Brain region selection guiding method, implantation path planning method and equipment

Abstract

The invention is suitable for the technical field of medical treatment, and provides a brain region selection guiding method, an implantation path planning method and equipment. According to the brain region selection guiding method, the probability that each brain region is selected as a first brain region can be displayed according to prior information of a target patient, and after the first brain region of the target patient is determined, based on the first brain region and the use probability of each type of brain region combination in a sample patient represented by brain region statistical information, the probability of each type of brain region combination in the sample patient is determined. The probability that each brain region of the target patient is selected as a second brain region is displayed, when the first brain region is the cranial entering region, the second brain region is the target region, and when the first brain region is the target region, the second brain region is the cranial entering region. According to the embodiment of the invention, the prior information of the target patient and the implant implantation condition of the sample patient can be utilized to assist the doctor in brain region selection of the implantation operation, the operation decision can be optimized, the doctor can be helped to rapidly plan a more reasonable and reliable implant implantation path, and the diagnosis efficiency and the treatment effect of the epilepsy patient can be improved.

Description

Technical Field

[0001] The present application belongs to the field of medical technology, and in particular relates to a method for guiding brain region selection, a method for planning an implantation path, and a device. Background Art

[0002] In the medical field, diseases are often treated by implanting stents, electrodes and other implants in the human body. As a cutting-edge medical diagnostic method, stereotactic electroencephalography (SEEG) surgery can accurately capture EEG signals through implanted electrodes to identify and locate the origin area of ​​epileptic seizures. During the brain implantation surgery, the implant needs to enter the skull from the cranial entry area and eventually act on the target area. The rationality of the implantation path is directly related to the patient's diagnosis and treatment effect. At present, implantation surgery is highly dependent on the doctor's clinical experience and energy investment, which affects the diagnostic efficiency and treatment effect of epilepsy patients. Summary of the invention

[0003] The embodiments of the present application provide a guidance method for brain region selection, an implantation path planning method and device, which can utilize the implantation conditions of sample patients to assist doctors in selecting brain regions, help optimize surgical decisions, help doctors quickly plan a more reasonable and reliable implantation path, and improve patients' diagnostic efficiency and treatment effects.

[0004] A first aspect of an embodiment of the present application provides a method for guiding brain region selection, comprising: determining a first brain region of a target patient, the first brain region being the cranial entry region or target region of an implant during an implantation operation on the target patient; based on the first brain region and brain region statistical information, displaying a first probability that each brain region of the target patient is selected as a second brain region; wherein the brain region statistical information represents the probability of use of each brain region combination in a sample patient in which an implant has been implanted, each of the brain region combinations comprising a cranial entry region and a target region where the implant implanted in the sample patient is located, and different brain region combinations have different cranial entry regions and / or different target regions; when the first brain region is the cranial entry region, the second brain region is the target region, and when the first brain region is the target region, the second brain region is the cranial entry region.

[0005] In some embodiments of the first aspect, the displaying of the first probability of each brain region of the target patient being selected as the second brain region based on the first brain region and the brain region statistical information comprises: querying the usage probability of each candidate brain region combination according to the first brain region and the brain region statistical information, when the first brain region is the cranial entry region, the candidate brain region combination is the brain region combination in which the cranial entry region is the same as the first brain region, and when the first brain region is the target region, the candidate brain region combination is the brain region combination in which the target region is the same as the first brain region; displaying the first probability of each brain region of the target patient being selected as the second brain region according to the usage probability of each candidate brain region combination.

[0006] In some embodiments of the first aspect, the brain region statistical information includes statistical information of different brain lobes; querying the usage probability of each candidate brain region combination based on the first brain region and the brain region statistical information includes: querying the usage probability of each candidate brain region combination based on a target brain lobe where the first brain region is located and the statistical information of the target brain lobe.

[0007] In some embodiments of the first aspect, before displaying the first probability that each brain region of the target patient is selected as the second brain region based on the first brain region and the brain region statistical information, the brain region selection guidance method further includes: acquiring retrospective data of the sample patient, the retrospective data characterizing the implant position in the skull of the sample patient; classifying the sample patient according to the implant position in the skull to obtain the brain region combination; determining the use probability of each of the brain region combinations; and generating the brain region statistical information according to the use probability of each of the brain region combinations.

[0008] In some embodiments of the first aspect, the classifying according to the implant position in the skull of the sample patient to obtain the brain region combination includes: determining the three-dimensional model data of the sample patient based on the retrospective data; mapping the implant of the sample patient in the three-dimensional model data to a standard brain space; and classifying the sample patient according to the implant position in the standard brain space to obtain the brain region combination.

[0009] In some embodiments of the first aspect, before determining the first brain region of the target patient, the method for guiding brain region selection further includes: acquiring prior information of the target patient; and displaying a second probability of each brain region being selected as the first brain region based on the prior information.

[0010] In some embodiments of the first aspect, determining the first brain region of the target patient includes: in response to a first selection operation among the brain regions, selecting a selected brain region as the first brain region.

[0011] A second aspect of an embodiment of the present application provides a method for planning an implantation path, comprising: displaying a first probability that each brain region of the target patient is selected as the second brain region according to the guiding method for brain region selection as described in any one of the first aspects; determining the second brain region among the various brain regions; and determining an implantation path of the implant of the target patient based on the first brain region and the second brain region.

[0012] In some embodiments of the second aspect, determining the implantation path of the implant of the target patient based on the first brain region and the second brain region includes: determining a target point in the target area and a cranial entry point in the cranial entry area; determining a candidate implantation path based on the target point and the cranial entry point, the candidate implantation path being used to connect a target point and a cranial entry point; when there are multiple candidate implantation paths, determining a cost value of each of the candidate implantation paths, the cost value being related to at least one of a distance between the candidate implantation path and a blood vessel, a distance between the candidate implantation path and a ventricle, a cranial entry angle of the candidate implantation path, and a gray-white matter sampling ratio of the candidate implantation path; determining the implantation path based on the cost value of each of the candidate implantation paths.

