Brain implant path planning system and brain implant path planning method

Abstract

This application relates to a brain implant path planning system and a brain implant path planning method. The brain implant path planning system includes: a strategy planning module and / or a rule planning module; the strategy planning module provides several pre-set strategy templates, obtains a target strategy template selected by the user from the several strategy templates, maps each first planning path in the target planning template to a target medical image, and obtains a second planning path in the target medical image corresponding to each first planning path; the rule planning module provides several pre-set rule templates, obtains a target rule template selected by the user from the several rule templates, and generates a third planning path in the target medical image according to the path planning rules in the target rule template. This provides a path planning tool that allows users to customize path templates, reducing or even eliminating manual adjustments to the planned path by the user, and improving path planning efficiency.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to a brain implant path planning system and a brain implant path planning method. Background Technology

[0002] Brain electrode implantation is a crucial minimally invasive surgical technique in neurosurgery, playing a vital role in the treatment of various neurological disorders. However, this technique faces a number of challenges, particularly in accurately determining the electrode implantation location.

[0003] Traditionally, with the development of artificial intelligence technology, an intelligent path planning method has emerged. This method can improve the efficiency of doctors manually drawing electrode implantation paths by collecting a large amount of retrospective data and learning to identify high-frequency planning paths.

[0004] However, traditional path planning methods suffer from low efficiency because the planning strategies are influenced by retrospective data. The planning strategies extracted from retrospective data from different hospitals and doctors vary greatly, making it difficult to accurately match the path planning habits of different doctors. Summary of the Invention

[0005] Therefore, it is necessary to provide a brain implant path planning system, brain implant path planning method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency of electrode path planning for brain electrode implantation, in order to address the above-mentioned technical problems.

[0006] In a first aspect, this application provides a brain implant path planning system, including: a strategy planning module and / or a rule planning module;

[0007] The strategy planning module is used to provide several pre-set strategy templates, obtain the target strategy template selected by the user from the several strategy templates, map each first planning path in the target strategy template to the target medical image, and obtain the second planning path in the target medical image corresponding to each first planning path.

[0008] The rule planning module provides several pre-defined rule templates and obtains the target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, it generates a third planning path in the target medical image.

[0009] In one embodiment, the strategy planning module includes a first strategy template generation submodule and / or a second strategy template generation submodule;

[0010] The first strategy template generation submodule is used to provide standard tissue images and generate strategy templates based on the planning paths entered by the user in the standard tissue images.

[0011] The second strategy template generation submodule is used to obtain retrospective data of users' historical path planning and analyze the retrospective data to obtain strategy templates.

[0012] In one embodiment, the rule planning module includes a rule template generation submodule;

[0013] The rule template generation submodule is used to obtain the path rules input by the user and generate rule templates.

[0014] In one embodiment, the system further includes: a display module;

[0015] The display module is used to display a list of policy templates and / or a list of rule templates; the policy template list includes options for each policy template; the rule template list includes options for each rule template.

[0016] In one embodiment, the display module is further configured to display strategy planning controls and rule planning controls;

[0017] The display module is also used to display a list of strategy templates when the user triggers the strategy planning control, and to display a list of rule templates when the user triggers the rule planning control.

[0018] In one embodiment, the display module is further configured to display a newly created strategy template control;

[0019] The display module is also used to display standard organization images when the user triggers the new strategy template control;

[0020] Correspondingly, the first strategy template generation submodule is also used to respond to the user's drawing operation on the standard organization image and generate a strategy template based on the planning path corresponding to the drawing operation.

[0021] In one embodiment, the display module is further configured to display a newly created rule template control;

[0022] The display module is also used to display the rule settings page when the user triggers the new rule template control; the rule settings page includes setting options for several rule parameters;

[0023] Correspondingly, the rule template generation submodule is also used to respond to user settings for several rule parameters and generate rule templates based on the parameter values ​​corresponding to the settings.

[0024] In one embodiment, the display module is also used to display medical images before and after path planning.

[0025] In one embodiment, the system further includes: a path risk warning module and a path adjustment module;

[0026] The path risk warning module is used to perform path risk detection on each secondary planned path in the target medical image and obtain the risk detection results.

[0027] The path adjustment module is used to adjust the second planned path that has the risk of path implantation when the risk detection results include the second planned path with the risk of path implantation, so as to obtain the adjusted second planned path.

[0028] Secondly, this application also provides a brain implant path planning method, applied to the brain implant path planning system in the first aspect, comprising:

[0029] The strategy planning module provides several pre-defined strategy templates and obtains the target strategy template selected by the user from these templates. Each first planning path in the target strategy template is mapped to the target medical image, resulting in several second planning paths in the target medical image; and / or,

[0030] The rule planning module provides several pre-defined rule templates and obtains the target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, a third planning path is generated in the target medical image.

[0031] Thirdly, this application also provides a brain implant path planning device, applied to the brain implant path planning system in the first aspect, comprising:

[0032] The strategy planning module provides several pre-defined strategy templates, obtains the target strategy template selected by the user from these templates, maps each first planning path in the target strategy template to the target medical image, and obtains several second planning paths in the target medical image; and / or,

[0033] The rule planning module provides several pre-defined rule templates and obtains the target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, it generates a third planning path in the target medical image.

[0034] Fourthly, this application also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the brain implant path planning method in the second aspect above.

[0035] Fifthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the brain implant path planning method described in the second aspect above.

[0036] In a sixth aspect, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the brain implant path planning method described in the second aspect above.

