Methods and related products for training microcatheter delivery models

Abstract

This disclosure discloses a method and related products for training a microcatheter delivery model. The method includes: constructing a physical simulation interventional surgery environment and an in vitro simulation interventional surgery environment; in the physical simulation interventional surgery environment, using a first image and a first force feedback as training data, using the microcatheter delivery model as a policy network of a reinforcement learning agent, and training it using a deep reinforcement learning algorithm to obtain a trained microcatheter delivery model; deploying the trained microcatheter delivery model in an in vitro simulation interventional surgery environment, and fine-tuning the trained microcatheter delivery model based on a second image, a second force feedback, and expected results. Utilizing the disclosed solution, the microcatheter can be more accurately delivered to the lesion location during actual surgery, and the success rate of catheter navigation in complex cerebral vascular pathways can be improved.

Description

Technical Field

[0001] The present disclosure generally relates to the field of computer vision technology. More specifically, the present disclosure relates to a method, electronic device, and computer-readable storage medium for training a microcatheter delivery model. Furthermore, the present disclosure also relates to a method, electronic device, and computer-readable storage medium for microcatheter delivery. Background Art

[0002] In the field of neurointervention, microcatheters are key devices for treating cerebrovascular diseases such as aneurysms, arteriovenous malformations, vascular stenosis, and acute stroke. However, due to the large curvature, numerous branches, and small diameter of cerebral blood vessels, microcatheters face numerous limitations during delivery. For example, when accessing branches such as the middle cerebral artery or posterior communicating artery, microcatheters require high flexibility and precise operation. Currently, delivering microcatheters to the lesion site not only consumes a significant amount of time, but also exposes doctors to radiation from digital subtraction angiography (DSA), which can cause irreversible damage to the doctor's health.

[0003] Although existing interventional robots can deliver microcatheters through remote control, there are still some problems. First, interventional robots require doctors to operate outside the operating room. Since doctors cannot directly touch the microcatheter and the robot lacks fine force feedback, doctors cannot sense the tension generated when the microcatheter moves, making it difficult to accurately control the force of microcatheter delivery. This not only prolongs the operation time, but may also expose the patient to more radiation. Secondly, inaccurate control may also lead to unexpected situations during the operation. For example, excessive force may cause vascular spasm in the patient, consumables to fall off, etc., endangering the patient's life safety.

[0004] In view of this, there is an urgent need to provide a solution for training microcatheter delivery models in order to achieve precise control of microcatheter delivery, improve the degree of automation of interventional robots in surgical applications, increase the success rate of catheter navigation in complex cerebral vascular pathways, reduce the errors and complication risks of human operation, and improve the accuracy of microcatheter positioning and treatment material delivery. Summary of the Invention

[0005] In order to at least solve one or more of the above-mentioned technical problems, the present disclosure proposes a solution for training a microcatheter delivery model in the following aspects.

[0006] In a first aspect, the present disclosure provides a method for training a microcatheter delivery model, comprising: constructing a physical simulation interventional surgery environment, the physical simulation interventional surgery environment comprising a blood vessel simulation model, a microcatheter simulation model and a mechanical model, wherein the mechanical model is used to perform mechanical control simulation on the microcatheter simulation model so that the microcatheter simulation model moves in the blood vessel simulation model and obtains a first force feedback in real time, and the first image in motion is simulated in real time using computer vision technology; constructing an in vitro simulation interventional surgery environment, the in vitro simulation interventional surgery environment comprising a blood vessel entity model, a surgical microcatheter, a camera, a control module and a mechanical module, wherein a force sensor is provided at the delivery end of the surgical microcatheter, and the force sensor is provided at the delivery end of the surgical microcatheter. The device is used to obtain a second force feedback in real time, the control module and the mechanical module are used to operate the surgical microcatheter so that the surgical microcatheter moves in the vascular entity model, and the camera is used to collect a second image in real time during movement; in the physical simulation interventional surgery environment, the first image and the first force feedback are used as training data, the microcatheter delivery model is used as a policy network of a reinforcement learning agent, and a deep reinforcement learning algorithm is used to train it to obtain a trained microcatheter delivery model; and the trained microcatheter delivery model is deployed in the in vitro simulated interventional surgery environment, and the trained microcatheter delivery model is fine-tuned based on the second image, the second force feedback and the expected result.

