Neurosurgery brain puncture surgical robot and electronic equipment

Abstract

The invention provides a neurosurgery brain puncture surgical robot and electronic equipment, in the neurosurgery brain puncture surgical robot, a control host is used for matching a brain CT image to be planned with a constructed intracranial hematoma puncture path database to determine a hematoma puncture planned path; and controlling the mechanical arm to drive the surgical instrument to reach the hematoma position of the brain according to the hematoma puncture planning path. In the embodiment of the invention, only the CT image scanned in the diagnosis process needs to be used as the brain CT image to be planned, other additional images do not need to be matched with the constructed intracranial hematoma puncture path database, planning of the intracranial hematoma puncture path can be completed in a short time by only using brain tissue, the planning efficiency is improved, and in addition, the method and the device have the advantages of being high in practicability and easy to popularize. The planning process does not need additional manual operation, the implementation difficulty is small for young doctors and grassroots doctors with less clinical experience, and on the basis, the treatment efficiency of cerebral hemorrhage patients is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a neurosurgery brain puncture operation robot and electronic equipment. Background Art

[0002] In neurosurgery application scenarios, such as in the treatment of cerebral hemorrhage, it is crucial to plan a reasonable hematoma puncture path. Current puncture path planning methods mainly include iterative learning-based methods and multi-constraint optimization methods.

[0003] For the iterative learning method, the initial path set is first generated based on the accessible area of ​​the needle tip, then the optimal path is selected based on the genetic simulated annealing algorithm, and finally the path is corrected based on the needle-tissue interaction model and the iterative learning algorithm. This method is mainly used on flexible needles. The above iterative process directly takes into account the premise that the flexible needle will have a large deformation during the puncture process. Intracerebral hemorrhage surgery requires puncture to the hematoma location with a rigid needle and insertion of a drainage tube for drainage. Therefore, the existing iterative learning-based method is not suitable for planning the puncture path of intracranial hematoma.

[0004] For the multi-constraint optimization method, the surgical risk is quantified into multiple constraints and assigned different weights. The surgical risks of different puncture paths are quantitatively analyzed by calculation to determine the optimal puncture path. However, in order to achieve the quantification of multiple constraints, multimodal imaging needs to be combined. At the same time, this method will also reconstruct multiple organs. As a result, the calculation is slow and time-consuming, which makes the path planning inefficient and is very unsuitable for emergency surgeries such as neurosurgery. In addition, in this planning scheme, doctors need to have rich clinical experience to perform manual intervention, which is difficult to implement for young doctors and grassroots doctors with less clinical experience.

[0005] Therefore, it is urgent to provide a puncture path planning solution that can overcome the above problems. Summary of the invention

[0006] In view of the above problems, an embodiment of the present application is proposed, which provides a neurosurgery brain puncture surgery robot and electronic equipment to at least solve the above problems.

[0007] One or more embodiments of the present application provide a neurosurgery brain puncture surgical robot, including a control host and a robotic arm, wherein the control host is used to match the brain CT image to be planned with a constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and control the robotic arm to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

[0008] According to another aspect of the present application, there is provided an electronic device, comprising: one or more processors; and a memory storing a program;

[0009] Among them, the program includes instructions, which, when executed by the processor, enable the processor to perform the following steps: match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and control the robotic arm to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

[0010] In the embodiment of the present application, only the CT image scanned during the diagnosis process is needed as the brain CT image to be planned, and no other additional images (MR, vascular images, etc.) are required. It is matched with the constructed intracranial hematoma puncture path database, so that the planning of the intracranial hematoma puncture path can be completed in a short time using only brain tissue, thereby improving the planning efficiency. Moreover, the planning process does not require additional manual operation. For young doctors and grassroots doctors with less clinical experience, the implementation difficulty is relatively small. On this basis, the treatment efficiency of patients with cerebral hemorrhage is effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 The present invention is a schematic structural diagram of a neurosurgery brain puncture surgery robot for the present application.

[0013] Figure 2 The process of performing contour evolution on the initial brain tissue model until the extracted brain tissue is obtained.

[0014] Figure 3 An exemplary flow chart for implementing registration according to an embodiment of the present application.

[0015] Figure 4 An electronic device is provided in an embodiment of the present application.

[0016] Figure 5 This is a flow chart of a method for planning a hematoma puncture path according to an embodiment of the present application. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0018] For ease of understanding, before describing the specific embodiments of the present application in detail, an example description is given of the application scenarios of the neurosurgery brain puncture surgical robot and electronic equipment of the present application.

[0019] Figure 1 This is a schematic diagram of the structure of a neurosurgery brain puncture surgery robot. Figure 1 As shown, the neurosurgery brain puncture surgical robot includes: a control host 101 and a robotic arm 102. The control host 101 is used to match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and control the robotic arm 102 to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

[0020] In the embodiment of the present application, only the CT image scanned during the diagnosis process is needed as the brain CT image to be planned, and no other additional images (MR, vascular images, etc.) are required. It is matched with the constructed intracranial hematoma puncture path database, so that the planning of the intracranial hematoma puncture path can be completed in a short time using only brain tissue, thereby improving the planning efficiency. Moreover, the planning process does not require additional manual operation. For young doctors and grassroots doctors with less clinical experience, the implementation difficulty is relatively small. On this basis, the treatment efficiency of patients with cerebral hemorrhage is effectively improved.

[0021] Optionally, the intracranial hematoma puncture path database includes clinical entry points and clinical targets corresponding to the hematoma puncture clinical path; the control host is used to perform the following steps to match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, including:

[0022] Extracting a hematoma region from the brain CT image to be planned, and calculating the center position of the hematoma region, so as to use the center position as a target point for path planning;

[0023] Transforming the target of the path planning into the constructed brain CT image standard space to match the clinical target of all hematoma puncture clinical pathways in the intracranial hematoma puncture path database;

[0024] The clinical entry point of the hematoma puncture clinical pathway where the matched clinical target is located is determined, and the clinical entry point is transformed to the space where the brain CT image to be planned is located, so as to obtain the entry point of the pathway planning.

[0025] To this end, in combination with the technical solution, the neurosurgery brain puncture surgery robot and its electronic equipment of the present invention bring significant technical benefits, which are specifically described as follows:

[0026] (1) Efficient and accurate path planning: By directly extracting the hematoma area from the brain CT image to be planned and calculating its center position as the target for path planning, the present invention simplifies the preliminary processing steps of path planning, avoids complex multimodal image fusion and multi-organ reconstruction, and thus significantly improves the efficiency of path planning. At the same time, the target is transformed into a standard space and matched with the clinical target in the database, ensuring the accuracy and reliability of path planning, and improving the success rate of puncture surgery based on the verified clinical pathway.