[0013] A third aspect of an embodiment of the present application provides a guiding device for brain region selection, comprising: a first brain region determination unit, used to determine a first brain region of a target patient, wherein the first brain region is a cranial entry region or a target region of an implant when performing an implantation operation on the target patient; a display unit, used to display a first probability that each brain region of the target patient is selected as a second brain region based on the first brain region and brain region statistical information; wherein the brain region statistical information represents the usage probability of each brain region combination in a sample patient in which an implant has been implanted, each of the brain region combinations comprising a cranial entry region and a target region where the implant implanted in the sample patient is located, and different brain region combinations have different cranial entry regions and / or different target regions; when the first brain region is the cranial entry region, the second brain region is the target region, and when the first brain region is the target region, the second brain region is the cranial entry region.

[0014] A fourth aspect of an embodiment of the present application provides an implant path planning device, comprising: a display unit, used to display a first probability that each brain region of the target patient is selected as the second brain region according to the guiding method for brain region selection described in any one of the first aspects; a second brain region determination unit, used to determine the second brain region among various brain regions; and a path planning unit, used to determine the implantation path of the implant of the target patient according to the first brain region and the second brain region.

[0015] A fifth aspect of an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the guidance method for brain region selection as described in any one of the first aspect when executing the computer program, or implements the steps of the implantation path planning method as described in any one of the second aspect when executing the computer program.

[0016] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the guidance method for selecting a brain region are implemented, or when the computer program is executed by a processor, the steps of the planning method for the implantation path are implemented.

[0017] A fifth aspect of an embodiment of the present application provides a computer program product, including a computer program, wherein when the computer program is executed, the method for guiding brain region selection described in the first aspect is executed, or, when the computer program is executed, the method for planning the implantation path described in the second aspect is executed.

[0018] In an embodiment of the present application, by determining the cranial entry area of ​​the implant during the implantation surgery on the target patient, based on the statistical information of the cranial entry area and the brain area, the first probability of each brain area of ​​the target patient being selected as the target area is displayed, or by determining the target area of ​​the implant during the implantation surgery on the target patient, based on the statistical information of the target area and the brain area, the first probability of each brain area of ​​the target patient being selected as the target area is displayed. The use probability of each brain area combination in the sample patients who have implanted the implant can be used to provide doctors with prompts on the probability of each brain area being selected, which can assist doctors in selecting the cranial entry area or target area, optimize surgical decisions, and help doctors quickly plan a more reasonable and reliable implantation path, thereby improving the diagnostic efficiency and treatment effect of epilepsy patients.

[0019] Moreover, based on the first brain region and the second brain region (i.e., the cranial entry area and the target area), the implantation path of the target patient can be determined to assist the doctor in planning the implantation path during the implantation surgery, thereby improving the accuracy of the path planning and the efficiency of the implantation surgery. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0021] Figure 1It is a schematic diagram of the implementation flow of the guiding method for selecting a brain region provided in an embodiment of the present application;

[0022] Figure 2 is a schematic diagram of a specific implementation flow of displaying a first probability provided by an embodiment of the present application;

[0023] Figure 3 It is a schematic diagram of a specific implementation process of generating brain region statistical information provided in an embodiment of the present application;

[0024] Figure 4 is a schematic diagram of brain region statistical information provided by an embodiment of the present application;

[0025] Figure 5 is a schematic diagram of statistical information of the occipital lobe provided in an embodiment of the present application;

[0026] Figure 6 is a schematic diagram of a heat map provided in an embodiment of the present application;

[0027] Figure 7 It is a schematic diagram of the implementation flow of the implantation path planning method provided in the embodiment of the present application;

[0028] Figure 8 It is a schematic diagram of a specific implementation process of determining an implantation path provided in an embodiment of the present application;

[0029] Fig. 9 is a structural schematic diagram of a guiding device for selecting a brain region provided in an embodiment of the present application;

[0030] Fig.10 It is a structural schematic diagram of an implantation path planning device provided in an embodiment of the present application;

[0031] Fig.11 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work are protected by the present application.

[0033] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0034] In the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0036] In the medical field, diseases are often treated by implanting stents, electrodes and other implants in the human body. As a cutting-edge medical diagnostic method, stereotactic EEG surgery can accurately capture EEG signals through implanted electrodes to identify and locate the origin area of ​​epileptic seizures. During the brain implantation surgery, the implant needs to enter the skull from the cranial entry area and eventually act on the target area. The rationality of the implantation path is directly related to the patient's diagnosis and treatment effect. At present, implantation surgery is highly dependent on the doctor's clinical experience and energy investment, which affects the diagnostic efficiency and treatment effect of epilepsy patients.

[0037] In view of this, the present application proposes a guiding method for brain region selection, which can use the implantation status of sample patients to assist doctors in selecting brain regions, optimize surgical decisions, help doctors quickly plan more reasonable and reliable implantation paths, and improve the diagnostic efficiency and treatment effects of epilepsy patients.

[0038] In order to illustrate the technical solution of the present application, a specific embodiment is provided below for illustration.

[0039] Please refer to Figure 1 , Figure 1 A schematic diagram of the implementation flow of a guidance method for brain region selection provided in an embodiment of the present application is shown. The method can be applied to an electronic device, which may be a computer, a smart phone, a medical device specifically used for image processing, or other smart devices, and the present application does not impose any limitation on this.

[0040] Specifically, the above-mentioned brain region selection guiding method may include the following steps S101 to S102.

[0041] Step S101, determining a first brain region of a target patient.

[0042] Among them, the target patients are those who need implant surgery path planning.