[0007] In some embodiments, the microcatheter delivery model is used as a policy network of a reinforcement learning agent and trained using a deep reinforcement learning algorithm, including: extracting visual features from the first image to obtain visual features contained in the first image, wherein the visual features include direction and position information of the microcatheter, vascular path, and geometric information and position information of the lesion target; determining a reward value using a reward function based on the visual features and the first force feedback; and optimizing parameters of the policy network according to the reward value.

[0008] In some embodiments, the reward function is used to reward a state of being close to the lesion target and being advanced safely, and to punish a state of being deviated from the lesion target and being advanced dangerously, and based on the visual feature and the first force feedback, determining the reward value using the reward function includes: determining the position state of the microcatheter and the lesion target based on the visual feature; determining the advancement state of the microcatheter based on the first force feedback; and determining the reward value based on the position state and the advancement state.

[0009] In some embodiments, determining the propulsion state of the microcatheter based on the first force feedback includes: determining whether the first force feedback is greater than or equal to a preset threshold; when the first force feedback is greater than or equal to the preset threshold, determining that the propulsion state of the microcatheter is dangerous propulsion; and when the first force feedback is less than the preset threshold, determining that the propulsion state of the microcatheter is safe propulsion.

[0010] In some embodiments, the microcatheter delivery model includes an input layer and an output layer; the input layer receives the first image and the first force feedback, and performs feature extraction on the first image and the first force feedback to obtain image features; the output layer generates an operation plan for the microcatheter simulation model based on the image features, and the operation plan includes an operation action and an operation force, and the operation action includes any one of forward, backward, twisting or stillness.

[0011] In some embodiments, the input layer includes a visual feature extraction module, a force feedback feature extraction module and a feature fusion module; the visual feature extraction module uses a convolutional neural network to extract visual features of the first image to obtain a feature map of the first image; the force feedback feature extraction module uses a fully connected network to extract geometric features of the first force feedback and the first image to obtain a feature vector; and the feature fusion module performs feature fusion on the feature map and the feature vector to obtain a feature vector in a unified state as the image feature.

[0012] In some embodiments, fine-tuning the trained microcatheter delivery model based on the second image, the second force feedback and the expected result includes: inputting the second image and the second force feedback into the trained microcatheter delivery model to generate an operation plan to obtain an operation plan for the surgical microcatheter; determining the difference between the operation plan and the expected result; and fine-tuning the parameters of the trained microcatheter delivery model according to the difference.

[0013] In a second aspect, the present disclosure provides a method for delivering a microcatheter, comprising: obtaining a vascular image and force feedback to be processed in a real interventional surgical environment; and inputting the vascular image and force feedback to be processed into a microcatheter delivery model trained by the method described in the first aspect and its multiple embodiments to generate an operation plan, so as to output an operation plan for the microcatheter in the real interventional surgical environment.

[0014] In a third aspect, the present disclosure provides an electronic device comprising: a processor; and a memory storing program instructions for training a microcatheter delivery model and / or for delivering a microcatheter, wherein when the program instructions are executed by the processor, the method described in the first aspect and its multiple embodiments and / or the method described in the second aspect are implemented.

[0015] In a fourth aspect, the present disclosure provides a computer-readable storage medium having stored thereon program instructions for training a microcatheter delivery model and / or for delivering a microcatheter, wherein when the program instructions are executed by a processor, the method described in the aforementioned first aspect and its multiple embodiments and / or the method described in the aforementioned second aspect and its multiple embodiments are implemented.

[0016] The solution for training the microcatheter delivery model, as provided above, constructs a physical simulation interventional surgical environment and an in vitro simulated interventional surgical environment, and combines it with a deep reinforcement learning algorithm to train and fine-tune the microcatheter delivery model. This allows the microcatheter delivery model to learn during training how to perform precise forward, backward, and rotational operations based on the microcatheter's movement within the blood vessels and force feedback information. This allows the microcatheter to be more accurately delivered to the lesion during actual surgery, improving the success rate of catheter navigation in complex cerebral vascular pathways and reducing the risk of errors and complications from human manipulation. Furthermore, by combining deep learning technology with surgical robots, more intelligent and automated surgical operations can be achieved, improving surgical precision and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the detailed description below with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0018] Figure 1 An exemplary flow chart of a method for training a microcatheter delivery model according to an embodiment of the present disclosure is shown;

[0019] Figure 2 An exemplary framework diagram of a system for training a microcatheter delivery model using a deep reinforcement learning algorithm according to an embodiment of the present disclosure is shown;

[0020] Figure 3 An exemplary flowchart of a process for training a microcatheter delivery model using a deep reinforcement learning algorithm according to an embodiment of the present disclosure is shown;

[0021] Figure 4An exemplary flow chart showing a method for delivering a microcatheter according to an embodiment of the present disclosure is shown;

[0022] Figure 5 An exemplary structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of this disclosure in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this disclosure, not all of them. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this disclosure.

