corresponding to the brain nucleus model to be processed and the brain nucleus corresponding to the first brain nucleus model have an adjacent relationship. The relative position information is used to characterize whether the current data point to be processed has a directly adjacent position relationship with the first brain nucleus model. The relative position information can include: directly adjacent position relationship and non-directly adjacent position relationship. The data point to be excluded refers to the data point that will be excluded from the brain nucleus model to be processed.

[0051] In this embodiment, the processing procedure for each data point to be processed is the same. Here, any one of the data points to be processed is taken as the current data point to be processed, and an exemplary description is given taking the current data point to be processed as an example. The data points in the brain nucleus model to be processed that face the first brain nucleus model have a direct adjacent relationship with the first brain nucleus model. The relative position information of these data points is a directly adjacent position relationship, and the relative position information of the remaining data points is a non-directly adjacent position relationship. In the specific application process, if the relative position information between the current data point to be processed and the first brain nucleus model is a non-directly adjacent position relationship, then the current data point to be processed can be determined as a data point to be excluded. Each data point to be processed can be processed in the same way, and finally, multiple data points to be excluded can be determined.

[0052] Exemplarily, the relative position information between the current data point to be processed and the first brain nucleus model can be determined by the intersection relationship between the surface normal of the current data point to be processed and the first brain nucleus model. Taking the current data point to be processed as an example for exemplary illustration, based on at least two adjacent data points of the current data point to be processed and the current data point to be processed, a grid surface corresponding to the current data point to be processed can be determined. According to the three-dimensional position coordinates of these three data points, the normal vector corresponding to this grid surface can be easily determined, and thus the surface normal of the current data point to be processed can be obtained. The first target space range to which the first brain nucleus model belongs is determined, and it can be determined whether the surface normal of the current data point to be processed is within the first target space range. If so, the intersection attribute is determined as the intersection attribute, and the corresponding relative position information is the directly adjacent position relationship; if not, the intersection attribute is determined as the non-intersection attribute, and the corresponding relative position information is the non-directly adjacent position relationship; based on this, in the case where the intersection attribute is the non-intersection attribute, the current data point to be processed can be determined as the data point to be excluded. Each data point to be processed can be processed in the same way, and finally one or more data points to be excluded can be determined.

[0053] Exemplarily, for the schematic diagrams of the brain nucleus model to be processed and the first brain nucleus model, see Figure 5 , Figure 5 In, the three-dimensional model composed of the left dark gray data points is the brain nucleus model to be processed, and the three-dimensional model composed of the right light gray data points is the first brain nucleus model. The rays on the surface of the brain nucleus model to be processed represent the surface normals of each data point to be processed. The intersection attribute corresponding to the surface normal located inside the first brain nucleus model is intersection, and the intersection attributes corresponding to the surface normals located outside the first brain nucleus model and showing a scattered distribution are non-intersection. The data points to be processed corresponding to these non-intersection surface normals are the data points to be excluded.

[0054] It can be understood that since there are neural pathways between adjacent brain nuclei, these neural pathways may also appear in the form of sharp protrusions in the three-dimensional model. Therefore, the purpose of this step is to: based on the relative position information between the data point to be processed and the first brain nucleus model, retain the data points to be processed that have a directly adjacent relationship with the first brain nucleus model. Through this internal sharp corner protection mechanism of the nucleus, the connectivity inside the nucleus can be effectively protected, and the internal structure between the combined nucleus models can be avoided from being damaged during the sharp corner removal process.

[0055] S130. Perform surface sharp protrusion removal processing on the brain nucleus model to be processed based on the data points to be excluded, and obtain the target brain nucleus model.

[0056] Among them, the target brain nucleus model refers to the brain nucleus model with sharp surface protrusions removed.

[0057] In this embodiment, based on determining one or more data points to be removed, these data points to be removed are removed from the brain nucleus model to be processed, that is, the processing step of removing sharp surface protrusions from the brain nucleus model to be processed is completed. At this time, the obtained brain nucleus model is the target brain nucleus model. Based on the above example, for the schematic diagram of the target brain nucleus model, see Figure 6 , such as Figure 6 shown, the target brain nucleus model does not contain surface normals showing a scattered distribution, and only the surface normals intersecting with the first brain nucleus model. That is, the data points to be removed have been removed from the brain nucleus model to be processed.

