A method and device for planning an electrode implantation site for brain surface pacing, and a storage medium

Abstract

The application provides a brain surface pulsation time electrode implantation point planning method and device and a storage medium. The method comprises the following steps: acquiring continuous multiple frames of blood vessel images collected in time sequence in real time at brain surface pulsation time; processing a target blood vessel image in the continuous multiple frames of blood vessel images to obtain a blood vessel segmentation graph; determining at least one target implantation point on the blood vessel segmentation graph according to a scattered arrangement rule of the implantation point, and generating a tracking image containing the target implantation point; determining real-time tracking trajectory data of the target implantation point based on the tracking image and the continuous multiple frames of blood vessel images; determining a real-time pulsation cycle of a brain blood vessel of an implantation region and a prediction trajectory result of the target implantation point based on the real-time tracking trajectory data; and planning an implantation strategy of the electrode according to the real-time pulsation cycle and the prediction trajectory result. The above method can plan the implantation strategy of the electrode in real time, so as to ensure the implantation accuracy and safety of the electrode according to the real-time situation of the patient during the operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electrode implantation, and particularly relates to a method and device for planning an electrode implantation point during brain surface pulsation and a storage medium. BACKGROUND

[0002] In the field of invasive brain-computer interface, planning of an electrode implantation point is a key step in the invasive brain-computer interface. If the planned implantation point is safe and accurate, not only can an accurate position be provided during implantation, but also great convenience can be brought to subsequent operations after the electrode is implanted. In current methods for planning an implantation point, a manual planning point method is mostly used. In this method, a planning point is selected by the human eye during surgery, and the planning point is adjusted according to the pulsation. Compared with an automatic planning method, the method depends on the experience and operation level of a doctor. SUMMARY

[0003] In view of the above technical problems in the prior art, the present application provides a method and device for planning an electrode implantation point during brain surface pulsation and a storage medium, which can plan an implantation strategy of an electrode in real time, such as implantation time and implantation position, to ensure the implantation accuracy and safety of the electrode according to real-time conditions of a patient during surgery.

[0004] The present application provides a method for planning an electrode implantation point during brain surface pulsation, which comprises the following steps.

[0005] Obtaining continuous multiple frames of blood vessel images collected in real time in time sequence during brain surface pulsation;

[0006] Processing a target blood vessel image in the continuous multiple frames of blood vessel images to obtain a blood vessel segmentation map;

[0007] Determining at least one target implantation point on the blood vessel segmentation map according to a scattered arrangement rule of the implantation point, and generating a tracking image containing the target implantation point;

[0008] Determining real-time tracking trajectory data of the target implantation point based on the tracking image and the continuous multiple frames of blood vessel images;

[0009] Determining a real-time pulsation period of a brain blood vessel of an implantation region and a prediction trajectory result of the target implantation point based on the real-time tracking trajectory data;

[0010] Planning an implantation strategy of an electrode according to the real-time pulsation period and the prediction trajectory result.

[0011] In some embodiments, the prediction trajectory result is determined by the following method:

[0012] predicting a future trajectory of the target implant point according to a real-time tracking trajectory data of the target implant point by using a target model, wherein the target model at least adopts a Kalman filtering algorithm;

[0013] updating parameters of the target model in real time according to the predicted trajectory of the target implant point and the real-time tracking trajectory data;

[0014] generating the predicted trajectory result after an error between the predicted trajectory of the target implant point and the real-time tracking trajectory data is within a preset threshold.

[0015] In some embodiments, the real-time beating cycle is determined by using the following method:

[0016] determining a wave crest and a wave trough of the motion trajectory of the target implant point according to the real-time tracking trajectory data;

[0017] determining a real-time beating cycle of the brain blood vessel of the implant region according to the wave crest and the wave trough of the motion trajectory of the target implant point.

[0018] In some embodiments, the target implant point is determined on the blood vessel segmentation map according to a scattered arrangement rule of implant points, and specifically includes:

[0019] a plurality of implantable points are determined on the blood vessel segmentation map according to a scattered arrangement rule of implant points;

[0020] the blood vessel segmentation map is divided into a plurality of planning regions based on the plurality of implantable points;

[0021] a target implant point is assigned to at least one of the planning regions from the plurality of implantable points.

[0022] In some embodiments, the target implant point is assigned to at least one of the planning regions from the plurality of implantable points, and specifically includes:

[0023] a number of target implant points of the planning region is determined according to a number of implantable points in the planning region, a total number of implantable points on the blood vessel segmentation map, and a total number of implanted electrodes.

[0024] In some embodiments, the tracking image is obtained by combining the target blood vessel image with the target implant point, and the plurality of continuous blood vessel images include a plurality of continuous to-be-registered images acquired in time sequence after the target blood vessel image is acquired;

[0025] the real-time tracking trajectory data of the target implant point is determined based on the tracking image and the plurality of continuous blood vessel images, and specifically includes:

[0026] sequentially and the tracking image for feature matching until the positions of the target implantation points on the plurality of the to-be-registered images are determined respectively;

[0027] The real-time tracking trajectory data is determined based on the tracking image and the positions of the target implantation points on each of the to-be-registered images.