[0027] (2) Reduce the computational burden and accelerate the decision-making process: Traditional multi-constraint optimization methods often take a long time because they involve a large amount of data processing and complex calculations. The present invention achieves fast matching by constructing and utilizing an intracranial hematoma puncture path database, greatly reducing the need for real-time calculations, so that puncture plans can be quickly formulated in emergency situations, which is particularly important for surgeries such as neurosurgery that require rapid response.

[0028] (3) Reduce operational difficulty and improve popularity: The path planning process of the present invention is highly automated, reducing the high dependence on the doctor's clinical experience. Even young doctors or grassroots doctors with less clinical experience can effectively perform puncture path planning with the assistance of the system, which not only reduces the difficulty of operation, but also promotes the application and promotion of advanced medical technology in a wider range.

[0029] (4) Combination of personalization and standardization: By matching the patient's specific hematoma location with a standardized puncture path database, the present invention achieves rapid generation of personalized treatment plans while ensuring the scientificity and safety of the treatment strategy. This combination not only takes into account the individual differences of patients, but also relies on proven clinical experience, thereby improving the overall effect of treatment.

[0030] Optionally, the control host is used to perform the following steps to extract the hematoma area from the brain CT image to be planned:

[0031] Preprocessing the brain CT image samples to obtain preprocessed brain CT image samples;

[0032] Performing enhancement processing on the preprocessed brain CT image samples to obtain a plurality of enhanced brain CT image samples for training the constructed neural network model to obtain a hematoma extraction model;

[0033] Based on the hematoma extraction model, the hematoma area is predicted for each CT image to be planned, and the predicted hematoma area is obtained;

[0034] A plurality of predicted hematomas extracted from a plurality of enhanced brain CT images are fused to obtain the extracted hematoma region.

[0035] To this end, in combination with this optional technical solution, the technical advantages of the neurosurgery brain puncture surgery robot and its electronic device in extracting the hematoma area are as follows:

[0036] (1) By preprocessing brain CT image samples, such as denoising and contrast correction, the image quality can be significantly improved, providing clearer and more accurate input data for subsequent hematoma extraction. This helps to reduce misjudgments or missed judgments caused by poor image quality, thereby improving the accuracy of hematoma area extraction.

[0037] (2) Enhancing the preprocessed brain CT image samples can highlight the characteristics of the hematoma area, making it easier for the neural network model to learn the unique properties of the hematoma during the training process. Through training with a large number of enhanced samples, the hematoma extraction model can more accurately identify and extract the hematoma area, further improving the accuracy.

[0038] (3) Multiple enhanced brain CT image samples: Using multiple enhanced brain CT image samples for model training can enable the hematoma extraction model to learn more diverse hematoma morphologies and features. This helps the model maintain a high level of recognition ability when facing brain CT images of different patients and different scanning conditions, thereby enhancing the robustness of the model.

[0039] (3) Fusion processing of predicted hematomas extracted from multiple enhanced brain CT images can comprehensively utilize the information of multiple images and reduce misjudgment caused by noise or artifacts in a single image. This fusion strategy further improves the robustness of hematoma region extraction.

[0040] Optionally, the preprocessed brain CT image samples are enhanced to obtain multiple enhanced brain CT image samples for training the constructed neural network model to obtain a hematoma extraction model; including: flipping the preprocessed brain CT image samples along the X, Y, and Z axes of the voxel space to obtain a total of 8 enhanced brain CT image samples.

[0041] To this end, combined with this technical solution, the pre-processed brain CT image samples are flipped along the X, Y, and Z axes of the voxel space, and a total of 8 enhanced brain CT image samples are obtained. The technical benefits brought by this method are as follows:

[0042] (1) Through the flipping operation, the original brain CT image samples are expanded into 8 different variants, which essentially greatly enriches the training data set through data augmentation technology without adding new patient data. This diversity helps the neural network model learn the characteristics of hematomas at different angles and directions, thereby improving the model's recognition ability and generalization performance of hematomas.

[0043] (2) Deep learning models, especially neural networks, are prone to overfitting during training due to insufficient data or uneven data distribution. Through data augmentation, we provide the model with more diverse training samples, which helps the model generalize better during training, reduces the risk of overfitting, and enables the model to maintain good performance on unseen data.

[0044] (3) The flipping operation simulates different scanning directions and angles that may be encountered in actual clinical practice. By training the model to recognize these flipped images, the model can be made more robust to factors such as scanning direction and patient position, and can accurately identify the hemorrhage area even when faced with images under different scanning conditions.

[0045] (4) Data augmentation is to generate new training samples through simple transformations based on the original data. This method does not require additional data collection and annotation costs and can be achieved only through calculations. Therefore, it is a method that efficiently utilizes computing resources to improve model performance.

[0046] (5) By flipping the brain CT images in three-dimensional space, the model needs to learn and understand the shape and position changes of the hematoma in three-dimensional space during the training process. This helps the model better capture the spatial characteristics of the hematoma and improve its ability to recognize the hematoma in three-dimensional space.

[0047] Optionally, the center position (X) of the hematoma area is calculated based on the following formula: 0 , Y 0 , Z 0 ):

[0048]

[0049]

[0050] Where i, j, k are the coordinates of the voxels in the hematoma area along the X, Y, and Z axes, M i,j,k is the gray value of the voxel at coordinate (i, j, k).

[0051] To this end, the condition M in the formula i,j,k >0 ensures that only voxels within the hematoma area are calculated, effectively eliminating interference from background and non-hematoma areas and improving the accuracy of the analysis.

[0052] Optionally, based on the following formula, the target point of the path planning is transformed into the physical space coordinates (X: c , Y c , Z c );

[0053]

[0054] in, is the direction matrix of the brain CT image to be planned, represents the spatial matrix of the brain CT image to be planned, represents the center coordinate vector of the hematoma area, Represents the origin coordinate vector of the brain CT image to be planned.

[0055] To this end, the precise calculation of the direction matrix and the space matrix is ​​used to achieve accurate correspondence between the physical space coordinates of the target and the hematoma area, thereby improving the accuracy of the puncture path.

[0056] Optionally, the target point of the path planning is transformed into the constructed brain CT image standard space, and matched with the clinical target points of all hematoma puncture clinical pathways in the intracranial hematoma puncture pathway database based on the following formula:

[0057]

[0058] Among them, (X db ,Y db ,Z db ) represents the coordinates of the clinical target in the standard space of the brain CT image, (X c ,Y c ,Z c ) represents the coordinates of the target point of path planning in the standard space of the brain CT image, D ec It represents the distance between the target point of path planning and the clinical target point in the standard space of the brain CT image.