[0037] The aforementioned brain implant path planning system, brain implant path planning method, device, computer equipment, storage medium, and computer program product, wherein the brain implant path planning system includes a strategy planning module and / or a rule planning module, wherein the strategy planning module is used to provide several pre-set strategy templates, obtain a target strategy template selected by the user from the several strategy templates, and map each first planning path in the target strategy template to a target medical image to obtain a second planning path in the target medical image corresponding to each first planning path; the rule planning module is used to provide several pre-set rule templates, obtain a target rule template selected by the user from the several rule templates, and generate a third planning path in the target medical image according to the path planning rules in the target rule template. In other words, this application provides a path planning tool that allows users to easily create custom path templates. Through this tool, users can create strategy and / or rule templates that better suit their needs. This allows users to use pre-defined path templates in the brain implant path planning system during the preoperative path planning stage. Since user-defined path templates are more closely matched to the user's actual operation than path templates obtained from retrospective data in big data, it can reduce or even eliminate the need for manual adjustments to the planned path, thus improving the overall efficiency of path planning. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in the embodiments or related technologies of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0039] Figure 1 This is a schematic diagram of the brain implant path planning system in one embodiment;

[0040] Figure 2 This is a schematic diagram of the strategy planning interface in one embodiment;

[0041] Figure 3 This is a schematic diagram of the interface for rule planning in one embodiment;

[0042] Figure 4 This is a flowchart illustrating the process of creating a standard template under the strategy template mode in one embodiment;

[0043] Figure 5 This is a schematic diagram of the interface for adding a new strategy template in one embodiment;

[0044] Figure 6 This is a flowchart illustrating path planning based on a standard template in one embodiment;

[0045] Figure 7 This is a schematic diagram of the interface for adding a rule template corresponding to a DBS technique in one embodiment;

[0046] Figure 8 This is a schematic diagram of the interface for adding a rule template corresponding to the SEEG technique in one embodiment;

[0047] Figure 9 This is a flowchart illustrating path planning in a rule template mode of one embodiment;

[0048] Figure 10 This is a schematic diagram of the path risk warning interface in one embodiment;

[0049] Figure 11 This is a flowchart illustrating a brain implant path planning method in one embodiment;

[0050] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0052] Brain electrode implantation is a crucial minimally invasive surgical procedure in neurosurgery, playing a vital role in treating various neurological disorders. However, this technique faces a series of challenges, particularly in accurately determining the electrode implantation location. The success of the surgery largely depends on the accuracy of the electrode implantation path, which not only affects treatment outcomes but also patient safety. Avoiding damage to critical brain structures, such as blood vessels and functional areas, during electrode implantation is a significant technical challenge. This requires surgeons to possess not only profound anatomical knowledge but also exceptional surgical skills and the ability to make rapid and accurate decisions. Traditional electrode implantation path planning methods have significant limitations; surgeons must precisely plan each electrode within a very short timeframe while simultaneously considering the avoidance of damage to critical structures such as blood vessels and ventricles. This process is not only time-consuming but also highly dependent on the surgeon's personal experience and skills, which to some extent limits the efficiency and safety of the surgery.

[0053] In related technologies, the method of manually drawing pathways by doctors is highly applicable and can fully meet the needs of different patients. However, this method is time-consuming and labor-intensive. It requires doctors to have sufficient clinical experience to design pathways and to confirm the safety of each pathway. With the development of big data analytics and medical technology, retrospective data can be used to assist doctors in path planning to improve the efficiency of path mapping. This approach requires collecting a large amount of retrospective data from different hospitals and doctors after historical path planning. Then, high-frequency path combinations are extracted from the retrospective data to form different path planning strategies. Based on this, doctors can choose the desired strategy to automatically generate electrode implantation paths in the patient's medical images. However, the path planning strategies extracted from retrospective data are easily influenced by the data itself, and there are significant differences in the strategies extracted from retrospective data from different hospitals and doctors. Therefore, after generating electrode implantation paths in medical images based on the path planning strategies generated from retrospective data, doctors still need to manually adjust each electrode implantation path to obtain the desired path, resulting in low path planning efficiency. Furthermore, since only some common high-frequency paths can be obtained from retrospective data, planning strategies cannot be generated for brain regions lacking path samples, significantly limiting its application scope and failing to cover all clinical needs.

[0054] Based on this, this application proposes a brain electrode implantation path planning method. This method integrates a strategy template mode and a rule template mode, fully considering the needs of different clinical application scenarios. Specifically, the strategy template mode converts doctors' rich path planning knowledge into templates for reuse in path planning; each strategy template can contain several planned paths. Furthermore, the rule template mode creates templates from doctors' planning rules for the planned paths, enabling path planning based on these rules; each rule template can contain several path planning rules. Simultaneously, the rule template mode also allows doctors to freely set constraints on the planned paths online to complete path planning for special cases.

[0055] Among them, the strategy template mode and the rule template mode provide doctors with a new way to construct path templates through image processing technology. This not only helps experienced doctors improve their work efficiency but also provides learning and guidance for less experienced doctors, enabling them to quickly master the key skills of path planning. By integrating the strategy template mode and the rule template mode, doctors can quickly plan both conventional and specific paths, switching between different application scenarios. This innovative method has significant clinical implications for improving the accuracy and safety of brain electrode implantation surgery, not only greatly improving the efficiency of path planning but also taking into account the needs of different scenarios.

[0056] Figure 1 A system architecture diagram of a brain implant path planning system is provided for embodiments of this application, as follows: Figure 1 As shown, the brain implant path planning system 10 may include a strategy planning module 11 and / or a rule planning module 12; wherein, the strategy planning module 11 is used to provide several pre-set strategy templates, obtain a target strategy template selected by the user from several strategy templates, and map each first planning path in the target strategy template to the target medical image to obtain a second planning path in the target medical image corresponding to each first planning path; the rule planning module 12 is used to provide several pre-set rule templates, obtain a target rule template selected by the user from several rule templates, and generate a third planning path in the target medical image according to the path planning rules in the target rule template.

[0057] The strategy template and rule template can be pre-set by the user based on the brain implant path planning system, according to clinical experience, path implantation specifications, and the actual situation of different lesions. In other words, the brain implant path planning system can provide users with a custom path planning template function, that is, provide users with a tool to customize path planning templates, so that different users can create path planning templates suitable for themselves. The path planning template can include strategy templates and rule templates.

[0058] For example, users can selectively develop strategy templates and / or rule templates based on different surgical procedures. For instance, for stereotactic electroencephalography (SEEG), users can selectively develop strategy templates, such as developing strategy templates for different brain regions based on different types of epilepsy; for deep brain stimulation (DBS), users can selectively develop rule templates, such as developing different rule templates for different nuclei. It should be noted that rule templates for different brain regions can also be developed for SEEG, or strategy templates for different nuclei can also be developed for DBS. This application does not specifically limit the template types applicable to different surgical procedures; however, the template types can include at least strategy templates and rule templates.

[0059] For example, if several strategy templates and several rule templates are pre-defined through a brain implant path planning system, in a practical application scenario, such as the preoperative path planning stage, the user can access the brain implant path planning system. The brain implant path planning system may also include a display module, which can be used to display a list of strategy templates and / or a list of rule templates. The list of strategy templates includes options for each strategy template; the list of rule templates includes options for each rule template. Here, "several" can mean one or more, that is, one or more strategy templates or one or more rule templates can be pre-defined.