[0058] The technical solution of the embodiment of the present invention screens multiple data points to be processed located in the corner area of the model from multiple data points of the brain nucleus model to be processed. Furthermore, for the multiple data points to be processed, based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed, the data points to be removed are determined from the multiple data points to be processed. Thus, based on the data points to be removed, the brain nucleus model to be processed is processed to remove sharp surface protrusions, and the target brain nucleus model is obtained. The technical solution of this embodiment reduces the sharp spikes in the model by identifying the corner feature points in the brain nucleus model and performing special processing on these points, improving the authenticity and aesthetics of the brain nucleus model; and by determining whether the surface normal of the corner feature point intersects the surface of the combined body of the brain nucleus model, it is decided whether to retain the corner feature point. This internal corner protection mechanism effectively protects the connectivity inside the nucleus and avoids damaging the internal structure between the combined nucleus models during the corner removal process, further improving the authenticity of the brain nucleus model.

[0059] Embodiment 2

[0060] Figure 7 This is a schematic diagram of a method for removing sharp surface protrusions from a model provided by an embodiment of the present invention. Based on the foregoing embodiment, S120 and S130 are further refined, and the specific implementation manner can refer to the technical solution of this embodiment. Among them, the same or corresponding technical terms as those in the above embodiment will not be elaborated here.

[0061] Such as Figure 7 shown, the method specifically includes the following steps:

[0062] S210. Screen multiple data points to be processed located in the corner area of the brain nucleus model to be processed from multiple data points of the brain nucleus model to be processed.

[0063] In this embodiment, after obtaining one or more data points to be processed, it is necessary to further determine the surface normal corresponding to each data point to be processed, so as to determine whether the current data point to be processed is determined as a data point to be excluded according to the corresponding surface normal. Next, the steps of S220 - S240 will be used to illustrate how to determine the surface normal of the data point to be processed.

[0064] S220. Determine the surface normal corresponding to the current data point to be processed.

[0065] In this embodiment, the relative position information between the current data point to be processed and the first brain nucleus model can be determined by the intersection relationship between the surface normal of the current data point to be processed and the first brain nucleus model. Therefore, the surface normal corresponding to the current data point to be processed can be determined first.

[0066] Optionally, the specific implementation method for determining the surface normal corresponding to the current data point to be processed may include the following steps:

[0067] S2201. Determine at least two first data points that are spatially adjacent to the current data point to be processed.

[0068] Among them, the first data point refers to the data point adjacent to the current data point to be processed.

[0069] In this embodiment, there may be multiple data points that are spatially adjacent to the current data point to be processed. Any two or more of them can be used as the first data points here.

[0070] S2202. Determine the target grid surface to which the current data point to be processed belongs from the current data point to be processed and at least two first data points.

[0071] Among them, the target grid surface refers to the grid surface that can contain the current data point to be processed.

[0072] Specifically, three points can form a unique plane. One or more grid surfaces can be formed according to the current data point to be processed and at least two first data points, and any one of these grid surfaces can be determined as the target grid surface to which the current data point to be processed belongs.

[0073] S2203. Determine the normal vector corresponding to the target grid surface as the surface normal of the current data point to be processed.

[0074] In this embodiment, on the basis of determining the target grid surface, according to the position coordinates on the grid surface and the geometric analysis theory, the normal vector corresponding to the target grid surface can be calculated, so that this normal vector can be determined as the surface normal of the current data point to be processed.

[0075] S230. Determine whether the current data point to be processed is a data point to be excluded based on the intersection property between the surface normal and the first brain nucleus model associated with the brain nucleus model to be processed.

[0076] Among them, the intersection property is used to characterize whether the surface normal of the current data point to be processed intersects the surface of the first brain nucleus model.

[0077] In this embodiment, if the intersection property between the surface normal of the current data point to be processed and the first brain nucleus model is the intersection property, it indicates that the relative position information corresponding to the current data point to be processed is a directly adjacent position relationship. In this case, the current data point to be processed may be located on the nerve fiber connection structure, and the current data point to be processed is not a data point to be excluded.

[0078] Optionally, if the intersection property between the surface normal of the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed is the non-intersection property, then determine the current data point to be processed as a data point to be excluded.

[0079] In this embodiment, if the intersection property between the surface normal of the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed is the non-intersection property, it indicates that the relative position information corresponding to the current data point to be processed is a non-directly adjacent position relationship. In this case, the current data point to be processed is not located on the nerve fiber connection structure, and then the current data point to be processed can be determined as a data point to be excluded.