[0059] To this end, the target coordinates of the path planning were converted into a standard space aligned with the brain CT image to ensure the precise correspondence between the puncture path and the clinical target.

[0060] Optionally, the control host is further used to perform the following steps to transform the target point of the path planning into the constructed brain CT image standard space:

[0061] The standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located.

[0062] To this end, in combination with this technical solution, a series of steps are performed to generate standard brain tissue images and clinical brain tissue images to be registered, which are then used for puncture path planning, which can bring the following technical benefits:

[0063] (1) Unified reference framework: By acquiring standard brain CT images and using them as the standard brain CT image space, a unified reference framework is provided for puncture path planning. This helps to eliminate individual differences between images of different patients, making the path planning results more consistent and comparable.

[0064] (2) Accurate brain tissue extraction: By extracting brain tissue from standard brain CT images and clinical brain CT images to be registered, clear and accurate brain tissue images can be obtained. This helps reduce noise and interference in the images and improves the accuracy and reliability of path planning.

[0065] (3) Improving registration accuracy: Using accurately extracted standard brain tissue and clinical brain tissue images for registration can improve the precision and accuracy of registration. This helps ensure that the clinical entry point and target point can be accurately transformed into the standard space, providing accurate positioning information for puncture path planning.

[0066] Optionally, the control host is further configured to perform the following steps to generate the standard brain tissue image and the clinical brain tissue image to be registered:

[0067] Acquire a standard brain CT image and use the image space where the image is located as the standard brain CT image space;

[0068] Performing brain tissue extraction on the standard brain CT image to obtain a standard brain tissue image;

[0069] A clinical brain CT image to be registered is acquired, and brain tissue is extracted from the clinical brain CT image to be registered to obtain a clinical brain tissue image to be registered.

[0070] To this end, in combination with this technical solution, a series of steps are performed to generate standard brain tissue images and clinical brain tissue images to be registered, which are then used for puncture path planning, which can bring the following technical benefits:

[0071] (1) Unified reference framework: By acquiring standard brain CT images and using them as the standard brain CT image space, a unified reference framework is provided for puncture path planning. This helps to eliminate individual differences between images of different patients, making the path planning results more consistent and comparable.

[0072] (2) Accurate brain tissue extraction: By extracting brain tissue from standard brain CT images and clinical brain CT images to be registered, clear and accurate brain tissue images can be obtained. This helps reduce noise and interference in the images and improves the accuracy and reliability of path planning.

[0073] (3) Improving registration accuracy: Using accurately extracted standard brain tissue and clinical brain tissue images for registration can improve the precision and accuracy of registration. This helps ensure that the clinical entry point and target point can be accurately transformed into the standard space, providing accurate positioning information for puncture path planning.

[0074] Optionally, the control host is specifically used to perform the following steps to generate the standard brain tissue image and the clinical brain tissue image to be registered:

[0075] Acquire and construct a clinical brain CT image library, wherein the clinical brain CT image library includes a plurality of clinical brain CT images;

[0076] A clinical brain CT image is selected from the clinical brain CT image library as a standard brain CT image, and a space where the standard brain CT image is located is used as the brain CT image standard space;

[0077] Using the remaining clinical brain CT images in the clinical brain CT image library as clinical brain CT images to be registered;

[0078] Extracting brain tissue from the standard brain CT image to obtain a standard brain tissue image;

[0079] Brain tissue is extracted from the clinical brain CT image to be registered to obtain a clinical brain tissue image to be registered.

[0080] In combination with this technical solution, by executing a series of specific steps to generate standard brain tissue images and clinical brain tissue images to be registered, and then used for puncture path planning, the following significant technical benefits can be achieved:

[0081] (1) Establishing unified standards:

[0082] A standard brain CT image is selected from the clinical brain CT image library, and the space in which it is located is used as the standard brain CT image space. This provides a unified and consistent reference framework for all subsequent puncture path planning, ensuring the comparability and consistency of the planning results.

[0083] (2) Make full use of image resources: By building a clinical brain CT image library and screening standard images, we can make full use of existing image resources. This not only improves the utilization rate of resources, but also provides rich data support for puncture path planning.

[0084] (3) Improve the accuracy of brain tissue extraction: By extracting brain tissue from standard brain CT images and clinical brain CT images to be registered, more accurate and clear brain tissue images can be obtained. This helps reduce noise and interference in the images and improve the accuracy and reliability of path planning.

[0085] (3) Optimizing the registration process: Using accurately extracted standard brain tissue and clinical brain tissue images for registration can improve the accuracy and efficiency of registration. This helps ensure that the clinical entry point and target point can be accurately transformed into the standard space, providing accurate positioning information for puncture path planning.

[0086] (4) Enhance surgical safety and effectiveness: Accurate puncture path planning can ensure that important brain tissue and vascular structures are avoided during surgery, reducing surgical risks. By combining standard brain tissue images with clinical brain tissue images for path planning, the safety and success rate of surgery can be improved, bringing better treatment effects and prognosis to patients.

[0087] Optionally, when constructing the intracranial hematoma puncture path database, the control host constructs the intracranial hematoma puncture path database based on the clinical entry points and clinical target points of the hematoma puncture clinical path corresponding to the clinical brain tissue image to be registered and the standard brain CT image in the brain CT image standard space.

[0088] Optionally, the control host takes the standard brain CT image or the clinical brain CT image to be registered as an object to be processed, and performs the following steps to extract brain tissue:

[0089] Determine the grayscale extremes of brain tissue;

[0090] Based on the brain tissue grayscale extreme value, brain tissue is extracted from the object to be processed.

[0091] Optionally, the control host is further configured to perform the following steps to determine the grayscale extreme value of brain tissue:

[0092] Converting the object to be processed into a voxel grayscale histogram of a brain image;

[0093] Based on the grayscale histogram, determining a first voxel grayscale extreme value and a second voxel grayscale extreme value, wherein a sum of the second sorting ratio and the first sorting ratio is equal to 1;

[0094] Based on the first voxel grayscale extreme value and the second voxel grayscale extreme value, a brain tissue grayscale extreme value is determined.