[0060] For example, the strategy planning module can provide a strategy template creation function, allowing users to create different strategy templates for different surgical procedures, brain regions, or nuclei, forming a strategy template list, which the display module can then display on the interface. Similarly, the rule planning module can provide a rule template creation function, allowing users to create different rule templates for different surgical procedures, brain regions, or nuclei, forming a rule template list, which the display module can then display on the interface. Based on this, during the preoperative path planning stage, users can select the desired strategy template from the strategy template list or the desired rule template from the rule template list for path planning.

[0061] For example, the brain implant path planning system can display both a strategy template list and a rule template list on the display interface, or display either the strategy template list or the rule template list separately, or display both the strategy template list and / or the rule template list when a display request is detected; of course, if no display request is detected, the strategy template list and / or the rule template list can be temporarily not displayed.

[0062] In one alternative implementation, refer to Figure 2 and Figure 3 As shown, the display interface may include at least a first display area and a second display area. The first display area can be used to display the target medical image of the target object. This medical image may include the medical image before path planning and the medical image after path planning. For example, before path planning, the medical image before path planning is displayed; after path planning, the medical image after path planning, including the planned path, is displayed. The second display area can serve as a functional area to display different functional controls and parameter information. For example, the display module can also be used to display strategy planning controls and rule planning controls. Based on this, the display module can also display a list of strategy templates when the user triggers the strategy planning control, and a list of rule templates when the user triggers the rule planning control.

[0063] refer to Figure 2 As shown, the second display area of ​​the display interface can be used to display path templates. A strategy planning control (i.e., strategy planning options) is set in the second display area. When the user selects the strategy planning control, a list of strategy templates can be displayed in the second display area. The list of strategy templates can include one or more strategy templates predefined by the user, such as insula template_right, insula template_left, frontal lobe template_right, frontal lobe template_left, temporal lobe template_right, temporal lobe template_left, etc.

[0064] Based on this, users can select the desired strategy template from the list of strategy templates displayed on the interface. That is, they can select the option corresponding to the desired strategy template. When the "Confirm" control is clicked on the display interface, the brain implant path planning system can plan a second planning path corresponding to each of the first planning paths in the target medical image of the target object based on the option of the strategy template selected by the user and according to all the first planning paths in the strategy template corresponding to that option. The system then overlays the planned second planning path on the target medical image of the target object, that is, displays the medical image including the path planning result (i.e., the second planning path) in the first display area of ​​the display interface.

[0065] refer to Figure 3As shown, a rule planning control (i.e., rule planning option) is also provided in the second display area. When the user selects the rule planning control, a list of rule templates can be displayed in the second display area. The list of rule templates can include one or more rule templates predefined by the user, such as insula_right, insula_left, occipital lobe_right, occipital lobe_left, parietal lobe_right, parietal lobe_left, frontal lobe_right, frontal lobe_left, temporal lobe_right, temporal lobe_left, etc.

[0066] Based on this, users can select the desired rule template from the list of rule templates displayed on the interface. By selecting the option corresponding to the desired rule template and clicking the "Confirm" button on the interface, the brain implant path planning system can plan a third planning path that satisfies all path planning rules in the target medical image of the target object, based on the user-selected rule template option and all path planning rules in that template. The planned third planning path is then overlaid and displayed on the target medical image of the target object, that is, the medical image including the path planning result (i.e., the third planning path) is displayed in the first display area of ​​the interface. It should be noted that a third planning path that satisfies all path planning rules can include one or more, for example, planning a third planning path in each of the symmetrical brain regions.

[0067] In one alternative implementation, after a user enters the brain implant path planning system, they can first select the target surgical procedure for the target object. The brain implant path planning system can selectively display the path template display area corresponding to the target surgical procedure. That is, if a strategy template exists for the target surgical procedure, the strategy template list can be displayed when the user clicks the strategy template control; if no strategy template exists for the target surgical procedure, the strategy template control can be in a non-triggered state or an empty strategy template list can be displayed. Similarly, if a rule template exists for the target surgical procedure, the rule template list can be displayed when the user clicks the rule template control; if no rule template exists for the target surgical procedure, the rule template control can be in a non-triggered state or an empty rule template list can be displayed.

[0068] The aforementioned brain implant path planning system includes a strategy planning module and / or a rule planning module. The strategy planning module provides several pre-set strategy templates and obtains a target strategy template selected by the user from these templates. It then maps each first planning path in the target strategy template to a target medical image, obtaining a second planning path in the target medical image corresponding to each first planning path. The rule planning module provides several pre-set rule templates and obtains a target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, it generates a third planning path in the target medical image. In other words, this application provides a path planning tool that allows users to easily create custom path templates. Through this tool, users can create strategy and / or rule templates that better suit their needs. This allows users to use pre-defined path templates in the brain implant path planning system during the preoperative path planning stage. Since user-defined path templates are more closely matched to the user's actual operation than path templates obtained from retrospective data in big data, it reduces or even eliminates the need for manual adjustments to the planned path, thus improving the overall efficiency of path planning.

[0069] In one exemplary embodiment, the strategy planning module can be used to provide a strategy template formulation function, thereby enabling users to formulate different strategy templates for different surgical procedures, different brain regions, or nuclei. This embodiment provides two methods for formulating strategy templates. For example, the strategy planning module may include a first strategy template generation submodule and / or a second strategy template generation submodule. The first strategy template generation submodule is used to provide standard tissue images and generate a strategy template based on the planning path input by the user in the standard tissue images. The second strategy template generation submodule is used to obtain the user's retrospective data and analyze the user's retrospective data to obtain the strategy template.

[0070] The following sections will describe in detail the two methods for creating strategy templates.

[0071] The first method involves users drawing their frequently used pathways as templates based on their experience and preferences, and saving them to generate a strategy template. That is, in standard tissue images, such as standard brain atlas space, users draw their frequently used pathways as templates according to their needs, and then check and confirm each template in a fused image containing the standard brain atlas space and the target area, ultimately generating a standard template, which can serve as a strategy template.

[0072] refer to Figure 4As shown, this illustrates a flowchart of a user-manually drawn template. The following section uses the SEEG technique as an example to describe in detail the process of generating a policy template in the first policy template generation submodule.

[0073] First, the brain mapping space can be a standard brain atlas, which contains at least the brain regions commonly used in the SEEG procedure.