[0028] In some embodiments, the implantation strategy of the electrode is planned according to the real-time pulsation cycle and the predicted trajectory result, specifically including:

[0029] In the case that the real-time pulsation cycle and the predicted trajectory result match at the first wave peak time, a second wave peak time is determined, wherein the second wave peak time is the next wave peak time of the first wave peak time;

[0030] The electrode is implanted into the target implantation point at the second wave peak time.

[0031] In some embodiments, the target blood vessel image in the continuous multiple frames of blood vessel images is processed to obtain a blood vessel segmentation image, specifically including:

[0032] The target blood vessel image is preprocessed to obtain a preprocessed image;

[0033] The preprocessed image is coarsely extracted to obtain a first extraction image;

[0034] The preprocessed image is finely extracted to obtain a second extraction image;

[0035] The first extraction image and the second extraction image are fused to obtain a blood vessel segmentation image.

[0036] Embodiments of the present application also provide a device for planning an electrode implantation point during brain surface pulsation, including:

[0037] An acquisition module is configured to acquire continuous multiple frames of blood vessel images which are collected in real time in time sequence during brain surface pulsation;

[0038] A processing module is configured to process a target blood vessel image in the continuous multiple frames of blood vessel images to obtain a blood vessel segmentation image;

[0039] A determination module is configured to determine at least one target implantation point on the blood vessel segmentation image according to a dispersion arrangement rule of implantation points, and generate a tracking image containing the target implantation point; determine real-time tracking trajectory data of the target implantation point based on the tracking image and the continuous multiple frames of blood vessel images; and determine a real-time pulsation cycle of a brain blood vessel in an implantation region and a predicted trajectory result of the target implantation point based on the real-time tracking trajectory data;

[0040] a planning module configured to plan an implantation strategy of the electrode according to the real-time beat cycle and the predicted trajectory result.

[0041] The embodiment of the present application also provides a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the brain surface beat planning method of an electrode implantation point.

[0042] Compared with the prior art, the embodiment of the present application has the beneficial effects that: the embodiment of the present application realizes the blood vessel segmentation map and the tracking image based on the real-time acquired blood vessel images, so as to obtain the real-time tracking trajectory data of the target implantation point, and according to the real-time beat cycle and the predicted trajectory result determined according to the real-time tracking trajectory data, the implantation strategy of the electrode such as the implantation time and the implantation position can be planned in real time, so as to ensure the implantation accuracy and safety of the electrode according to the real-time situation of the patient in the operation process, and compared with the scheme of relying on the experience and operation level of the doctor for implantation, the implantation efficiency is effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0043] In the drawings, which are not necessarily drawn to scale, like numerals can describe similar components in different views. The drawings are intended to illustrate various embodiments in accordance with the application and as such do not provide limitations to the scope of the application. Wherever possible, the same reference numbers will be used throughout the drawings to depict the same or like parts. Such embodiments are illustrative rather than limiting as to the present devices or methods.

[0044] Figure 1 The first flowchart of the planning method of the embodiment of the present application;

[0045] Figure 2 The schematic diagram of the blood vessel segmentation map of the planning method of the embodiment of the present application;

[0046] Figure 3 The schematic diagram of the tracking image of the planning method of the embodiment of the present application;

[0047] Figure 4 The second flowchart of the planning method of the embodiment of the present application;

[0048] Figure 5 The third flowchart of the planning method of the embodiment of the present application;

[0049] Figure 6 The schematic diagram of the planning method of the embodiment of the present application for presenting the implantable point on the blood vessel segmentation map;

[0050] Figure 7A fourth flow chart for a planning method of embodiments of the present application;

[0051] Figure 8 A fifth flow chart for a planning method of embodiments of the present application;

[0052] Figure 9 A schematic diagram of a first extraction map for a planning method of embodiments of the present application;

[0053] Figure 10 A schematic diagram of a second extraction map for a planning method of embodiments of the present application;

[0054] Figure 11 A block diagram of a structure of a planning device for electrode implantation points for brain surface activation of embodiments of the present application. DETAILED DESCRIPTION

[0055] It is to be understood that various alterations and modifications can be made to the embodiments described herein. Therefore, the above description should not be taken as limiting, but merely as exemplification of the embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the application.

[0056] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the application and, together with the general description of the application given above, and the detailed description of the embodiments given below, serve to explain the principles of the present application.

[0057] These and other characteristics of the present application will become apparent from the following description of the preferred forms given, by way of non-limiting example only, with reference to the attached drawings.

[0058] It is also to be understood that even though a few examples of implementations of the present application are described in detail, many alternatives, modifications, additions, and deletions can be made to these specific examples by those skilled in the art without departing from the intended spirit and scope of the present application, as recited in the following claims.

[0059] The above and other aspects, features, and advantages of the present application will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, when properly considered together.

[0060] Specific embodiments of the present application are described hereinbelow, with reference to the drawings; however, these embodiments are merely examples of the present application, which can be implemented in numerous ways. Well-known and / or redundant functions and structures are not described in detail to avoid obscuring the present application unnecessarily. Therefore, specific structural and functional details disclosed herein are not intended to limit the present application, but merely as a representative basis for teaching one skilled in the art to variously employ the present application in virtually any appropriate detailed structure.

[0061] This specification may use the phrases “in one embodiment,” “in another embodiment,” “in yet another embodiment,” or “in other embodiments,” all of which may refer to one or more of the same or different embodiments according to this application.