[0024] It should be understood that the terms “include” and “comprising” used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0025] It should also be understood that the terminology used in this disclosure is for the purpose of describing specific embodiments only and is not intended to limit the disclosure. As used in this disclosure and the claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should be further understood that the term "and / or" as used in this disclosure and the claims refers to any and all possible combinations of one or more of the associated listed items, including and including these combinations.

[0026] As used in this specification and claims, the term “if” can be interpreted as “when” or “upon” or “in response to determining” or “in response to detecting,” depending on the context. Similarly, the phrase “if it is determined” or “if [described condition or event] is detected” can be interpreted as meaning “upon determination” or “in response to determining” or “upon detection of [described condition or event]” or “in response to detecting [described condition or event],” depending on the context.

[0027] The specific embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.

[0028] While developing the solution disclosed herein, the inventors discovered that directly training a microcatheter delivery model in a real interventional surgical setting is too costly. To reduce training costs, the microcatheter input model can be first trained in a physical simulated interventional surgical setting, then fine-tuned in an in vitro simulated interventional surgical setting. Once the microcatheter input model is verified and stable, it can be used in real interventional procedures.

[0029] Figure 1 An exemplary flow chart of a method 100 for training a microcatheter delivery model according to an embodiment of the present disclosure is shown. It is understood that the method 100 can be executed by any appropriate device with data processing capabilities, including but not limited to a terminal device, a processor, and a server.

[0030] Based on this, Figure 1 As shown, at step S101, method 100 can construct a physical simulation interventional surgery environment and an in vitro simulation interventional surgery environment. Specifically, the physical simulation interventional surgery environment can include a vascular simulation model (also referred to as a vascular anatomical model), a microcatheter simulation model, and a mechanical model, wherein the mechanical model is used to perform mechanical control simulation on the microcatheter simulation model so that the microcatheter simulation model moves in the vascular simulation model and obtains force feedback (for the sake of distinction, this can be referred to as first force feedback) of the microcatheter simulation model in real time. In the physical simulation interventional surgery environment, a virtual vascular image (for the sake of distinction, this can be referred to as a first image) in motion (i.e., the microcatheter simulation model moves in the vascular simulation model) can be simulated in real time using computer vision technology.

[0031] Here, computer vision technology is used to obtain a virtual vascular image in motion, which can be achieved through the following operations: for the three-dimensional virtual scene of the microcatheter simulation model moving in the vascular simulation model, different projection angles are selected for projection to generate a DSA two-dimensional silhouette image similar to that during the operation. Specifically, the projection can be to project the vascular simulation model in white and the microcatheter simulation model in black.

[0032] The aforementioned vascular simulation model is a 3D anatomical model used to simulate real cerebral blood vessels. This model can be obtained by performing 3D reconstruction and aneurysm segmentation using 3D tomographic images such as computed tomography (CT), magnetic resonance imaging (MRI), and computed tomography angiography (CTA). Aneurysm segmentation can obtain geometric and positional information about the target lesion.

[0033] The geometric information of the lesion target may include the morphological characteristics of the lesion, the diameter of the blood vessels at the lesion, and the degree of stenosis. Here, the morphological characteristics of the lesion may include the size, shape, and volume of the lesion. The position information of the lesion target may include one or more of spatial positioning, vascular branch positioning, and vascular path tracking. Spatial positioning refers to the specific position of the lesion target in three-dimensional space, and the specific position can be represented by a coordinate system; vascular branch positioning refers to the vascular branch where the lesion is located; vascular path tracking refers to the path and distribution of the lesion along the blood vessel. The path of the lesion along the blood vessel refers to the specific direction and position of the lesion in the vascular network, which includes the starting point of the lesion, the vascular branches passed through, and the end point. The distribution of the lesion refers to the distribution characteristics of the lesion in the vascular network, including the length, width, density, and curvature of the lesion.

