Model generation method and system, and device, storage medium and program product

Abstract

Provided in the present disclosure are a model generation method and system, and a device, a storage medium and a program product. The generation method comprises: acquiring several ultrasound images of a target object and corresponding probing position information; extracting a contour feature point set of the target object from each ultrasound image, wherein the contour feature point set comprises a plurality of contour feature points; on the basis of the contour feature point set and probing position information corresponding to each ultrasound image, obtaining spatial position information of each contour feature point; on the basis of the spatial position information of each contour feature point, obtaining a target projection point of each contour feature point on a standard three-dimensional model corresponding to the target object; and on the basis of the spatial position information and target projection point of each contour feature point, calibrating the standard three-dimensional model to obtain a target three-dimensional model of the target object. The present disclosure automatically completes the quick construction of a three-dimensional cardiac model without manual intervention, thereby significantly shortening the time for constructing the three-dimensional cardiac model before surgery.

Description

Model generation method, system, device, storage medium and program product Technical Field

[0001] The present disclosure relates to the field of data processing technology, and in particular to a model generation method, system, device, storage medium, and program product. Background Art

[0002] During intracardiac surgery (such as minimally invasive surgery for atrial fibrillation), surgeons usually need to use an intracardiac echocardiography (ICE) catheter to draw a three-dimensional model of part of the heart chamber before performing treatment. These three-dimensional models of the heart chambers can help doctors establish three-dimensional spatial information between the heart chamber tissues and establish the relative position of surgical instruments in the heart chambers. Doctors can also mark points on the three-dimensional model to record part of the surgical operation process. By constructing a three-dimensional model of the heart chambers, doctors can plan surgical operations more accurately, navigate surgical instruments, locate the diseased area, and minimize surgical risks and improve the success rate of surgery.

[0003] However, currently, the process of drawing three-dimensional models using an ICE catheter is still manual. First, the operator needs to accurately manipulate the ICE handle to position the ICE probe to the desired location. The operator then manually selects a diastolic image at that location and gradually constructs the cardiac cavity model by outlining and tracing the edges. This construction method is not only time-consuming, but the resulting model often lacks accuracy and loses many details. Furthermore, the operator requires a high level of industry knowledge to determine and select ultrasound images that meet specific requirements during the appropriate cardiac cycle. Summary of the Invention

[0004] The technical problem to be solved by the present disclosure is to overcome the defects of the prior art in which the ICE catheter is used to draw a three-dimensional model of the cardiac cavity, such as a long time consumption and a rough and inaccurate model, and to provide a model generation method, system, device, storage medium and program product.

[0005] The present disclosure solves the above technical problems through the following technical solutions:

[0006] According to a first aspect of the present disclosure, a method for generating a model is provided, the method comprising:

[0007] Acquire several ultrasonic images of the target object and corresponding searchlight position information;

[0008] Extracting a contour feature point set of the target object in each of the ultrasound images, wherein the contour feature point set includes a plurality of contour feature points;

[0009] Obtaining spatial position information of each contour feature point based on the contour feature point set and the searchlight position information corresponding to each ultrasound image;

[0010] Obtaining, according to the spatial position information of each of the contour feature points, a target projection point of each of the contour feature points on a standard three-dimensional model corresponding to the target object;

[0011] Based on the spatial position information of each of the contour feature points and the target projection point, the standard three-dimensional model is calibrated to obtain a target three-dimensional model of the target object.

[0012] Preferably, the step of obtaining a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object according to the spatial position information of each contour feature point comprises:

[0013] Obtaining a set of intersection points between the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model;

[0014] Based on the spatial position information, aligning each of the contour feature point sets with the corresponding intersection point set using a preset alignment method;

[0015] Each of the aligned contour feature points in each contour feature point set is mapped to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point.

[0016] Preferably, the step of obtaining a set of intersection points between the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model comprises:

[0017] Creating a virtual sector of the contour feature point set based on the spatial position information and the corresponding searchlight position information;

[0018] Obtaining the intersection point set of the virtual sector and the standard three-dimensional model;

[0019] The step of aligning each of the contour feature point sets with the corresponding intersection point set using a preset alignment method based on the spatial position information includes:

[0020] Obtaining first centroid positions corresponding to a plurality of the contour feature points in the contour feature point set and second centroid positions corresponding to a plurality of intersection points in the intersection point set;

[0021] Based on the first center of gravity position and the second center of gravity position, move each of the contour feature points in the contour feature point set by the same displacement along the same direction, so that the contour feature point set is aligned with the corresponding intersection point set;

[0022] and / or,

[0023] The step of mapping each of the aligned contour feature points in each contour feature point set to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point comprises:

[0024] Mapping each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each of the contour feature points;

[0025] Obtaining the distance between each of the contour feature points and the corresponding initial projection point to obtain the total distance corresponding to the contour feature point set;

[0026] If the total distance does not meet the preset condition, each of the contour feature points is moved by the same preset distance to expand or shrink each of the contour feature points in space;

[0027] Repeating the step of mapping each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point until the total distance meets the preset condition;

[0028] The initial projection point corresponding to each of the contour feature points is used as the target projection point.

[0029] Preferably, the step of calibrating the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain the target three-dimensional model of the target object includes:

[0030] Obtaining the nearest neighbor point of each target projection point on the standard three-dimensional model;

[0031] Based on the spatial position information, each of the nearest neighbor points is moved to the position of the contour feature point corresponding to the target projection point;

[0032] Acquire a set of points to be updated, the set of points to be updated including points to be updated whose distances to the nearest neighbor points are less than a preset threshold;

[0033] Each of the to-be-updated points in the to-be-updated point set is calibrated using a preset algorithm to obtain the target three-dimensional model.