[0062] This application provides a method for planning electrode implantation points during brain surface pulsation, which can be applied to electrode implantation point planning platforms, electrode implantation point planning devices, etc.

[0063] like Figure 1 As shown, the above-mentioned method for planning electrode implantation points during brain surface pulsation includes steps S101 to S106.

[0064] Step S101: Acquire continuous multi-frame vascular images in real time sequence during brain surface pulsation.

[0065] The blood vessels depicted in the aforementioned vascular images are specifically subpial cerebral blood vessels. The implantation area for the electrodes is near these subpial cerebral blood vessels. It should be noted that the pulsation of subpial cerebral blood vessels differs from the regularity of heart rate and pulse. The pulsation of the brain surface is caused by the combined action of respiration and blood vessels, and can be intermittent and irregular. Therefore, the pulsation of subpial cerebral blood vessels will vary depending on the patient's real-time condition. Real-time acquisition of multiple consecutive frames of vascular images ensures the real-time planning of the electrode implantation point during surgery, allowing for electrode implantation tailored to the patient's specific situation.

[0066] Specifically, multiple consecutive frames of vascular images can be acquired through a micro-vision system during brain surface pulsation. Of course, other image acquisition devices can also be used to acquire vascular images, and this application does not make any specific limitations on this.

[0067] Step S102: Process the target blood vessel image in the continuous multi-frame blood vessel images to obtain a blood vessel segmentation map.

[0068] like Figure 2 As shown, Figure 2 The image shown is a segmented image of blood vessels, specifically a binary image after segmentation, where the white area represents the implantation area and the black area represents the blood vessels.

[0069] Step S103: Determine at least one target implantation point on the vascular segmentation map according to the dispersion arrangement rule of the implantation points, and generate a tracking image containing the target implantation point.

[0070] like Figure 3 As shown, Figure 3 The image shown is a tracking image. The blue dots in the image represent target implantation points, and the blue numbers near the blue dots are the numbers of each target implantation point. The 32 target implantation points shown in the image are just examples. When implanting electrodes, one or more of these points can be selected for electrode implantation.

[0071] The dispersion arrangement rule of the implantation points can be understood as meeting the electrode size, the spacing between electrodes, the safe spacing of the electrodes from the blood vessels, and the like, and the purpose is to ensure that the positions of the implantation points can guarantee the safe implantation of the electrodes.

[0072] The tracking image can be presented on the blood vessel segmentation map in combination with the target implantation point, or can be presented on the target blood vessel image in combination with the target implantation point, for the purpose of optimizing the display effect, Figure 3 The tracking image shown in the middle is presented on the target blood vessel image in combination with the target implantation point.

[0073] Step S104: determining real-time tracking trajectory data of the target implantation point based on the tracking image and the continuous multiple frames of blood vessel images.

[0074] The target implantation point can be determined for each target implantation point to determine real-time tracking trajectory data. The real-time tracking trajectory data can be understood as determining the position of the target implantation point on each blood vessel image, thereby obtaining the total trajectory data of the implantation point on the continuous multiple frames of blood vessel images.

[0075] Step S105: determining the real-time pulsation cycle of the brain blood vessels of the implantation region and the predicted trajectory result of the target implantation point based on the real-time tracking trajectory data.

[0076] The real-time pulsation cycle is obtained according to the continuously collected multiple frames of blood vessel images when the brain is pulsating during the operation. The pulsation cycle is real-time, and is affected by individual differences of patients. The pulsation cycle of the brain blood vessels under the soft meninges will have occasional irregularities. Therefore, the real-time pulsation cycle can ensure the targeted implantation for patients, and further ensure the implantation accuracy and safety for different patients.

[0077] The predicted trajectory result of the target implantation point can be understood as a predicted trajectory result of the target implantation point, which can be calculated by a model.

[0078] Step S106: planning an implantation strategy of the electrodes according to the real-time pulsation cycle and the predicted trajectory result.

[0079] The implantation strategy can include implantation timing, implantation position, and the like, so that the electrodes can be accurately implanted based on the implantation strategy. The object of executing the implantation strategy can be a surgical robot, or a doctor. The present application does not make specific limitations thereon, and can accurately execute the determined implantation strategy. When the execution object is a surgical robot, the surgical robot can be in place in advance based on the predicted trajectory result, so as to ensure the accuracy and automation degree of the implantation operation.

[0080] When the electrodes to be implanted are multiple, the positions of the electrodes to be implanted can be determined in multiple target implantation points, and the electrodes are sequentially implanted into the positions of the corresponding target implantation points.

[0081] The above method can provide good guarantee for obtaining accurate implantation strategy through real-time tracking of the target implantation point during brain surface pulsation and predicted trajectory results, and improve the efficiency in the implantation process.

[0082] The calculation of the real-time pulsation cycle and the predicted trajectory result in the above method can achieve real-time performance of 30 frames per second, so that the purpose of accurately planning the implantation strategy of the electrode according to the real-time pulsation cycle and the predicted trajectory result can be achieved during the operation.