[0034] The aforementioned microcatheter simulation model can be built using discrete elastic rod technology to simulate the bending, torsional, and tensile forces of the microcatheter. Discrete elastic rod technology is a physical model used to simulate deformable linear objects. This technology discretizes a continuous elastic rod into a series of nodes and rod elements, with the nodes connected by rod elements. During the simulation, elastic forces and constraints are introduced between these rod elements to simulate the deformation and movement of the object.

[0035] Specifically, by setting the bending, torsional, and tensile stiffness between the rod elements, the deformation behavior of the microcatheter during bending, twisting, and stretching can be simulated, respectively. Subsequently, during the simulation, by setting the fixed and free ends of the microcatheter and applying bending forces, the deformation of the microcatheter can be observed; applying torque at one end of the microcatheter can observe the torsion angle and stress distribution of the microcatheter; and applying tension at both ends of the microcatheter can observe the elongation and stress distribution of the microcatheter.

[0036] In the simulation, each node can be considered a rack, and the number of racks depends on the degree of discretization. A greater number of racks improves simulation accuracy, but also increases computational complexity. When an elastic rod is subjected to external forces (such as wall pressure, thrust, friction, and blood flow impact), these forces act on the rod's nodes and are transmitted through the nodes to adjacent nodes, gradually building tension within the rod. The aforementioned mechanical model can be used to calculate the tension generated by the microcatheter due to external forces (such as wall pressure, thrust, friction, and blood flow impact between the rod and the vessel wall during delivery). This allows the force returned to the microcatheter delivery end to be derived, known as the first force feedback. In practical applications, this first force feedback is used to assess the safety of microcatheter advancement.

[0037] The aforementioned in vitro simulated interventional surgical environment may include a physical model of a blood vessel, a surgical microcatheter, a camera, a control module, and a mechanical module. The delivery end of the surgical microcatheter is provided with a force sensor for obtaining real-time force feedback (for the sake of distinction, this may be referred to as secondary force feedback). The control module and mechanical module are used to operate the surgical microcatheter to cause it to move within the physical model of the blood vessel. The camera is used to capture real-time images of the blood vessel during movement (i.e., the surgical microcatheter moving within the physical model of the blood vessel) (for the sake of distinction, this may be referred to as secondary images).

[0038] In the disclosed embodiment, the vascular entity model can use a vascular model made of materials such as silicone, gelatin or agar gel, and can also be added with a simulated blood circulation system. The surgical microcatheter is a microcatheter used for real surgery. When the control module and the mechanical module are used to operate the surgical microcatheter, the control module is specifically used to convert the operation scheme output by the microcatheter delivery model into a signal that can control the movement of the mechanical module, so as to control the operation of the mechanical module. The mechanical module is specifically used to mechanically control the delivery end of the surgical microcatheter, including forward, backward and rotation. In actual operation, two sets of roller units with opposite directions can be used. By driving the roller units to rotate under the drive of the motor, the friction force is used to push the guide wire forward or withdraw backward, so that the surgical microcatheter moves forward or backward, and the two sets of roller units are moved up and down to rotate the surgical microcatheter.

[0039] Then, at step S102, method 100 can use the aforementioned first image and first force feedback as training data in a physical simulation interventional surgery environment, use the microcatheter delivery model as a policy network of a reinforcement learning agent, and train it using a deep reinforcement learning algorithm to obtain a trained microcatheter delivery model.

[0040] In the disclosed embodiments, the microcatheter delivery model may include, but is not limited to, an input layer and an output layer. The input layer may receive the aforementioned first image and first force feedback and perform feature extraction on the first image and first force feedback to obtain image features. The output layer generates an operation plan for the microcatheter simulation model based on the image features. The operation plan may include an operation action and an operation force. The operation action may include any of forward, backward, twisting, or static movement. The operation force may include strength and direction.

[0041] In the disclosed embodiment, the input layer may include a visual feature extraction module, a force feedback feature extraction module, and a feature fusion module. The visual feature extraction module uses a convolutional neural network to extract visual features from the first image to obtain a feature map of the first image. These feature maps can be regarded as abstract representations of the first image at different levels and scales, and the pixel value in each feature map represents the feature response intensity of the corresponding position. The multi-layer structure of the convolutional neural network makes the feature extraction hierarchical. The low-level convolution layer extracts low-level features of the first image, such as edges, textures, etc.; the high-level convolution layer extracts higher-level features, such as the global morphology of the blood vessels, the overall posture of the microcatheter, etc.