[0080] In this embodiment, the target brain nucleus model can be a point cloud model or a target brain nucleus surface model composed of at least one mesh patch. If the target brain nucleus model is a point cloud model, execute according to the steps of S261 to obtain the target brain nucleus point cloud model; if the target brain nucleus model is a surface model, execute according to the steps of S2621 - S2624 to obtain the target brain nucleus surface model.

[0081] S241. Delete the data points to be excluded from the multiple data points in the brain nucleus model to be processed to obtain the target brain nucleus point cloud model.

[0082] In this embodiment, deleting the data points to be excluded determined in the above steps from the multiple data points in the brain nucleus model to be processed can obtain the target brain nucleus point cloud model.

[0083] S2421. Obtain multiple non-overlapping mesh patches to be processed composed of the multiple data points of the brain nucleus model to be processed.

[0084] Among them, the mesh patch to be processed is the mesh surface that will be processed.

[0085] In this embodiment, multiple non-overlapping to-be-processed mesh patches can be formed by multiple data points on the surface of the to-be-processed brain nucleus model. In the specific application process, these to-be-processed mesh patches can be stored in the ply file of the to-be-processed brain nucleus model in the form of "faces". Here, the multiple to-be-processed mesh patches stored in the ply file of the to-be-processed brain nucleus model can be obtained.

[0086] S2422. Determine a target mesh patch including the data points to be removed from the to-be-processed mesh patches.

[0087] Among them, the target mesh patch is the mesh patch to be deleted from the surface patches of the to-be-processed brain nucleus model.

[0088] In this embodiment, for each to-be-processed mesh patch, if the to-be-processed mesh patch contains the data points to be removed, it is determined as the target mesh patch. Based on this, one or more target mesh patches can be obtained.

[0089] S2423. Remove the target mesh patch from the multiple to-be-processed mesh patches of the to-be-processed brain nucleus model to obtain a to-be-repaired brain nucleus surface model.

[0090] In this embodiment, on the basis of obtaining the target mesh patch, these target mesh patches can be deleted from the multiple to-be-processed mesh patches of the to-be-processed brain nucleus model. At this time, the originally surface-closed to-be-processed brain nucleus model becomes a to-be-repaired brain nucleus surface model with surface holes.

[0091] S2424. Perform hole filling processing and surface smoothing processing on the to-be-repaired brain nucleus surface model to obtain a target brain nucleus surface model.

[0092] In this embodiment, a hole filling algorithm and a surface smoothing algorithm (for example, Laplace smoothing algorithm, mean curvature flow smoothing algorithm, etc.) can be pre-configured. By using the hole filling algorithm to perform hole filling processing on the to-be-repaired brain nucleus surface model and using the surface smoothing algorithm to perform surface smoothing processing on the to-be-repaired brain nucleus surface model, the target brain nucleus surface model can be obtained.

[0093] Optionally, the specific implementation manner of performing hole filling processing on the to-be-repaired brain nucleus surface model may include: performing hole detection on the to-be-repaired brain nucleus surface model to determine the hole area in the to-be-repaired brain nucleus surface model; and using the Poisson surface reconstruction algorithm to perform hole filling processing on the hole area.

[0094] Among them, the Poisson surface reconstruction algorithm is a method for generating a three-dimensional surface model from point cloud data. It is based on the Poisson equation and generates the surface by globally optimizing the normal vectors in the point cloud. This algorithm is applicable to point cloud data with noise and irregular distributions, especially those applications that require generating smooth surfaces. The core of this algorithm is to solve a Poisson equation based on the normal vector field of the point cloud. Specifically, given a point cloud data set and its corresponding normal vector set, Poisson surface reconstruction generates a scalar field by solving a Poisson equation, such that the gradient of the scalar field is as consistent as possible with the normal vector field, thereby obtaining a smooth three-dimensional surface. The Poisson surface reconstruction algorithm generally includes the following steps: First, estimate the normal vectors in the point cloud; then, construct an octree to divide the space; finally, use the Poisson equation to solve and generate the surface.