[0083] The present application realizes the obtaining of the blood vessel segmentation map and the tracking image based on the real-time acquired blood vessel image through the continuous multiple frames of blood vessel images acquired in time sequence during the brain surface pulsation, so as to obtain the real-time tracking trajectory data of the target implantation point. According to the real-time pulsation cycle and the predicted trajectory result determined according to the real-time tracking trajectory data, the implantation strategy of the electrode such as implantation time and implantation position can be planned in real time, so as to ensure the accuracy of the implantation of the electrode according to the real-time situation of the patient during the operation. Compared with the scheme of relying on the experience and operation level of the doctor for implantation, the implantation efficiency is effectively improved.

[0084] In some embodiments, as shown in Figure 4 The predicted trajectory result is determined by steps S201 to S203 below.

[0085] Step S201: predicting the trajectory of the target implantation point in the future according to the real-time tracking trajectory data of the target implantation point by using a target model; wherein the target model at least adopts Kalman filtering algorithm.

[0086] Step S202: updating the parameters of the target model in real time according to the predicted trajectory of the target implantation point and the real-time tracking trajectory data.

[0087] Step S203: generating the predicted trajectory result when the error between the predicted trajectory of the target implantation point and the real-time tracking trajectory data is within a preset threshold.

[0088] In this way, the trajectory of the target implantation point can be accurately predicted by using the target model, and after adjusting the parameters of the target model according to the predicted trajectory and the real-time tracking trajectory, the predicted trajectory result meeting the implantation requirements can be generated when the error meets the preset threshold, thereby improving the real-time performance and effectiveness of the predicted trajectory result calculation.

[0089] The target model can dynamically adjust parameters in combination with real-time tracking trajectory data by using at least the Kalman filtering algorithm, so as to improve the accuracy of the predicted trajectory of the target implantation point output by the target model. It should be noted that, since the Kalman filtering algorithm requires certain prior knowledge, the result of the target model may not be accurate at the beginning, and the accuracy of the output result will be significantly increased after the target model is predicted for a period of time. When the prediction error is within a preset threshold, it is determined that the implantation requirement is met, and the implantation strategy of the electrode can be accurately planned based on the real-time beat cycle and the obtained prediction trajectory result.

[0090] In some embodiments, as shown in Figure 5 The real-time beat cycle is determined by using steps S301 to S302 below.

[0091] Step S301: determining the wave crest and wave trough of the motion trajectory of the target implantation point according to the real-time tracking trajectory data.

[0092] Step S302: determining the real-time beat cycle of the brain blood vessel of the implantation region according to the wave crest and wave trough of the motion trajectory of the target implantation point.

[0093] In this way, after the wave crest and wave trough of the motion trajectory of the target implantation point are determined based on the obtained real-time tracking trajectory data, the real-time beat cycle of the brain blood vessel of the implantation region can be accurately calculated, a more accurate implantation strategy can be planned for the implantation surgery, and the safety of the patient and the postoperative recovery effect can be improved.

[0094] It can be understood that, in the case where a plurality of target implantation points are determined on the tracking image, the real-time beat cycle can be determined based on the wave crest and wave trough of the motion trajectory of one of the target implantation points. Specifically, one of the plurality of target implantation points is determined as the position of the electrode to be implanted this time, and the real-time beat cycle can be determined based on the wave crest and wave trough of the motion trajectory of the target implantation point.

[0095] Before the wave crest and wave trough of the motion trajectory of the target implantation point are determined according to the real-time tracking trajectory data, the tracking trajectory data can be preprocessed, which can include but is not limited to filtering, interpolation, smoothing processing, etc. The purpose is to eliminate noise points and smooth the trajectory, and then the wave crest and wave trough are calculated respectively, and the real-time beat cycle of the brain blood vessel is determined by the wave crest and wave trough.

[0096] After the wave crest and wave trough of the motion trajectory of the target implantation point are obtained, a data statistical method can be used to obtain the real-time beat cycle, such as a histogram statistical method. The present application does not make specific limitations thereto, and the real-time beat cycle can be accurately calculated.

[0097] In some embodiments, step S103, which involves determining at least one target implantation point on the vascular segmentation map according to the dispersion arrangement rule of the implantation points, specifically includes:

[0098] Multiple implantable points are determined on the vascular segmentation map according to the dispersed arrangement rules of the implantation points;

[0099] The vascular segmentation map is divided into multiple planning regions based on the multiple implantable points;

[0100] For at least one of the planned areas, a target implantation point is assigned among the plurality of implantable points.

[0101] In this way, by first determining multiple implantable points and then assigning target implantable points to each planning area based on the multiple implantable points, the reliability of determining target implantable points among multiple implantable points is improved, and the requirements for the arrangement of electrode implantable points are met.

[0102] The above-mentioned rules for the dispersion of implantation points are related to at least one of the following: electrode diameter, electrode spacing, and distance between the electrode and the blood vessel.

[0103] The aforementioned planning areas can be coded according to row and column rules. When a large area is obstructed by a blood vessel, it will be divided into smaller areas, thus improving the dispersion of implantable sites. Specifically, clustering methods can be used to divide multiple planning areas. For example, several adjacent implantable sites can be grouped into one area. After obtaining the clustering results, the areas containing implantable sites belonging to the same category can be grouped into one area.

[0104] like Figure 6 As shown, Figure 6 To illustrate implantable points on a vascular segmentation map, the green box represents the surgical area. This vascular segmentation map shows multiple implantable points (green dots), and the red numbers near each implantable point are the codes for the corresponding planned areas. For example, Figure 6 There are 22 planning areas shown in the figure. Planning area No. 1 has 1 implantable point, planning area No. 2 has 1 implantable point, planning area No. 3 has 1 implantable point, planning area No. 4 has 4 implantable points, and so on. These will not be elaborated here.