[0095] To this end, by converting the object to be processed (standard brain CT image or clinical brain CT image to be registered) into a voxel grayscale histogram of the brain image, the grayscale distribution of different tissues in the image can be intuitively displayed. Based on the grayscale histogram, by setting the first sorting ratio and the second sorting ratio (the sum of the two is 1), the first voxel grayscale extreme value (such as the minimum grayscale value) and the second voxel grayscale extreme value (such as the maximum grayscale value) can be determined. These two extreme values ​​help to define the grayscale range of brain tissue. Using the determined first voxel grayscale extreme value and the second voxel grayscale extreme value, the brain tissue grayscale extreme value can be further accurately calculated, thereby providing an accurate grayscale threshold for brain tissue extraction.

[0096] Optionally, when the control host determines the first voxel grayscale extreme value and the second voxel grayscale extreme value based on the grayscale histogram, the control host comprises the following steps:

[0097] Based on the grayscale histogram, sorting the voxel grayscale values ​​of the brain image to obtain a voxel grayscale value sequence;

[0098] Based on a set first sorting ratio, the voxel grayscale value sequence is intercepted to obtain a first voxel grayscale extreme value;

[0099] The voxel grayscale value sequence is intercepted based on the set second sorting ratio to obtain a second voxel grayscale extreme value.

[0100] To this end, by sorting the voxel grayscale values ​​of the brain image and intercepting the voxel grayscale value sequence based on the set first sorting ratio and second sorting ratio, the first voxel grayscale extreme value and the second voxel grayscale extreme value can be accurately determined. These two extreme values ​​jointly define the grayscale range of the brain tissue, which helps to accurately distinguish brain tissue from non-brain tissue in the subsequent brain tissue extraction process. Since the brain CT images of different patients may have differences in grayscale distribution, by setting the adjustable first sorting ratio and second sorting ratio, different imaging conditions can be flexibly adapted. This flexibility ensures that brain tissue can be accurately extracted under different conditions and improves the robustness of the technology.

[0101] Optionally, when determining the grayscale extreme value of the brain tissue based on the first voxel grayscale extreme value and the second voxel grayscale extreme value, the control host fuses the first voxel grayscale extreme value and the second voxel grayscale extreme value to obtain the grayscale extreme value of the brain tissue.

[0102] Optionally, the first voxel grayscale extreme value is smaller than the second voxel grayscale extreme value, and its specific size can be flexibly determined according to the application scenario, and the embodiment of the present application does not limit its uniqueness.

[0103] When the grayscale extreme value of the first voxel and the grayscale extreme value of the second voxel are fused to obtain the grayscale extreme value of the brain tissue, the fusion can be performed, for example, by linear interpolation. Based on the grayscale extreme value of the first voxel and the grayscale extreme value of the second voxel, it is equivalent to obtaining a robust grayscale minimum and maximum value from the grayscale histogram, eliminating the interference of skin, bones and other tissues, and only calculating for brain tissue, which greatly reduces the amount of calculation for registration and effectively improves the efficiency of path planning.

[0104] Optionally, when the control host extracts brain tissue of the object to be processed based on the brain tissue grayscale extreme value, the control host includes the following steps:

[0105] Determining basic parameters of the brain tissue based on the grayscale extreme value of the brain tissue;

[0106] Based on the basic parameters of the brain tissue, construct an initial model of the brain tissue;

[0107] The initial brain tissue model is subjected to contour evolution to obtain extracted brain tissue.

[0108] To this end, by using the previously determined brain tissue grayscale extremes, the control host can more accurately define the grayscale range of the brain tissue, thereby extracting the basic parameters of the brain tissue, such as size, shape, and position. These basic parameters provide accurate data support for the subsequent construction of the initial model of the brain tissue, which helps to reduce errors in the extraction process. Based on the basic parameters of the brain tissue, the control host can construct an initial model of the brain tissue. This model is a preliminary description of the morphology and structure of the brain tissue, providing a reliable starting point for the subsequent contour evolution. The accuracy of the initial model is crucial to the final brain tissue extraction results, as it ensures that the evolution process proceeds in the right direction. By performing contour evolution on the initial model of the brain tissue, the control host can gradually approach the real brain tissue boundary, thereby extracting accurate brain tissue images. The contour evolution process is a dynamic adjustment and optimization process, which can modify the initial model according to the actual situation of the image data to further improve the accuracy of the extraction.

[0109] Optionally, when the control host determines the basic parameters of the brain tissue based on the grayscale extreme value of the brain tissue, the control host includes the following steps:

[0110] Traversing each voxel on the object to be processed, and based on the grayscale extreme value of the brain tissue, respectively counting the grayscale value distribution intensity of the voxel in the X-axis, Y-axis and Z-axis directions to determine the center coordinates of the brain tissue;

[0111] According to the total number of voxels on the object to be processed whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel, the radius of the sphere when the central coordinate is the center of the sphere is calculated as the radius of the brain tissue.

[0112] To this end, by traversing each voxel on the object to be processed and counting the grayscale distribution intensity of the voxels in the X-axis, Y-axis and Z-axis directions based on the grayscale extreme value of the brain tissue, the central coordinates of the brain tissue can be accurately determined. This method takes into account the grayscale distribution characteristics of the brain tissue in three-dimensional space, avoids deviations in a single direction, and thus improves the accuracy of brain tissue positioning. According to the total number of voxels with grayscale values ​​greater than the grayscale extreme value of the brain tissue on the object to be processed and the physical size of a single voxel, the radius of a sphere with the central coordinate as the center of the sphere can be calculated as the radius of the brain tissue. This calculation method fully considers the actual grayscale distribution and volume characteristics of the brain tissue, making the calculated radius more accurate and reliable. By determining the central coordinates and radius of the brain tissue, accurate initial parameters can be provided for the subsequent brain tissue extraction process, which helps to reduce the number of iterations and calculations in the extraction process and improve the extraction efficiency. Accurate central coordinates and radius parameters can guide the construction of the initial model of the brain tissue, making the model more consistent with the actual shape and size of the brain tissue, which helps to converge to the real brain tissue boundary faster in the subsequent contour evolution process and improve the accuracy of extraction.