[0095] In the specific application process, a preset hole detection algorithm can be adopted to detect holes in the surface model of the brain nucleus to be repaired, and determine each hole area in the model. Exemplarily, for the schematic diagrams of the surface model of the brain nucleus to be repaired and the hole areas, refer to Figure 8 , such as Figure 8 shown, the area outlined by the black lines on the surface of the surface model of the brain nucleus to be repaired is the hole area. Furthermore, each hole area is processed for hole filling through the Poisson surface reconstruction algorithm.

[0096] Next, a specific example is used to illustrate a method for removing sharp protrusions on the model surface provided by this embodiment. Figure 9 It is a specific implementation flowchart of a method for removing sharp protrusions on the model surface.

[0097] Such as Figure 9 shown, this method mainly includes the following steps:

[0098] S1. Detect sharp corners. The purpose of this step is to identify and record the sharp corner feature points in the point cloud file. This step specifically includes the following contents: (1) Point cloud reconstruction: Reconstruct the point cloud file in ply format of the brain nucleus model to be processed, that is, recreate a ply format point cloud file to store the final obtained target brain nucleus model in the newly created ply point cloud file; (2) Feature extraction: By analyzing the distribution density of the data points in the point cloud, identify the data point dense areas, which may be the sharp corner areas of the model; (3) Data recording: Record the coordinates and relevant face information of the data points to be processed in the sharp corner area of the model for subsequent processing.

[0099] S2. Inner sharp corner protection. The purpose of this step is to protect the connectivity inside the nuclear cluster and avoid damaging the internal structure between the combined nuclear cluster models during the sharp corner removal process. The specific content of this step is as follows: (1) Normal calculation: Calculate the surface normals of these data points to be processed. (2) Intersection judgment: Perform an intersection test between these surface normals and the surface of the first brain nuclear cluster model. (3) Retention or removal decision: If the surface normal of a certain data point to be processed intersects with the surface of the first brain nuclear cluster model, retain this data point; if not, mark it as a point to be removed.

[0100] S3. Remove sharp corners. The specific content of this step is as follows: (1) Point deletion: Delete the marked sharp corner points from the brain nuclear cluster model to be processed. (2) Face update: Delete the faces related to these points to maintain the consistency of the point cloud data. (3) Hole detection: Identify the holes generated due to the removal of sharp corners.

[0101] S4. Fill holes. The specific content of this step is as follows: (1) Hole identification: Use an algorithm to identify the hole area. (2) Hole filling: Apply existing technologies, such as Poisson surface reconstruction, etc., to fill the holes.

[0102] S5. Smooth the surface. The specific content of this step is as follows: (1) Surface smoothing algorithm: Apply a surface smoothing algorithm, such as Laplacian smoothing or mean curvature flow, to smooth the point cloud surface. (2) Quality inspection: Check the smoothed point cloud data to ensure that no new errors or distortions are introduced.

[0103] The technical solution of the embodiment of the present invention screens a plurality of data points to be processed located in the sharp corner area from a plurality of data points of the brain nucleus model to be processed; furthermore, for at least one data point to be processed, at least two first data points that are spatially adjacent to the current data point to be processed are determined. From the current data point to be processed and the at least two first data points, the target grid plane to which the current data point to be processed belongs is determined, and the normal vector corresponding to the target grid plane is determined as the surface normal of the current data point to be processed. In this way, the surface normal of each data point to be processed can be determined quickly and efficiently. Subsequently, if the intersection attribute between the surface normal of the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed is a non-intersecting attribute, the current data point to be processed is determined as a data point to be removed. In one way, the data points to be removed can be deleted from the plurality of data points in the brain nucleus model to be processed to obtain a target brain nucleus point cloud model; in another way, a plurality of non-overlapping processed grid patches composed of the plurality of data points of the brain nucleus model to be processed can be obtained. Furthermore, a target grid patch including the data points to be removed is determined from the processed grid patches, and then the target grid patch is removed from the plurality of processed grid patches of the brain nucleus model to be processed to obtain a brain nucleus surface model to be repaired. Thus, hole filling processing and surface smoothing processing are performed on the brain nucleus surface model to be repaired to obtain a target brain nucleus surface model. The technical solution provided in this embodiment restores the integrity of the point cloud by repairing the holes generated by removing the sharp corners after removing the sharp corner data points. This hole repair technology not only eliminates the sharp corners but also maintains the continuity and integrity of the brain nucleus model, further improving the authenticity and aesthetics of the brain nucleus model.