[0034] Preferably, the step of calibrating each of the to-be-updated points in the to-be-updated point set by a preset algorithm to obtain the target three-dimensional model includes:

[0035] Obtaining the original curvature of the point of the standard three-dimensional model;

[0036] Obtaining the curvature of each of the to-be-updated points in the to-be-updated point set to obtain the point calibration curvature of the to-be-updated point set;

[0037] A genetic algorithm is used to calculate the target position of each point to be updated by taking the minimum difference between the calibration curvature of the point and the original curvature of the point as the optimization goal;

[0038] Each of the points to be updated is moved to the corresponding target position to obtain the target three-dimensional model.

[0039] Preferably, the target object includes the entire target organ or the entire target tissue;

[0040] and / or,

[0041] The target object includes the entire target organ or several target sites contained in the entire target tissue;

[0042] The contour feature point set of the target object includes a sub-contour feature point set corresponding to each target part;

[0043] The step of obtaining, based on the spatial position information of each contour feature point, a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object comprises:

[0044] Acquire all sub-contour feature point sets corresponding to the same target part, and the spatial position information corresponding to each sub-contour feature point set;

[0045] Based on each sub-contour feature point set and the spatial position information, obtaining a sub-projection point of each sub-contour feature point on the sub-standard three-dimensional model corresponding to the target part;

[0046] The step of calibrating the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object includes:

[0047] Based on the spatial position information of each sub-contour feature point corresponding to the target part and the sub-projection point, calibrate the sub-standard three-dimensional model corresponding to the target part to obtain a sub-three-dimensional model of each target part;

[0048] The target three-dimensional model of the target object is obtained based on the sub-three-dimensional model of each target part.

[0049] Preferably, the entire target organ or the entire target tissue includes a heart;

[0050] The target sites include the left atrium, left ventricle, right atrium, right ventricle, aorta, pulmonary artery, and superior vena cava.

[0051] According to a second aspect of the present disclosure, there is provided a model generation system, the generation system comprising an acquisition module, an extraction module, a mapping module, a projection module, and a calibration module;

[0052] The acquisition module is used to acquire a plurality of ultrasonic images of the target object and corresponding searchlight position information;

[0053] The extraction module is used to extract a contour feature point set of the target object in each of the ultrasound images, wherein the contour feature point set includes a plurality of contour feature points;

[0054] The mapping module is used to obtain the spatial position information of each contour feature point based on the contour feature point set and the searchlight position information corresponding to each ultrasound image;

[0055] The projection module is used to obtain a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object according to the spatial position information of each contour feature point;

[0056] The calibration module is used to calibrate the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object.

[0057] Preferably, the projection module includes an acquisition unit, an alignment unit and a mapping unit:

[0058] The acquisition unit is used to acquire a set of intersection points between the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model;

[0059] The alignment unit is configured to align each of the contour feature point sets with the corresponding intersection point set using a preset alignment method based on the spatial position information;

[0060] The mapping unit is used to map each of the aligned contour feature points in each contour feature point set to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point.

[0061] Preferably, the acquisition unit includes a creation subunit and an acquisition subunit, and the alignment unit includes a center of gravity subunit and an alignment subunit;

[0062] The creation subunit is used to create a virtual sector of the contour feature point set based on the spatial position information and the corresponding searchlight position information;

[0063] The acquisition subunit is used to acquire the intersection point set between the virtual sector and the standard three-dimensional model;

[0064] The center of gravity determination subunit is used to obtain the first center of gravity positions corresponding to the plurality of contour feature points in the contour feature point set, and the second center of gravity positions corresponding to the plurality of intersection points in the intersection point set;

[0065] The alignment subunit is configured to move each of the contour feature points in the contour feature point set by the same displacement along the same direction based on the first center of gravity position and the second center of gravity position, so as to align the contour feature point set with the corresponding intersection point set;

[0066] and / or,

[0067] The mapping unit includes a mapping subunit, a distance subunit, an adjustment subunit and a determination subunit;

[0068] The mapping subunit is used to map each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each of the contour feature points;

[0069] The distance subunit is used to obtain the distance between each of the contour feature points and the corresponding initial projection point, and obtain the total distance corresponding to the contour feature point set;

[0070] The adjusting subunit is configured to move each of the contour feature points by the same preset distance if the total distance does not satisfy a preset condition, so as to expand or shrink each of the contour feature points in space;

[0071] calling the mapping subunit to repeatedly execute the step of mapping each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point until the total distance meets the preset condition;

[0072] The determining subunit is configured to use the initial projection point corresponding to each of the contour feature points as the target projection point.

[0073] Preferably, the calibration module includes an extraction unit, a movement unit and a calibration unit;

[0074] The extraction unit is used to obtain the nearest neighbor point of each target projection point on the standard three-dimensional model;

[0075] The moving unit is used to move each of the nearest neighbor points to the position of the contour feature point corresponding to the target projection point based on the spatial position information;

[0076] The extraction unit is further configured to obtain a set of points to be updated, wherein the set of points to be updated includes points to be updated whose distances to the nearest neighboring points are less than a preset threshold;

[0077] The calibration unit is used to calibrate each of the to-be-updated points in the to-be-updated point set using a preset algorithm to obtain the target three-dimensional model.

[0078] Preferably, the calibration unit includes an original curvature calculation subunit, a calibration curvature calculation subunit, an optimization subunit and a movement subunit;

[0079] The original curvature calculation subunit is used to obtain the original curvature of the point of the standard three-dimensional model;

[0080] The calibration curvature calculation subunit is used to obtain the curvature of each of the to-be-updated points in the to-be-updated point set, and obtain the point calibration curvature of the to-be-updated point set;

[0081] The optimization subunit is used to calculate the target position of each point to be updated by using a genetic algorithm with the difference between the point calibration curvature and the point original curvature being minimized as the optimization target;

[0082] The moving subunit is used to move each of the to-be-updated points to the corresponding target position to obtain the target three-dimensional model.