[0113] Optionally, when the control host traverses each voxel on the object to be processed and, based on the grayscale extreme value of the brain tissue, respectively counts the grayscale value distribution intensity of the voxel in the X-axis, Y-axis and Z-axis directions to determine the center coordinates of the brain tissue, the control host includes the following steps:

[0114] For each voxel on the object to be processed, the difference between its gray value and the gray extreme value of the brain tissue is calculated as a first difference, and compared with the second difference, the minimum value is taken, and the minimum value is subjected to non-negative truncation processing to be used as the gray intensity value of each voxel, wherein the second difference is the difference between the gray extreme value of the second voxel and the gray extreme value of the brain tissue;

[0115] Summing the voxel grayscale intensity values ​​to obtain the overall grayscale intensity distribution value of the voxels on the object to be processed;

[0116] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the X-axis direction is calculated according to the coordinates of each voxel in the X-axis direction, the physical size information of the voxel in the X-axis direction, and the grayscale intensity value;

[0117] The gray value distribution intensities of all voxels in the X-axis direction are summed to obtain the gray value distribution intensities of the voxels in the X-axis direction, so as to calculate the ratio of the gray value distribution intensities to the overall gray value distribution value, and the ratio is used as the center coordinate of the brain tissue in the X-axis direction;

[0118] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the Y-axis direction is calculated according to the coordinate of each voxel in the Y-axis direction, the physical size information of the voxel in the Y-axis direction, and the grayscale intensity value;

[0119] Adding the grayscale value distribution intensities of all voxels in the Y-axis direction to obtain the grayscale value distribution intensity of the voxels in the Y-axis direction, so as to calculate the ratio of the grayscale value distribution intensity to the overall grayscale intensity distribution value, and using the ratio as the center coordinate of the brain tissue in the Y-axis direction;

[0120] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the Z-axis direction is calculated according to the coordinates of each voxel in the Z-axis direction, the physical size information of the voxel in the Z-axis direction, and the grayscale intensity value;

[0121] The grayscale value distribution intensity of all voxels in the Z-axis direction is summed to obtain the grayscale value distribution intensity of the voxels in the Z-axis direction, so as to calculate the ratio of the grayscale value distribution value to the overall grayscale intensity distribution value, and the ratio is used as the central coordinate of the brain tissue in the Z-axis direction.

[0122] To this end, by calculating the difference between the grayscale value of each voxel and the grayscale extreme value of the brain tissue, and comparing it with the second difference (the difference between the grayscale extreme value of the second voxel and the grayscale extreme value of the brain tissue), taking the minimum value for non-negative truncation processing, the grayscale intensity value of the voxel can be obtained. This method takes into account the distribution range of the grayscale value of the brain tissue, making the calculation of the grayscale intensity more accurate. Not only the overall grayscale intensity distribution value is calculated, but also the grayscale value distribution intensity of the voxels in the X-axis, Y-axis and Z-axis directions are calculated respectively. Through this comprehensive analysis, the central coordinates of the brain tissue in three-dimensional space can be determined more accurately. By calculating the ratio of the grayscale value distribution intensity in each axis direction to the overall grayscale intensity distribution value, the central coordinates of the brain tissue in each axis direction can be determined. This method avoids deviations in a single direction and improves the accuracy of determining the central coordinates.

[0123] Optionally, when the control host calculates the radius of the sphere when the central coordinate is the center of the sphere according to the total number of voxels on the object to be processed whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel, the control host comprises the following steps:

[0124] Calculating the product of the total number of voxels on the object to be processed whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel to obtain the total volume of the voxels;

[0125] The total volume of the voxels is adjusted based on the set spherical shape factor and then a cube root calculation is performed to obtain a cube root as the radius of the sphere.

[0126] To this end, the accurate total volume of voxels can be obtained by calculating the product of the total number of voxels with grayscale values ​​greater than the grayscale extreme value of brain tissue on the object to be processed and the physical size of a single voxel. This method takes into account the volume characteristics of brain tissue in actual image data, making the calculation of the total volume of voxels more accurate. A set spherical shape factor is introduced to adjust the total volume of voxels. This is because the actual shape of brain tissue may not be a perfect sphere. By adjusting the shape factor, it can be closer to the shape of the actual brain tissue, thereby improving the accuracy of the calculation of the sphere radius. By taking the cube root of the adjusted total voxel volume, the sphere radius can be directly obtained. This method is concise and clear, and the calculation results are accurate and reliable.

[0127] Optionally, the basic parameters of the brain tissue include the center coordinates of the brain tissue and the radius of the brain tissue. When the control host constructs the initial model of the brain tissue based on the basic parameters of the brain tissue, the control host includes the following steps:

[0128] Obtain multiple constructed faces and divide each face into multiple triangles;

[0129] For each triangle, the distance from each vertex to the central coordinate is adjusted to be close to half of the radius of the brain tissue, so that the area composed of the adjusted multiple faces is used as the initial model of the brain tissue.

[0130] To this end, by adjusting the distance from the vertex to the center coordinate of each triangle to be close to half the radius of the brain tissue, the shape and size of the initial model can be ensured to be closer to the actual brain tissue. This method takes into account the spatial position and volume characteristics of the brain tissue and improves the accuracy of the initial model. Using multiple faces and dividing them into multiple triangles can more finely represent the complex shape of the brain tissue. By combining these adjusted faces, a more accurate and realistic initial model of the brain tissue can be constructed.

[0131] Optionally, when the control host performs contour evolution on the initial model of the brain tissue to obtain the extracted brain tissue, the control host comprises the following steps:

[0132] Based on each contour point on the initial brain tissue model, constructing contour evolution tension along the X-axis, Y-axis, and Z-axis directions;

[0133] Each contour point is moved toward the edge of the sphere defined by the radius of the brain tissue so that the contour evolution tension constructed for each contour point along the X-axis, Y-axis, and Z-axis directions reaches a balance until a spherical area covering the sphere defined by the radius of the brain tissue is obtained.

[0134] To this end, a new model is constructed based on each contour point on the initial brain tissue model along the X-axis, Y-axis,

[0135] The contour evolution tension in the Z-axis direction can achieve precise control of the contour evolution process. This control method ensures that the contour evolution can proceed in the expected direction and speed, thereby improving the accuracy of brain tissue extraction. Each contour point is moved toward the edge of the sphere defined by the radius of the brain tissue until the contour evolution tension along the X-axis, Y-axis, and Z-axis directions constructed for the contour point reaches a balance, which can ensure the stability and consistency of the contour evolution process. This balance state helps to avoid deviations and distortions in the contour evolution process, thereby obtaining more accurate brain tissue extraction results.

[0136] like Figure 2 As shown, the process of performing contour evolution on the initial model of brain tissue until the extracted brain tissue is obtained. Initially, the initial model of brain tissue is a spherical area with the central coordinate as the center of the sphere and a radius of half the radius of the brain tissue. After the contour evolution is completed, the area obtained is not completely a spherical area, so as to serve as the extracted brain tissue.

[0137] Optionally, the control host is used to perform the following steps to register the standard brain tissue image and the clinical brain tissue image to be registered, so as to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located:

[0138] Based on the characteristic points of the brain tissue anatomical structure, multiple registration control points and corresponding spline basis functions are constructed;

[0139] Based on all the registration control points and the corresponding spline basis functions, an image registration model is constructed;

[0140] Based on the image registration model, the standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the hematoma puncture clinical pathway corresponding to the clinical brain tissue image to be registered to the space where the standard brain tissue image is located.