[0074] Secondly, path template drawing is performed. For a specific type of strategy template, such as a frontal lobe template, the brain regions to be sampled and the target regions are selected based on the brain atlas space. Then, a target point is selected on the target region to be sampled, and an entry point is selected on the entry region. Connecting the target point and the entry point forms a path. It should be noted that when the epileptic focus is located in the frontal lobe region, a corresponding frontal lobe template can be drawn. This frontal lobe template can include multiple paths from different entry regions to different target regions. The entry region can be in the frontal lobe region or other brain regions. The target region can also be located in the frontal lobe region, or other brain regions, because frontal lobe epilepsy may also require electrode implantation in other brain regions. Therefore, the frontal lobe template can include multiple paths from different entry regions to different target regions.

[0075] Next, the path template is generated and confirmed. The above path drawing steps are repeated to draw all the paths in the required strategy template, forming a standard template for a specific brain region, that is, a strategy template for a specific brain region. Taking the frontal lobe template as an example, the above drawing method can be used to draw multiple paths in the frontal lobe template. Among them, multiple paths in the frontal lobe template can include paths formed by different target areas and different entry points. For example, the brain atlas space can include brain region 1, brain region 2, brain region 3, brain region 4, etc. Then, multiple paths in the frontal lobe template can include path a between brain region 1 (target point) and brain region 2 (entry point), path b between brain region 1 (target point) and brain region 3 (entry point), path c between brain region 2 (target point) and brain region 3 (entry point), path d between brain region 3 (target point) and brain region 4 (entry point), etc. It can even include paths formed by the target point and the entry point in the same brain region, such as path e between brain region 4 (target point) and brain region 4 (entry point), etc.

[0076] Using the above method of drawing strategy templates, users can draw different strategy templates according to their needs, such as: insular template, frontal lobe template, temporal lobe template, etc., or insular template_right, insular template_left, frontal lobe template_right, frontal lobe template_left, temporal lobe template_right, temporal lobe template_left, etc.; among them, the insular template can include insular template_right and insular template_left, that is, the strategy template corresponding to the right insular region and the strategy template corresponding to the left insular region.

[0077] For example, in the case of a brain implant path planning system that includes a display module, the display module can also be used to display a new strategy template control, such as... Figure 2 The "+New Strategy Template" shown in the image; based on this, the display module can also be used to display standard organizational images when the user triggers the New Strategy Template control; correspondingly, the first strategy template generation submodule can also be used to respond to the user's drawing operation on the standard organizational images and generate a strategy template based on the planning path corresponding to the drawing operation.

[0078] like Figure 5 As shown, when the New Strategy Template control is triggered, standard tissue images can be displayed in the first display area of ​​the display interface. For example, the target medical image of the target object can be replaced with a standard tissue image. Alternatively, when the New Strategy Template control is triggered, the user is redirected to another display interface, where standard tissue images are displayed in the first display area of ​​that interface. Additionally, the second display area of ​​the display interface (or another display interface) displays functional controls and parameter information related to path template drawing. For example, functional controls can include at least a Add Path control, Set as Target control, Set as Ingress Point control, Undo control, and Restore control. Parameter information can include at least path list information and tissue list information. Taking the tissue list information as an example, it can display information about various tissues (such as brain regions) in the standard tissue image, and different tissues can be marked and distinguished using different attribute parameters.

[0079] When drawing a path template, a path can be drawn in the standard tissue image displayed in the first display area by triggering the Add Path control. Optionally, during the path drawing process, a marker point can be added to the standard tissue image, and the marker point can be set as a target point or entry point by triggering the Set as Target Point control or Set as Cranial Entry Point control, so that a planned path can be formed after the target point and entry point are marked. Furthermore, after the planned path is formed, the completed planned path can be displayed in the path list in the second display area.

[0080] Using the same drawing operation, multiple planning paths can be drawn, thus forming a new strategy template. For example, after drawing all the planning paths in the strategy template, the save / edit control in the second display area can be triggered to generate a strategy template based on the drawn planning paths. Optionally, after triggering the save / edit control, it can automatically jump back to... Figure 2 The display interface shown shows the newly added strategy templates under strategy planning, such as strategy template 7. At this time, the user can also rename the newly added strategy template to generate a strategy template for a specific brain region.

[0081] The second approach involves generating a strategy template based on retrospective data from the user's (doctor's) history of path planning. This retrospective data can include path planning data for multiple patients treated by the user in the past, as well as medical images of the target object after actual electrode implantation based on the path planning data. These medical images include the electrode path after implantation.

[0082] For example, the user can collect target medical images of different target objects after electrode implantation, such as computed tomography (CT) images, vascular images, etc. Vascular images may include, but are not limited to, at least one of time-of-flight (TOF) images, phase contrast (PC) images, CT angiography (CTA) images, and contrast-enhanced (T1C) images using a longitudinal relaxation time constant (T1). These medical images include detailed information about the implanted electrodes. Additionally, structural images of the target object, such as T1 images and T2 images, are acquired; these are high-resolution brain magnetic resonance imaging (MRI) images that provide detailed views of brain structures. Next, based on the medical images after electrode implantation, three-dimensional reconstruction of the SEEG electrodes is performed to determine the position and shape of the electrodes in three-dimensional space.

[0083] Next, after reconstruction, registration algorithms can be used to register the target medical image (e.g., CT image) of the target object (e.g., a patient) with structural images (e.g., T1 images). Registration is a technique that aligns images acquired from different sources or at different time points, ensuring spatial consistency. Through the registration process, the reconstructed SEEG electrodes can be accurately mapped onto the T1 images, allowing for the assessment of the relationship between electrode placement and brain structures within a unified reference frame. Subsequently, for further analysis and comparison, all T1 images are registered to the Montreal Neurological Institute (MNI) space, a standardized brain template that allows for comparison of brain data from different individuals within the same reference frame. Based on this, all reconstructed electrodes can be mapped to the MNI space. This allows for the unified mapping of all electrode implantation paths in retrospective data to the MNI space, achieving data standardization. Ultimately, this method enables the aggregation and comprehensive analysis of electrode implantation information from different patients.

