A method and device for determining ablation parameters of high-frequency pulse electric field ablation

Abstract

The present invention discloses a method and device for determining ablation parameters for high-frequency pulse electric field ablation. The method comprises: acquiring collected medical image data containing depth information, determining a corresponding lesion box containing three-dimensional information of the lesion based on the medical image data; analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion; starting from a plurality of equally spaced points on the boundary of the electrode contour, emitting a preset number of rays to the surrounding area, obtaining the number of intersections between the rays and the lesion box to determine whether the electrode and the lesion are in contact; when the electrode and the lesion are in contact, determining the corresponding ablation parameters based on the contour and depth of the lesion, the ablation parameters including pulse voltage, pulse width, number of pulse trains, and number of discharges. The present invention solves the problem of inaccurate ablation parameter determination in the prior art.

Description

Technical Field

[0001] The present invention relates to the technical field of pulse electric field ablation, and in particular to a method and device for determining ablation parameters of high-frequency pulse electric field ablation. Background Art

[0002] Pulsed electric field ablation technology generates a high-voltage pulsed electric field with a pulse width of milliseconds, microseconds, or even nanoseconds. It releases extremely high energy in a short period of time, which can cause a large number of irreversible micropores in cell membranes and even intracellular organelles such as the endoplasmic reticulum, mitochondria, and nuclei. This can then cause apoptosis of diseased cells, thereby achieving the desired therapeutic purpose.

[0003] The selection of ablation parameters is closely related to the treatment effect, patient safety and post-treatment recovery. Therefore, how to scientifically and accurately determine the ablation parameters of high-frequency pulsed electric field ablation is the key to the development of this technology.

[0004] In the existing technology, when determining ablation parameters, the ablation parameters set based on experience are often used as the ablation parameters for each ablation, which cannot adapt well to different lesions. In particular, when a second ablation is required, it relies more on the doctor's personal experience to determine the ablation parameters required for the second ablation. As a result, the accuracy of the ablation parameters is relatively low, making the treatment effect unstable and difficult to achieve the expected results. Summary of the Invention

[0005] In view of this, an object of the present invention is to provide a method and apparatus for determining ablation parameters for high-frequency pulse electric field ablation, aiming to solve the problem of inaccurate ablation parameter determination in the prior art.

[0006] The embodiment of the present invention is implemented as follows:

[0007] A method for determining ablation parameters for high-frequency pulse electric field ablation, the method comprising:

[0008] Acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of the lesion according to the medical image data;

[0009] Analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion;

[0010] Starting from a plurality of equally spaced points on the boundary of the electrode outline, a preset number of rays are emitted to the surrounding area, and the number of intersections between the rays and the lesion box is obtained to determine whether the electrode and the lesion are in contact;

[0011] When the electrode is in contact with the lesion, corresponding ablation parameters are determined according to the contour and depth of the lesion, wherein the ablation parameters include pulse voltage, pulse width, number of pulse trains, and number of discharges.

[0012] Furthermore, in the above-mentioned method for determining ablation parameters of high-frequency pulsed electric field ablation, the step of acquiring collected medical image data containing depth information and determining a corresponding lesion box containing three-dimensional information of a lesion according to the medical image data includes:

[0013] Inputting the medical image data into a pre-trained lesion localization model to obtain a corresponding lesion box containing three-dimensional information of the lesion;

[0014] The training process of the lesion localization model is as follows:

[0015] Collecting a preset number of historical medical image data, each of which corresponds to a training data set with a preset frame outlining the lesion range and annotating the lesion type;

[0016] The training data set is divided into a training set, a validation set and a test set according to a preset ratio, and then the training data set is input into a preset neural network for training until the model parameters tend to be stable to obtain the lesion localization model.

[0017] Furthermore, in the above-mentioned method for determining ablation parameters of high-frequency pulsed electric field ablation, the step of analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion includes:

[0018] Inputting the lesion box into a pre-trained lesion segmentation model to obtain the corresponding lesion contour and depth as well as the contour of the electrode used to ablate the lesion;

[0019] The training process of the lesion segmentation model is as follows:

[0020] Collecting a preset number of historical lesion boxes and the contours and depths of the corresponding lesions and the contours of electrodes used to ablate the lesions to obtain a training data set;

[0021] The training data set is divided into a training set, a validation set and a test set according to a preset ratio, and then the training data set is input into a preset neural network for training until the model parameters tend to be stable to obtain the lesion segmentation model.