[0141] Therefore, by selecting the characteristic points of the brain tissue anatomical structure as the registration control points, it can be ensured that the registration process fully considers the morphological and structural characteristics of the brain tissue. This registration method based on characteristic points can more accurately reflect the correspondence between brain tissues, thereby improving the registration accuracy. Using spline basis functions to model the registration control points can more flexibly describe the deformation and displacement between brain tissues. The spline basis function has good locality and smoothness, and can effectively handle the complex deformation in brain tissue images, further improving the registration accuracy.

[0142] Figure 3 This is an exemplary flow chart for implementing registration in the embodiment of the present application. Figure 3As shown in FIG. 1 , the standard brain tissue image is taken as the fixed image, and the clinical brain tissue image to be registered is taken as the moving image. The registration process is that the moving image is deformed to adapt to the fixed image.

[0143] See also Figure 3 , the overall process of registration is as follows:

[0144] (1) Pyramid is a pyramid module that realizes the registration of different resolutions: first, coarse registration is performed on the low-resolution moving image, and then fine registration is performed on the full-resolution moving image to effectively improve the efficiency of registration.

[0145] (2) Sampler is a sampler. Under normal circumstances, it is unnecessary to loop through all voxels in the fixed image. Therefore, the fixed image is sampled by the sampler to obtain a subset, and the subset is aligned with the moving image to greatly improve the efficiency of the alignment.

[0146] (3) Interpolator is an interpolator. During the optimization process, numerical calculations are required at non-voxel positions of the moving image. Therefore, an interpolator is needed to perform grayscale interpolation to obtain the grayscale values ​​of non-voxel positions on the moving image.

[0147] (4) Metric is a similarity measure used to evaluate the similarity between the moving image and the fixed image. It serves as the cost function of the optimization problem in the registration process. The registration process is the process of minimizing the cost function. The transformation parameters are continuously optimized to minimize the metric between the moving image and the fixed image after transformation.

[0148] (5) Optimiser is an optimizer. In order to solve the optimization problem and obtain the optimal transformation parameters, an iterative optimization strategy is usually adopted. According to the optimizer calculation, it is continuously iterated until the metric reaches the minimum value or meets other stopping conditions.

[0149] (6) Transform refers to the registration transformation type. The transformation type adopted by the present invention is the above-mentioned image registration model. To this end, in the above-mentioned optimizer, the control points are continuously optimized.

[0150] The detailed working process of the above-mentioned pyramid, sampler, interpolator, metric, and optimiser is the prior art and will not be described in detail in the embodiments of this application. Figure 4An electronic device is provided in an embodiment of the present application. Figure 4 As shown, the electronic device includes: one or more processors 401; and a memory 402 for storing programs; wherein the program includes instructions, and when the instructions are executed by the processor, the processor executes the following steps: matching the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and controlling the robotic arm to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

[0151] Figure 5 This is a flow chart of a method for planning a hematoma puncture path in an embodiment of the present application. Figure 5 As shown, it includes:

[0152] S501, obtaining a brain CT image to be planned;

[0153] S502, matching the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path.

[0154] Optionally, in the planning method of the hematoma puncture planning path, the intracranial hematoma puncture path database includes clinical entry points and clinical targets corresponding to the hematoma puncture clinical path; the step of matching the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path includes:

[0155] Extracting a hematoma region from the brain CT image to be planned, and calculating the center position of the hematoma region, so as to use the center position as a target point for path planning;

[0156] Transforming the target of the path planning into the constructed brain CT image standard space to match the clinical target of all hematoma puncture clinical pathways in the intracranial hematoma puncture path database;

[0157] The clinical entry point of the hematoma puncture clinical pathway where the matched clinical target is located is determined, and the clinical entry point is transformed to the space where the brain CT image to be planned is located, so as to obtain the entry point of the pathway planning.

[0158] Optionally, in the planning method of the hematoma puncture planning path, the step of extracting the hematoma area from the brain CT image to be planned includes:

[0159] Preprocessing the brain CT image samples to obtain preprocessed brain CT image samples;

[0160] Performing enhancement processing on the preprocessed brain CT image samples to obtain a plurality of enhanced brain CT image samples for training the constructed neural network model to obtain a hematoma extraction model;

[0161] Based on the hematoma extraction model, the hematoma area is predicted for each CT image to be planned, and the predicted hematoma area is obtained;

[0162] A plurality of predicted hematomas extracted from a plurality of enhanced brain CT images are fused to obtain the extracted hematoma region.

[0163] Optionally, in the planning method of the hematoma puncture planning path, the preprocessed brain CT image samples are enhanced to obtain multiple enhanced brain CT image samples to train the constructed neural network model to obtain a hematoma extraction model; including: flipping the preprocessed brain CT image samples along the X, Y, and Z axes of the voxel space to obtain a total of 8 enhanced brain CT image samples.

[0164] Optionally, in the planning method of the hematoma puncture planning path, the center position (Z 0 , Y 0 , Z 0 ):

[0165]

[0166] Where i, j, k are the coordinates of the voxels in the hematoma area along the X, Y, and Z axes, M i,j,k is the gray value of the voxel at coordinate (i, j, k).

[0167] Optionally, in the planning method of the hematoma puncture planning path, based on the following formula, the target point of the path planning is transformed into the physical space coordinates (Z c , Y c , Z c );

[0168]

[0169] in, is the direction matrix of the brain CT image to be planned, represents the spatial matrix of the brain CT image to be planned, represents the center coordinate vector of the hematoma area, Represents the origin coordinate vector of the brain CT image to be planned.

[0170] Optionally, in the planning method of the hematoma puncture planning path, the target point of the path planning is transformed into the constructed brain CT image standard space, and matched with the clinical targets of all hematoma puncture clinical pathways in the intracranial hematoma puncture path database based on the following formula:

[0171]

[0172] Among them, (Z db ,Y db ,Z db ) represents the coordinates of the clinical target in the standard space of the brain CT image, (X c ,Y c ,Z b ) represents the coordinates of the target point of path planning in the standard space of the brain CT image, D ec It represents the distance between the target point of path planning and the clinical target point in the standard space of the brain CT image.

[0173] Optionally, in the planning method of the hematoma puncture planning path, the transforming of the target point of the path planning into the constructed brain CT image standard space includes:

[0174] The standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located.