[0043] In the embodiments of the present application, brain regions are also referred to as brain functional partitions, which refer to local areas in the brain with specific functions. In brain implant surgery, it is necessary to place an intracranial implant in the target patient. The implant can be an electrode, which is used to record intracranial EEG activity and determine the epileptogenic zone. The first brain region is the cranial entry area or target area of ​​the implant when the target patient is implanted. Among them, the cranial entry area refers to the brain region where the exact point where the implant enters the cranial cavity is located. The target area refers to the brain region that the implant ultimately needs to reach during the implant surgery, and the target area is usually an abnormal area (such as the area where the epileptogenic focus is located).

[0044] Step S102: Based on the first brain region and the brain region statistical information, display the first probability that each brain region of the target patient is selected as the second brain region.

[0045] In an embodiment of the present application, the brain region statistical information represents the usage probability of each brain region combination in the sample patients who have implanted the implant, and each brain region combination may include the cranial entry region and the target region where the implant is implanted in the sample patient, and different brain region combinations have different cranial entry regions and / or different target regions. In other words, the two brain region combinations have different cranial entry regions, different target regions, or both the cranial entry regions and the target regions are different.

[0046] Among them, the sample patients are patients who have completed implantation of the implant. For each sample patient, the usage frequency of the corresponding brain region combination can be increased by one according to the cranial entry area and target area where the implant is located. For example, for the implant of the sample patient numbered 001, assuming that the cranial entry area is the middle temporal gyrus and the target area is the hippocampus, the usage frequency of the brain region combination with the cranial entry area being the middle temporal gyrus and the target area being the hippocampus can be increased by one. For the implant of the sample patient numbered 002, assuming that the cranial entry area is the superior temporal gyrus and the target area is the insula, the usage frequency of the brain region combination with the cranial entry area being the superior temporal gyrus and the target area being the insula can be increased by one. By statistically analyzing a sufficient number of sample patients, brain region statistical information that can characterize the usage probability of each brain region combination can be formed. The usage probability of each brain region combination can be the usage frequency of the brain region combination divided by the total usage frequency. The total usage frequency can refer to the total usage frequency of all brain region combinations. When the brain region combination is divided according to cerebral lobes, the total usage frequency may also refer to the total usage frequency of the brain region combination corresponding to the cerebral lobe to which the brain region combination belongs. The brain region combination corresponding to the cerebral lobe refers to the brain region combination in which at least one of the cranial entry region and the target region belongs to the cerebral lobe. For example, for the brain region combination whose cranial entry region is the lingual gyrus and whose target region is the lateral occipital cortex, the usage frequency of the brain region combination can be divided by the total usage frequency of the brain region combination corresponding to the occipital lobe.

[0047] It should be noted that, in a single brain region combination, the cranial entry region and the target region can be the same brain region or different brain regions, and this application does not impose any limitation on this.

[0048] In some embodiments of the present application, when the first brain region is the cranial entry region, the second brain region is the target region. Based on the cranial entry region and brain region statistical information, the use probability of the brain region combination with the same cranial entry region can be queried, and based on the use probability, the first probability of each brain region of the target patient being selected as the target region is displayed, so that the doctor can refer to the displayed first probability to select the target region for implanting the implant in the target patient. As an example, the first probability is positively correlated with the use probability, for example, the first probability is equal to the use probability.

[0049] As an example, the first brain region (cranial entry region) is the lateral occipital cortex. At this time, you can query brain region combinations with the same cranial entry region, for example, brain region combination 1 (cranial entry region is the lateral occipital cortex, target region is white matter), brain region combination 2 (cranial entry region is the lateral occipital cortex, target region is fusiform gyrus), brain region combination 3 (cranial entry region is the lateral occipital cortex, target region is fusiform gyrus), then based on the usage probabilities of these three brain region combinations, if the usage probability of brain region combination 1 is the highest, then the first probability of white matter as the second brain region (target region) is the highest.

[0050] In other embodiments of the present application, when the first brain region is the target region, the second brain region is the cranial entry region. Based on the target region and brain region statistical information, the use probability of the brain region combination with the same target region can be queried, and according to the level of the use probability, the first probability of each brain region of the target patient being selected as the cranial entry region is displayed, so that the doctor can refer to the displayed first probability to select the cranial entry region for implanting the target patient.

[0051] As an example, the first brain region (target area) is the hippocampus. At this time, you can query brain region combinations with the same target area, for example, brain region combination 4 (the cranial entry area is the middle temporal gyrus and the target area is the hippocampus) and brain region combination 5 (the cranial entry area is the lateral occipital cortex and the target area is the hippocampus) are queried. Then, based on the usage probability of these two brain region combinations, if the usage probability of brain region combination 4 is the highest, then the first probability of the middle temporal gyrus as the second brain region (cranial entry area) is the highest.

[0052] The present application does not limit the display method of probability. In some embodiments of the present application, a brain image of a target patient can be displayed. In the brain image, each brain region is filled with a target color, and the target color of each brain region corresponds to its first probability of being selected as the second brain region. For example, the brain region can be filled with different depths of red, and the depth of red is proportional to the first probability of being selected as the second brain region. In other embodiments of the present application, the first probability can also be displayed in the form of text, numerical value, etc.

[0053] In an embodiment of the present application, by determining the cranial entry area of ​​the implant during the implantation surgery on the target patient, based on the statistical information of the cranial entry area and the brain area, the first probability of each brain area of ​​the target patient being selected as the target area is displayed, or by determining the target area of ​​the implant during the implantation surgery on the target patient, based on the statistical information of the target area and the brain area, the first probability of each brain area of ​​the target patient being selected as the target area is displayed. The use probability of each brain area combination in the sample patients who have implanted the implant can be used to provide doctors with prompts on the probability of each brain area being selected, which can assist doctors in selecting the cranial entry area or target area, optimize surgical decisions, and help doctors quickly plan a more reasonable and reliable implantation path, thereby improving the diagnostic efficiency and treatment effect of epilepsy patients.