[0083] Preferably, the target object includes the entire target organ or the entire target tissue;

[0084] and / or,

[0085] The target object includes the entire target organ or several target sites contained in the entire target tissue;

[0086] The contour feature point set of the target object includes a sub-contour feature point set corresponding to each target part;

[0087] The projection module is further configured to obtain all sub-contour feature point sets corresponding to the same target part, and the spatial position information corresponding to each sub-contour feature point set; based on each sub-contour feature point set and the spatial position information, obtain a sub-projection point of each sub-contour feature point on the sub-standard three-dimensional model corresponding to the target part;

[0088] The calibration module is also used to calibrate the sub-standard three-dimensional model corresponding to the target part based on the spatial position information and the sub-projection point of each sub-contour feature point corresponding to the target part to obtain a sub-three-dimensional model of each target part; based on the sub-three-dimensional model of each target part, the target three-dimensional model of the target object is obtained.

[0089] Preferably, the entire target organ or the entire target tissue includes a heart;

[0090] The target sites include the left atrium, left ventricle, right atrium, right ventricle, aorta, pulmonary artery, and superior vena cava.

[0091] According to a third aspect of the present disclosure, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and used to run on the processor, wherein when the processor executes the computer program, the generation method described in the first aspect of the present disclosure is implemented.

[0092] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the generating method described in the first aspect of the present disclosure is implemented.

[0093] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the generating method according to the first aspect of the present disclosure is implemented.

[0094] On the basis of conforming to the common sense in this field, the above-mentioned preferred conditions can be arbitrarily combined to obtain the preferred embodiments of the present disclosure.

[0095] The positive progress of the present disclosure lies in: by combining the overall information of the standard heart model with the local information of the intracardiac ultrasound image, a genetic algorithm is used to quickly fit the patient's heart three-dimensional model, thereby realizing the rapid construction of the heart three-dimensional model during the operation. This allows the ICE catheter operator to automatically complete operations such as ultrasound image selection and three-dimensional model construction without additional manual intervention, significantly shortening the time required to construct the heart three-dimensional model before the operation, greatly simplifying the workflow of the ICE catheter operator, and reducing the risk of the operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] FIG1 is a flow chart of a method for generating a model according to Embodiment 1 of the present disclosure;

[0097] FIG2 is a flow chart of step S4 of the method for generating a model in Example 1 of the present disclosure;

[0098] FIG3 is a schematic diagram of image distribution of an intersection set according to Embodiment 1 of the present disclosure;

[0099] FIG4 is a first schematic diagram of the alignment process between the contour feature point set and the intersection point set in Embodiment 1 of the present disclosure;

[0100] FIG5 is a second schematic diagram of the alignment process between the contour feature point set and the intersection point set in Example 1 of the present disclosure;

[0101] FIG6 is a flow chart of step S43 of the method for generating a model in Example 1 of the present disclosure;

[0102] FIG7 is a flow chart of step S5 of the method for generating a model in Example 1 of the present disclosure;

[0103] FIG8 is a flow chart of step S54 of the method for generating a model in Example 1 of the present disclosure;

[0104] FIG9 is a flow chart of an algorithm according to Embodiment 1 of the present disclosure;

[0105] FIG10 is a schematic diagram of modules of a system for generating a model according to Embodiment 2 of the present disclosure;

[0106] FIG11 is a schematic structural diagram of an electronic device according to Embodiment 3 of the present disclosure. DETAILED DESCRIPTION

[0107] The present disclosure is further illustrated below by way of examples, but the present disclosure is not limited to the scope of the examples.

[0108] In the embodiments of the present disclosure, prefixes such as "first" and "second" are used only to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. In the embodiments of the present disclosure, the use of prefixes such as ordinal numbers to distinguish description objects does not constitute a limitation on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and no unnecessary limitations should be constituted due to the use of such prefixes. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0109] In the embodiments of the present disclosure, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0110] Example 1

[0111] In a specific embodiment of the present disclosure, a method for generating a model is provided, as shown in FIG1 , the method comprising:

[0112] S1. Acquire several ultrasonic images of the target object and corresponding searchlight position information;

[0113] S2. extracting a contour feature point set of the target object in each ultrasound image, where the contour feature point set includes multiple contour feature points;

[0114] S3. Based on the contour feature point set and searchlight position information corresponding to each ultrasound image, obtain the spatial position information of each contour feature point;

[0115] S4. Obtaining a target projection point of each contour feature point on a standard three-dimensional model corresponding to the target object based on the spatial position information of each contour feature point;

[0116] S5. Calibrate the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object.

[0117] Specifically, in order to achieve rapid construction of a target three-dimensional model, it is necessary to pre-construct a standard three-dimensional model of the target object. The target object includes the entire target organ or the entire target tissue. For example, taking the entire heart organ as an example, the heart scan data of several patients using in vitro ultrasound, CT (computer tomography) and MRI (magnetic resonance imaging) can be collected, and a three-dimensional model of the heart can be constructed using a three-dimensional reconstruction algorithm. The average shape model of each three-dimensional heart model can be obtained using a statistical shape model construction method as the standard three-dimensional model of the heart. The three-dimensional reconstruction algorithm can use a pixel-based volume reconstruction method and / or a contour point-based surface reconstruction method. When constructing the statistical shape model, the Fourier decomposition method can be used, or the point distribution model method can be used.

