Photoacoustic microimaging brain electrode implantation site and path planning method and system

Abstract

The invention provides a photoacoustic microimaging brain electrode implantation site and path planning method and system, and the method comprises the steps: an image extraction step: employing a brain blood vessel enhanced extraction algorithm to extract an input photoacoustic microscanning imaging result, and obtaining brain blood vessel information; the cerebrovascular information comprises two-dimensional image information and a three-dimensional reconstruction result of the cerebrovascular; and an implantation site and path acquisition step: processing the cerebrovascular information by using a brain electrode implantation site path planning algorithm to obtain an implantation site on the two-dimensional image and an implantation path in the three-dimensional structure. According to the invention, the brain blood vessel enhanced extraction algorithm is adopted, so that the problems of high photoacoustic scanning imaging noise and poor imaging quality are solved, and accurate extraction and three-dimensional reconstruction of the brain blood vessel are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a method and system for photoacoustic microscopy imaging of brain electrode implantation sites and path planning thereof. Background Art

[0002] Electrode implantation techniques include single-needle electrode implantation and multi-needle electrode implantation (for example, two electrodes or four electrodes are implanted each time). Compared with single-needle electrodes, multi-needle electrodes can usually obtain higher quality signals and are currently the most commonly used electrode implantation mode in animal experiments. In practical applications, before performing electrode implantation operations, it is often necessary to reasonably plan the electrode implantation position (or implantation site). Taking brain electrode implantation as an example, compared with scalp electrodes, neural electrodes implanted in the cerebral cortex can record neurophysiological signals with a higher signal-to-noise ratio, which can help researchers understand changes in brain activity more effectively. The cerebral cortex has a large number of blood vessels. If the implantation position planning is not accurate enough, the electrode may be implanted into the vascular area, causing bleeding risks. Therefore, it is very important to ensure the accuracy of the planned electrode implantation site.

[0003] Photoacoustic microscopy (PAM) is a preoperative imaging result based on photoacoustic microscopy (PAM). Its resolution can reach tens to dozens of microns, and it can image both surface and deep blood vessels. It is a new type of biomedical imaging method, commonly used for imaging of blood vessels and other tissues. Under 532nm laser excitation, cerebral blood vessels have a strong contrast with surrounding brain tissues, which can achieve high-contrast, high signal-to-noise ratio cerebral vascular imaging.

[0004] However, photoacoustic scanning imaging has high noise and unsatisfactory imaging quality, which may cause bleeding during surgery or even damage brain function.

[0005] In a Chinese patent document with publication number CN116196097A, a method and device for electrode implantation site planning, a readable storage medium, and a terminal are disclosed, wherein the method includes: determining an original image collected for an electrode implantation object; performing image segmentation on the original image to determine a non-implantation area and an implantable area; determining multiple first candidate implantation sites in the implantable area based on a first preset electrode distance; for each first candidate implantation site, determining a preliminary pairing point of the first candidate implantation site based on the first preset electrode distance; and screening each first candidate implantation site and its preliminary pairing point based on a second preset electrode distance to determine the electrode implantation site planning result. This patent document uses traditional imaging means with low spatial resolution, which cannot capture all vascular information, and only considers site planning on a two-dimensional image plane, without considering spatial path planning on a three-dimensional plane. Summary of the invention

[0006] In view of the defects in the prior art, the object of the present invention is to provide a method and system for photoacoustic microscopy imaging of brain electrode implantation sites and path planning.

[0007] A method for photoacoustic microscopy brain electrode implantation site and path planning according to the present invention includes:

[0008] Image extraction step: using a cerebral vascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebral vascular information; the cerebral vascular information includes two-dimensional image information and three-dimensional reconstruction results of the cerebral vessels;

[0009] Implantation site and path acquisition steps: Use the brain electrode implantation site path planning algorithm to process the cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

[0010] Preferably, the image extraction step comprises:

[0011] Step S1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy image to improve the signal-to-noise ratio;

[0012] Step S1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the imaging blur area, the area where the resolution does not reach the preset value, and the imaging deformation area, so as to improve the resolution of the photoacoustic microscopy image;

[0013] Step S1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain two-dimensional image information of the cerebral blood vessels;

[0014] Step S1.4: combining the depth information of the photoacoustic microscopy imaging with the two-dimensional image information of the cerebral blood vessels, and performing back-projection calculation to obtain a three-dimensional reconstruction result of the cerebral blood vessels.

[0015] Preferably, the denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

[0016] Preferably, the image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

[0017] Preferably, the blood vessel segmentation model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output a mask image corresponding to the blood vessel area; the training of the deep neural network is based on supervised learning, and the supervised information is a rough segmentation annotation of the blood vessel.