[0054] In some embodiments of the present application, before determining the first brain region of the target patient, the guiding method for brain region selection may further include: obtaining prior information of the target patient, and displaying a second probability of each brain region being selected as the first brain region based on the prior information.

[0055] Specifically, the target patient's prior information refers to information related to the target patient that can be obtained in advance, including but not limited to the target patient's symptom description information, age information, and preliminary imaging examination results. This prior information can comprehensively reflect the patient's health status and possible disease characteristics, and provide an analytical basis for subsequent probability analysis.

[0056] In some embodiments of the present application, prior information may be input into a conditional probability model, and the conditional probability model is used to output a second probability that each brain region is selected as the first brain region under specific conditions.

[0057] In some embodiments of the present application, the conditional probability model can be obtained based on the retrospective data of the sample patient. Retrospective data may include, but are not limited to, clinical data, imaging data, and electroencephalogram data. Among them, the clinical data reflects the personal attributes of the sample patient, including but not limited to the basic information of the sample patient (such as height, weight, age), medical history, clinical manifestations, and treatment response. Imaging data is used to record the location and characteristics of abnormal areas, including but not limited to magnetic resonance imaging (MRI) images, computed tomography (CT) images, and positron emission computed tomography (PET) images. Electroencephalogram data is used to record the timing data of brain electrical activity, including but not limited to SEEG data and electroencephalogram (EEG) data.

[0058] In order to improve the reliability of the conditional probability model, the above retrospective data can be preprocessed. Data preprocessing includes but is not limited to: 1. Data cleaning: delete missing values, outliers, and handle inconsistent data formats. 2. Data conversion: convert non-numeric data (such as gender, medical history) to numeric data for easy model processing. 3. Data standardization: scale all numeric data to a uniform range, such as between 0 and 1, to eliminate the impact of different dimensions.

[0059] For retrospective data, feature extraction can be performed to determine the features most relevant to brain region selection. For example, the above features can be the age of sample patients in clinical data, the symptoms of clinical manifestations in clinical data (such as convulsions, head and eye deviation), diagnostic information in imaging data, auxiliary examination information in medical laboratories, etc.

[0060] The characteristics of each sample patient are defined as conditional space, and the selection of different brain regions as target regions / entry regions is defined as event space. The probability of a certain brain region being selected as a target region / entry region under each combination of characteristics can be estimated. This application does not limit the method of estimating probability. The following are some feasible estimation methods:

[0061] 1. Frequency method: P(A|B)=P(A∩B) / P(B); P(A|B) is the probability of event A occurring when event B occurs, corresponding to the probability of a certain brain area being selected as the target area / entry area (event A) under given features (event B); P(A∩B) represents the probability of event A and event B occurring at the same time; P(B) represents the probability of event B occurring.

[0062] 2. Bayesian method: P(A|B)=P(B|A)*P(A) / P(B), where P(A|B) is the posterior probability, i.e. the conditional probability of event A occurring under the condition that event B occurs. P(B|A) is the likelihood probability, i.e. the conditional probability of event B occurring under the condition that event A occurs. P(A) is the prior probability of event A occurring. P(B) is the marginal probability of event B occurring.

[0063] 3. Maximum Likelihood Estimate (MLE), which estimates the probability of a brain region being selected as the target region / entry region under each feature combination by maximizing the likelihood function.

[0064] 4. Probabilistic graphical model: used for conditional dependencies between variables. By learning the probabilistic graphical model, the probability of a certain brain area being selected as the target area / entry area under each feature combination can be estimated.

[0065] 5 Monte Carlo method, through random sampling, estimates the probability of a brain area being selected as the target area / entry area under each feature combination.

[0066] 6. Machine learning method: Use a classifier to predict the probability of a certain brain area being selected as the target area / entry area under each combination of features, such as decision tree, random forest, support vector machine, etc.

[0067] The above conditional probability model can give the probability of a certain brain region being selected as the target region / entry region for each sample patient with each feature combination. By inputting the prior information of the target patient into the conditional probability model, the feature combination of the target patient can be determined, and the second probability of each brain region being selected as the first brain region under the same feature combination can be analyzed, and the second probability can be displayed.

[0068] In the implementation of the present application, a conditional probability model can be constructed by combining multi-dimensional information such as clinical data, imaging data, and electroencephalogram data of sample patients to display the second probability of a brain region being selected as the first brain region, which can provide scientific guidance for doctors to select the first brain region.

[0069] In other embodiments, the feature combination of the target patient can also be determined based on prior information, and the second probability of the brain region being selected as the first brain region can be determined and displayed according to the mapping relationship between the feature combination and the probability of the brain region being selected as the first brain region.

[0070] The present application does not limit the display method of probability. In some embodiments of the present application, the above-mentioned second probability can be presented in the form of a probability map, and the probability map can be directly output by the conditional probability model. In the probability map, the second probability of each brain region can be represented by different shades of color, so that the user can intuitively select the first brain region based on the probability map. In other embodiments of the present application, the second probability can also be displayed in the form of text, numerical values, etc.

[0071] Accordingly, determining the first brain region of the target patient may include: in response to the first selection operation in each brain region, using the selected brain region as the first brain region. In this way, the user can intuitively select the required first brain region based on the displayed second probability. In other implementations, the brain region with the highest second probability may also be automatically determined as the first brain region.

[0072] In some embodiments of the present application, Figure 2 As shown, based on the first brain region and the brain region statistical information, displaying the first probability that each brain region of the target patient is selected as the second brain region may include: step S201 to step S202.

[0073] Step S201, querying the usage probability of each candidate brain region combination according to the first brain region and brain region statistical information.