[0118] Step S1: By using an intracorporeal ultrasound device (eg, an ICE catheter), a plurality of ultrasound images D = {D1, D2, ..., D n}, and simultaneously record the searchlight position information matrix L = {L1, L2, ..., L n For example, the target object may be a heart, and an ultrasound image of the heart during isovolumetric relaxation and corresponding searchlight position information may be obtained using an ICE catheter. The searchlight position information may be provided by a hardware probe or inferred and predicted based on an intracardiac probe position model, and this embodiment does not impose any specific limitations on this.

[0119] After acquiring several ultrasound images, ultrasound image contour extraction and contour structure edge processing are performed on each ultrasound image in step S2 to obtain the contour of the target object in each ultrasound image. Each contour is composed of multiple contour feature points, and each contour feature point together constitutes a contour feature point set corresponding to the contour.

[0120] For example, the heart contour of each ultrasound image is extracted, and then the complete heart contour feature point set C = {C1, C2, ..., C m}, where each contour has and only corresponds to one probe position. Each contour consists of a cluster of contour feature points, so there is C i =P i ={p1,p2,...,p u}, where P i C is the contour feature point set i A two-dimensional point set, pu For contour C i The value of the u-th point on the u =(x u ,y u ,0.0,0.0).

[0121] Since each ultrasound image is a two-dimensional image, it needs to be converted into a three-dimensional space to obtain the three-dimensional spatial information of the target object. Therefore, in step S3, each contour feature point can be converted into an expression in the three-dimensional space according to the contour feature point set and searchlight position information corresponding to each ultrasound image. For example, u Take point as an example, p u By using C i The corresponding searchlight position information 4×4 matrix L i Multiplying them, we get p u The expression in three-dimensional space, namely p u '=p u ×L i Similarly, by multiplying all points in the contour feature point set corresponding to the target object with the searchlight position information matrix corresponding to the contour where it is located, the expression of the entire target object contour in three-dimensional space can be obtained.

[0122] After obtaining the spatial information of each contour feature point in the target object, each contour feature point can be mapped to the corresponding standard 3D model of the target object in step S4 to obtain the target projection point of each contour feature point on the standard 3D model. Then, in step S5, the standard 3D model is calibrated based on the spatial position information of each contour feature point and the target projection point, and the target 3D model of the target object is quickly fitted.

[0123] This specific embodiment establishes a standard three-dimensional model of the heart and uses the contour edge data of part of the heart collected by ultrasound images to fit the standard three-dimensional model to the patient's actual heart shape, quickly fitting the patient's three-dimensional heart model, thereby achieving rapid construction of the three-dimensional heart model during surgery. This allows ICE catheter operators to automatically complete operations such as ultrasound image selection and three-dimensional model construction without additional manual intervention, thereby significantly shortening the time required to construct the three-dimensional heart model before surgery.

[0124] In one specific embodiment, as shown in FIG2 , step S4 includes:

[0125] S41, obtaining a set of intersection points between a contour feature point set corresponding to each ultrasound image and a standard three-dimensional model;

[0126] S42, aligning each contour feature point set with the corresponding intersection point set using a preset alignment method based on the spatial position information;

[0127] S43 , mapping each contour feature point in each aligned contour feature point set to a standard three-dimensional model to obtain a target projection point corresponding to each contour feature point.

[0128] Wherein, step S41 includes:

[0129] S411, creating a virtual sector of the contour feature point set based on the spatial position information and the corresponding searchlight position information;

[0130] S412: Obtain a set of intersection points between the virtual sector and the standard three-dimensional model.

[0131] Step S42 includes:

[0132] S421, obtaining first centroid positions corresponding to multiple contour feature points in the contour feature point set, and second centroid positions corresponding to multiple intersection points in the intersection point set;

[0133] S422 : Based on the first center of gravity position and the second center of gravity position, move each contour feature point in the contour feature point set in the same direction by the same displacement, so as to align the contour feature point set with the corresponding intersection point set.

[0134] Specifically, the contour feature points obtained in step S3 may be aligned with the standard three-dimensional model using global alignment and local alignment methods respectively.

[0135] For example, the whole point set C of the contour feature point set C is obtained by step S3 = {C1, C2, ..., C m} and its corresponding searchlight position information matrix L={L1,L2,...,L n}. So, for the contour feature point set C j , there is only one searchlight position information matrix L j Correspondingly, use the searchlight position information matrix L j , you can create a contour feature point set C j The corresponding virtual fan F intersects with the standard three-dimensional model LA to obtain the contour feature point set C j The intersection point set I with the standard three-dimensional model LA. At this time, the image distribution of the intersection point set can be closed or open, as shown in FIG3 .

[0136] When the image distribution of the intersection set is closed, that is, when the distance between two points in the intersection set is less than the threshold θ, the global alignment method can be used, that is, by dividing the contour feature point set C j The center of gravity of the intersection point set I is moved to the center of gravity of the intersection point set I. At the same time, the contour feature point set C j All points in will move the same displacement as the center of gravity, as shown in Figure 4.

[0137] When the image distribution of the intersection set is not closed, that is, when the distance between two points in the intersection set is greater than or equal to the threshold θ, the local alignment method can be used, that is, by placing the contour feature point set C j The center points of the two endpoints of are moved to the center points of the two endpoints of the intersection set I. Similarly, the contour feature point set C j All points in will move the same displacement as the center point of the endpoint, as shown in Figure 5. The endpoint can be determined based on the number of adjacent points. For example, when the endpoint is within the range If there is only one adjacent point in the contour, the point is one of the endpoints of the contour feature point set.

[0138] Similarly, by performing the above alignment processing steps on all contour feature point sets C of the target object, a complete contour point set C' aligned with the standard three-dimensional model can be obtained.

[0139] By mapping each contour feature point in each aligned contour feature point set to the standard three-dimensional model in step S43 , a target projection point of each contour feature point on the standard three-dimensional model can be obtained.