[0084] Furthermore, each electrode in the MNI space is precisely located and classified. The MNI space is a standardized brain template that allows for the accurate description of electrode positions in a unified coordinate system. For each electrode, the cranial entry region number and the target region number are calculated. Here, the cranial entry region refers to the DKT (Desikan-Killiany Atlas) brain region where the exact point of electrode entry into the cranial cavity is located, and the target region is the brain region where the electrode target is located, that is, the final positioned brain region of the electrode, which is also in the DKT brain region. By numbering the cranial entry region and the target region of the electrode, the electrodes in the MNI space can be systematically classified according to their positions in the DKT brain region. This classification not only helps to understand the distribution pattern of electrode implantation but also is of great significance for optimizing the implantation strategy.

[0085] Exemplarily, all electrodes in the MNI space can be clustered first to screen out the high-frequency paths from all electrodes and retain the high-frequency paths in the MNI space for subsequent strategy template generation operations. When screening the high-frequency paths, the electrodes for each cranial entry region and each target region can be clustered to obtain the high-frequency paths for each cranial entry region and each target region; the determination process of the high-frequency paths will be described in detail below taking a single cranial entry region and target region as an example, that is, the electrodes with the same cranial entry region and the same target region are classified, and the clustering algorithm is used to extract the high-frequency paths of the electrodes with the same cranial entry region and the same target region; for example, among all the electrodes in the same cranial entry region and the same target region, multiple electrodes with cranial entry points within a certain range and target points within a certain range are clustered into one category. When the number of electrodes in this category reaches a certain quantity threshold, the multiple electrodes in this category can be fused into an electrode implantation path as a high-frequency path for this cranial entry region and this target region; for example: there are N electrodes in cranial entry region 1 and target region 1, and among these N electrodes, M (M < N) electrodes have cranial entry points within the preset range of a certain cranial entry point coordinate and their target points are within the preset range of a certain target point coordinate, then these M electrodes can be aggregated into one category of electrodes; furthermore, when the number M of these M electrodes is greater than or equal to the preset quantity threshold T (T < N), it indicates that the electrodes formed by this cranial entry point coordinate and target point coordinate appear frequently, and these M electrodes can be fused into an electrode implantation path and used as a high-frequency path; for example, the electrode formed by this cranial entry point coordinate and target point coordinate can be used as this high-frequency path, or the electrode formed by the average cranial entry point coordinate and average target point coordinate of these M electrodes can be used as this high-frequency path, etc. There may be more than one high-frequency path or multiple high-frequency paths may be set in each group of electrodes with the same cranial entry region and the same target region.

[0086] Building upon this, after obtaining all high-frequency paths in the MNI space through clustering, hierarchical clustering can be further performed on all high-frequency paths to obtain multiple strategy templates. Hierarchical clustering involves performing similarity clustering on all high-frequency paths in the MNI space from the patient's perspective, thereby grouping multiple patients with highly similar high-frequency paths into one class and obtaining the corresponding strategy template for that class. This strategy template includes at least several highly similar high-frequency paths from those patients. For example, after obtaining all high-frequency paths, each patient may only contain a portion of all high-frequency paths. Using the number of all high-frequency paths as the vector dimension, a path vector (one-dimensional vector), also known as a patient identifier, can be determined for each patient. High-frequency paths present in the patient's path vector can be set to 1, and high-frequency paths not present in the patient's path vector can be set to 0. Next, similarity clustering is performed on all patients' path vectors, grouping the path vectors of multiple patients with a similarity greater than or equal to a preset similarity threshold into one class, and determining the corresponding strategy template for that class based on the high-frequency paths of the multiple patients in that class.

[0087] For example, when determining the strategy template, high-frequency paths among the high-frequency paths of multiple patients corresponding to a class, whose repetition frequency is greater than or equal to a preset threshold, can be added to the strategy template corresponding to that class to generate the strategy template for that class. For example, after hierarchical clustering, if the similarity between the path vectors of patients a, b, and c is greater than or equal to a preset similarity threshold, such as 0.8, then patients a, b, and c can be clustered into one class. Next, assuming that patient a has 15 high-frequency paths, patient b has 12 high-frequency paths, and patient c has 10 high-frequency paths, if a certain high-frequency path 1 is present in patients a, b, and c, that is, the frequency of high-frequency path 1 appearing in the class is greater than or equal to 2, then high-frequency path 1 can be added to the strategy template corresponding to that class as an electrode path in the strategy template. If high-frequency path 2 is present in patients a, b, and c, that is, the frequency of high-frequency path 2 appearing in the class is less than 2, then high-frequency path 2 is discarded and not used as an electrode path in the strategy template corresponding to that class.

[0088] Based on the hierarchical clustering method described above, after clustering all high-frequency paths from the patient's perspective using similarity, multiple strategy templates can be obtained, such as strategy template 1, strategy template 2, strategy template 3, ..., strategy template n, etc. These multiple strategy templates may contain high-frequency paths with the same entry cranial region and / or the same target region. When planning paths based on these strategy templates, users can match and select from these strategy templates according to the current patient's epilepsy type, so as to plan paths based on the target strategy template that matches the current patient's epilepsy type.

[0089] For example, for multiple strategy templates obtained by hierarchical clustering, the strategy template can be labeled according to the characteristics of the high-frequency paths contained in each strategy template. For example, the epilepsy type that matches the strategy template can include one epilepsy type or multiple epilepsy types. In this way, when performing path planning, the corresponding strategy template can be quickly matched and selected, thereby improving the efficiency of path planning.

[0090] For example, when high-frequency paths are extracted from all electrode implantation paths of each patient through clustering, users can also select the required high-frequency paths from all high-frequency paths extracted by the clustering algorithm based on their own clinical experience and add them to the path set. Then, the high-frequency paths in the path set are classified from the patient's perspective by hierarchical clustering algorithm to form different strategy templates.

[0091] By using the above two methods, an electrode path strategy template that is more compatible with the user's clinical experience and habits can be formed. This allows the user to perform efficient path planning based on the pre-set strategy template, thereby improving the efficiency of path planning.

[0092] For example, refer to Figure 6 The diagram illustrates the operation of path planning using strategy templates. When using strategy templates for path planning, the user can select the desired strategy template from a standard template library, specifically from the list of strategy templates displayed in the second display area. The brain implant path planning system then maps all paths from the user-selected strategy template from the MNI space to the target medical image space of the target object, and displays the target medical image of the target object containing the planned paths in the first display area. Furthermore, the user can confirm and adjust the planned paths in the mapped medical image to generate the final electrode path.