[0022] Furthermore, in the above-mentioned method for determining ablation parameters of high-frequency pulsed electric field ablation, the step of obtaining the number of intersections between the ray and the lesion box to determine whether the electrode and the lesion are in contact with each other includes:

[0023] When the number of intersections between the ray and the lesion box is an even number, it is determined that the electrode is not in contact with the lesion;

[0024] When the number of intersections between the ray and the lesion box is an odd number, it is determined that the electrode is in contact with the lesion.

[0025] Furthermore, in the above-mentioned method for determining ablation parameters of high-frequency pulsed electric field ablation, the step of determining the corresponding ablation parameters according to the contour and depth of the lesion includes:

[0026] Inputting the contour and depth of the lesion into a preset first ablation parameter determination model to obtain corresponding ablation parameters;

[0027] The training process of the first ablation parameter determination model is as follows:

[0028] Collecting historical ablation parameters and corresponding ablation depths and ablation lengths as a training data set, and inputting the training data set into a naive Bayesian network for training to obtain the first ablation parameter determination model;

[0029] The naive Bayesian network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and ablation parameters through multiple trainings;

[0030] Among them, the mathematical expression of Bayes' formula is:

[0031] ；

[0032] in, P(A|B) Indicates that in the event B Conditions under which events occur A The probability of occurrence, P(B|A) Indicates that in the event A Conditions under which events occur B The probability of occurrence, P(A) It's an event A The prior probability of independent occurrence, P(B) It's an event B The marginal probability of occurrence, event A={pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}.

[0033] Furthermore, the method for determining ablation parameters for high-frequency pulsed electric field ablation, wherein, after the step of determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion, further comprises:

[0034] After completing an ablation discharge using the ablation parameters, acquiring impedance data, contour, and depth of the lesion within a preset time period after the ablation discharge is completed;

[0035] The impedance data and the contour and depth of the lesion after the ablation discharge are input into a pre-trained second ablation parameter determination model to obtain corresponding secondary ablation parameters.

[0036] Furthermore, in the above-mentioned method for determining ablation parameters of high-frequency pulsed electric field ablation, the training process of the second ablation parameter determination model is as follows:

[0037] Collecting historical impedance data, secondary ablation parameters, and corresponding ablation depth and ablation length as a training data set, and inputting the training data set into a Markov network for training to obtain the second ablation parameter determination model;

[0038] The Markov network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and secondary ablation parameters through multiple training;

[0039] Among them, the mathematical expression of Bayes' formula is:

[0040] ；

[0041] in, P(A|B, I) Indicates that in the event B Conditions under which events occur A, I The probability of occurrence, P(B|A, I) Indicates that in the event A Conditions under which events occur B, I The probability of occurrence, P(A|I) Indicates that in the event A The probability of event I occurring under the condition that P(B|I) Indicates that in the event B Conditions under which events occur I The probability of occurrence, event A = {pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}, I Impedance data.

[0042] Another object of the present invention is to provide a device for determining ablation parameters for high-frequency pulsed electric field ablation, the device comprising:

[0043] an acquisition module, configured to acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of a lesion according to the medical image data;

[0044] a segmentation module, configured to analyze and segment the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion;

[0045] A collision module is used to emit a preset number of rays from multiple equally spaced points on the boundary of the electrode outline to the surrounding area, and obtain the number of intersections between the rays and the lesion box to determine whether the electrode and the lesion are in contact;

[0046] a determination module, configured to determine corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion;

[0047] or for determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion, and for obtaining impedance data, contour and depth of the lesion after one ablation discharge using the corresponding ablation parameters, and determining corresponding secondary ablation parameters based on the impedance data and the contour and depth of the lesion;

[0048] The ablation parameters and the secondary ablation parameters both include pulse voltage, pulse width, number of pulse trains, and number of discharges.

[0049] Another object of the present invention is to provide a readable storage medium having a computer program stored thereon, wherein the program implements the steps of the above method when executed by a processor.

[0050] Another object of the present invention is to provide an electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the above method are implemented when the processor executes the program.