[0140] In one specific embodiment, as shown in FIG6 , step S43 includes:

[0141] S431, mapping each contour feature point in the contour feature point set to a standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point;

[0142] S432, obtaining the distance between each contour feature point and the corresponding initial projection point, and obtaining the total distance corresponding to the contour feature point set;

[0143] S433: If the total distance does not meet the preset condition, each contour feature point is moved by the same preset distance to expand or shrink each contour feature point in space;

[0144] Repeat step S431 until the total distance meets the preset condition;

[0145] S434: Use the initial projection point corresponding to each contour feature point as the target projection point.

[0146] Specifically, because ultrasound depth is adjustable, as the depth increases, visible tissue becomes smaller and more imaged content appears within a fixed-size image sector; whereas, as the depth decreases, visible tissue becomes larger and more detailed within the image sector. After the alignment process in step S42, the overall bounding box size of the contour feature points needs to be converged to the bounding box size range of the standard 3D model.

[0147] Therefore, in a specific implementable method, each contour feature point can be projected onto the surface of the standard three-dimensional model through step S431 to obtain the initial projection point of each contour feature point on the standard three-dimensional model. Since the contour feature point may be outside the standard three-dimensional model (for example, the patient's heart is larger) or inside the standard three-dimensional model (for example, the patient's heart is smaller), when the contour feature point is outside the standard three-dimensional model, the initial projection point can be obtained by intersecting the line connecting the contour feature point to the center of gravity / center position point with the standard three-dimensional model surface. When the contour feature point is inside the standard three-dimensional model, a ray is drawn from the center of gravity / center position point to the contour feature point, and the extension line of this ray will intersect with the standard three-dimensional model surface to obtain the initial projection point.

[0148] Then, step S432 is used to obtain the distance between each contour feature point and the corresponding initial projection point, and the distance sum is calculated. Then, step S433 is used to perform overall reduction and enlargement to minimize the distance sum. When the contour feature point is outside the standard three-dimensional model, the distance sum is reduced by overall reduction. When the contour feature point is inside the standard three-dimensional model, the distance sum is reduced by overall enlargement. After multiple adjustments, the distance sum is minimized, and then step S434 is used to set the initial projection point corresponding to each contour feature point when the distance sum is minimized as the target projection point.

[0149] In a specific practicable manner, the parameter α is obtained so that the distance between the contour feature point set C' and the projection point of the standard three-dimensional model is minimized. Thus, the projection point C"=αC' of the patient's true heart contour in the three-dimensional space of the standard three-dimensional model is obtained. Specifically, through step S42, the contour feature point set C after alignment processing can be obtained. j ', let the point set Q be the contour feature point set C j Projection on the standard three-dimensional model can be used to obtain the parameter α, so that argmin f(x) = ∑(αC j '-Q), so there is C j ”=αC j '.

[0150] In a specific embodiment, as shown in FIG7 , step S5 includes:

[0151] S51, obtaining the nearest neighbor point of each target projection point on the standard three-dimensional model;

[0152] S52, based on the spatial position information, moving each nearest neighbor point to the position of the contour feature point corresponding to the target projection point;

[0153] S53, obtaining a set of points to be updated, the set of points to be updated including points to be updated whose distances to their nearest neighbor points are less than a preset threshold;

[0154] S54 , calibrating each to-be-updated point in the to-be-updated point set using a preset algorithm to obtain a target three-dimensional model.

[0155] Specifically, S4 can be used to obtain the projection points of the patient's true heart contour on the standard three-dimensional heart model. Since the projection points are virtual points, not real points, it is necessary to obtain the nearest neighbor points of these projection points on the standard three-dimensional heart model through step S51. While maintaining the model's topological structure, the positions of these nearest neighbor points are moved to the positions of the corresponding projection points through step S52 to obtain a corrected heart model. For example, the nearest neighbor point Q' of the patient's heart projection point P" on the standard three-dimensional heart model is obtained, and then the position coordinates of these points are equal to the projection point, that is, Q'=P", and the displacement vector V=P"-Q' of these points is calculated.

[0156] Next, the updated positions of the nearest neighbor points remain unchanged. Step S53 retrieves points on the standard 3D model whose distances from the nearest neighbor points are less than a preset threshold. Step S54 uses a preset algorithm to determine the new positions of the points to be updated, and the points to be updated are moved to the corresponding new positions to obtain the target 3D model. For example, after obtaining the displacement vector V between the nearest neighbor point Q' and the projected point P", the near-geodesic method is used to retrieve a set of points PB to be updated whose neighboring distances to the projected point set P" are less than β.

[0157] In a specific embodiment, as shown in FIG8 , step S54 includes:

[0158] S541, obtaining the original curvature of the point of the standard three-dimensional model;

[0159] S542, obtaining the curvature of each to-be-updated point in the to-be-updated point set, and obtaining the point calibration curvature of the to-be-updated point set;

[0160] S543, using a genetic algorithm to minimize the difference between the point calibration curvature and the point original curvature as the optimization goal, and calculating the target position of each point to be updated;

[0161] S544: Move each point to be updated to the corresponding target position to obtain a target three-dimensional model.

[0162] Specifically, in a 3D model, curvature refers to the degree of curvature or radius of curvature at a point on a surface. It describes the local geometric characteristics of the surface near that point. The magnitude of the curvature is proportional to the degree of curvature of the surface, while the radius of curvature represents the inverse of the curvature. If the radius of curvature is small, the surface is more curved near that point; if the radius of curvature is large, the surface is relatively flat.