[0105] After the target implantation points are allocated, some planning areas may have no target implantation points or one or more target implantation points. This application does not make specific restrictions on this.

[0106] In some embodiments, allocating target implantation points among the plurality of implantable points for at least one of the planned areas specifically includes:

[0107] The number of target implant points of the planning area is determined according to the number of implantable points in the planning area, the total number of implantable points on the blood vessel segmentation map, and the total number of implanted electrodes.

[0108] The number of target implant points of each planning area can be calculated by the following formula:

[0109] ;

[0110] wherein, is the number of implantable points in the planning area; is the total number of implantable points on the blood vessel segmentation map; is the total number of implanted electrodes.

[0111] The planning areas can be sorted in descending order according to the number of implantable points in each planning area, and the number of target implant points of each planning area is calculated in turn based on the order.

[0112] After determining the number of target implant points of the planning area, a target implant point can be selected from the multiple implantable points in the planning area according to the distance from the blood vessel. For example, in the planning area numbered 19 in Figure 6 , there are 3 implantable points, and after determining that the planning area numbered 19 has 1 target implant point, one of the 3 implantable points can be selected as the target implant point according to the distance from the blood vessel, for example, the implantable point farthest from the blood vessel is determined as the target implant point.

[0113] In some embodiments, the tracking image is obtained by combining the target blood vessel image and the target implant point, and the continuous multiple frames of blood vessel images include continuous multiple frames of to-be-registered images sequentially acquired after the target blood vessel image is acquired. As Figure 7 shown, the step S104 of determining the real-time tracking trajectory data of the target implant point based on the tracking image and the continuous multiple frames of blood vessel images specifically includes steps S401 to S402.

[0114] Step S401: sequentially performing feature matching between the continuous multiple frames of to-be-registered images and the tracking image until the positions of the target implant points on multiple to-be-registered images are determined.

[0115] Step S402: determining the real-time tracking trajectory data based on the tracking image and the positions of the target implant points on each to-be-registered image.

[0116] Thus, after obtaining the tracking image, the positions of the target implantation points on the plurality of to-be-registered images can be determined by sequentially registering the other blood vessel images with the tracking image, so that accurate real-time tracking trajectory data is obtained after registration.

[0117] The target blood vessel image in the plurality of continuous frames of blood vessel images can be understood as a starting image. After the tracking image is calculated based on the starting image, there is no need to calculate the blood vessel images of subsequent frames, but after the blood vessel images of the subsequent frames are sequentially matched with the tracking image, the positions of the target implantation points on the blood vessel images of the subsequent frames can be obtained, without the need to calculate the target implantation point trajectory data in all blood vessel images, thereby reducing the calculation amount, and accurate real-time tracking trajectory data can be obtained through feature matching.

[0118] The feature matching can use a traditional feature point-based matching method, such as SIFT (Scale-Invariant Feature Transform), SURF (Speeded-Up Robust Features), or a deep learning method. Preferably, a deep learning model with a Transformer architecture can be used for tracking of the target implantation point.

[0119] In some embodiments, as shown in FIG. 5, Figure 8 The step S106 of planning an implantation strategy of the electrode according to the real-time beating cycle and the predicted trajectory result specifically includes steps S501-S502.

[0120] The step S501 includes determining a second peak time when the real-time beating cycle and the predicted trajectory result match at a first peak time, wherein the second peak time is a next peak time of the first peak time.

[0121] The step S502 includes controlling the electrode to be implanted into the target implantation point at the second peak time.

[0122] Thus, when the real-time beating cycle and the predicted trajectory result match at the first peak time, the parameters of the target model after adjustment can meet the implantation requirements, and the electrode can be implanted at the next peak time, thereby meeting the accuracy of electrode implantation.

[0123] Specifically, the second peak time can be determined by the following formula:

[0124] ;

[0125] wherein T1 is the first peak time, T2 is a valley time after the first peak time, and T3 is the second peak time.

[0126] The above is to take the wave peak as the implantation timing. In some other embodiments, a wave trough can be selected as the implantation timing. Specifically, the second wave trough time can be determined in a case where the real-time beating cycle and the predicted trajectory result match at the first wave trough time; the second wave trough time is the next wave trough time of the first wave trough time; and the electrode is controlled to be implanted to the target implantation point at the second wave trough time.

[0127] The above-mentioned matching of the real-time beating cycle and the predicted trajectory result at the first wave peak time can be understood as that the error between the predicted trajectory result and the real-time beating cycle at the first wave peak time is within a preset threshold, that is, the real-time beating cycle and the predicted trajectory result are matched.

[0128] In some embodiments, the processing of the target blood vessel image in the continuous multiple frames of blood vessel images in step S102 obtains a blood vessel segmentation map, specifically including:

[0129] The target blood vessel image is preprocessed to obtain a preprocessed image;

[0130] The preprocessed image is coarsely extracted to obtain a first extraction map;

[0131] The preprocessed image is finely extracted to obtain a second extraction map;

[0132] The first extraction map and the second extraction map are fused to obtain a blood vessel segmentation map.