[0051] Compared with the prior art, the present invention can determine the lesion box containing three-dimensional information of the lesion by acquiring medical image data, so that the contour and depth of the lesion and the electrode contour can be determined based on the potential information about the length, width and depth of the lesion inside the lesion box. The contour and depth of the lesion are then used to determine the ablation parameters for each treatment, and the corresponding ablation parameters can be accurately provided when facing different lesions. This solves the problem of inaccurate ablation parameter determination in the prior art. In addition, the use of a collision algorithm to detect whether the electrode and the lesion are in contact can further improve the accuracy of lesion elimination and avoid thermal damage.

[0052] In addition, the present invention has at least the following beneficial effects:

[0053] 1. It can automatically identify and locate the lesion location and automatically calculate the size of the lesion;

[0054] 2. It can automatically identify the position of the catheter with integrated electrodes and determine whether the catheter is placed in place;

[0055] 3. Establish a feedback method that can recommend the next ablation parameters based on the change in impedance after discharge. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of a method for determining ablation parameters for high-frequency pulsed electric field ablation provided by the first embodiment of the present invention;

[0057] Figure 2 This is a structural block diagram of an ablation parameter determination device for high-frequency pulse electric field ablation in the third embodiment of the present invention.

[0058] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0059] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0060] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0062] Example 1

[0063] See also Figure 1 , shown is a method for determining ablation parameters of high-frequency pulse electric field ablation in the first embodiment of the present invention, the method includes steps S10 to S13.

[0064] Step S10: Acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of the lesion according to the medical image data.

[0065] Among them, the medical image data can be CT image data, which is data collected by the CT device in DICOM format. This format is the native data output by the CT device, which contains grayscale images and corresponding depth values. The medical image data contains lesion range and lesion type information, and then the corresponding lesion box containing three-dimensional information of the lesion can be obtained by analyzing the medical image data.

[0066] Specifically, the medical image data is input into the pre-trained lesion localization model to obtain a corresponding lesion box containing three-dimensional information of the lesion. Among them, the medical image data containing depth information and pictures obtained by CT scanning is input into the lesion localization model, and the model can obtain a specific lesion box containing three-dimensional information of the lesion.

[0067] Exemplarily, a preset number of historical medical image data are collected, and each historical medical image data is framed with a preset frame to define the lesion range and annotate the lesion type to obtain a training data set. For example, 8,000 CT scans are collated, and each CT data is framed with the lesion range and lesion type information. The frame is a three-dimensional cube and the corresponding lesion type. The CT data, the eight vertex coordinates of the three-dimensional cube, and the lesion type are used as a set of data. Thirty thousand sets of data are used as training data sets and input into a preset neural network for training using an AI training server. This continues until the training stop conditions are met, such as when the model parameters become stable or the preset number of training times is reached.

[0068] Furthermore, the training set, validation set, and test set can be divided into preset proportions of 80%, 15%, and 5%, respectively, for training, validation, and testing. The preset neural network can use YOLO, specifically YOLOv8. The trained model has mastered the inherent logic of using CT data to locate lesions. Therefore, the input CT data can output data containing 8 coordinates of a three-dimensional cube and the type of lesion. Among them, the three-dimensional cube composed of 8 coordinates is the lesion box. The lesion box contains the length, width, and depth information of the lesion.

[0069] In addition, in some preferred embodiments of the present invention, in order to further obtain more accurate lesion box data, after obtaining the above-mentioned three-dimensional cube, the cube can be subjected to an intersection and union operation to filter out repeated data in the range and obtain the final lesion box as output data.

[0070] Step S11 : analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion.

[0071] Among them, to determine the ablation parameters during ablation discharge, it is necessary to obtain the contour and depth data of the corresponding lesion, so that the corresponding ablation parameters can be accurately set according to the contour and depth data of the lesion, and the contour of the electrode used to ablate the lesion (hereinafter collectively referred to as "electrode contour") can be obtained. The position information of the electrode can also be determined based on the electrode contour, that is, the appropriate ablation position can be determined, so that the above information can be displayed on the display screen of the ablation device. The operator of the device can confirm the insertion of the catheter integrated with the electrode into the specified position based on the electrode contour and position information. In specific implementation, it can also be implemented in conjunction with the position sensor on the catheter. The reason is that the catheter cannot actually move in a straight line. The sensor can accurately determine whether it touches other tissues and adjust the position of the catheter on the insertion path in real time until the catheter moves to the correct position.