[0104] Embodiment III

[0105] Figure 10 It is a schematic structural diagram of a device for removing sharp protrusions on the surface of a model provided by an embodiment of the present invention. The device includes: a data point screening module 310, a data point to be removed determination module 320, and a target model determination module 330.

[0106] Among them, the data point screening module 310 is used to screen a plurality of data points to be processed located in the sharp corner area from a plurality of data points of the brain nucleus model to be processed;

[0107] The data point to be removed determination module 320 is used to determine the data points to be removed from the plurality of data points to be processed based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed;

[0108] A target model determination module 330, configured to perform surface sharp protrusion removal processing on the to-be-processed brain nucleus model based on the to-be-excluded data points, so as to obtain a target brain nucleus model.

[0109] Based on the above device, optionally, the data point screening module 310 includes:

[0110] A sub-model determination unit, configured to divide the to-be-processed brain nucleus model into at least one to-be-processed area with consistent spatial ranges;

[0111] A sharp corner area determination unit, configured to determine a model sharp corner area from the to-be-processed brain nucleus model based on the data point distribution density information of the at least one to-be-processed area;

[0112] A to-be-processed point determination unit, configured to determine multiple data points located within the model sharp corner area as to-be-processed data points.

[0113] Based on the above device, optionally, the data point screening module 310 further includes:

[0114] An adjacent point determination unit, configured to, for the multiple data points in the to-be-processed brain nucleus model, determine adjacent data points to the current data point based on the spatial position information of the current data point and a preset spatial neighborhood range;

[0115] A to-be-processed point determination unit, configured to determine multiple to-be-processed data points located in the model sharp corner area based on the total number of the adjacent data points and a preset number threshold.

[0116] Based on the above device, optionally, the to-be-processed point determination unit is specifically configured to determine data points whose total number of the adjacent data points is greater than or equal to the preset number threshold as multiple to-be-processed data points suspected of being located in the model sharp corner area.

[0117] Based on the above device, optionally, the excluded data point determination module 320 includes:

[0118] A surface normal determination unit, configured to determine a surface normal corresponding to the current to-be-processed data point;

[0119] An excluded data point determination unit, configured to determine whether the current to-be-processed data point is an to-be-excluded data point based on the intersection attribute between the surface normal and a first brain nucleus model associated with the to-be-processed brain nucleus model.

[0120] Based on the above device, optionally,

[0121] The model surface sharp protrusion removal device further includes: A surface normal determination unit, including:

[0122] A first data point determination subunit, configured to determine, for the at least one data point to be processed, any two first data points that are spatially adjacent to the current data point to be processed;

[0123] A target grid plane determination subunit, configured to determine, from the current data point to be processed and the two first data points, a target grid plane to which the current data point to be processed belongs;

[0124] A surface normal determination subunit, configured to determine the normal vector corresponding to the target grid plane as the surface normal of the current data point to be processed.

[0125] Based on the above device, optionally, an excluded data point determination unit is specifically configured to, if the intersection attribute between the surface normal of the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed is a non-intersecting attribute, determine the current data point to be processed as an excluded data point.

[0126] Based on the above device, optionally, a target model determination module 330 is specifically configured to delete the excluded data point from the multiple data points in the brain nucleus model to be processed, to obtain a target brain nucleus point cloud model.

[0127] Based on the above device, optionally, the target model determination module 330 further includes:

[0128] A to-be-processed patch determination unit, configured to obtain multiple non-overlapping to-be-processed grid patches formed by the multiple data points of the brain nucleus model to be processed;

[0129] A target patch determination unit, configured to determine, from the to-be-processed grid patches, a target grid patch that includes the excluded data point;

[0130] A to-be-repaired model determination unit, configured to exclude the target grid patch from the multiple to-be-processed grid patches of the brain nucleus model to be processed, to obtain a to-be-repaired brain nucleus surface model;

[0131] A target model determination unit, configured to perform hole filling processing and surface smoothing processing on the to-be-repaired brain nucleus surface model, to obtain a target brain nucleus surface model.

[0132] Based on the above device, optionally, the target model determination unit is specifically configured to perform hole detection on the to-be-repaired brain nucleus surface model, to determine a hole area in the to-be-repaired brain nucleus surface model; and perform hole filling processing on the hole area by using a Poisson surface reconstruction algorithm.