[0133] In this way, by coarsely and finely extracting the preprocessed image respectively, the blood vessel segmentation map obtained by fusing the first extraction map and the second extraction map can more accurately represent the situation of the blood vessels in the target blood vessel image, specifically, the first extraction map can represent the large outline of the blood vessels, and the second extraction map can represent the situation of small capillaries.

[0134] As shown in Figure 9 and Figure 10 , the first extraction map is shown in Figure 9 , the second extraction map is shown in Figure 10 , and the fusion of the first extraction map and the second extraction map can obtain the blood vessel segmentation map as shown in Figure 2 .

[0135] The above-mentioned preprocessing of the target blood vessel image includes but is not limited to normalization, image filtering, image enhancement, scaling and other processing methods of the target blood vessel image.

[0136] The above-mentioned coarse extraction processing at least includes enhancing the contrast of the preprocessed image, binarizing the image after the contrast enhancement, to obtain the first extraction map which can extract the large outline of the blood vessels.

[0137] The above-mentioned fine extraction process includes at least multiple opening and closing operations on the preprocessed image, noise reduction, and strengthening of the connection of small blood vessels, so as to obtain a binary image containing the segmentation results of capillaries, i.e., the second extraction image.

[0138] This application also provides a device 100 for planning electrode implantation points during brain surface pulsation. For example... Figure 11 As shown, the electrode implantation point planning device 100 during brain surface pulsation includes an acquisition module 101, a processing module 102, a determination module 103, and a planning module 104. The acquisition module 101 acquires multiple consecutive frames of vascular images collected in real-time during brain surface pulsation. The processing module 102 processes the target vascular images in the multiple consecutive frames of vascular images to obtain a vascular segmentation map. The determination module 103 determines at least one target implantation point on the vascular segmentation map according to the dispersion arrangement rules of the implantation points and generates a tracking image containing the target implantation point; determines real-time tracking trajectory data of the target implantation point based on the tracking image and the multiple consecutive frames of vascular images; and determines the real-time pulsation cycle of the cerebral blood vessels in the implantation area and the predicted trajectory result of the target implantation point based on the real-time tracking trajectory data. The planning module 104 plans the electrode implantation strategy according to the real-time pulsation cycle and the predicted trajectory result.

[0139] This application utilizes real-time acquisition of multiple consecutive frames of vascular images during brain surface pulsation to obtain vascular segmentation and tracking images based on the real-time acquired vascular images. This results in real-time tracking trajectory data for the target implantation point. Furthermore, based on the real-time pulsation cycle determined by the real-time tracking trajectory data and the predicted trajectory results, the application can plan the electrode implantation strategy in real time, such as implantation time and location. This ensures the accuracy of electrode implantation during surgery based on the patient's real-time condition, effectively improving implantation efficiency compared to implantation methods that rely on the doctor's experience and skill level.

[0140] In some embodiments, the determining module 103 is further configured to: predict the future trajectory of the target implantation point using a target model based on the real-time tracking trajectory data of the target implantation point; wherein the target model employs at least a Kalman filter algorithm; update the parameters of the target model in real time based on the predicted trajectory of the target implantation point and the real-time tracking trajectory data; and generate the predicted trajectory result after the error between the predicted trajectory of the target implantation point and the real-time tracking trajectory data is within a preset threshold.

[0141] In some embodiments, the determining module 103 is further configured to: determine the peaks and troughs of the motion trajectory of the target implantation point based on the real-time tracking trajectory data; and determine the real-time pulsation cycle of the cerebral blood vessels in the implantation area based on the peaks and troughs of the motion trajectory of the target implantation point.

[0142] In some embodiments, the determining module 103 is further configured to determine a plurality of implantable points on the blood vessel segmentation map according to a dispersion arrangement rule of the implantable points; divide the blood vessel segmentation map into a plurality of planning regions based on the plurality of implantable points; and assign a target implantable point to at least one of the planning regions from the plurality of implantable points.

[0143] In some embodiments, the determining module 103 is further configured to determine a number of target implantable points of the planning region according to a number of implantable points in the planning region, a total number of implantable points on the blood vessel segmentation map, and a total number of implanted electrodes.

[0144] In some embodiments, the tracking image is obtained by combining the target blood vessel image with the target implantable point, and the plurality of continuous blood vessel images are a plurality of continuous images to be registered which are sequentially captured after the target blood vessel image is captured. The determining module 103 is further configured to sequentially perform feature matching between the plurality of continuous images to be registered and the tracking image until positions of the target implantable points on the plurality of continuous images to be registered are determined; and determine the real-time tracking trajectory data based on the tracking image and the positions of the target implantable points on the plurality of continuous images to be registered.

[0145] In some embodiments, the planning module 104 is further configured to determine a second peak time when the real-time pulsation cycle and the predicted trajectory result match at a first peak time, wherein the second peak time is a next peak time of the first peak time; and control the electrode to be implanted to a target implantable point at the second peak time.

[0146] In some embodiments, the processing module 102 is further configured to pre-process the target blood vessel image to obtain a pre-processed image; perform coarse extraction processing on the pre-processed image to obtain a first extracted image; perform fine extraction processing on the pre-processed image to obtain a second extracted image; and fuse the first extracted image and the second extracted image to obtain the blood vessel segmentation map.