[0018] Preferably, the implantation point and path acquisition step includes:

[0019] Step S2.1: Calculate the two-dimensional cerebral vascular probability density map to obtain the implantation site; select the starting points for the number of implantation sites in the two-dimensional image plane so that the distance between any two of them is greater than or equal to the specified shortest distance between adjacent sites, the optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites, and the optimization condition is to keep the distance between any two points greater than or equal to the shortest distance between adjacent sites;

[0020] Step S2.2: Calculate the three-dimensional cerebral vascular probability density map to obtain the implantation path; select the implantation direction with the determined implantation site as the starting point in the three-dimensional space, and under the condition of specifying the implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

[0021] A system for photoacoustic microscopy brain electrode implantation site and path planning thereof provided by the present invention comprises:

[0022] Image extraction module: using cerebrovascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebrovascular information; the cerebrovascular information includes 2D image information and 3D reconstruction results of cerebrovascular;

[0023] Implantation site and path acquisition module: Use the brain electrode implantation site path planning algorithm to process cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

[0024] Preferably, the image extraction module comprises:

[0025] Module M1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy images to improve the signal-to-noise ratio;

[0026] Module M1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the blurred imaging area, the area where the resolution does not reach the preset value, and the imaging deformation area, and improve the resolution of the photoacoustic microscopy image;

[0027] Module M1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain the two-dimensional image information of the cerebral blood vessels;

[0028] Module M1.4: Combine the depth information of photoacoustic microscopy with the two-dimensional image information of cerebral blood vessels, and perform back-projection calculation to obtain the three-dimensional reconstruction results of cerebral blood vessels.

[0029] Preferably, the denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

[0030] Preferably, the image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

[0031] Preferably, the blood vessel segmentation model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output a mask image corresponding to the blood vessel area; the training of the deep neural network is based on supervised learning, and the supervised information is a rough segmentation annotation of the blood vessel.

[0032] Preferably, the implantation point and path acquisition module includes:

[0033] Module M2.1: Calculate the two-dimensional cerebral vascular probability density map to obtain the implantation site; select the starting points of the specified number of implantation sites in the two-dimensional image plane so that the distance between them is greater than or equal to the specified shortest distance between adjacent sites. The optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites. The optimization condition is to keep the distance between two points greater than or equal to the shortest distance between adjacent sites.

[0034] Module M2.2: Calculate the three-dimensional cerebral vascular probability density map to obtain the implantation path; select the implantation direction with the determined implantation site as the starting point in the three-dimensional space. Under the condition of specifying the implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] 1. The present invention solves the problem of large noise and poor imaging quality in photoacoustic scanning imaging by adopting a cerebral blood vessel enhancement extraction algorithm, and realizes accurate extraction and three-dimensional reconstruction of cerebral blood vessels.

[0037] 2. The present invention is based on the extracted cerebral blood vessels and three-dimensional reconstruction, and adopts a brain electrode implantation site path planning algorithm to avoid blood vessels during the brain electrode implantation process, thereby reducing bleeding during the operation and potential damage to brain function, achieving the effect of improving the safety and success rate of the operation, and providing great help to doctors.

[0038] Other beneficial effects of the present invention will be explained in the specific implementation manner through the introduction of specific technical features and technical solutions. Through the introduction of these technical features and technical solutions, those skilled in the art should be able to understand the beneficial technical effects brought about by the technical features and technical solutions. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0040] Figure 1 The figure is a flow chart of the method of the present invention.

[0041] Figure 2 This is a flow chart of the cerebral blood vessel enhancement extraction algorithm of the present invention.

[0042] Figure 3 This is a flow chart of the brain electrode implantation site path planning algorithm of the present invention. DETAILED DESCRIPTION

[0043] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0044] Reference Figure 1 As shown, a method for photoacoustic microscopy imaging of brain electrode implantation sites and path planning thereof comprises:

[0045] Image extraction step: using a cerebral vascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebral vascular information; the cerebral vascular information includes two-dimensional image information and three-dimensional reconstruction results of the cerebral vessels;

[0046] Specific:

[0047] Step S1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy image to improve the signal-to-noise ratio;

[0048] Step S1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the imaging blur area, the area where the resolution does not reach the preset value, and the imaging deformation area, so as to improve the resolution of the photoacoustic microscopy image;

[0049] Step S1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain two-dimensional image information of the cerebral blood vessels;

[0050] Step S1.4: combining the depth information of the photoacoustic microscopy imaging with the two-dimensional image information of the cerebral blood vessels, and performing back-projection calculation to obtain a three-dimensional reconstruction result of the cerebral blood vessels.