[0042] The force feedback feature extraction module uses a fully connected network to extract geometric features from the first force feedback and the first image to obtain a fixed-length feature vector. This feature vector contains information related to the geometric features in the first force feedback and the first image, such as the magnitude, direction, and location of the point of application of the force. The force feedback feature extraction module not only considers the first force feedback itself but also incorporates the geometric information in the first image, making the extracted geometric features more comprehensive and able to better reflect the force conditions and geometric state of the microcatheter inside the blood vessel. The feature fusion module performs feature fusion on the feature map and feature vector to obtain a unified feature vector as the image feature.

[0043] The reinforcement learning agent described above is an AI agent that learns optimal behavior policies by interacting with its environment. It receives observations from the environment, takes actions, receives rewards, and updates its policy to maximize cumulative rewards. The workflow of a reinforcement learning agent typically includes the following steps: observation, action, reward, and learning updates.

[0044] Specifically, observation refers to the agent observing the state of the current environment and obtaining the necessary information to make decisions. These observations can be a combination of multiple data types. Action refers to the agent selecting an action based on its current strategy based on the observed state. This action aims to maximize the expected reward. Reward refers to the feedback provided by the environment based on the agent's actions, usually in the form of rewards. The reward quantifies the degree of success of the agent's actions in achieving the goal. Learning update refers to the agent using reward information to update its strategy to improve future actions. The goal is to maximize the cumulative reward based on past results. To make it easier to understand how, we will later combine Figure 2 and Figure 3 The system and process of training the microcatheter delivery model using deep reinforcement learning algorithm are described in detail respectively.

[0045] Next, at step S103 , the method 100 may deploy the trained microcatheter delivery model in an in vitro simulated interventional surgery environment, and fine-tune the trained microcatheter delivery model based on the second image, the second force feedback, and the expected result.

[0046] Specifically, the second image and second force feedback can be input into the trained microcatheter delivery model to generate an operation plan for the surgical microcatheter. Next, the difference between the operation plan and the expected outcome can be determined. In practical applications, the expected outcome can be an operation plan for the surgical microcatheter developed by a senior neurointerventionalist based on the second image and second force feedback. Thus, the difference between the operation plan generated by the microcatheter delivery model and the expected outcome can include differences in the operating motion and the operating force (strength and direction). Finally, based on these differences, an optimization algorithm (such as gradient descent) can be used to fine-tune the parameters of the trained microcatheter delivery model to ensure the model's output is as accurate as possible and minimize the loss function. This results in a more universal, safe, and stable microcatheter delivery model for real-world use.

[0047] Combination of the above Figure 1 The paper describes a method for training a microcatheter delivery model. By constructing a physical simulation interventional surgical environment and an in vitro simulated interventional surgical environment, combined with a deep reinforcement learning algorithm, the microcatheter delivery model is trained and fine-tuned. This allows the microcatheter delivery model to learn how to precisely move forward, backward, and rotate based on the microcatheter's movement within the blood vessels and force feedback information. This allows the microcatheter to be more accurately delivered to the lesion during actual surgery, improving the success rate of microcatheter navigation in complex cerebral vascular pathways and reducing the risk of errors and complications from manual manipulation. Furthermore, by combining deep learning technology with surgical robots, more intelligent and automated surgical operations can be achieved, improving surgical precision and safety.

[0048] Figure 2 FIG. 2 shows an exemplary framework diagram of a system 200 for training a microcatheter delivery model using a deep reinforcement learning algorithm according to an embodiment of the present disclosure. Figure 2 As shown, system 200 may include a reinforcement learning environment 201 , an observation module 202 , a reward module 203 , an agent module 204 , a control module 205 , and a mechanical module 206 .

[0049] The aforementioned reinforcement learning environment 201 may include a blood vessel (such as the aforementioned blood vessel simulation model) and a microcatheter (such as the aforementioned microcatheter simulation model). The observation module 202 may include a visual feedback collection module 2021 and a force feedback collection module 2022. The visual feedback collection module 2021 is used to collect real-time intraoperative imaging information (such as digital subtraction angiography). Subsequent analysis of this imaging information can extract information about the direction and position of the microcatheter, the vascular path, and the geometry and position of the lesion target. This information can be used to assess the microcatheter's current position, posture, and positional status relative to the lesion target. The force feedback collection module 2022 is used to collect the force reflected back to the microcatheter delivery end due to wall pressure, thrust, friction, and blood flow impact. This force is used to assess the safety of microcatheter advancement.