[0163] Therefore, in order to simultaneously obtain the heart shape information of the standard three-dimensional heart model and the real-time information of the patient's heart contour, the heart shape information can be evaluated by the maximum curvature, Gaussian curvature, and mean curvature. Taking the mean curvature as an example, the position of the nearest neighbor point can be fixed, and the total amount of mean curvature change between the new model and the original model can be minimized by adjusting the position of the point to be updated, that is, the goal is Where X and Y are the distributions of the model points, and f(X) and f(Y) are the total average curvature changes under the distributions of X and Y points.

[0164] To solve the single-objective optimization problem described above, a constrained optimization genetic algorithm can be used. The algorithm flow is shown in Figure 9. This involves parameter encoding, population initialization, fitness evaluation, and updating the population through selection, crossover, and compilation operators. The constrained optimization rule has two conditions: 1) only the positions of the updated points in the projected point set of the patient's true cardiac contour are updated; and 2) the direction vectors of the updated positions of these updated points must be equal to the sum of the parameters of the displacement vectors of the projected point set.

[0165] For example, after obtaining the displacement vector V and the set of points to be updated PB, since the number of points in the set of points to be updated PB is w, the point to be updated pb and the point w in the projection point set P" are pb The points are adjacent, and the displacement vectors of these points are V'. According to the constraint optimization rule, the update direction of the point to be updated pb satisfies the following formula:

[0166] Among them, T pb is the update direction of the neighboring point pb, λ is the distance weight vector, and its vector length is w pb , with a value range of [0, γ]. Therefore, we can encode and randomly initialize the population GW0 based on the length w, which has a value range of [0, γ]. Then, based on the update direction of the projection point set PB of each member in the population, we obtain the updated 3D heart model and its average curvature change. The average curvature change of each member in the population is sorted in ascending order, with the first N members as the parents. Through crossover and mutation operations, we increase the diversity of the population members, and finally obtain the next generation group GW1.

[0167] The above sorting, selection, crossover and mutation operations are looped. Finally, when the current N members are no longer updated, the loop stops and the result that meets the optimization goal is finally obtained.

[0168] In one embodiment, the target object includes several target sites contained in the entire target organ or the entire target tissue;

[0169] The contour feature point set of the target object includes a sub-contour feature point set corresponding to each target part;

[0170] Step S4 includes:

[0171] Obtain all sub-contour feature point sets corresponding to the same target part, as well as the spatial position information corresponding to each sub-contour feature point set;

[0172] Based on each sub-contour feature point set and spatial position information, a sub-projection point of each sub-contour feature point on the sub-standard three-dimensional model corresponding to the target part is obtained;

[0173] Step S5 includes:

[0174] Based on the spatial position information and sub-projection points of each sub-contour feature point corresponding to the target part, the sub-standard three-dimensional model corresponding to the target part is calibrated to obtain a sub-three-dimensional model of each target part;

[0175] Based on the sub-3D model of each target part, a target 3D model of the target object is obtained.

[0176] Specifically, taking the heart as an example, the heart is composed of multiple target parts, wherein the target parts include the left atrium, left ventricle, right atrium, right ventricle, aorta, pulmonary artery, and superior vena cava. When extracting the contour feature point set of the target object in each ultrasound image in step S2, the image segmentation algorithm can be used to perform semantic segmentation of the ultrasound image to achieve accurate segmentation between different cardiac chambers, and through contour structure edge processing, the cardiac cavity contour edge is extracted, and the interference component of the ultrasound image fan edge is removed, and finally the true cardiac cavity edge is obtained, thereby obtaining the sub-contour feature point set corresponding to each target part. Among them, the image segmentation algorithm can be a deep learning image segmentation method or a traditional image threshold segmentation method.

[0177] When constructing a 3D heart model, corresponding 3D models are constructed for different parts of the heart. It is understood that the standard 3D model includes sub-standard 3D models corresponding to each part. Taking the left atrium as an example, by acquiring the left atrial contour in each ultrasound image and obtaining the spatial position information corresponding to each left atrial contour feature point based on the searchlight position information of each ultrasound image, each left atrial contour is mapped onto the standard 3D model of the left atrium. The standard 3D model of the left atrium is then calibrated to obtain the corresponding sub-3D model of the left atrium. The specific calibration process can be found in other embodiments. Similarly, after obtaining the sub-3D models corresponding to each target part of the heart, the target 3D model of the entire heart can be obtained.

[0178] This specific embodiment constructs a corresponding sub-3D model for each target part of the target object, which can fully reflect the characteristic details of each target part, better fit the shape of the patient's actual target object, and facilitate doctors to make accurate judgments and estimates.

[0179] This embodiment combines the overall information of the standard heart model with the local information of the intracardiac ultrasound image and uses a genetic algorithm to quickly fit the patient's three-dimensional heart model, thereby achieving rapid construction of the three-dimensional heart model during surgery. This allows the ICE catheter operator to automatically complete operations such as ultrasound image selection and three-dimensional model construction without additional manual intervention, significantly shortening the time required to construct the three-dimensional heart model before surgery, greatly simplifying the ICE catheter operator's workflow, and reducing surgical risks.

[0180] Example 2

[0181] FIG10 is a module diagram of a model generation system provided by an exemplary embodiment of the present disclosure, wherein the generation system includes an acquisition module 100 , an extraction module 200 , a mapping module 300 , a projection module 400 , and a calibration module 500 ;

[0182] The acquisition module 100 is used to acquire a plurality of ultrasonic images of the target object and corresponding searchlight position information;

[0183] The extraction module 200 is used to extract a contour feature point set of a target object in each ultrasound image, where the contour feature point set includes a plurality of contour feature points;

[0184] The mapping module 300 is used to obtain the spatial position information of each contour feature point based on the contour feature point set and searchlight position information corresponding to each ultrasound image;

[0185] The projection module 400 is used to obtain a target projection point of each contour feature point on a standard three-dimensional model corresponding to the target object according to the spatial position information of each contour feature point;

[0186] The calibration module 500 is used to calibrate the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object.