[0072] Specifically, a lesion segmentation model can be used to output the corresponding lesion contour and depth and electrode contour based on the input lesion box. Among them, UNET is selected as the neural network for training the lesion segmentation model, which is mainly used to mark the lesion contour. The training data set is obtained using the historical lesion box and the corresponding lesion contour and depth, electrode contour. For example, the lesion box data can be obtained, the lesion contour and electrode contour in the lesion box can be calibrated, and the lesion box and the corresponding marked lesion contour and electrode contour data are composed of a training data set. The training set, validation set and test set can be divided into preset ratios of 80%, 15% and 5%, which are used for training, validation and testing respectively. The lesion segmentation model masters the internal logic of the segmentation of the lesion contour and the electrode contour, so that the lesion box is input into the lesion segmentation model for reasoning. The potential data of the length, width and depth of the lesion contained in the lesion box can be analyzed, thereby obtaining the required lesion contour and depth and electrode contour.

[0073] Step S12: Starting from a plurality of equally spaced points on the boundary of the electrode outline, a preset number of rays are emitted to the surrounding area, and the number of intersections between the rays and the lesion box is obtained to determine whether the electrode fits the lesion.

[0074] Among them, after determining the two major factors affecting the ablation parameters, namely the lesion contour and the electrode contour, it is necessary to detect the degree of fit between the electrode and the lesion, so as to ensure that pulse ablation is performed when the electrode and the lesion are in fit. Specifically, in this embodiment, a collision algorithm is used to determine whether the electrode and the lesion are in fit. In the specific implementation, a plurality of equally spaced points on the boundary of the electrode contour are used as starting points, and a preset number of rays are emitted to the surrounding area. For example, 50 points are selected and 50 rays are emitted. When the number of intersections between the rays and the lesion box (i.e., the number of collisions) is an even number, it is determined that the electrode and the lesion are not in fit; when the number of intersections between the rays and the lesion box is an odd number, it is determined that the electrode and the lesion are in fit.

[0075] Step S13: When the electrode is in contact with the lesion, corresponding ablation parameters are determined according to the contour and depth of the lesion, wherein the ablation parameters include pulse voltage, pulse width, number of pulse trains, and number of discharges.

[0076] Among them, after the contour (such as length) and depth information of the lesion are known, the corresponding ablation parameters can be accurately determined based on the contour and depth information of the lesion. Specifically, the ablation parameters include pulse voltage, pulse width, number of pulse trains and number of discharges. These parameters can be pushed to the operator of the ablation device, such as a doctor, so that the doctor can operate the ablation device according to these parameters. In addition, in an embodiment of the present invention, the type of lesion can also be determined based on the analysis of medical image data. After determining the type of lesion, electrode catheters of different types or functions can be selected to adapt to the current ablation operation.

[0077] Specifically, a model is trained by using a model training method to train a model that can determine the corresponding ablation parameters according to the contour and depth of the lesion, wherein animals can be used to collect training data sets. For example, when mice or pigs are used as research subjects, the rectum of mice or the gallbladder, pancreas, liver and digestive tract of pigs can be selected as test subjects. For example, a discharge ablation test is performed on mice, and the corresponding ablation parameters and ablation effects are recorded. The ablation effect can be evaluated by comparing the difference between the ablation length and ablation depth after the current ablation and the ablation length and ablation depth after the previous ablation. That is, when the difference between the two is within the corresponding preset range at the same time, it can be judged that the expected ablation effect is achieved. The ablation length and ablation depth in the data set are the length and depth of the lesion after ablation, and the ablation length and ablation depth during the first ablation are the initial length and initial depth of the lesion. The ablation parameters and the corresponding ablation length and ablation depth constitute a training data set and are input into the naive Bayesian network for training to obtain the first ablation parameter determination model. It should be noted that in actual training, the ablation parameters, ablation depth and ablation length constitute a training data set, and the ablation length and ablation depth are determined by the ablation parameters. However, in actual prediction, it is necessary to determine the ablation parameters through the contour and depth information of the lesion, namely the ablation length and ablation depth, so the naive Bayesian network is used. The naive Bayesian network can adjust the optimal relationship between the ablation length and ablation depth of the discharge ablation and the ablation parameters through multiple training based on the Bayesian probability formula, so that the corresponding ablation parameters can be accurately output under the premise of known ablation length and ablation depth of the lesion.

[0078] More specifically, the mathematical expression of Bayes' formula is:

[0079] ；

[0080] in, P(A|B) Indicates that in the event B Conditions under which events occur A The probability of occurrence, P(B|A) Indicates that in the event A Conditions under which events occur B The probability of occurrence, P(A) It's an event A The prior probability of independent occurrence, P(B) It's an event B The marginal probability of occurrence, event A={pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}.