[0051] Among them, the denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

[0052] The image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

[0053] The blood vessel segmentation model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output a mask image corresponding to the blood vessel area; the training of the deep neural network is based on supervised learning, and the supervised information is the coarse segmentation annotation of the blood vessels.

[0054] Implantation site and path acquisition steps: Use the brain electrode implantation site path planning algorithm to process the cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

[0055] Specific:

[0056] Step S2.1: Calculate the two-dimensional cerebral vascular probability density map to obtain the implantation site; select the starting points for the number of implantation sites in the two-dimensional image plane so that the distance between any two of them is greater than or equal to the specified shortest distance between adjacent sites, the optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites, and the optimization condition is to keep the distance between any two points greater than or equal to the shortest distance between adjacent sites;

[0057] Step S2.2: Calculate the three-dimensional cerebral vascular probability density map to obtain the implantation path; select the implantation direction with the determined implantation site as the starting point in the three-dimensional space, and under the condition of specifying the implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

[0058] The present invention solves the problem of high noise and poor imaging quality in photoacoustic scanning by adopting a brain blood vessel enhancement extraction algorithm, and realizes accurate extraction and three-dimensional reconstruction of brain blood vessels. Based on the extracted brain blood vessels and three-dimensional reconstruction, the path planning algorithm for brain electrode implantation sites is adopted to avoid blood vessels during brain electrode implantation, reduce bleeding during surgery and potential damage to brain function, and achieve the effect of improving surgical safety and success rate.

[0059] The above is a basic embodiment of the present invention. The technical solution of the present invention is further described below through a preferred embodiment.

[0060] Example 1

[0061] Reference Figure 1 As shown, a method for photoacoustic microscopy imaging of brain electrode implantation sites and path planning thereof comprises:

[0062] Step S1: for the input photoacoustic microscopy scanning imaging results, execute the cerebral blood vessel enhancement extraction algorithm to extract the two-dimensional image information and three-dimensional reconstruction results of the cerebral blood vessels.

[0063] Reference Figure 2 As shown in the figure, the process of the cerebral vascular enhancement extraction algorithm is as follows: first, perform a denoising operation on the original acquired photoacoustic microscopy image, and use the trained denoising model to remove the noise caused by background interference, instrument electromagnetic radiation, imaging tissue movement, etc., to improve the signal-to-noise ratio of the photoacoustic microscopy image. Next, use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the imaging blur area, low resolution area and imaging deformation area, and improve the resolution of the photoacoustic microscopy image. Further, use the trained vascular segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain the two-dimensional image information of the cerebral blood vessels. Finally, the depth information of the photoacoustic microscopy imaging is combined with the two-dimensional image information of the cerebral blood vessels obtained in the previous step, and the three-dimensional reconstruction result of the cerebral blood vessels is obtained by back-projection calculation.

[0064] The denoising model is implemented based on a deep neural network, which inputs the original photoacoustic microscopy image and outputs a high signal-to-noise ratio photoacoustic microscopy image with noise removed. The training of the deep neural network is based on supervised learning, and the supervisory information is the denoising and noise adding process.

[0065] The image enhancement model is implemented based on a deep neural network. Its function is to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution. The training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

[0066] The blood vessel segmentation model is implemented based on a deep neural network. Its function is to input the original photoacoustic microscopy image and output the mask image corresponding to the blood vessel area. The training of the deep neural network is based on supervised learning, and the supervised information is the rough segmentation annotation of the blood vessels.

[0067] Step S2: For the extracted cerebral vascular information, a brain electrode implantation site path planning algorithm is executed to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

[0068] The selection of implantation sites is calculated based on the two-dimensional cerebral vascular probability density map. The main calculation method is to select the starting point of the number of implantation sites in the two-dimensional image plane so that the distance between them is greater than or equal to the specified shortest distance between adjacent sites. The optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites, and the optimization condition is to keep the distance between two points greater than or equal to the shortest distance between adjacent sites.