[0187] In a specific embodiment, the projection module 400 includes an acquisition unit, an alignment unit, and a mapping unit:

[0188] The acquisition unit is used to obtain the intersection point set of the contour feature point set corresponding to each ultrasound image and the standard three-dimensional model;

[0189] The alignment unit is used to align each contour feature point set with the corresponding intersection point set using a preset alignment method based on the spatial position information;

[0190] The mapping unit is used to map each contour feature point in each aligned contour feature point set to a standard three-dimensional model to obtain a target projection point corresponding to each contour feature point.

[0191] In a specific embodiment, the acquisition unit includes a creation subunit and an acquisition subunit, and the alignment unit includes a center of gravity determination subunit and an alignment subunit;

[0192] The creation subunit is used to create a virtual sector of the contour feature point set based on the spatial position information and the corresponding searchlight position information;

[0193] The acquisition subunit is used to obtain the intersection point set between the virtual sector and the standard three-dimensional model;

[0194] The center of gravity determination subunit is used to obtain the first center of gravity positions corresponding to the multiple contour feature points in the contour feature point set and the second center of gravity positions corresponding to the multiple intersection points in the intersection point set;

[0195] The alignment subunit is used to move each contour feature point in the contour feature point set by the same displacement along the same direction based on the first center of gravity position and the second center of gravity position, so as to align the contour feature point set with the corresponding intersection point set;

[0196] In one embodiment, the mapping unit includes a mapping subunit, a distance subunit, an adjustment subunit, and a determination subunit;

[0197] The mapping subunit is used to map each contour feature point in the contour feature point set to a standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point;

[0198] The distance subunit is used to obtain the distance between each contour feature point and the corresponding initial projection point, and obtain the total distance corresponding to the contour feature point set;

[0199] The adjusting subunit is used to move each contour feature point by the same preset distance if the total distance does not meet the preset condition, so as to expand or shrink each contour feature point in the spatial position;

[0200] Calling the mapping subunit to repeatedly execute the step of mapping each contour feature point in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point until the total distance meets a preset condition;

[0201] The determination subunit is used to take the initial projection point corresponding to each contour feature point as the target projection point.

[0202] In one embodiment, the calibration module 500 includes an extraction unit, a movement unit, and a calibration unit;

[0203] The extraction unit is used to obtain the nearest neighbor point of each target projection point on the standard three-dimensional model;

[0204] The moving unit is used to move each nearest neighbor point to the position of the contour feature point corresponding to the target projection point based on the spatial position information;

[0205] The extraction unit is further used to obtain a set of points to be updated, the set of points to be updated including points to be updated whose distances to their nearest neighbor points are less than a preset threshold;

[0206] The calibration unit is used to calibrate each to-be-updated point in the to-be-updated point set using a preset algorithm to obtain a target three-dimensional model.

[0207] In one embodiment, the calibration unit includes an original curvature calculation subunit, a calibration curvature calculation subunit, an optimization subunit, and a movement subunit;

[0208] The original curvature calculation subunit is used to obtain the original curvature of the points of the standard three-dimensional model;

[0209] The calibration curvature calculation subunit is used to obtain the curvature of each to-be-updated point in the to-be-updated point set, and obtain the point calibration curvature of the to-be-updated point set;

[0210] The optimization subunit is used to use a genetic algorithm to minimize the difference between the point calibration curvature and the point original curvature as the optimization goal, and calculate the target position of each point to be updated;

[0211] The moving subunit is used to move each point to be updated to the corresponding target position to obtain the target three-dimensional model.

[0212] In one embodiment, the target object includes the entire target organ or the entire target tissue;

[0213] In one embodiment, the target object includes several target sites contained in the entire target organ or the entire target tissue;

[0214] The contour feature point set of the target object includes a sub-contour feature point set corresponding to each target part;

[0215] The projection module 400 is further configured to obtain all sub-contour feature point sets corresponding to the same target part, and spatial position information corresponding to each sub-contour feature point set; based on each sub-contour feature point set and the spatial position information, obtain a sub-projection point of each sub-contour feature point on the sub-standard three-dimensional model corresponding to the target part;

[0216] The calibration module 500 is also used to calibrate the sub-standard three-dimensional model corresponding to the target part based on the spatial position information and sub-projection point of each sub-contour feature point corresponding to the target part, so as to obtain a sub-three-dimensional model of each target part; and based on the sub-three-dimensional model of each target part, obtain the target three-dimensional model of the target object.

[0217] In one embodiment, the entire target organ or the entire target tissue comprises a heart;

[0218] Target sites include the left atrium, left ventricle, right atrium, right ventricle, aorta, pulmonary artery, and superior vena cava.

[0219] Since the system embodiments generally correspond to the method embodiments, reference will be made to the description of the method embodiments for relevant details. The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components of the units may or may not be physical units, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the disclosed solution.

[0220] This embodiment combines the overall information of the standard heart model with the local information of the intracardiac ultrasound image and uses a genetic algorithm to quickly fit the patient's three-dimensional heart model, thereby achieving rapid construction of the three-dimensional heart model during surgery. This allows the ICE catheter operator to automatically complete operations such as ultrasound image selection and three-dimensional model construction without additional manual intervention, significantly shortening the time required to construct the three-dimensional heart model before surgery, greatly simplifying the ICE catheter operator's workflow, and reducing surgical risks.

[0221] Example 3

[0222] Figure 11 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present disclosure. The electronic device includes a memory, a processor, and a computer program stored in the memory and executed on the processor. When the processor executes the computer program, it implements the model generation method provided in any of the above-mentioned embodiments. The electronic device 30 shown in Figure 11 is merely an example and should not limit the functionality or scope of use of the embodiments of the present disclosure.