[0074] When the first brain region is the cranial entry region, the candidate brain region combination is a combination of brain regions that are the same as the first brain region, and a single cranial entry region and different target regions can form different brain region combinations. When the first brain region is the target region, the candidate brain region combination is a combination of brain regions that are the same as the first brain region, and a single target region and different cranial entry regions can form different brain region combinations.

[0075] Specifically, Figure 3 As shown, before displaying the first probability that each brain region of the target patient is selected as the second brain region based on the first brain region and the brain region statistical information, the guided method for brain region selection further includes steps S301 to S304.

[0076] Step S301, obtaining retrospective data of sample patients.

[0077] The retrospective data characterizes the location of the implant in the brain of the sample patient. As mentioned above, the retrospective data may include but is not limited to clinical data, imaging data, and electroencephalogram data, which can characterize the location of the implant implanted in the brain of the sample patient.

[0078] Step S302: Classify the sample patients according to the implant locations in their skulls to obtain each brain region combination.

[0079] According to the position of the implant in the skull of the sample patient, the cranial entry area and target area of ​​the implant can be determined. By classifying according to the cranial entry area and target area, each brain region combination can be determined. For example, when the cranial entry area and target area of ​​the implant of the sample patient are the middle temporal gyrus and the hippocampus respectively, the cranial entry area of ​​the middle temporal gyrus and the target area of ​​the hippocampus can be recorded as a brain region combination.

[0080] Step S303: determining the usage probability of each brain region combination.

[0081] For each sample patient, the frequency of use of the brain region combination formed by the cranial implant insertion area and the target area can be increased by 1. By counting the situations of different sample patients, the use probability of each brain region combination can be obtained.

[0082] In some embodiments of the present application, classifying the sample patients according to the positions of implants in their skulls to obtain each brain region combination may include: determining the three-dimensional model data of the sample patients based on retrospective data, mapping the implants of the sample patients in the three-dimensional model data to a standard brain space, and classifying the sample patients according to the positions of their implants in the standard brain space to obtain each brain region combination.

[0083] Specifically, the retrospective data may include a CT image and a T1 structural image of the sample patient. Determining the three-dimensional model data of the sample patient based on the retrospective data may include: performing three-dimensional reconstruction based on the CT image to obtain the three-dimensional model data. Mapping the implant of the sample patient in the three-dimensional model data to the standard brain space may include: registering the CT image and the T1 structural image data to map the implant in the three-dimensional model data to the T1 structural image; registering the T1 structural image to the standard brain space; and classifying the implant according to the position of the sample patient in the standard brain space.

[0084] Specifically, the CT images contain detailed information about the sample patient after the implant was implanted, while the T1 structural image data can provide a detailed view of the sample patient's brain structure. Using CT images for three-dimensional reconstruction, the position and shape of the implant can be reproduced in three-dimensional space.

[0085] In order to facilitate the comparison of implant conditions of different sample patients in the same reference frame, the implant conditions of different sample patients need to be placed in the standard brain space for classification. The standard brain space refers to the MNI (Montreal Neurological Institute) space. The coordinate transformation relationship between the MNI space and the T1 structural image is a priori information. The three-dimensional model data derived from the CT image and the T1 structural image can be aligned using the registration algorithm to ensure the spatial consistency of the two. Subsequently, the T1 structural image is registered to the standard brain space, and the sample patient can be classified according to the implant position in the standard brain space.

[0086] Step S304: generating brain region statistical information according to the usage probability of each brain region combination.

[0087] By numbering different brain region combinations, implants in the MNI space can be classified according to their locations in the skull, and the usage probabilities of different brain region combinations can be calculated and counted to generate brain region statistics. Figure 4 As shown in the figure, the brain region combination No. 1 is: the entry area is the middle temporal gyrus, the target area is the hippocampus, and the use probability is 11.6%. The brain region combination No. 2 is: the entry area is the lateral occipital cortex, the target area is the lingual gyrus, and the use probability is 6.5%. The brain region combination No. 3 is: the entry area is the superior temporal gyrus, the target area is the insula, and the use probability is 4.7%.

[0088] In order to improve query efficiency, brain region statistical information may include statistical information of different brain lobes. By dividing the brain lobes, brain regions with higher correlation may be classified into a category to form statistical information of brain lobes. Among them, brain lobes are components of the brain, which may include but are not limited to frontal lobes, temporal lobes, insular lobes, occipital lobes, and parietal lobes. Each brain lobe may include one or more brain regions.

[0089] Accordingly, based on the first brain region and the brain region statistical information, the usage probability of each candidate brain region combination is queried, including: based on the target brain lobe where the first brain region is located and the statistical information of the target brain lobe, the usage probability of each candidate brain region combination is queried.

[0090] The statistical information of the target lobe records the probability of using a combination of brain regions where at least one of the entry region and the target region belongs to the target lobe. Figure 5 The statistical information of the occipital lobe is shown. The brain region combination No. 2 is: the entry area is the lateral occipital cortex, the target area is the lingual gyrus, and the use probability is 58.8%. The brain region combination No. 20 is: the entry area is the lateral occipital cortex, the target area is the white matter, and the use probability is 11.3%. The brain region combination No. 32 is: the entry area is the lateral occipital cortex, the target area is the fusiform gyrus, and the use probability is 8.2%. The brain region combination No. 34 is: the entry area is the lateral occipital cortex, the target area is the fusiform gyrus, and the use probability is 8.2%. The brain region combination No. 73 is: the entry area is the lateral occipital cortex, the target area is the lateral occipital cortex, and the use probability is 3.1%. The brain region combination No. 74 is: the entry area is the lateral occipital cortex, the target area is the anterior fusiform gyrus, and the use probability is 3.1%. The brain region combination No. 82 is: the entry area is the lateral occipital cortex, the target area is the paracalcarine gyrus, and the use probability is 2.1%. The brain region combination numbered 89 is: the entry area is the lateral occipital cortex, the target area is the hippocampus, and the probability of use is 2.1%. The brain region combination numbered 91 is: the entry area is the lateral occipital cortex, the target area is the isthmus of the cingulate gyrus, and the probability of use is 2.1%. The brain region combination numbered 100 is: the entry area is the cuneus, the target area is the lingual gyrus, and the probability of use is 1.0%. If the first brain region is located in the occipital lobe, the statistical information of the occipital lobe can be queried to obtain the probability of use of each candidate brain region combination. In this way, there is no need to query the statistical information of brain region combinations in other lobes, which helps to improve query efficiency.