[0133] The technical solution of the embodiment of the present invention screens multiple data points located in the sharp corner area from multiple data points of the to-be-processed brain nucleus model. Furthermore, for the multiple to-be-processed data points, based on the relative position information between the current to-be-processed data point and the first brain nucleus model associated with the to-be-processed brain nucleus model, the data points to be removed are determined from the multiple to-be-processed data points. Thus, based on the data points to be removed, the surface sharp protrusions of the to-be-processed brain nucleus model are removed to obtain the target brain nucleus model. The technical solution of this embodiment reduces the sharp spikes in the model by identifying the sharp corner feature points in the brain nucleus model and performing special processing on these points, improving the authenticity and aesthetics of the brain nucleus model; and by determining whether the surface normal of the sharp corner feature point intersects the surface of the combined body of the brain nucleus model, it is decided whether to retain the sharp corner feature point. This internal sharp corner protection mechanism effectively protects the connectivity inside the nucleus, avoiding the destruction of the internal structure between the combined nucleus models during the sharp corner removal process, and further improving the authenticity of the brain nucleus model.

[0134] The model surface sharp protrusion removal device provided by the embodiment of the present invention can execute the model surface sharp protrusion removal method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the method.

[0135] It should be noted that the various units and modules included in the above system are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the embodiments of the present invention.

[0136] Embodiment 4

[0137] Figure 11 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. Figure 11 It shows a block diagram of an exemplary electronic device 40 suitable for implementing the embodiment mode of the embodiment of the present invention.

[0138] Figure 11 The shown electronic device 40 is only an example and should not bring any limitation to the functions and usage scope of the embodiments of the present invention.

[0139] As Figure 11 shown, the electronic device 40 is presented in the form of a general computing device. The components of the electronic device 40 may include but are not limited to: one or more processors or processing units 401, a system memory 402, and a bus 403 connecting different system components (including the system memory 402 and the processing unit 401).

[0140] The bus 403 represents one or more of several types of bus architectures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor bus, or a local bus using any of a variety of bus architectures. By way of example, and without limitation, these architectures include the Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MAC) bus, Enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

[0141] The electronic device 40 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 40, including both volatile and nonvolatile media, removable and non-removable media.

[0142] The system memory 402 can include computer system readable media in the form of volatile memory, such as random access memory (RAM) 404 and / or cache memory 405. The electronic device 40 may further include other removable / non-removable, volatile / nonvolatile computer system storage media. By way of example only, a storage system 406 can be provided for reading from and writing to a non-removable, nonvolatile magnetic medium ( Figure 11 not shown and typically referred to as a "hard disk drive"). Although Figure 11 not shown in the figures, a disk drive for reading from and writing to a removable nonvolatile disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical medium) can be provided. In these instances, each drive can be connected to the bus 403 by one or more data media interfaces. The memory 402 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the present invention.

[0143] A program / utility 408 having a set (at least one) of program modules 407 can be stored, for example, in the memory 402. Such program modules 407 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data, and each of these examples or some combination thereof may include an implementation of a networking environment. The program modules 407 typically carry out the functions and / or methods of the embodiments described herein.

[0144] The electronic device 40 can also communicate with one or more external devices 409 (such as a keyboard, a pointing device, a display, etc.), and can also communicate with one or more devices that enable a user to interact with the electronic device 40, and / or communicate with any device that enables the electronic device 40 to communicate with one or more other computing devices (such as a network card, a modem, etc.). Such communication can be carried out through the input / output (I / O) interface 411. Moreover, the electronic device 40 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) through the network adapter 412. As shown in the figure, the network adapter 412 communicates with other modules of the electronic device 40 through the bus 403. It should be understood that although Figure 11 not shown in the figure, other hardware and / or software modules can be used in combination with the electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, etc.

[0145] The processing unit 401 executes various functional applications and page processing by running programs stored in the system memory 402, such as implementing the method for removing sharp protrusions on the model surface provided by the embodiments of the present invention.

[0146] Embodiment 5

[0147] The embodiments of the present invention also provide a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute a method for removing sharp protrusions on the model surface when executed by a computer processor. The method includes:

[0148] Screening a plurality of data points to be processed located in the sharp corner area of the brain nucleus model to be processed from a plurality of data points of the brain nucleus model to be processed;

[0149] For the plurality of data points to be processed, based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed, determining the data points to be removed from the plurality of data points to be processed;

[0150] Performing surface sharp protrusion removal processing on the brain nucleus model to be processed based on the data points to be removed to obtain a target brain nucleus model.