[0081] In summary, the method for determining ablation parameters for high-frequency pulsed electric field ablation in the above-mentioned embodiment of the present invention can determine the lesion box containing three-dimensional information of the lesion by acquiring medical image data, so that the contour and depth of the lesion and the electrode contour can be determined based on the potential information about the length, width and depth of the lesion inside the lesion box, thereby using the contour and depth of the lesion to determine the ablation parameters for each time, and accurately providing corresponding ablation parameters when facing different lesions. This solves the problem of inaccuracy in determining ablation parameters in the prior art. In addition, the use of a collision algorithm to detect whether the electrode and the lesion are in contact can further improve the accuracy of lesion elimination and avoid thermal damage.

[0082] Example 2

[0083] This embodiment also provides a method for determining ablation parameters for high-frequency pulsed electric field ablation. The method for determining ablation parameters for high-frequency pulsed electric field ablation in this embodiment differs from the method for determining ablation parameters for high-frequency pulsed electric field ablation in Example 1 in that:

[0084] After step S13, the following steps are also included:

[0085] After completing an ablation discharge using the ablation parameters, acquiring impedance data, contour, and depth of the lesion within a preset time period after the ablation discharge is completed;

[0086] Specifically, the current impedance data is obtained through an external impedance analyzer, and the impedance data and the contour and depth of the lesion after the end of this ablation discharge are input into the pre-trained second ablation parameter determination model to obtain the corresponding secondary ablation parameters. It should be noted that the secondary ablation refers to the need to ablate the lesion again after the end of the previous ablation discharge, including the second ablation, the third ablation and the Nth ablation. In addition, it should be noted that before performing the secondary ablation, the current temperature data can also be obtained to determine whether to continue the ablation based on the data. For example, when the temperature is too high, the ablation is stopped directly, and when the temperature is normal, the secondary ablation is continued to avoid the occurrence of thermal damage. In addition, since the impedance of the lesion will undergo relatively drastic changes at the end of discharge, multiple impedance values ​​may appear within the preset time. In order to ensure the accuracy of the secondary ablation parameter determination, multiple impedance data with similar values ​​can be screened out within the preset time period to obtain the average value as the impedance data of the lesion, or the one with the same value in greater number can be screened out as the impedance data of the lesion. For example, the preset time period can be selected within 5S or 10S after the end of discharge. The specific range can be selected according to actual conditions and is not limited here.

[0087] Among them, an ablation parameter adjustment feedback mechanism is established. After an ablation discharge is performed, the effect of this ablation discharge can be evaluated, that is, whether the corresponding ablation length and ablation depth are achieved under the acquired impedance data. Therefore, if the discharge effect does not meet expectations, that is, the ablation length and ablation depth of the lesion after the ablation discharge do not reach the corresponding ablation length and ablation depth, that is, the difference between the ablation length and ablation depth of the lesion after the ablation discharge and the ablation length and ablation depth of the lesion after the previous ablation discharge are not simultaneously within the corresponding preset range, the ablation parameters can be improved to obtain secondary ablation parameters, which can improve the therapeutic effect of secondary ablation of the lesion.

[0088] It should be noted that in this embodiment, the impedance data after discharge is used to assist the ablation depth and ablation length to obtain the secondary ablation parameters. Compared with the determination of ablation parameters based entirely on ablation length and ablation depth, the accuracy of the determination of the secondary ablation parameters is effectively improved. In the specific implementation, impedance is introduced as additional information, the Bayesian formula is re-established, and a Markov network is established based on the formula. The network is deployed to the training server for AI training. Each set of training data contains secondary ablation parameters, impedance data, ablation length and ablation depth after ablation discharge, and the first ablation parameter determination model Similarly, during training, a training dataset consisting of secondary ablation parameters, impedance data, ablation depth, and ablation length is used to determine the ablation length and depth. However, in actual prediction, the secondary ablation parameters need to be determined using impedance data, the contour and depth information of the lesion, namely the ablation length and ablation depth. The Markov network needs to construct a Markov chain. Therefore, the ablation length and ablation depth after the ablation discharge, the secondary ablation parameters, and the impedance data are used as states for probabilistic deduction, thus forming a Markov model. Bayesian formulas are also used for probability calculations.