[0091] Step S202 : displaying the first probability of each brain region of the target patient being selected as the second brain region according to the usage probability of each candidate brain region combination.

[0092] The first probability is positively correlated with the usage probability of the corresponding candidate brain region combination. That is, for a certain brain region, the higher the usage probability of the candidate brain region combination formed by it and the first brain region, the more sample patients use this candidate brain region combination for implantation surgery, and the higher the first probability of this brain region being selected as the second brain region. Figure 5 For example, if the first brain region is the cranial entry region and the cranial entry region is the lateral occipital cortex, then the first probability that the second brain region is the lingual gyrus is the highest. For the second brain region with a use probability of 0, it can be directly shielded, and the target region that is not related to the cranial entry region or the cranial entry region that is not related to the target region can be automatically shielded, showing the first probability that each brain region of the target patient is selected as the second brain region.

[0093] The first probability of each brain region being selected as the second brain region can be displayed in the form of a heat map. Figure 6 For example, the red depth of the brain region in the heat map is proportional to the first probability of being selected, that is, the higher the probability of the brain region being selected, the higher the red depth.

[0094] In the implementation manner of the present application, by statistically analyzing the probabilities of cranial entry areas and target areas being selected under different set conditions in retrospective data, empirical guidance can be provided to doctors in the selection of cranial entry areas and target areas during SEEG planning, which helps to optimize the surgical decision-making process.

[0095] Correspondingly, such as Figure 7 As shown, Figure 7 A schematic diagram of the implementation flow of an implant path planning method provided in the present application is shown. The method can be applied to an electronic device, which can be a computer, a smart phone, a medical device specifically used for image processing, or other smart devices, and the present application does not impose any restrictions on this.

[0096] Specifically, the above-mentioned brain region selection guiding method may include the following steps S701 to S703.

[0097] Step S701 : displaying the first probability that each brain region of the target patient is selected as the second brain region.

[0098] The display method of the first probability can refer to the previous article Figure 1-Figure 6 The description of the guidance method for brain region selection is not elaborated in this application.

[0099] Step S702: determining a second brain region among various brain regions.

[0100] Specifically, the user can intuitively select the required second brain region based on the displayed first probability. In response to the second selection operation in each brain region, the selected brain region is used as the second brain region. In other implementations, the brain region with the highest first probability can also be automatically determined as the second brain region.

[0101] Step S703, determining an implantation path of the implant of the target patient according to the first brain region and the second brain region.

[0102] In an embodiment of the present application, the implantation path of the target patient's implant can be determined based on the first brain region and the second brain region (i.e., the cranial entry region and the target region), assisting the doctor in completing the planning of the implantation path during the implantation surgery, thereby improving the accuracy of the path planning and the efficiency of the implantation surgery.

[0103] Specifically, Figure 8As shown, determining the implantation path of the implant of the target patient according to the first brain region and the second brain region may include: step S801 to step S804.

[0104] Step S801, determining a target point in a target area and a cranial entry point in a cranial entry area.

[0105] The cranial entry point refers to the starting point where the implant is added to the skull, and the target point refers to the final location of the implant. This application does not limit the selection method of the target point and the cranial entry point. Specifically, the target point can be set at a preset interval in the target area, and a cranial entry point suitable for the implant to enter can be selected in the epidermal area within the cranial entry area. The number of both the cranial entry point and the target point can be one or more.

[0106] Step S802: Determine candidate implantation paths based on the target point and the cranial entry point.

[0107] Among them, the candidate implantation pathway connects a target point and a cranial entry point.

[0108] Step S803, when there are multiple candidate implantation paths, determine the cost of each candidate implantation path.

[0109] When there is only one candidate implantation path, that is, the target point and the cranial entry point are both unique, the candidate implantation path can be directly used as the implantation path for the target patient. In practical applications, there are generally multiple candidate implantation paths, and the cost value of each candidate implantation path can be determined.

[0110] The cost value is related to at least one of the distance between the candidate implantation path and the blood vessel, the distance between the candidate implantation path and the ventricle, the cranial entry angle of the candidate implantation path, and the gray-white matter sampling ratio of the candidate implantation path.

[0111] Specifically, the distance between the candidate implantation path and the blood vessels and the distance from the ventricles are negatively correlated with the cost value. Among them, the ventricles are the ventricular duct system inside the brain parenchyma, which can include the left and right lateral ventricles, the third ventricle and the fourth ventricle. The ventricles are cavities located in the cerebrum, diencephalon and brainstem. The ependyma is lined around the ventricular walls and the chambers are filled with cerebrospinal fluid. The greater the distance between the candidate implantation path and the blood vessels, the easier it is for the implant to contact the blood vessels when implanted. The greater the distance between the candidate implantation path and the ventricles, the easier it is for the implant to contact the ventricles when implanted, and the higher the surgical risk. The cranial entry angle of the candidate implantation path is positively correlated with the cost value. The cranial entry angle affects the convenience of surgical operation and the size of surgical side effects of the patient. Generally, the smaller the better. The gray-white matter sampling ratio of the candidate implantation path refers to the ratio between gray matter and white matter, which is negatively positive with the cost value. The higher the gray-white matter sampling ratio, the more neurons there are, which indicates a better treatment effect.