[0151] The computer storage medium of the embodiments of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program, which can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0152] The computer-readable signal media may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0153] The program code contained on the computer-readable media may be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0154] The computer program code for performing the operations of the embodiments of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0155] Embodiment Six

[0156] Figure 12 A schematic structural diagram of a medical system provided by an embodiment of the present application. The system includes: an implantable medical device 510, a display 520, and a processor 530. The implantable medical device 510, the display 520, and the processor 530 can communicate with each other interactively. A memory is provided in the processor, and the memory stores a computer program. The processor is configured to execute the computer program and, when executing the computer program, implement a method for removing sharp protrusions on the surface of a model.

[0157] Among them, the implantable medical device 510 at least includes a pulse generator implanted into the body of a target user and electrode leads implanted into the brain of the target user. At least a plurality of electrode contacts are provided at the implanted end of the electrode leads, and the pulse generator is connected to the electrode leads.

[0158] The display 520 is configured to display the target brain nucleus model.

[0159] The processor 530 is configured to obtain a brain image of a target user with at least one stimulating electrode implanted in the brain, construct a to-be-processed brain nucleus model according to the brain image, execute a method for removing sharp protrusions on the surface of the model, perform surface sharp protrusion removal processing on the to-be-processed brain nucleus model to obtain a target brain nucleus model, and display the target brain nucleus model by using the display.

[0160] In the technical solution of the embodiment of the present application, the medical system includes an implantable medical device, a display, and a processor. When the medical system is specifically applied, the processor screens a plurality of to-be-processed data points located in the sharp corner area of the to-be-processed brain nucleus model from a plurality of data points of the to-be-processed brain nucleus model; for the plurality of to-be-processed data points, based on the relative position information between the current to-be-processed data point and a first brain nucleus model associated with the to-be-processed brain nucleus model, determines to-be-eliminated data points from the plurality of to-be-processed data points; and performs surface sharp protrusion removal processing on the to-be-processed brain nucleus model based on the to-be-eliminated data points to obtain a target brain nucleus model, so that the target brain nucleus model can be displayed on the display. The technical solution of this embodiment reduces the sharp spikes in the model by identifying the sharp corner feature points in the brain nucleus model and performing special processing on these points, improving the authenticity and aesthetics of the brain nucleus model; and determines whether to retain the sharp corner feature point by judging whether the surface normal of the sharp corner feature point intersects the surface of the combination of the brain nucleus models, and this internal sharp corner protection mechanism effectively protects the connectivity inside the nucleus, avoiding damage to the internal structure between the combined nucleus models during the sharp corner removal process, and further improving the authenticity of the brain nucleus model.

[0161] Note that the above is only a preferred embodiment of the present invention and the technical principles applied. Those skilled in the art will understand that the present invention is not limited to the specific embodiments here, and various obvious changes, re-adjustments and substitutions can be made by those skilled in the art without departing from the protection scope of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments only. Without departing from the concept of the present invention, more other equivalent embodiments can be included, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for removing sharp protrusions on the surface of a model, characterized in that, Including: Screening a plurality of data points to be processed located in the sharp corner area of the brain nucleus model to be processed from a plurality of data points of the brain nucleus model to be processed; For the plurality of data points to be processed, based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed, determining the data points to be excluded from the plurality of data points to be processed; Based on the data points to be excluded, performing a surface sharp protrusion removal process on the brain nucleus model to be processed to obtain a target brain nucleus model.

2. The method according to claim 1, characterized in that, The screening of a plurality of data points to be processed located in the sharp corner area of the brain nucleus model to be processed from a plurality of data points of the brain nucleus model to be processed includes: Dividing the brain nucleus model to be processed into at least one area to be processed with consistent spatial ranges; Based on the data point distribution density information of the at least one area to be processed, determining the sharp corner area of the model from the brain nucleus model to be processed; Determining the plurality of data points located within the sharp corner area of the model as the data points to be processed.

3. The method according to claim 1, wherein The screening of a plurality of data points to be processed located in the sharp corner area of the brain nucleus model to be processed from a plurality of data points of the brain nucleus model to be processed includes: For the plurality of data points in the brain nucleus model to be processed, based on the spatial position information of the current data point and a preset spatial neighborhood range, determining the adjacent data points of the current data point; Based on the total number of the adjacent data points and a preset number threshold, determining a plurality of data points to be processed located in the sharp corner area of the model.