[0089] Specifically, the mathematical expression of the Bayesian formula is:

[0090] ；

[0091] in, P(A|B, I) Indicates that in the event B Conditions under which events occur A, I The probability of occurrence, P(B|A, I) Indicates that in the event A Conditions under which events occur B, I The probability of occurrence, P(A|I) Indicates that in the event A The probability of event I occurring under the condition that P(B|I) Indicates that in the event B Conditions under which events occur I The probability of occurrence, event A = {pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}, I Impedance data.

[0092] For example, after the discharge is completed, the impedance data 5 seconds after the discharge is completed, and the contour and depth of the lesion after the ablation discharge are input into the Markov network again. At this time, the Markov network can be adjusted according to the results of this discharge to obtain the ablation parameters required for the second discharge, namely the secondary ablation parameters.

[0093] In summary, the method for determining ablation parameters for high-frequency pulsed electric field ablation in the above-mentioned embodiment of the present invention can determine the lesion box containing three-dimensional information of the lesion by acquiring medical image data, so that the contour and depth of the lesion and the electrode contour can be determined based on the potential information about the length, width and depth of the lesion inside the lesion box, thereby using the contour and depth of the lesion to determine the ablation parameters for each time, and accurately providing corresponding ablation parameters when facing different lesions. This solves the problem of inaccuracy in determining ablation parameters in the prior art. In addition, the use of a collision algorithm to detect whether the electrode and the lesion are in contact can further improve the accuracy of lesion elimination and avoid thermal damage.

[0094] Example 3

[0095] See also Figure 2 , shown is an ablation parameter determination device for high-frequency pulse electric field ablation proposed in a third embodiment of the present invention, the device comprising:

[0096] An acquisition module 100 is configured to acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of a lesion according to the medical image data;

[0097] A segmentation module 200 is used to analyze and segment the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion;

[0098] The collision module 300 is used to emit a preset number of rays from multiple equally spaced points on the boundary of the electrode outline to the surrounding area, and obtain the number of intersections between the rays and the lesion box to determine whether the electrode and the lesion are in contact;

[0099] a determination module 400 for determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion;

[0100] or for determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion, and for obtaining impedance data, contour and depth of the lesion after one ablation discharge using the corresponding ablation parameters, and determining corresponding secondary ablation parameters based on the impedance data and the contour and depth of the lesion;

[0101] The ablation parameters and the secondary ablation parameters both include pulse voltage, pulse width, number of pulse trains, and number of discharges.

[0102] Furthermore, in the above-mentioned ablation parameter determination device for high-frequency pulsed electric field ablation, the step of acquiring collected medical image data containing depth information and determining a corresponding lesion box containing three-dimensional information of a lesion according to the medical image data includes:

[0103] Inputting the medical image data into a pre-trained lesion localization model to obtain a corresponding lesion box containing three-dimensional information of the lesion;

[0104] The training process of the lesion localization model is as follows:

[0105] Collecting a preset number of historical medical image data, each of which corresponds to a training data set with a preset frame outlining the lesion range and annotating the lesion type;

[0106] The training data set is divided into a training set, a validation set and a test set according to a preset ratio, and then the training data set is input into a preset neural network for training until the model parameters tend to be stable to obtain the lesion localization model.

[0107] Furthermore, in the above-mentioned device for determining ablation parameters of high-frequency pulsed electric field ablation, the step of analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion includes:

[0108] Inputting the lesion box into a pre-trained lesion segmentation model to obtain the corresponding lesion contour and depth as well as the contour of the electrode used to ablate the lesion;

[0109] The training process of the lesion segmentation model is as follows:

[0110] Collecting a preset number of historical lesion boxes and the contours and depths of the corresponding lesions and the contours of electrodes used to ablate the lesions to obtain a training data set;

[0111] The training data set is divided into a training set, a validation set and a test set according to a preset ratio, and then the training data set is input into a preset neural network for training until the model parameters tend to be stable to obtain the lesion segmentation model.

[0112] Furthermore, in the above-mentioned ablation parameter determination device for high-frequency pulse electric field ablation, the step of obtaining the number of intersections between the ray and the lesion box to determine whether the electrode and the lesion are in contact includes:

[0113] When the number of intersections between the ray and the lesion box is an even number, it is determined that the electrode is not in contact with the lesion;

[0114] When the number of intersections between the ray and the lesion box is an odd number, it is determined that the electrode is in contact with the lesion.