[0093] In one exemplary embodiment, the rule planning module can be used to provide a rule template creation function, thereby enabling users to create different rule templates for different surgical procedures, different brain regions, or nuclei. The rule template includes at least one path rule, and based on this at least one path rule, a planned path that satisfies the rule template can be generated. Exemplarily, the rule planning module may include a rule template generation submodule, which is used to obtain path rules input by the user and generate a rule template.

[0094] The following section uses the DBS technique as an example to describe in detail the process of generating rule templates by the rule template generation submodule.

[0095] First, the brain atlas space is acquired. A standard brain atlas is used and a nucleus mask is generated simultaneously. For example, the brain atlas can be corrected by ACPC, and the brain atlas and nuclei are fused and displayed in the ACPC corrected view. The line connecting the midpoint of the posterior border of the anterior commissure (AC) to the midpoint of the anterior border of the posterior commissure (PC) is called the AC-PC line.

[0096] Secondly, the path template is drawn. For example, the target area (i.e., the target nucleus) can be selected or the target point can be drawn based on the ACPC coordinate system. Relevant rule parameters are set as the constraints corresponding to the target area (or target point). The rule parameters may include, but are not limited to, parameters such as ventricular distance, vascular distance, sagittal plane angle (Arc) and transverse plane angle (Ring). Optionally, the rule parameters may also include the weights corresponding to each parameter. Among them, the sagittal plane angle (Arc) refers to the angle between the planned path and the sagittal plane on the coronal image, and the transverse plane angle (Ring) refers to the angle between the planned path and the transverse plane on the coronal image.

[0097] Next, rule templates corresponding to the target region (or target point) can be generated based on the various rule parameters. Different constraints can be set for different target regions to generate rule templates for different target regions, such as GPI rule templates for GPI nuclei and STN rule templates for STN nuclei. For example, for the same target region (or target point), multiple different rule templates can be set to obtain multiple planning paths for that target region (or target point). It should be noted that a planning path satisfying a rule template can be generated based on a rule template, or a planning path satisfying a rule template can be generated separately in symmetrical brain regions based on a rule template, thus obtaining two planning paths.

[0098] For example, in the case of a brain implant path planning system that includes a display module, the display module can also be used to display a newly created rule template control, such as... Figure 3 The "+Create New Rule Template" shown in the image; based on this, the display module can also be used to display the rule settings page when the user triggers the Create New Rule Template control. The rule settings page includes setting options for several rule parameters; correspondingly, the rule template generation submodule can also be used to respond to the user's setting operations on several rule parameters and generate a rule template based on the parameter values ​​corresponding to the setting operations.

[0099] like Figure 7 As shown, when the new rule template control is triggered, the rule settings page can be displayed in the second display area of ​​the interface. This rule settings page is used to set or modify the rule parameters corresponding to a certain rule template; see reference. Figure 7As shown, taking newly created rule template 3 as an example, the target area, i.e., the target nucleus, for rule template 3 can include the L-GPi nucleus. The rule parameters corresponding to the L-GPi nucleus can include vascular distance, ventricular distance, Arc angle, Ring angle, and the weights corresponding to each parameter. After setting the various rule parameters, a rule template for the L-GPi nucleus, i.e., rule template 3, can be generated based on the set rule parameters.

[0100] In addition, for the SEEG procedure, corresponding rule templates can be set for different brain regions, such as... Figure 8 The image shows the rule settings page corresponding to the SEEG procedure. Taking the rule template corresponding to the right insula as an example, the rule template for the right insula can include constraints corresponding to different brain region combinations. These brain region combinations include the brain region containing the entry region (T) and the brain region containing the target region (E). At least one of the entry region and target region is different in different brain region combinations, such as different entry regions and the same target region, or the same entry region and different target regions, or both different entry regions and target regions. Based on the constraints corresponding to the brain region combinations, one or more planning paths corresponding to that brain region combination can be generated. In the case of multiple brain region combinations, several planning paths can be generated based on this rule template.

[0101] For example, when the entry area and the target area coincide, it can be considered that only a single brain region is involved; when the entry area and the target area do not coincide, it can be considered that two brain regions are involved, such as... Figure 8 Each brain region combination shown involves two brain regions. For multiple brain region combinations corresponding to a single brain region or multiple brain region combinations corresponding to two brain regions, the corresponding constraints can be the same. For example, when a single brain region is selected, the constraints generated based on the set rule parameters can simultaneously apply to multiple brain region combinations corresponding to that single brain region; that is, the constraints for multiple brain region combinations corresponding to a single brain region are all the same. Of course, the constraints for multiple brain region combinations corresponding to a single brain region can also be different. The rule setting method for two brain regions is the same as for single brain regions, and will not be repeated here.

[0102] It should be noted that, for a single brain region, when the rule template includes multiple brain region combinations corresponding to the single brain region, if the entry areas or brain regions contained in the multiple brain region combinations are the same, then the constraints corresponding to the multiple brain region combinations should be different. That is, for the same brain region combination, multiple different constraints can be set to generate multiple planning paths. If the entry areas or brain regions contained in the multiple brain region combinations are not the same, then the constraints corresponding to the multiple brain region combinations can be the same or different.

[0103] For example, the planning parameters corresponding to the brain region combination under the SEEG procedure may include, but are not limited to, at least one of the following parameters: path spacing, blood vessel distance, ventricle distance, contact point distance, path length, and entry angle. Of course, the planning parameters may also include the weights corresponding to each parameter.

[0104] For example, continue to refer to Figure 6 As shown, when using rule templates for path planning, users can also select the rule template they need from the standard template library, that is, select the required rule template from the list of rule templates displayed in the second display area. Then, the brain implant path planning system can generate the corresponding planned path in the target medical image of the target object according to the various rule parameters in the rule template selected by the user, and display the target medical image of the target object containing the planned path in the first display area. Furthermore, users can also confirm and adjust the planned path in the medical image to generate the final electrode path to be used.

[0105] In an exemplary embodiment, based on the strategy template creation function corresponding to the strategy planning control and the rule template creation function corresponding to the rule planning control, the brain implant path planning system proposed in this application can realize not only the strategy template mode but also the rule template mode. In the strategy template mode, path planning can be performed according to a pre-set strategy template; in the rule template mode, path planning can be performed according to a pre-set rule template. Furthermore, the rule template mode also supports online input of new path rules, i.e., new constraints, such as through... Figure 3 The newly created rule template shown is used to implement online path planning in rule template mode.