[0147] The embodiments of the present application further provide a computer readable storage medium storing a computer program, the computer program being executed by a processor to implement the steps of the method for planning an implantable point of an electrode in a brain surface pulsation according to any of the above embodiments.

[0148] It is noted that, according to various units in various embodiments of the present application, the units can be implemented as computer-executable instructions stored on a memory, which are executed by a processor to implement corresponding steps; or can be implemented as hardware with corresponding logic computing capability; or can be implemented as a combination of software and hardware (firmware). In some embodiments, the processor can be implemented as any one of FPGA, ASIC, DSP chip, SOC (System on Chip), MPU (such as but not limited to Cortex), etc. The processor can be communicatively coupled to the memory and configured to execute computer-executable instructions stored therein. The memory can include read-only memory (ROM), flash memory, random access memory (RAM), dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM, static memory (for example, flash memory, static random access memory), etc., on which computer-executable instructions are stored in any format. The computer-executable instructions can be accessed by the processor, read from the ROM or any other suitable storage location, and loaded into the RAM for execution by the processor to implement the wireless communication method according to various embodiments of the present application.

[0149] It should be noted that, in various components of the system of the present application, logical division is made to the components therein according to the functions to be implemented thereby, but the present application is not limited thereto, and various components can be re-divided or combined as needed, for example, some components can be combined into a single component, or some components can be further decomposed into more sub-components.

[0150] Various component embodiments of the present application can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the system according to embodiments of the present application. The present application can also be implemented as a device or apparatus program (for example, a computer program and a computer program product) for executing part or all of the methods described herein. Such program implementing the present application can be stored on a computer readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form. In addition, the present application can be implemented by means of hardware including a number of different elements and by means of a properly programmed computer. In unit claims that recite a number of devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not indicate any order. These words can be interpreted as names.

[0151] Furthermore, although example embodiments have been described herein, the scope of coverage of this patent will include any and all embodiments having equivalent elements, modifications, omissions, combinations (e.g., of

[0152] The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) can be used in combination with each other. For example, other embodiments utilizing such can be employed without departing from the scope of the application. Additionally, the various features described above can be grouped together or divided into separate embodiments of the present application. The scope of the present application is not to be interpreted merely by the foregoing description but rather be determined by reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. The embodiments disclosed in this application are to be considered merely illustrative of the principles of the application and are not intended to limit the scope of the application to the embodiments disclosed in this written description. Rather, the scope of the application is to be determined solely by the appended claims, along with the full scope of equivalents to which such claims are entitled. The claims should not be read to require the presence of all limitations listed in the claims or inherent in the disclosed embodiments.

[0153] The above embodiments are only exemplary embodiments of the present application, not intended to limit the present application, and the protection scope of the present application is defined by the claims. Those skilled in the art can make various modifications or equivalent replacements to the present application within the spirit and protection scope of the present application, and such modifications or equivalent replacements should also be considered to fall within the protection scope of the present application.

Claims

1. A device for planning electrode implantation sites for brain surface pulsations, characterized by, The method comprises the following steps: acquiring a plurality of continuous blood vessel images collected in real time in time sequence when the brain pulsates; processing a target blood vessel image in the plurality of continuous blood vessel images to obtain a blood vessel segmentation image; determining at least one target implantation point on the blood vessel segmentation image according to a dispersion arrangement rule of implantation points, and generating a tracking image containing the target implantation point; determining real-time tracking trajectory data of the target implantation point based on the tracking image and the plurality of continuous blood vessel images; determining a real-time pulsation cycle of a brain blood vessel of an implantation region and a predicted trajectory result of the target implantation point based on the real-time tracking trajectory data; planning an implantation strategy of an electrode according to the real-time pulsation cycle and the predicted trajectory result; wherein the planning module is further configured to: in the case that the real-time pulsation cycle and the predicted trajectory result match at a first wave peak time, determine a second wave peak time; wherein the second wave peak time is a next wave peak time of the first wave peak time; control the electrode to be implanted into the target implantation point at the second wave peak time; or in the case that the real-time pulsation cycle and the predicted trajectory result match at a first wave valley time, determine a second wave valley time; wherein the second wave valley time is a next wave valley time of the first wave valley time; control the electrode to be implanted into the target implantation point at the second wave valley time.

2. The device for planning of brain surface pulsatile electrode implantation points according to claim 1, characterized in that, The determining module is further configured to: predict a trajectory of the target implantation point in the future according to the real-time tracking trajectory data of the target implantation point by using a target model; wherein the target model at least adopts a Kalman filtering algorithm; update parameters of the target model in real time according to the predicted trajectory of the target implantation point and the real-time tracking trajectory data; generate the predicted trajectory result after an error between the predicted trajectory of the target implantation point and the real-time tracking trajectory data is within a preset threshold.

3. The device for planning of brain surface pulsatile electrode implantation points according to claim 1 or 2, characterized in that, The determining module is further configured to: determine wave peaks and wave valleys of a motion trajectory of the target implantation point according to the real-time tracking trajectory data; determine a real-time pulsation cycle of a brain blood vessel of an implantation region according to the wave peaks and wave valleys of the motion trajectory of the target implantation point.

4. The device for planning of brain surface pulsatile electrode implantation points according to claim 1, characterized in that, The determining module is further configured to: determine a plurality of implantable points on the blood vessel segmentation image according to the dispersion arrangement rule of implantation points; divide the blood vessel segmentation image into a plurality of planning regions based on the plurality of implantable points; assign a target implantation point to at least one of the planning regions from the plurality of implantable points.