[0115] Furthermore, in the above-mentioned device for determining ablation parameters for high-frequency pulsed electric field ablation, the step of determining the corresponding ablation parameters according to the contour and depth of the lesion includes:

[0116] Inputting the contour and depth of the lesion into a preset first ablation parameter determination model to obtain corresponding ablation parameters;

[0117] The training process of the first ablation parameter determination model is as follows:

[0118] Collecting historical ablation parameters and corresponding ablation depths and ablation lengths as a training data set, and inputting the training data set into a naive Bayesian network for training to obtain the first ablation parameter determination model;

[0119] The naive Bayesian network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and ablation parameters through multiple trainings;

[0120] Among them, the mathematical expression of Bayes' formula is:

[0121] ；

[0122] in, P(A|B) Indicates that in the event B Conditions under which events occur A The probability of occurrence, P(B|A) Indicates that in the event A Conditions under which events occur B The probability of occurrence, P(A) It's an event A The prior probability of independent occurrence, P(B) It's an event B The marginal probability of occurrence, event A={pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}.

[0123] Furthermore, the ablation parameter determination device for high-frequency pulsed electric field ablation, wherein, after the step of determining the corresponding ablation parameter according to the contour and depth of the lesion when the electrode is in contact with the lesion, further comprises:

[0124] After completing an ablation discharge using the ablation parameters, acquiring impedance data, contour, and depth of the lesion within a preset time period after the ablation discharge is completed;

[0125] The impedance data and the contour and depth of the lesion after the ablation discharge are input into a pre-trained second ablation parameter determination model to obtain corresponding secondary ablation parameters.

[0126] Furthermore, in the above-mentioned ablation parameter determination device for high-frequency pulse electric field ablation, the training process of the second ablation parameter determination model is:

[0127] Collecting historical impedance data, secondary ablation parameters, and corresponding ablation depth and ablation length as a training data set, and inputting the training data set into a Markov network for training to obtain the second ablation parameter determination model;

[0128] The Markov network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and secondary ablation parameters through multiple training;

[0129] Among them, the mathematical expression of Bayes' formula is:

[0130] ；

[0131] in, P(A|B, I) Indicates that in the event B Conditions under which events occur A, I The probability of occurrence, P(B|A, I) Indicates that in the event A Conditions under which events occur B, I The probability of occurrence, P(A|I) Indicates that in the event A The probability of event I occurring under the condition that P(B|I) Indicates that in the event B Conditions under which events occur I The probability of occurrence, event A = {pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}, I Impedance data.

[0132] The functions or operation steps implemented when the above modules are executed are substantially the same as those in the above method embodiments and will not be repeated here.

[0133] Example 4

[0134] Another aspect of the present invention further provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in any one of the above-mentioned embodiments 1 to 2.

[0135] Example 5

[0136] On the other hand, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and runnable on the processor, and when the processor executes the program, the steps of the method described in any one of the above embodiments one to two are implemented.

[0137] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0138] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or for use in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0139] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0140] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0141] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0142] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A method for determining ablation parameters for high-frequency pulse electric field ablation, characterized in that: The method comprises: Acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of the lesion according to the medical image data; Analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion; Starting from a plurality of equally spaced points on the boundary of the electrode outline, a preset number of rays are emitted to the surrounding area, and the number of intersections between the rays and the lesion box is obtained to determine whether the electrode and the lesion are in contact; When the electrode is in contact with the lesion, corresponding ablation parameters are determined according to the contour and depth of the lesion, wherein the ablation parameters include pulse voltage, pulse width, number of pulse trains, and number of discharges; The step of acquiring the collected medical image data containing depth information and determining the corresponding lesion box containing three-dimensional information of the lesion according to the medical image data includes: Inputting the medical image data into a pre-trained lesion localization model to obtain a corresponding lesion box containing three-dimensional information of the lesion; The training process of the lesion localization model is as follows: Collecting a preset number of historical medical image data, each of which corresponds to a training data set with a preset frame outlining the lesion range and annotating the lesion type; Dividing the training data set into a training set, a validation set, and a test set according to a preset ratio, and then inputting the training data set into a preset neural network for training until the model parameters tend to be stable to obtain the lesion localization model; The step of analyzing and segmenting the lesion box to obtain the contour and depth of the lesion and the contour of the electrode for ablating the lesion includes: Inputting the lesion box into a pre-trained lesion segmentation model to obtain the corresponding lesion contour and depth as well as the contour of the electrode used to ablate the lesion; The training process of the lesion segmentation model is as follows: Collecting a preset number of historical lesion boxes and the contours and depths of the corresponding lesions and the contours of electrodes used to ablate the lesions to obtain a training data set; Dividing the training data set into a training set, a validation set, and a test set according to a preset ratio, and then inputting the training data set into a preset neural network for training until the model parameters tend to be stable to obtain the lesion segmentation model; The step of determining corresponding ablation parameters according to the contour and depth of the lesion includes: Inputting the contour and depth of the lesion into a preset first ablation parameter determination model to obtain corresponding ablation parameters; The training process of the first ablation parameter determination model is as follows: Collecting historical ablation parameters and corresponding ablation depths and ablation lengths as a training data set, and inputting the training data set into a naive Bayesian network for training to obtain the first ablation parameter determination model; The naive Bayesian network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and ablation parameters through multiple trainings; Among them, the mathematical expression of Bayes' formula is: ； in, P(A|B) Indicates that in the event B Conditions under which events occur A The probability of occurrence, P(B|A) Indicates that in the event A Conditions under which events occur B The probability of occurrence, P(A) It's an event A The prior probability of independent occurrence, P(B) It's an event B The marginal probability of occurrence, event A={pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}.