[0112] Preferably, the distance between the candidate implantation path and the blood vessel, the distance between the candidate implantation path and the ventricle, the cranial entry angle of the candidate implantation path, and the gray-white matter sampling ratio of the candidate implantation path can be weightedly added to obtain the cost value.

[0113] Step S804, determining the implantation path according to the cost value of each candidate implantation path.

[0114] Specifically, according to the cost value of each candidate implantation path, the candidate implantation path with the lowest cost value can be selected as the implantation path, that is, the candidate implantation path with the farthest blood vessel distance and ventricle distance, the smallest cranial entry angle, and the highest gray-white matter sampling ratio should be selected as much as possible to balance safety and reliability.

[0115] It should be noted that, for the sake of simplicity of description, the aforementioned method embodiments are all expressed as a series of action combinations, but those skilled in the art should know that the present application is not limited to the described order of actions, because according to the present application, certain steps can be performed in other orders.

[0116] like Fig. 9 The figure is a schematic structural diagram of a guiding device 900 for selecting a brain region provided in an embodiment of the present application. The guiding device 900 for selecting a brain region is configured on an electronic device.

[0117] Specifically, the guiding device 900 for selecting a brain region may include:

[0118] A first brain region determining unit 901 is used to determine a first brain region of a target patient, where the first brain region is a cranial entry region or a target region of an implant when performing an implantation operation on the target patient;

[0119] The display unit 902 is used to display the first probability of each brain region of the target patient being selected as the second brain region based on the first brain region and the brain region statistical information; wherein the brain region statistical information represents the use probability of each brain region combination in the sample patients who have implanted the implant, each of the brain region combinations includes the cranial entry region and the target region where the implant implanted in the sample patient is located, and different brain region combinations have different cranial entry regions and / or different target regions; when the first brain region is the cranial entry region, the second brain region is the target region, and when the first brain region is the target region, the second brain region is the cranial entry region.

[0120] In some embodiments of the present application, the display unit 902 can be specifically used to: query the usage probability of each candidate brain region combination according to the first brain region and the brain region statistical information, when the first brain region is the cranial entry region, the candidate brain region combination is the brain region combination in which the cranial entry region is the same as the first brain region, and when the first brain region is the target region, the candidate brain region combination is the brain region combination in which the target region is the same as the first brain region; according to the usage probability of each candidate brain region combination, display the first probability that each brain region of the target patient is selected as the second brain region.

[0121] In some embodiments of the present application, the brain region statistical information includes statistical information of different brain lobes; the display unit 902 can be specifically used to: query the usage probability of each combination of the candidate brain regions based on the target brain lobe where the first brain region is located and the statistical information of the target brain lobe.

[0122] In some embodiments of the present application, the display unit 902 can also be specifically used to: obtain retrospective data of the sample patient, the retrospective data characterizing the implant location in the sample patient's skull; classify the sample patient according to the implant location in the skull to obtain the brain region combination; determine the use probability of each of the brain region combinations; and generate the brain region statistical information based on the use probability of each of the brain region combinations.

[0123] In some embodiments of the present application, the display unit 902 can be specifically used to: determine the three-dimensional model data of the sample patient based on the retrospective data; map the implant of the sample patient in the three-dimensional model data to a standard brain space; and classify the implants of the sample patient according to their positions in the standard brain space to obtain the brain region combination.

[0124] In some embodiments of the present application, the display unit 902 can also be specifically used to: obtain prior information of the target patient; input the prior information into a conditional probability model, and display the second probability of each brain region output by the conditional probability model being selected as the first brain region.

[0125] In some embodiments of the present application, the first brain region determining unit 901 may be specifically configured to: in response to a first selection operation among various brain regions, use the selected brain region as the first brain region.

[0126] It should be noted that, for the convenience and simplicity of description, the specific working process of the above-mentioned brain region selection guiding device 900 can be referred to Figures 1 to 6 The corresponding process of the method will not be repeated here.

[0127] like Fig.10Shown is a schematic structural diagram of an implant path planning device 1000 provided in an embodiment of the present application, wherein the implant path planning device 1000 is configured on an electronic device.

[0128] Specifically, the implantation path planning device 1000 may include:

[0129] A display unit 902, used to display a first probability that each brain region of the target patient is selected as the second brain region;

[0130] A second brain region determining unit 1001, configured to determine the second brain region from among the brain regions;

[0131] The path planning unit 1002 is used to determine an implantation path of the implant of the target patient according to the first brain region and the second brain region.

[0132] In some embodiments of the present application, the path planning unit 1002 can be specifically used to: determine a target point within a target area and an entry point within an entry point area; determine a candidate implantation path based on the target point and the entry point, wherein the candidate implantation path points from a target point to a entry point; when there are multiple candidate implantation paths, determine a cost value for each of the candidate implantation paths, wherein the cost value is related to at least one of the distance between the candidate implantation path and a blood vessel, the distance between the candidate implantation path and a ventricle, the entry angle of the candidate implantation path, and the gray-white matter sampling ratio of the candidate implantation path; determine the implantation path based on the cost value of each of the candidate implantation paths.

[0133] It should be noted that, for the convenience and simplicity of description, the specific working process of the implantation path planning device 1000 can be referred to Figures 7 and 8 The corresponding process of the method will not be repeated here.

[0134] like Fig.11 FIG. 1 is a schematic diagram of an electronic device 11 provided in an embodiment of the present application. Specifically, the electronic device 11 may include: a processor 110, a memory 111, and a computer program 112 stored in the memory 111 and executable on the processor 110, such as a guide program for selecting a brain region. When the processor 110 executes the computer program 112, the steps in the above-mentioned guide method for selecting a brain region are implemented, such as Figure 1 Alternatively, when the processor 110 executes the computer program 112, the steps in the above-mentioned implant path planning method embodiments are implemented, for example Figure 7 Steps S701 to S703 are shown.