5. The device for planning of brain surface pulsatile electrode implantation points according to claim 4, characterized in that, The determining module is further configured to: determine a number of target implantation points of the planning region according to a number of implantable points in the planning region, a total number of implantable points on the blood vessel segmentation image, and a total number of implanted electrodes.

6. The device for planning brain surface pulsatile electrode implantation points according to claim 1 or 2, characterized in that, The tracking image is obtained by combining the target blood vessel image with the target implantation point, the plurality of continuous blood vessel images contain a plurality of continuous to-be-registered images collected in time sequence after the target blood vessel image is collected; and the determining module is further configured to: sequentially and the tracking image for feature matching until the positions of the target implant points on the plurality of the to-be-registered images are determined respectively; determining the real-time tracking trajectory data based on the tracking image and the positions of the target implant points on each of the to-be-registered images.

7. The device for planning of brain surface pulsatile electrode implantation points according to claim 1, characterized in that, The processing module is further configured to: pre-process the target blood vessel image to obtain a pre-processed image; performing coarse extraction processing on the pre-processed image to obtain a first extraction image; performing fine extraction processing on the pre-processed image to obtain a second extraction image; and fusing the first extraction image and the second extraction image to obtain a blood vessel segmentation image.

8. A computer readable storage medium storing a computer program, characterized in that, The computer program, when executed by a processor, implements the steps of a method for planning an electrode implant point during brain surface pulsation, the method comprising: acquiring a plurality of continuous frames of blood vessel images collected in real time in time sequence during brain surface pulsation; processing a target blood vessel image in the plurality of continuous frames of blood vessel images to obtain a blood vessel segmentation image; determining at least one target implant point on the blood vessel segmentation image according to a dispersion arrangement rule of implant points, and generating a tracking image containing the target implant point; determining real-time tracking trajectory data of the target implant point based on the tracking image and the plurality of continuous frames of blood vessel images; determining a real-time pulsation period of a brain blood vessel in an implant region and a predicted trajectory result of the target implant point based on the real-time tracking trajectory data; planning an implant strategy of an electrode according to the real-time pulsation period and the predicted trajectory result; wherein the planning of the implant strategy of the electrode according to the real-time pulsation period and the predicted trajectory result specifically comprises: determining a second wave peak time when the real-time pulsation period and the predicted trajectory result match at a first wave peak time, wherein the second wave peak time is a next wave peak time of the first wave peak time; controlling the electrode to be implanted into the target implant point at the second wave peak time.

9. The computer-readable storage medium of claim 8, wherein, The predicted trajectory result is determined by the following method: predicting a future trajectory of the target implant point according to real-time tracking trajectory data of the target implant point by using a target model, wherein the target model at least uses a Kalman filtering algorithm; updating parameters of the target model in real time according to the predicted trajectory of the target implant point and the real-time tracking trajectory data; generating the predicted trajectory result when an error between the predicted trajectory of the target implant point and the real-time tracking trajectory data is within a preset threshold.

10. The computer-readable storage medium of claim 8 or 9, wherein, The real-time pulsation period is determined by the following method: determining wave peaks and wave troughs of a motion trajectory of the target implant point according to the real-time tracking trajectory data; determining a real-time pulsation period of a brain blood vessel in an implant region according to the wave peaks and wave troughs of the motion trajectory of the target implant point.

11. The computer-readable storage medium of claim 8, wherein, The determination of at least one target implant point on the blood vessel segmentation image according to a dispersion arrangement rule of implant points specifically comprises: determining a plurality of implantable points on the blood vessel segmentation image according to the dispersion arrangement rule of implant points; dividing the blood vessel segmentation image into a plurality of planning regions based on the plurality of implantable points; allocating target implant points to at least one of the planning regions from the plurality of implantable points.

12. The computer-readable storage medium of claim 11, wherein, The target implant point is assigned to at least one of the planning areas in the multiple implantable points, specifically comprising: The number of target implant points of the planning area is determined according to the number of implantable points in the planning area, the total number of implantable points on the blood vessel segmentation map, and the total number of implanted electrodes.

13. The computer-readable storage medium of claim 8 or 9, wherein, The tracking image is obtained by combining the target blood vessel image with the target implant point, and the continuous multiple frames of blood vessel images include continuous multiple frames of to-be-registered images sequentially acquired after the target blood vessel image is acquired; The real-time tracking trajectory data of the target implant point is determined based on the tracking image and the continuous multiple frames of blood vessel images, specifically comprising: The continuous multiple frames of to-be-registered images are sequentially and feature-matched with the tracking image until the positions of the target implant points on multiple to-be-registered images are determined; The real-time tracking trajectory data is determined based on the tracking image and the positions of the target implant points on each to-be-registered image.

14. The computer-readable storage medium of claim 8, wherein, The target blood vessel image in the continuous multiple frames of blood vessel images is processed to obtain a blood vessel segmentation map, specifically comprising: The target blood vessel image is pre-processed to obtain a pre-processed image; The pre-processed image is coarsely extracted to obtain a first extraction image; The pre-processed image is finely extracted to obtain a second extraction image; The first extraction image and the second extraction image are fused to obtain a blood vessel segmentation map.

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