[0175] Optionally, in the planning method for hematoma puncture planning path, generating the standard brain tissue image and the clinical brain tissue image to be registered includes:

[0176] Acquire a standard brain CT image and use the image space where the image is located as the standard brain CT image space;

[0177] Performing brain tissue extraction on the standard brain CT image to obtain a standard brain tissue image;

[0178] A clinical brain CT image to be registered is acquired, and brain tissue is extracted from the clinical brain CT image to be registered to obtain a clinical brain tissue image to be registered.

[0179] Optionally, in the planning method for hematoma puncture planning path, generating the standard brain tissue image and the clinical brain tissue image to be registered includes:

[0180] Acquire and construct a clinical brain CT image library, wherein the clinical brain CT image library includes a plurality of clinical brain CT images;

[0181] A clinical brain CT image is selected from the clinical brain CT image library as a standard brain CT image, and a space where the standard brain CT image is located is used as the brain CT image standard space;

[0182] Using the remaining clinical brain CT images in the clinical brain CT image library as clinical brain CT images to be registered;

[0183] Extracting brain tissue from the standard brain CT image to obtain a standard brain tissue image;

[0184] Brain tissue is extracted from the clinical brain CT image to be registered to obtain a clinical brain tissue image to be registered.

[0185] Optionally, in the planning method of the hematoma puncture planning path, the construction of the intracranial hematoma puncture path database includes: constructing the intracranial hematoma puncture path database based on the clinical entry points and clinical target points of the hematoma puncture clinical path corresponding to the clinical brain tissue image to be registered and the standard brain CT image in the standard brain CT image space.

[0186] Optionally, in the planning method of the hematoma puncture planning path, the standard brain CT image or the clinical brain CT image to be registered is used as the object to be processed, and the following steps are performed to extract brain tissue:

[0187] Determine the grayscale extremes of brain tissue;

[0188] Based on the brain tissue grayscale extreme value, brain tissue is extracted from the object to be processed.

[0189] Optionally, the control host is further configured to perform the following steps to determine the grayscale extreme value of brain tissue:

[0190] Converting the object to be processed into a voxel grayscale histogram of a brain image;

[0191] Based on the grayscale histogram, determining a first voxel grayscale extreme value and a second voxel grayscale extreme value, wherein a sum of the second sorting ratio and the first sorting ratio is equal to 1;

[0192] Based on the first voxel grayscale extreme value and the second voxel grayscale extreme value, a brain tissue grayscale extreme value is determined.

[0193] Optionally, determining the first voxel grayscale extreme value and the second voxel grayscale extreme value based on the grayscale histogram includes:

[0194] Based on the grayscale histogram, sorting the voxel grayscale values ​​of the brain image to obtain a voxel grayscale value sequence;

[0195] Based on a set first sorting ratio, the voxel grayscale value sequence is intercepted to obtain a first voxel grayscale extreme value;

[0196] The voxel grayscale value sequence is intercepted based on the set second sorting ratio to obtain a second voxel grayscale extreme value.

[0197] Optionally, in the planning method of the hematoma puncture planning path, determining the brain tissue grayscale extreme value based on the first voxel grayscale extreme value and the second voxel grayscale extreme value includes: fusing the first voxel grayscale extreme value and the second voxel grayscale extreme value to obtain the brain tissue grayscale extreme value.

[0198] Optionally, in the planning method of the hematoma puncture planning path, the brain tissue extraction of the object to be processed based on the brain tissue grayscale extreme value includes:

[0199] Determining basic parameters of the brain tissue based on the grayscale extreme value of the brain tissue;

[0200] Based on the basic parameters of the brain tissue, construct an initial model of the brain tissue;

[0201] The initial brain tissue model is subjected to contour evolution to obtain extracted brain tissue.

[0202] Optionally, in the planning method for hematoma puncture planning path, determining basic parameters of brain tissue based on the grayscale extreme value of brain tissue includes:

[0203] Traversing each voxel on the object to be processed, and based on the grayscale extreme value of the brain tissue, respectively counting the grayscale value distribution intensity of the voxel in the X-axis, Y-axis and Z-axis directions to determine the center coordinates of the brain tissue;

[0204] According to the total number of voxels on the object to be processed whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel, the radius of the sphere when the central coordinate is the center of the sphere is calculated as the radius of the brain tissue.

[0205] Optionally, in the planning method of the hematoma puncture planning path, traversing each voxel on the object to be treated, and based on the grayscale extreme value of the brain tissue, respectively counting the grayscale value distribution intensity of the voxel in the X-axis, Y-axis and Z-axis directions to determine the center coordinates of the brain tissue, includes:

[0206] For each voxel on the object to be processed, the difference between its gray value and the gray extreme value of the brain tissue is calculated as a first difference, and compared with the second difference, the minimum value is taken, and the minimum value is subjected to non-negative truncation processing to be used as the gray intensity value of each voxel, wherein the second difference is the difference between the gray extreme value of the second voxel and the gray extreme value of the brain tissue;

[0207] Summing the voxel grayscale intensity values ​​to obtain the overall grayscale intensity distribution value of the voxels on the object to be processed;

[0208] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the X-axis direction is calculated according to the coordinates of each voxel in the X-axis direction, the physical size information of the voxel in the X-axis direction, and the grayscale intensity value;

[0209] The gray value distribution intensities of all voxels in the X-axis direction are summed to obtain the gray value distribution intensities of the voxels in the X-axis direction, so as to calculate the ratio of the gray value distribution intensities to the overall gray value distribution value, and the ratio is used as the center coordinate of the brain tissue in the X-axis direction;

[0210] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the Y-axis direction is calculated according to the coordinate of each voxel in the Y-axis direction, the physical size information of the voxel in the Y-axis direction, and the grayscale intensity value;

[0211] Adding the grayscale value distribution intensities of all voxels in the Y-axis direction to obtain the grayscale value distribution intensity of the voxels in the Y-axis direction, so as to calculate the ratio of the grayscale value distribution intensity to the overall grayscale intensity distribution value, and using the ratio as the center coordinate of the brain tissue in the Y-axis direction;

[0212] For each voxel on the object to be processed, the grayscale value distribution intensity of each voxel in the Z-axis direction is calculated according to the coordinates of each voxel in the Z-axis direction, the physical size information of the voxel in the Z-axis direction, and the grayscale intensity value;

[0213] The grayscale value distribution intensity of all voxels in the Z-axis direction is summed to obtain the grayscale value distribution intensity of the voxels in the Z-axis direction, so as to calculate the ratio of the grayscale value distribution value to the overall grayscale intensity distribution value, and the ratio is used as the central coordinate of the brain tissue in the Z-axis direction.