[0135] When the processor 110 executes the computer program 112, the functions of each module / unit in the above-mentioned device embodiments are realized, for example Fig. 9 The functions of the first brain region determination unit 901 and the display unit 902 are, for example, Fig.10 The functions of the display unit 902 , the second brain region determination unit 1001 and the path planning unit 1002 are shown.

[0136] The computer program may be divided into one or more modules / units, which are stored in the memory 111 and executed by the processor 110 to complete the present application. The one or more modules / units may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the electronic device 11.

[0137] The electronic device 11 may include, but is not limited to, a processor 110 and a memory 111. Those skilled in the art will appreciate that Fig.11 It is only an example of the electronic device 11 and does not constitute a limitation of the electronic device 11. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 11 may also include input and output devices, network access devices, buses, etc.

[0138] The processor 110 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), off-the-shelf programmable gate arrays or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0139] The memory 111 may be an internal storage unit of the electronic device 11, such as a hard disk or memory of the electronic device 11. The memory 111 may also be an external storage device of the electronic device 11, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 11. Further, the memory 111 may also include both an internal storage unit of the electronic device 11 and an external storage device. The memory 111 is used to store the computer program and other programs and data required by the electronic device 11. The memory 111 may also be used to temporarily store data that has been output or is to be output.

[0140] It should be noted that, for the convenience and brevity of description, the structure of the electronic device 11 can also refer to the specific description of the structure in the method embodiment, which will not be repeated here.

[0141] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0142] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0143] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0144] In the embodiments provided in the present application, it should be understood that the disclosed devices / equipment and methods can be implemented in other ways. For example, the device / equipment embodiments described above are merely schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0145] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0146] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0147] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0148] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for guiding brain region selection, characterized in that: include: Determining a first brain region of a target patient, where the first brain region is a cranial entry region or a target region of an implant during an implantation operation on the target patient; Based on the first brain region and brain region statistical information, the first probability of each brain region of the target patient being selected as the second brain region is displayed; wherein the brain region statistical information represents the usage probability of each brain region combination in the sample patients who have implanted the implant, each of the brain region combinations includes the cranial entry region and the target region where the implant implanted in the sample patient is located, and different brain region combinations have different cranial entry regions and / or different target regions; when the first brain region is the cranial entry region, the second brain region is the target region, and when the first brain region is the target region, the second brain region is the cranial entry region.

2. The method for guiding brain region selection according to claim 1, characterized in that: The displaying, based on the first brain region and the brain region statistical information, of a first probability that each brain region of the target patient is selected as the second brain region comprises: querying the use probability of each candidate brain region combination according to the first brain region and the brain region statistical information, wherein when the first brain region is a cranial entry region, the candidate brain region combination is the brain region combination in which the cranial entry region and the first brain region are the same, and when the first brain region is a target region, the candidate brain region combination is the brain region combination in which the target region and the first brain region are the same; According to the usage probability of each combination of the candidate brain regions, a first probability of each brain region of the target patient being selected as the second brain region is displayed.

3. The method for guiding brain region selection according to claim 2, characterized in that: The brain region statistical information includes statistical information of different brain lobes; The querying, based on the first brain region and the brain region statistical information, the usage probability of each candidate brain region combination includes: According to the target brain lobe where the first brain region is located and the statistical information of the target brain lobe, the usage probability of each combination of the candidate brain regions is queried.

4. The method for guiding brain region selection according to claim 1, characterized in that: Before displaying the first probability that each brain region of the target patient is selected as the second brain region based on the first brain region and the brain region statistical information, the brain region selection guiding method further includes: Acquiring retrospective data of the sample patient, wherein the retrospective data characterizes the location of the implant in the skull of the sample patient; Classifying the sample patients according to the locations of the implants in their skulls to obtain the brain region combination; determining the probability of using each combination of the brain regions; The brain region statistical information is generated according to the usage probability of each combination of the brain regions.

5. The method for guiding brain region selection according to claim 4, characterized in that: The brain region combination is obtained by classifying according to the implant location in the brain of the sample patient, including: Determining the three-dimensional model data of the sample patient according to the retrospective data; Mapping the implant of the sample patient in the three-dimensional model data to a standard brain space; In the standard brain space, the sample patients are classified according to their implant positions to obtain the brain region combination.

6. The method for guiding brain region selection according to claim 1, characterized in that: Before determining the first brain region of the target patient, the method for guiding brain region selection further includes: Acquiring prior information of the target patient; According to the prior information, a second probability of each brain region being selected as the first brain region is displayed.

7. The method for guiding brain region selection according to claim 6, characterized in that: The determining the first brain region of the target patient includes: in response to a first selection operation among the brain regions, using the selected brain region as the first brain region.

8. A method for planning an implantation path, characterized in that: include: The method for guiding brain region selection according to any one of claims 1 to 7, wherein the method displays a first probability that each brain region of the target patient is selected as the second brain region; determining the second brain region among various brain regions; An implantation path of the implant of the target patient is determined based on the first brain region and the second brain region.

9. The implantation path planning method according to claim 8, characterized in that: The step of determining an implantation path of the implant of the target patient according to the first brain region and the second brain region comprises: Determine the target point within the target area and the cranial entry point within the cranial entry area; Determining a candidate implantation path according to the target point and the cranial entry point, wherein the candidate implantation path is used to connect a target point and a cranial entry point; When there are multiple candidate implantation paths, determining a cost value of each candidate implantation path, wherein the cost value is related to at least one of a distance between the candidate implantation path and a blood vessel, a distance between the candidate implantation path and a ventricle, an angle of cranial entry of the candidate implantation path, and a gray-white matter sampling ratio of the candidate implantation path; The implantation path is determined according to the cost value of each candidate implantation path.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the guidance method for brain region selection as described in any one of claims 1 to 7 are implemented, or, when the processor executes the computer program, the steps of the implantation path planning method as described in any one of claims 8 to 9 are implemented.

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