[0050] The aforementioned reward module 203 can calculate a reward value using information returned by the visual feedback collection module 2021 and the force feedback collection module 2022. Typically, the reward value increases when the microcatheter reaches the target area and approaches the lesion target, while the reward value decreases when the microcatheter does not reach the target area and moves away from the lesion target. Furthermore, the reward module 203 penalizes unsafe operation by decreasing the reward value if the microcatheter contacts the vessel wall or if the pressure on the vessel wall exceeds a preset threshold.

[0051] The aforementioned agent module 204 utilizes a deep reinforcement learning model, employing a policy network and a value network to implement control decisions. The model inputs are visual features (i.e., real-time imaging information collected by the visual feedback collection module 2021) and mechanical signals (forces collected by the force feedback collection module 2022). The model outputs an operation plan for the microcatheter. Furthermore, the control module 205 is responsible for converting the operation plan output by the model into a signal capable of controlling the movement of the mechanical module. The mechanical module 206 is responsible for mechanically controlling the delivery end of the microcatheter, including forward, backward, and rotational movements.

[0052] Figure 3 An exemplary flow chart of the process 300 of training a microcatheter delivery model using a deep reinforcement learning algorithm according to an embodiment of the present disclosure is shown. Figure 3 The description is a specific implementation of the above step S102. Figure 1 The features described can apply analogously here.

[0053] like Figure 3As shown, at step S301, visual features can be extracted from the first image to obtain visual features contained in the first image. These visual features may include, but are not limited to, the direction and position information of the microcatheter, the vascular path, and the geometric and positional information of the lesion target. Next, at step S302, a reward function can be used to determine a reward value based on the visual features and the first force feedback. Finally, at step S303, the parameters of the policy network can be optimized based on the reward value.

[0054] To improve the efficiency and safety of microcatheter delivery, in the disclosed embodiments, a reward function is used to reward states that approach the target lesion and advance safely, and to penalize states that deviate from the target lesion and advance dangerously. In other words, the reward function can determine a positive reward value for states that approach the target lesion and advance safely, and a negative reward value for states that deviate from the target lesion and advance dangerously.

[0055] In practice, at step S302, the following operations can be performed to determine a reward value using a reward function based on the visual features and the first force feedback: determining the positional state of the microcatheter relative to the lesion target based on the visual features; determining the advancement state of the microcatheter based on the first force feedback; and determining the reward value based on the positional state and advancement state. Here, the positional state can include an approaching state and a deviating state, and the recommended state can include a safe advancement state and a risky advancement state.

[0056] In the disclosed embodiment, the position information of the microcatheter may include one or more of spatial positioning and vascular branch positioning. Spatial positioning refers to the specific position of the microcatheter in three-dimensional space, and the specific position can be represented by a coordinate system; vascular branch positioning refers to the vascular branch where the microcatheter is located. Thus, when determining the positional status of the microcatheter and the lesion target based on visual features, it can be determined according to the direction and position information of the microcatheter and the position information of the lesion target. Specifically, when it is determined that the microcatheter is moving in a direction away from the lesion target according to the direction and position information of the microcatheter and the position information of the lesion target, the positional status of the microcatheter and the lesion target can be determined to be a deviation state; when it is determined that the microcatheter is moving in a direction close to the lesion target according to the direction and position information of the microcatheter and the position information of the lesion target, the positional status of the microcatheter and the lesion target can be determined to be a close state.

[0057] In the embodiment disclosed herein, the propulsion state of the microcatheter is determined based on the first force feedback, and specifically the following operations can be performed: determine whether the microcatheter contacts the blood vessel wall. If the microcatheter does not contact the blood vessel wall, the propulsion state of the microcatheter can be determined to be safe propulsion. Conversely, if the microcatheter contacts the blood vessel wall, it can be determined first whether the first force feedback is greater than or equal to a preset threshold. It can be understood that although it is difficult to directly obtain the wall-touching pressure exerted on the microcatheter during the actual pushing process, there is a proportional relationship between the wall-touching pressure and the first force feedback. Therefore, we can indirectly evaluate the size of the wall-touching pressure exerted on the microcatheter inside the blood vessel through the size of the first force feedback, thereby determining whether the recommended state of the microcatheter is safe propulsion or dangerous recommendation.