[0106] like Figure 9 As shown, this diagram illustrates the workflow for online path planning in rule-based template mode. During online path planning, users can select the target area to be sampled based on the usage scenario, set corresponding constraints, and complete path planning based on the target medical image of the target object.

[0107] Target selection: The selection varies depending on the scenario. For SEEG, the user can select one or two brain regions to sample; for DBS, the user needs to select the target nucleus.

[0108] Constraint Settings: To ensure the planned path avoids critical tissues such as blood vessels and ventricles, and meets sampling requirements, constraints need to be set for path planning. The rule parameters for the SEEG procedure include, but are not limited to, blood vessel distance, ventricle distance, entry angle, path length, and gray-white matter sampling ratio. The rule parameters for the DBS procedure include, but are not limited to, blood vessel distance, ventricle distance, Arc angle, and Ring angle. Users can set the planning parameters for constraints according to their needs.

[0109] Path generation: Based on the target area selected by the user and the constraints set, a planned path that meets the constraints is generated based on the target medical image of the target object. The planned path is then overlaid and displayed on the target medical image of the target object. The user can confirm, adjust and use the generated planned path.

[0110] For example, in the rule template mode, both the rule template and the constraints set online are constraints set for the planned path. These constraints can include distance constraints between the planned path and key tissues such as blood vessels and ventricles. Therefore, the planned path generated in medical images can directly avoid interference with key tissues such as blood vessels and ventricles, making the planned path a safe and effective path. However, for the strategy template mode, since the interference between the template path and key tissues such as blood vessels and ventricles is not considered when the strategy template is formulated, when all template paths in the strategy template are mapped to medical images, interference problems may occur between the planned path in the mapped medical images and key tissues such as blood vessels and ventricles, causing varying degrees of damage to these key tissues.

[0111] Therefore, for path planning based on strategy templates, the brain implant path planning system may also include a risk warning function and a path adjustment function; for example, the brain implant path planning system may also include a path risk warning module and a path adjustment module; wherein, the path risk warning module can be used to perform path risk detection on each second planned path in the target medical image to obtain a risk detection result; the path adjustment module can be used to adjust the second planned path with path implantation risk when the risk detection result includes a second planned path with path implantation risk to obtain an adjusted second planned path.

[0112] For example, when each first planning path (also known as a template path) in the user-selected target strategy template is mapped to the target medical image of the target object to obtain a second planning path in the target medical image corresponding to each first planning path, the path risk warning module in the brain implant path planning system can perform path risk detection on each second planning path in the target medical image. That is, it can determine whether each second planning path meets the preset interference conditions. If the preset interference conditions are met, it indicates that the second planning path has planning risks; otherwise, if the preset interference conditions are not met, it indicates that the second planning path does not have planning risks and is a safe and effective planning path that can be used for subsequent electrode implantation. The preset interference conditions can be used to characterize whether there is interference between the planned path and key tissues such as blood vessels and ventricles, including but not limited to the distance between the planned path and key tissues such as blood vessels and ventricles being less than or equal to a preset distance threshold.

[0113] refer to Figure 10 As shown, the display module can also be used to display a path risk warning control. When the user triggers the path risk warning control, the path risk warning module can perform path risk detection on each second planned path in the target medical image and obtain a risk detection result. The risk detection result can include whether each second planned path in the target medical image has an interference risk. For example, a path list can be displayed in the second display area. The path list can include all planned paths in the target medical image (such as all second planned paths), and can also display parameter information such as the name, color, length, and risk detection result of each planned path. Optionally, different attribute parameters can be used to distinguish between planned paths with interference risk and planned paths without interference risk. These attribute parameters can include, but are not limited to, at least one of parameters such as color, pattern, and line. For example, in the risk detection result of the path list, red can be used to indicate that the planned path has an interference risk, and green can be used to indicate that the planned path has no interference risk.

[0114] For example, when a user selects a planned path, risk warning information can also be displayed in the first display area. For instance, a user can select a planned path in the target medical image or select a planned path from the path list in the second display area (such as selecting path 5). When the planned path selected by the user has an interference risk, a risk warning message "Current path has interference" can be displayed above the target medical image in the first display area to enhance the warning effect on the user.

[0115] For example, for planned paths with interference risks, the brain implant path planning system can support manual adjustment by the user, such as manually adjusting the planned path with interference risks in the target medical image to obtain a planned path without interference risks. For example, the brain implant path planning system can also support automatic adjustment of planned paths with interference risks, so that the path adjustment module can automatically adjust the planned path with interference risks, reducing the tediousness of user operations and improving the efficiency of path adjustment. Optionally, the path adjustment module can perform fully automatic adjustment of planned paths with interference risks, or it can perform semi-automatic adjustment. Semi-automatic adjustment can mean first performing automatic adjustment, then manually adjusting the automatically adjusted planned path, or first performing manual adjustment, then automatically fine-tuning the manually adjusted planned path, to obtain a planned path without interference risks.

[0116] For example, the display module can also be used to display a blood vessel avoidance recommendation control. When the user triggers the blood vessel avoidance recommendation control, the path adjustment module can adjust the planned paths with interference risk in the target medical image respectively, and output the adjusted recommended path corresponding to each planned path with interference risk, so that the user can select and confirm.

[0117] In this embodiment, the brain implant path planning system may also include a path risk warning module and a path adjustment module, so as to detect interference risks and adjust the planned path in the target medical image, which enriches the function of the brain implant path planning system and improves the integrity of the brain implant path planning system.

[0118] In one exemplary embodiment, a brain implant path planning method is also proposed, which can be applied to the brain implant path planning system in any of the above embodiments. The method includes:

[0119] The strategy planning module provides several pre-defined strategy templates and obtains the target strategy template selected by the user from these templates. Each first planning path in the target planning template is mapped to the target medical image, resulting in a second planning path in the target medical image corresponding to each first planning path; and / or,

[0120] The rule planning module provides several pre-defined rule templates and obtains the target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, a third planning path is generated in the target medical image.

[0121] The brain implant path planning system may include a strategy planning module and / or a rule planning module. The strategy planning module can provide strategy templates, and the rule planning module can provide rule templates. Users can choose any strategy template or rule template for path planning. Of course, based on the rule planning module, users can also set new path planning rules online to suit different target objects in different scenarios, thereby improving the scope of application.