4. The method according to claim 3, wherein The determining of a plurality of data points to be processed suspected of being located in the sharp corner area of the model based on the total number of the adjacent data points and a preset number threshold includes: Determining the data points with the total number of the adjacent data points greater than or equal to the preset number threshold as the plurality of data points to be processed suspected of being located in the sharp corner area of the model.

5. The method according to claim 1, characterized in that The determining of the data points to be excluded from the plurality of data points to be processed based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed includes: Determining the surface normal corresponding to the current data point to be processed; Based on the intersection property between the surface normal and the first brain nucleus model associated with the brain nucleus model to be processed, determining whether the current data point to be processed is a data point to be excluded.

6. The method according to claim 5, characterized in that The determining of the surface normal corresponding to the current data point to be processed includes: Determining any two first data points spatially adjacent to the current data point to be processed; From the current data point to be processed and the two first data points, determining the target grid surface to which the current data point to be processed belongs; Determining the normal vector corresponding to the target grid surface as the surface normal of the current data point to be processed.

7. The method according to claim 5, characterized in that, The determining of whether the current data point to be processed is a data point to be excluded based on the intersection property between the surface normal and the first brain nucleus model associated with the brain nucleus model to be processed includes: If the intersection property between the surface normal of the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed is a non-intersection property, then determining the current data point to be processed as a data point to be excluded.

8. The method according to claim 1, characterized in that, The target brain nucleus model is a point cloud model. Based on the data points to be removed, the surface sharp protrusions of the brain nucleus model to be processed are removed to obtain the target brain nucleus model, including: Deleting the data points to be removed from the multiple data points in the brain nucleus model to be processed to obtain a target brain nucleus point cloud model.

9. The method according to claim 1, wherein The target brain nucleus model is a target brain nucleus surface model composed of at least one mesh patch. Based on the data points to be removed, the surface sharp protrusions of the brain nucleus model to be processed are removed to obtain the target brain nucleus model. Obtaining a plurality of non-overlapping mesh patches to be processed composed of the multiple data points of the brain nucleus model to be processed. Determining a target mesh patch including the data points to be removed from the mesh patches to be processed. Removing the target mesh patch from the multiple mesh patches to be processed of the brain nucleus model to be processed to obtain a brain nucleus surface model to be repaired. Performing hole filling processing and surface smoothing processing on the brain nucleus surface model to be repaired to obtain a target brain nucleus surface model.

10. The method according to claim 9, wherein The hole filling processing of the brain nucleus surface model to be repaired includes: Performing hole detection on the brain nucleus surface model to be repaired to determine the hole area in the brain nucleus surface model to be repaired. Performing hole filling processing on the hole area by using a Poisson surface reconstruction algorithm.

11. A device for removing sharp protrusions on the surface of a model, characterized in that, The device includes: A data point screening module for screening a plurality of data points to be processed located in the sharp corner area of the brain nucleus model to be processed from the multiple data points of the brain nucleus model to be processed. A data point removal determination module for determining the data points to be removed from the multiple data points to be processed based on the relative position information between the current data point to be processed and the first brain nucleus model associated with the brain nucleus model to be processed. A target model determination module for removing the surface sharp protrusions of the brain nucleus model to be processed based on the data points to be removed to obtain a target brain nucleus model.

12. A medical system, characterized in that, The medical system includes: An implantable medical device, which at least includes a pulse generator implanted into the body of a target user and an electrode lead implanted into the brain of the target user. At least a plurality of electrode contacts are arranged at the implanted end of the electrode lead, and the pulse generator is connected to the electrode lead. A display for displaying the target brain nucleus model. A processor configured to obtain a brain image of a target user with at least one stimulating electrode implanted in the brain, construct a brain nucleus model to be processed according to the brain image, perform surface sharp protrusion removal processing on the brain nucleus model to be processed by executing the model surface sharp protrusion removal method according to any one of claims 1-10 to obtain a target brain nucleus model, and display the target brain nucleus model by using the display.

13. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method for removing sharp protrusions on the model surface according to any one of claims 1-10.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the method for removing sharp protrusions on the model surface according to any one of claims 1-10 when the computer instructions are executed by a processor.

15. A computer program product, characterized in that, The computer program product includes a computer program which, when executed by a processor, implements the method for removing sharp protrusions on the model surface according to any one of claims 1-10.

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