[0058] Furthermore, if the first force feedback is less than a preset threshold, the microcatheter's advancement state is determined to be safe advancement. Conversely, if the first force feedback is greater than or equal to the preset threshold, the microcatheter's advancement state is determined to be dangerous advancement. It will be appreciated that those skilled in the art may select a specific value for the preset threshold based on actual needs, and this disclosure does not impose any specific limitations thereon.

[0059] Combined with the above Figures 1 to 3 A method for training a microcatheter delivery model is described. Accordingly, the present disclosure also provides a method for delivering a microcatheter. Figure 4 , which shows an exemplary flow chart of a method 400 for delivering a microcatheter according to an embodiment of the present disclosure. It is understood that the method 400 can be executed by any appropriate device with data processing capabilities, such as but not limited to a processor, a terminal device, and a server.

[0060] like Figure 4 As shown, at step S401, method 400 can obtain a vascular image to be processed and force feedback in a real interventional surgical environment. Next, at step S402, method 400 can input the vascular image to be processed (e.g., digital subtraction angiography) and force feedback into a microcatheter delivery model to generate an operation plan, thereby outputting an operation plan for the microcatheter in the real interventional surgical environment. The microcatheter delivery model herein is trained and generated according to the aforementioned method for training a microcatheter delivery model and its various embodiments.

[0061] In actual interventional surgery, using a microcatheter delivery model to generate microcatheter operation plans allows for more accurate microcatheter delivery to the lesion site, improving the success rate of microcatheter navigation within complex cerebral vascular pathways and reducing the risk of human error and complications. This helps improve surgical safety and shortens postoperative recovery time for patients.

[0062] Next, combine Figure 5An electronic device 500 provided in an embodiment of the present application is exemplarily introduced. Figure 5 As shown, the electronic device 500 of the embodiment of the present application may include a processor 501 , a memory 502 and a communication bus 503 .

[0063] In a specific embodiment, the processor 501 may be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a CPU, a controller, a microcontroller, and a microprocessor. It is understood that for different devices, the electronic device used to implement the above-mentioned processor functions may also be other electronic devices, which is not specifically limited in this embodiment.

[0064] In the embodiment of the present application, the communication bus 503 is used to realize the connection and communication between the processor 501 and the memory 502; the memory 502 stores program instructions for training the microcatheter delivery model and / or for delivering the microcatheter; when the processor 501 executes the program instructions stored in the memory 502, the present application is realized. Figures 1 to 3 The method described for training a microcatheter delivery model or a combination thereof Figure 4 A method for delivering a microcatheter is described.

[0065] Combination of the above Figure 5 The present invention describes an electronic device for training a microcatheter delivery model and / or for delivering microcatheters that can be used to implement the present invention. It should be understood that the device structure or architecture herein is merely exemplary, and the implementation and implementation entities of the present invention are not limited thereto, but may be modified without departing from the spirit of the present invention. It is understood that the description of the various embodiments in this disclosure emphasizes the differences between the various embodiments, and the similarities or corresponding aspects thereof can be referenced to each other. For the purpose of brevity, this disclosure will not elaborate on each one.

[0066] According to the above description in conjunction with the accompanying drawings, those skilled in the art will also understand that the embodiments of the present application can also be implemented by a software program. Therefore, the present application also provides a computer-readable storage medium. The computer-readable storage medium stores program instructions for training a microcatheter delivery model and / or for delivering a microcatheter. The program instructions can be used to implement the present application in conjunction with the accompanying drawings. Figures 1 to 3 The method described for training a microcatheter delivery model or a combination thereof Figure 4 A method for delivering a microcatheter is described.

[0067] It should be noted that although the operations of the present method are described in a particular order in the accompanying drawings, this does not require or imply that the operations must be performed in that particular order, or that all of the operations shown must be performed to achieve the desired results. Rather, the steps depicted in the flowcharts may be performed in a different order. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into a single step, and / or a single step may be broken down into multiple steps.