[0069] The selection of implantation path is calculated based on the three-dimensional cerebral vascular probability density map. The main calculation method is to select the implantation direction with the implantation site as the starting point in the three-dimensional space. Under the condition of the specified implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

[0070] Reference Figure 3 As shown, the path planning algorithm for brain electrode implantation sites includes implantation site planning based on two-dimensional images and path planning based on three-dimensional reconstruction results. Based on the two-dimensional image information of cerebral blood vessels, the probability density distribution map of implantation sites is calculated. The calculation is mainly based on the image distance between each pixel in the image and the nearest blood vessel. In the two-dimensional coordinate system of the image, the closer the distance to the blood vessel, the smaller the probability of the pixel corresponding to the implantation site. The implantation site probability distribution map obtained in this way reflects the distance between each implantation site and the nearest blood vessel. The greater the corresponding probability, the farther the distance from the blood vessel, the safer the implantation process, and the lower the probability of penetrating the cerebral blood vessels. After artificially setting the number of implantation sites and the shortest distance between adjacent sites, the implantation sites that meet the requirements are selected based on the implantation site probability density map to achieve implantation site planning based on two-dimensional images. The main calculation formula is as follows:

[0071] Based on the two-dimensional image information of cerebral blood vessels, the probability density distribution map M is first calculated. For each pixel point (u, v) in the distribution map, its probability density value is defined as proportional to the distance d(u, v) to the nearest blood vessel, that is,

[0072] M(u,v)=kd(u,v)

[0073] And normalize the image, the normalized image is Normalization is calculated by the following formula:

[0074]

[0075] After calculating the corresponding normalized two-dimensional probability density distribution map, N implantation sites are randomly generated in the area where the initial threshold is set to be greater than 0.5, where N represents the number of implantation sites set manually, and the corresponding implantation site position is defined as At the same time, the shortest distance between the two implantation sites is set to s, and the implantation site P is optimized based on the above conditions:

[0076]

[0077] Finally, the optimal implantation site planning is obtained through optimization iteration

[0078] Similarly, based on the 3D reconstruction information of cerebral blood vessels, the probability density distribution map of the implantation path is calculated, and the calculation is mainly based on the spatial distance between each voxel point and the nearest blood vessel in the 3D voxel space. Combined with the completed 2D implantation site planning, taking the 2D implantation site planning as the starting point, after artificially setting the implantation depth, and on the premise of avoiding blood vessels, a 3D implantation path that meets the requirements is calculated, realizing path planning based on the 3D reconstruction results.

[0079] In the in vivo brain electrode implantation animal experiment, the method provided by the present invention achieved a bleeding site of less than 150 microns for each electrode implantation, making the operation success rate reach more than 80%.

[0080] The present invention also provides a system for photoacoustic microscopy imaging brain electrode implantation sites and path planning thereof. The system for photoacoustic microscopy imaging brain electrode implantation sites and path planning thereof can be realized by executing the process steps of the method for photoacoustic microscopy imaging brain electrode implantation sites and path planning thereof, that is, those skilled in the art can understand the method for photoacoustic microscopy imaging brain electrode implantation sites and path planning thereof as a preferred implementation of the system for photoacoustic microscopy imaging brain electrode implantation sites and path planning thereof.

[0081] Specifically, a system for photoacoustic microscopy imaging of brain electrode implantation sites and path planning thereof includes:

[0082] Image extraction module: using cerebrovascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebrovascular information; the cerebrovascular information includes 2D image information and 3D reconstruction results of cerebrovascular;

[0083] Implantation site and path acquisition module: Use the brain electrode implantation site path planning algorithm to process cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

[0084] The image extraction module comprises:

[0085] Module M1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy images to improve the signal-to-noise ratio;

[0086] Module M1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the blurred imaging area, the area where the resolution does not reach the preset value, and the imaging deformation area, and improve the resolution of the photoacoustic microscopy image;

[0087] Module M1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain the two-dimensional image information of the cerebral blood vessels;

[0088] Module M1.4: Combine the depth information of photoacoustic microscopy with the two-dimensional image information of cerebral blood vessels, and perform back-projection calculation to obtain the three-dimensional reconstruction results of cerebral blood vessels.

[0089] The denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

[0090] The image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

[0091] The blood vessel segmentation model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output a mask image corresponding to the blood vessel area; the training of the deep neural network is based on supervised learning, and the supervised information is the rough segmentation annotation of the blood vessel.

[0092] The implantation point and path acquisition module includes:

[0093] Module M2.1: Calculate the two-dimensional cerebral vascular probability density map to obtain the implantation site; select the starting points of the specified number of implantation sites in the two-dimensional image plane so that the distance between them is greater than or equal to the specified shortest distance between adjacent sites. The optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites. The optimization condition is to keep the distance between two points greater than or equal to the shortest distance between adjacent sites.