[0214] Optionally, in the planning method of the hematoma puncture planning path, the calculation of the radius of the sphere when the central coordinate is the center of the sphere according to the total number of voxels on the object to be treated whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel includes:

[0215] Calculating the product of the total number of voxels on the object to be processed whose grayscale value is greater than the grayscale extreme value of the brain tissue and the physical size of a single voxel to obtain the total volume of the voxels;

[0216] The total volume of the voxels is adjusted based on the set spherical shape factor and then a cube root calculation is performed to obtain a cube root as the radius of the sphere.

[0217] Optionally, the basic parameters of the brain tissue include the central coordinates of the brain tissue and the radius of the brain tissue, and constructing the initial model of the brain tissue based on the basic parameters of the brain tissue includes:

[0218] Obtain multiple constructed faces and divide each face into multiple triangles;

[0219] For each triangle, the distance from each vertex to the central coordinate is adjusted to be close to half of the radius of the brain tissue, so that the area composed of the adjusted multiple faces is used as the initial model of the brain tissue.

[0220] Optionally, the step of performing contour evolution on the initial model of the brain tissue to obtain the extracted brain tissue includes:

[0221] Based on each contour point on the initial brain tissue model, constructing contour evolution tension along the X-axis, Y-axis, and Z-axis directions;

[0222] Each contour point is moved toward the edge of the sphere defined by the radius of the brain tissue so that the contour evolution tension constructed for each contour point along the X-axis, Y-axis, and Z-axis directions reaches a balance until a spherical area covering the sphere defined by the radius of the brain tissue is obtained.

[0223] Optionally, registering the standard brain tissue image and the clinical brain tissue image to be registered, so as to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located, includes:

[0224] Based on the characteristic points of the brain tissue anatomical structure, multiple registration control points and corresponding spline basis functions are constructed;

[0225] Based on all the registration control points and the corresponding spline basis functions, an image registration model is constructed;

[0226] Based on the image registration model, the standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the hematoma puncture clinical pathway corresponding to the clinical brain tissue image to be registered to the space where the standard brain tissue image is located.

[0227] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A neurosurgery brain puncture surgery robot, characterized in that: include: A control host and a robotic arm, wherein the control host is used to match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and control the robotic arm to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

2. The neurosurgery brain puncture surgery robot according to claim 1, characterized in that: The intracranial hematoma puncture pathway database includes clinical entry points and clinical targets corresponding to the hematoma puncture clinical pathway; The control host is used to perform the following steps to match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path: Extracting a hematoma region from the brain CT image to be planned, and calculating the center position of the hematoma region, so as to use the center position as a target point for path planning; Transforming the target of the path planning into the constructed brain CT image standard space to match the clinical target of all hematoma puncture clinical pathways in the intracranial hematoma puncture path database; The clinical entry point of the hematoma puncture clinical pathway where the matched clinical target is located is determined, and the clinical entry point is transformed to the space where the brain CT image to be planned is located, so as to obtain the entry point of the pathway planning.

3. The neurosurgery brain puncture surgery robot according to claim 1, characterized in that: The control host is used to perform the following steps to extract the hematoma area from the brain CT image to be planned: Preprocessing the brain CT image samples to obtain preprocessed brain CT image samples; Performing enhancement processing on the preprocessed brain CT image samples to obtain a plurality of enhanced brain CT image samples for training the constructed neural network model to obtain a hematoma extraction model; Based on the hematoma extraction model, the hematoma area is predicted for each CT image to be planned, and the predicted hematoma area is obtained; A plurality of predicted hematomas extracted from a plurality of enhanced brain CT images are fused to obtain the extracted hematoma region.

4. The neurosurgery brain puncture surgery robot according to claim 3, characterized in that: The preprocessed brain CT image samples are enhanced to obtain multiple enhanced brain CT image samples to train the constructed neural network model to obtain a hematoma extraction model; including: flipping the preprocessed brain CT image samples along the X, Y, and Z axes of the voxel space to obtain a total of 8 enhanced brain CT image samples.

5. The neurosurgery brain puncture surgery robot according to claim 1, characterized in that: The center position (X0, Y0, Z0) of the hematoma area is calculated based on the following formula: Where i, j, k are the coordinates of the voxels in the hematoma area along the X, Y, and Z axes, M i,j,k is the gray value of the voxel at coordinate (i, j, k).

6. The neurosurgery brain puncture surgery robot according to claim 1, characterized in that: Based on the following formula, the target point of the path planning is transformed into the physical space coordinates (X c , Y c , Z c ); in, is the direction matrix of the brain CT image to be planned, represents the spatial matrix of the brain CT image to be planned, represents the center coordinate vector of the hematoma area, Represents the origin coordinate vector of the brain CT image to be planned.

7. The neurosurgery brain puncture surgery robot according to claim 1, characterized in that: The control host is also used to perform the following steps to transform the target point of the path planning into the constructed brain CT image standard space: The standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located.

8. The neurosurgery brain puncture surgery robot according to claim 7, characterized in that: The control host is also used to perform the following steps to generate the standard brain tissue image and the clinical brain tissue image to be registered: Acquire a standard brain CT image and use the image space where the image is located as the standard brain CT image space; Performing brain tissue extraction on the standard brain CT image to obtain a standard brain tissue image; A clinical brain CT image to be registered is acquired, and brain tissue is extracted from the clinical brain CT image to be registered to obtain a clinical brain tissue image to be registered.

9. The neurosurgery brain puncture surgery robot according to claim 7, characterized in that: The control host is used to perform the following steps to register the standard brain tissue image and the clinical brain tissue image to be registered, so as to transform the clinical entry point and clinical target point of the clinical brain tissue image to be registered corresponding to the hematoma puncture clinical pathway to the space where the standard brain tissue image is located: Based on the characteristic points of the brain tissue anatomical structure, multiple registration control points and corresponding spline basis functions are constructed; Based on all the registration control points and the corresponding spline basis functions, an image registration model is constructed; Based on the image registration model, the standard brain tissue image and the clinical brain tissue image to be registered are registered to transform the clinical entry point and clinical target point of the hematoma puncture clinical pathway corresponding to the clinical brain tissue image to be registered to the space where the standard brain tissue image is located.

10. An electronic device, characterized in that: include: one or more processors; as well as A memory for storing programs; Among them, the program includes instructions, which, when executed by the processor, enable the processor to perform the following steps: match the brain CT image to be planned with the constructed intracranial hematoma puncture path database to determine the hematoma puncture planning path, and control the robotic arm to drive the surgical instrument to the hematoma position in the brain according to the hematoma puncture planning path.

Images