[0068] Although multiple embodiments of the present application have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art can conceive of many changes, modifications, and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein can be adopted. The accompanying claims are intended to define the scope of protection of the present application and therefore cover equivalents or alternatives within the scope of these claims.

Claims

1. A method for training a microcatheter delivery model, comprising: Constructing a physical simulation interventional surgery environment, the physical simulation interventional surgery environment including a blood vessel simulation model, a microcatheter simulation model, and a mechanical model, wherein the mechanical model is used to perform mechanical control simulation on the microcatheter simulation model so that the microcatheter simulation model moves within the blood vessel simulation model and obtains first force feedback in real time, and a first image of the movement is simulated in real time using computer vision technology; Constructing an in vitro simulated interventional surgery environment, the in vitro simulated interventional surgery environment comprising a physical model of a blood vessel, a surgical microcatheter, a camera, a control module, and a mechanical module, wherein a force sensor is provided at a delivery end of the surgical microcatheter, the force sensor is used to obtain a second force feedback in real time, the control module and the mechanical module are used to operate the surgical microcatheter to move the surgical microcatheter in the physical model of the blood vessel, and the camera is used to capture a second image of the movement in real time; In the physical simulation interventional surgery environment, using the first image and the first force feedback as training data, using the microcatheter delivery model as a policy network of a reinforcement learning agent, and training the microcatheter delivery model using a deep reinforcement learning algorithm to obtain a trained microcatheter delivery model; and The trained microcatheter delivery model is deployed in the in vitro simulated interventional surgery environment, and the trained microcatheter delivery model is fine-tuned based on the second image, the second force feedback and the expected result.

2. The method according to claim 1, wherein The microcatheter delivery model is used as a policy network for a reinforcement learning agent and trained using a deep reinforcement learning algorithm, including: Performing visual feature extraction on the first image to obtain visual features contained in the first image, wherein the visual features include direction and position information of the microcatheter, vascular path, and geometric information and position information of the lesion target; determining a reward value using a reward function based on the visual feature and the first force feedback; and Optimizing the parameters of the policy network according to the reward value.

3. The method according to claim 2, wherein: The reward function is used to reward a state of being close to the lesion target and advancing safely, and to punish a state of being deviated from the lesion target and advancing dangerously, and determining a reward value using the reward function based on the visual feature and the first force feedback includes: determining the position status of the microcatheter and the lesion target based on the visual features; determining an advancement state of the microcatheter based on the first force feedback; and The reward value is determined based on the position state and the propulsion state.

4. The method according to claim 3, wherein: Determining the propulsion state of the microcatheter based on the first force feedback includes: determining whether the first force feedback is greater than or equal to a preset threshold; When the first force feedback is greater than or equal to a preset threshold, determining that the propulsion state of the microcatheter is dangerous propulsion; and When the first force feedback is less than the preset threshold, it is determined that the propulsion state of the microcatheter is safe propulsion.

5. The method according to any one of claims 1 to 4, wherein: The microcatheter delivery model includes an input layer and an output layer; The input layer receives the first image and the first force feedback, and performs feature extraction on the first image and the first force feedback to obtain image features; The output layer generates an operation plan for the microcatheter simulation model based on the image features, where the operation plan includes an operation action and an operation force, and the operation action includes any one of forward, backward, twisting, or stillness.

6. The method according to claim 5, wherein: The input layer includes a visual feature extraction module, a force feedback feature extraction module and a feature fusion module; The visual feature extraction module uses a convolutional neural network to extract visual features from the first image to obtain a feature map of the first image; The force feedback feature extraction module uses a fully connected network to extract geometric features from the first force feedback and the first image to obtain a feature vector; and The feature fusion module performs feature fusion on the feature map and the feature vector to obtain a feature vector in a unified state as the image feature.

7. The method according to claim 1, wherein Fine-tuning the trained microcatheter delivery model based on the second image, the second force feedback, and the expected result includes: Inputting the second image and the second force feedback into the trained microcatheter delivery model to generate an operation plan to obtain an operation plan for the surgical microcatheter; determining discrepancies between the operational plan and the expected results; and The parameters of the trained microcatheter delivery model are fine-tuned according to the differences.

8. An electronic device comprising: processor; as well as A memory storing program instructions for training a microcatheter delivery model, wherein when the program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium storing program instructions for training a microcatheter delivery model, wherein the program instructions, when executed by a processor, implement the method according to any one of claims 1 to 7.

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