[0094] Module M2.2: Calculate the three-dimensional cerebral vascular probability density map to obtain the implantation path; select the implantation direction with the determined implantation site as the starting point in the three-dimensional space. Under the condition of specifying the implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

[0095] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0096] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for photoacoustic microscopy imaging of brain electrode implantation sites and path planning, characterized in that: include: Image extraction step: using a cerebral vascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebral vascular information; The cerebral blood vessel information includes two-dimensional image information and three-dimensional reconstruction results of the cerebral blood vessels; Implantation site and path acquisition steps: Use the brain electrode implantation site path planning algorithm to process the cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

2. The method for photoacoustic microscopy brain electrode implantation site and path planning according to claim 1, characterized in that: The image extraction step comprises: Step S1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy image to improve the signal-to-noise ratio; Step S1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the imaging blur area, the area where the resolution does not reach the preset value, and the imaging deformation area, so as to improve the resolution of the photoacoustic microscopy image; Step S1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain two-dimensional image information of the cerebral blood vessels; Step S1.4: combining the depth information of the photoacoustic microscopy imaging with the two-dimensional image information of the cerebral blood vessels, and performing back-projection calculation to obtain a three-dimensional reconstruction result of the cerebral blood vessels.

3. The method for photoacoustic microscopy brain electrode implantation site and path planning according to claim 2, characterized in that: The denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

4. The method for photoacoustic microscopy brain electrode implantation site and path planning according to claim 2, characterized in that: The image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

5. The method for photoacoustic microscopy brain electrode implantation site and path planning according to claim 2, characterized in that: The blood vessel segmentation model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output a mask image corresponding to the blood vessel area; the training of the deep neural network is based on supervised learning, and the supervised information is the rough segmentation annotation of the blood vessel.

6. The method for photoacoustic microscopy brain electrode implantation site and path planning according to claim 2, characterized in that: The implantation point and path acquisition step comprises: Step S2.1: Calculate the two-dimensional cerebral vascular probability density map to obtain the implantation site; select the starting points for the number of implantation sites in the two-dimensional image plane so that the distance between any two of them is greater than or equal to the specified shortest distance between adjacent sites, the optimization goal is to maximize the sum of the cumulative probability values ​​of all implantation sites, and the optimization condition is to keep the distance between any two points greater than or equal to the shortest distance between adjacent sites; Step S2.2: Calculate the three-dimensional cerebral vascular probability density map to obtain the implantation path; select the implantation direction with the determined implantation site as the starting point in the three-dimensional space, and under the condition of specifying the implantation depth, the optimization goal is to maximize the sum of the cumulative probability values ​​on all implantation paths.

7. A system for photoacoustic microscopy imaging of brain electrode implantation sites and path planning, characterized in that: include: Image extraction module: uses the cerebrovascular enhancement extraction algorithm to extract the input photoacoustic microscopy scanning imaging results to obtain cerebrovascular information; The cerebral blood vessel information includes two-dimensional image information and three-dimensional reconstruction results of the cerebral blood vessels; Implantation site and path acquisition module: Use the brain electrode implantation site path planning algorithm to process cerebrovascular information to obtain the implantation site on the two-dimensional image and the implantation path in the three-dimensional structure.

8. The system for photoacoustic microscopy brain electrode implantation site and path planning according to claim 7, characterized in that: The image extraction module comprises: Module M1.1: Use the trained denoising model to denoise the acquired photoacoustic microscopy images to improve the signal-to-noise ratio; Module M1.2: Use the trained image enhancement model to enhance the photoacoustic microscopy image, correct the blurred imaging area, the area where the resolution does not reach the preset value, and the imaging deformation area, and improve the resolution of the photoacoustic microscopy image; Module M1.3: Use the trained blood vessel segmentation model to segment the blood vessels in the photoacoustic microscopy image, remove the background and other tissue structure information, and obtain the two-dimensional image information of the cerebral blood vessels; Module M1.4: Combine the depth information of photoacoustic microscopy with the two-dimensional image information of cerebral blood vessels, and perform back-projection calculation to obtain the three-dimensional reconstruction results of cerebral blood vessels.

9. The system for photoacoustic microscopy brain electrode implantation site and path planning according to claim 8, characterized in that: The denoising model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the signal-to-noise ratio photoacoustic microscopy image with noise removed; the training of the deep neural network is based on supervised learning, and the supervised information is the denoising and noisy process.

10. The system for photoacoustic microscopy brain electrode implantation site and path planning according to claim 8, characterized in that: The image enhancement model includes a deep neural network, which is used to input the original photoacoustic microscopy image and output the photoacoustic microscopy image with enhanced resolution; the training of the deep neural network is based on supervised learning, and the supervised information is the restored difference between the original high-resolution image and the downsampled image.

Images