2. The method for determining ablation parameters of high-frequency pulse electric field ablation according to claim 1, characterized in that: The step of obtaining the number of intersections between the ray and the lesion box to determine whether the electrode is in contact with the lesion comprises: When the number of intersections between the ray and the lesion box is an even number, it is determined that the electrode is not in contact with the lesion; When the number of intersections between the ray and the lesion box is an odd number, it is determined that the electrode is in contact with the lesion.

3. The method for determining ablation parameters for high-frequency pulse electric field ablation according to claim 1, characterized in that: After the step of determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion, the method further includes: After completing an ablation discharge using the ablation parameters, acquiring impedance data, contour, and depth of the lesion within a preset time period after the ablation discharge is completed; The impedance data and the contour and depth of the lesion after the ablation discharge are input into a pre-trained second ablation parameter determination model to obtain corresponding secondary ablation parameters.

4. The method for determining ablation parameters of high-frequency pulse electric field ablation according to claim 3, characterized in that: The training process of the second ablation parameter determination model is: Collecting historical impedance data, secondary ablation parameters, and corresponding ablation depth and ablation length as a training data set, and inputting the training data set into a Markov network for training to obtain the second ablation parameter determination model; The Markov network is based on the Bayesian probability formula and adjusts the optimal relationship between the ablation depth, ablation length and secondary ablation parameters through multiple training; Among them, the mathematical expression of Bayes' formula is: ； in, P(A|B, I) Indicates that in the event B Conditions under which events occur A, I The probability of occurrence, P(B|A,I) Indicates that in the event A Conditions under which events occur B, I The probability of occurrence, P(A|I) Indicates that in the event A The probability of event I occurring under the condition that P (B∣I) Indicates that in the event B Conditions under which events occur I The probability of occurrence, event A = {pulse voltage, pulse width, number of pulse trains, number of discharges}, event B ={ablation length, ablation depth}, I Impedance data.

5. A device for determining ablation parameters for high-frequency pulse electric field ablation, characterized in that: A method for determining ablation parameters for high-frequency pulsed electric field ablation according to any one of claims 1 to 4, the device comprising: an acquisition module, configured to acquire collected medical image data containing depth information, and determine a corresponding lesion box containing three-dimensional information of a lesion according to the medical image data; a segmentation module, configured to analyze and segment the lesion box to obtain the contour and depth of the lesion and the contour of the electrode used to ablate the lesion; A collision module is used to emit a preset number of rays from multiple equally spaced points on the boundary of the electrode outline to the surrounding area, and obtain the number of intersections between the rays and the lesion box to determine whether the electrode and the lesion are in contact; a determination module, configured to determine corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion; or for determining corresponding ablation parameters according to the contour and depth of the lesion when the electrode is in contact with the lesion, and for obtaining impedance data, contour and depth of the lesion after one ablation discharge using the corresponding ablation parameters, and determining corresponding secondary ablation parameters based on the impedance data and the contour and depth of the lesion; The ablation parameters and the secondary ablation parameters both include pulse voltage, pulse width, number of pulse trains, and number of discharges.

6. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

7. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the method according to any one of claims 1 to 4 are implemented when the processor executes the program.

Images