[0122] In other words, this brain implant path planning system can implement not only strategy template patterns but also rule template patterns, such as... Figure 11 As shown, in strategy template mode, users can set strategy templates. After completing the drawing of the strategy template, it is also possible to modify and use the template. Users can modify the pre-drawn strategy template before using it, or they can directly use the pre-drawn strategy template to realize path planning. In rule template mode, users can select the target area for the target medical image of the current target object, set constraints, and then perform path planning in the target medical image according to the set constraints. Optionally, for the target area and constraints selected online by the user, new rule templates can also be generated and added to the rule template list for subsequent reuse.

[0123] For example, the brain implant path planning method may further include: when the current planning mode is detected to be a strategy template mode, displaying a pre-set strategy template, i.e., displaying a strategy template list, and performing path planning based on the target strategy template selected by the user from the strategy template list; when the current planning mode is detected to be a rule template mode, on the one hand, displaying a pre-set rule template, i.e., displaying a rule template list, and performing path planning based on the target rule template selected by the user from the rule template list; on the other hand, displaying a rule setting page, and performing path planning based on new constraints entered by the user in the rule setting page.

[0124] For example, the brain implant path planning method may further include: displaying a strategy template mode control and a rule template mode control, wherein if a user triggers an operation on the strategy template mode control, the current planning mode can be determined to be a strategy template mode; and if a user triggers an operation on the rule template mode control, the current planning mode can be determined to be a rule template mode.

[0125] By employing this path planning method, users can leverage the aforementioned path planning tools to develop strategy templates and / or rule templates that better suit their needs. This allows users to utilize pre-defined path templates within the brain implant path planning system during the preoperative planning stage. Since user-defined path templates are more closely aligned with actual user operations compared to templates obtained from retrospective data in large datasets, manual adjustments to the planned path can be reduced or even eliminated, thus improving overall path planning efficiency. Furthermore, this path planning tool provides doctors with flexibility for less common case studies, assisting them in setting matching path constraints online. This enables efficient path planning for specific cases. The embodiments described in this application, with their innovative, efficient, and personalized characteristics, represent a significant technological breakthrough in the field of neurosurgery, possessing broad clinical application prospects and profound social value.

[0126] Based on the same inventive concept, this application also provides a brain implant path planning device for implementing the brain implant path planning method described above. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more brain implant path planning device embodiments provided below can be found in the limitations of the brain implant path planning method described above, and will not be repeated here.

[0127] In one exemplary embodiment, a brain implant path planning device is provided, comprising: a strategy planning module and / or a rule planning module, wherein:

[0128] The strategy planning module provides several pre-defined strategy templates and obtains the target strategy template selected by the user from the several strategy templates. It maps each first planning path in the target strategy template to the target medical image to obtain several second planning paths in the target medical image.

[0129] The rule planning module provides several pre-defined rule templates and obtains the target rule template selected by the user from these templates. Based on the path planning rules in the target rule template, it generates a third planning path in the target medical image.

[0130] Each module in the aforementioned brain implant path planning device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0131] In one exemplary embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When executed by the processor, the computer program implements a brain implant path planning method. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0132] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0133] In one exemplary embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the brain implant path planning method in any of the above embodiments.

[0134] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the brain implant path planning method in any of the above embodiments.

[0135] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the brain implant path planning method in any of the above embodiments.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0137] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0138] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A brain implant path planning system, characterized in that, The system includes: a strategy planning module and / or a rule planning module; The strategy planning module is used to provide several pre-set strategy templates, obtain the target strategy template selected by the user from the several strategy templates, map each first planning path in the target strategy template to the target medical image, and obtain the second planning path in the target medical image corresponding to each first planning path. The rule planning module is used to provide several pre-set rule templates, obtain the target rule template selected by the user from the several rule templates, and generate a third planning path in the target medical image according to the path planning rules in the target rule template.

2. The system according to claim 1, characterized in that, The strategy planning module includes a first strategy template generation submodule and / or a second strategy template generation submodule; The first strategy template generation submodule is used to provide standard tissue images and generate strategy templates based on the planning paths input by the user in the standard tissue images; The second strategy template generation submodule is used to obtain retrospective data of users' historical path planning and analyze the retrospective data to obtain a strategy template.

3. The system according to claim 1, characterized in that, The rule planning module includes a rule template generation submodule; The rule template generation submodule is used to obtain the path rules input by the user and generate rule templates.

4. The system according to any one of claims 1-3, characterized in that, The system also includes: a display module; The display module is used to display a list of strategy templates and / or a list of rule templates; the list of strategy templates includes options for each strategy template; the list of rule templates includes options for each rule template.

5. The system according to claim 4, characterized in that, The display module is also used to display strategy planning controls and rule planning controls; The display module is also configured to display the list of strategy templates when the user triggers the strategy planning control, and to display the list of rule templates when the user triggers the rule planning control.

6. The system according to claim 4, characterized in that, The display module is also used to display the newly created strategy template control; The display module is also used to display the standard organization image when the user triggers the new strategy template control; Correspondingly, the first strategy template generation submodule is also used to respond to the user's drawing operation on the standard organization image and generate a strategy template based on the planning path corresponding to the drawing operation.

7. The system according to claim 4, characterized in that, The display module is also used to display a newly created rule template control; The display module is also used to display a rule settings page when the user triggers the new rule template control; the rule settings page includes setting options for several rule parameters; Correspondingly, the rule template generation submodule is also used to respond to the user's setting operation on the several rule parameters, and generate a rule template based on the parameter values ​​corresponding to the setting operation.

8. The system according to claim 4, characterized in that, The display module is also used to display medical images before and / or after path planning.

9. The system according to claim 1, characterized in that, The system also includes: a path risk warning module and a path adjustment module; The path risk warning module is used to perform path risk detection on each of the second planned paths in the target medical image and obtain the risk detection results; The path adjustment module is used to adjust the second planned path with path implantation risk when the risk detection result includes a second planned path with path implantation risk, so as to obtain an adjusted second planned path.

10. A method for planning the path of a brain implant, characterized in that, The method, applied to the brain implant path planning system as described in any one of claims 1-9, comprises: The strategy planning module provides several pre-defined strategy templates and obtains the target strategy template selected by the user from these templates. Each first planning path in the target strategy template is mapped to the target medical image, resulting in several second planning paths in the target medical image; and / or, The rule planning module provides several pre-set rule templates and obtains the target rule template selected by the user from the several rule templates. Based on the path planning rules in the target rule template, a third planning path is generated in the target medical image.

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