[0223] As shown in FIG11 , the electronic device 30 may be a general-purpose computing device, such as a server device. Components of the electronic device 30 may include, but are not limited to, the at least one processor 31, the at least one memory 32, and a bus 33 connecting various system components (including the memory 32 and the processor 31).

[0224] The bus 33 includes a data bus, an address bus, and a control bus.

[0225] The memory 32 may include a volatile memory, such as a random access memory (RAM) 321 and / or a cache memory 322 , and may further include a read-only memory (ROM) 323 .

[0226] The memory 32 may also include a program tool 325 (or utility) having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0227] The processor 31 executes various functional applications and data processing by running the computer program stored in the memory 32, such as the model generation method provided in any of the above embodiments.

[0228] The electronic device 30 can also communicate with one or more external devices 34 (e.g., a keyboard, pointing device, etc.). Such communication can occur via an input / output (I / O) interface 35. Furthermore, the electronic device 30 can communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 36. As shown, the network adapter 36 communicates with other modules of the electronic device 30 via a bus 33. It should be understood that, although not shown, other hardware and / or software modules can be used in conjunction with the electronic device 30, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0229] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided and embodied by multiple units / modules.

[0230] Example 4

[0231] An embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the model generation method provided in any of the above embodiments.

[0232] The readable storage medium may include, but is not limited to, a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0233] Example 5

[0234] An embodiment of the present disclosure further provides a computer program product, including a computer program, which, when executed by a processor, implements the model generation method provided in any of the above embodiments.

[0235] The program code for executing the computer program product of the present disclosure may be written in any combination of one or more programming languages, and the program code may be executed entirely on the user device, partially on the user device, as a standalone software package, partially on the user device and partially on a remote device, or entirely on the remote device.

[0236] While specific embodiments of the present disclosure have been described above, those skilled in the art will appreciate that these are merely illustrative and that the scope of protection of the present disclosure is defined by the appended claims. Those skilled in the art may make various changes or modifications to these embodiments without departing from the principles and essence of the present disclosure, and such changes and modifications are intended to fall within the scope of protection of the present disclosure.

Claims

1. A method for generating a model, characterized in that: The generation method comprises: Acquire several ultrasonic images of the target object and corresponding searchlight position information; Extracting a contour feature point set of the target object in each of the ultrasound images, wherein the contour feature point set includes a plurality of contour feature points; Obtaining spatial position information of each contour feature point based on the contour feature point set and the searchlight position information corresponding to each ultrasound image; Obtaining, according to the spatial position information of each of the contour feature points, a target projection point of each of the contour feature points on a standard three-dimensional model corresponding to the target object; calibrating the standard three-dimensional model based on the spatial position information of each of the contour feature points and the target projection point to obtain a target three-dimensional model of the target object; The step of obtaining, based on the spatial position information of each contour feature point, a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object comprises: Obtaining a set of intersection points between the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model; Based on the spatial position information, aligning each of the contour feature point sets with the corresponding intersection point set using a preset alignment method; Each of the aligned contour feature points in each contour feature point set is mapped to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point.

2. The generation method according to claim 1, characterized in that The step of obtaining the intersection point set of the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model includes: Creating a virtual sector of the contour feature point set based on the spatial position information and the corresponding searchlight position information; Obtaining the intersection point set of the virtual sector and the standard three-dimensional model; The step of aligning each of the contour feature point sets with the corresponding intersection point set using a preset alignment method based on the spatial position information includes: Obtaining first centroid positions corresponding to a plurality of the contour feature points in the contour feature point set and second centroid positions corresponding to a plurality of intersection points in the intersection point set; Based on the first center of gravity position and the second center of gravity position, move each of the contour feature points in the contour feature point set by the same displacement along the same direction, so that the contour feature point set is aligned with the corresponding intersection point set; and / or, The step of mapping each of the aligned contour feature points in each contour feature point set to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point comprises: Mapping each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each of the contour feature points; Obtaining the distance between each of the contour feature points and the corresponding initial projection point to obtain the total distance corresponding to the contour feature point set; If the total distance does not meet the preset condition, each of the contour feature points is moved by the same preset distance to expand or shrink each of the contour feature points in space; Repeating the step of mapping each of the contour feature points in the contour feature point set to the standard three-dimensional model to obtain an initial projection point corresponding to each contour feature point until the total distance meets the preset condition; The initial projection point corresponding to each of the contour feature points is used as the target projection point.

3. The generation method according to claim 1, characterized in that The step of calibrating the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object includes: Obtaining the nearest neighbor point of each target projection point on the standard three-dimensional model; Based on the spatial position information, each of the nearest neighbor points is moved to the position of the contour feature point corresponding to the target projection point; Acquire a set of points to be updated, the set of points to be updated including points to be updated whose distances to the nearest neighbor points are less than a preset threshold; Each of the to-be-updated points in the to-be-updated point set is calibrated using a preset algorithm to obtain the target three-dimensional model.

4. The generation method according to claim 3, characterized in that The step of calibrating each of the to-be-updated points in the to-be-updated point set by a preset algorithm to obtain the target three-dimensional model comprises: Obtaining the original curvature of the point of the standard three-dimensional model; Obtaining the curvature of each of the to-be-updated points in the to-be-updated point set to obtain the point calibration curvature of the to-be-updated point set; A genetic algorithm is used to calculate the target position of each point to be updated by taking the minimum difference between the calibration curvature of the point and the original curvature of the point as the optimization goal; Each of the points to be updated is moved to the corresponding target position to obtain the target three-dimensional model.

5. The generation method according to any one of claims 1 to 4, characterized in that The target object includes the entire target organ or the entire target tissue; and / or, The target object includes the entire target organ or several target sites contained in the entire target tissue; The contour feature point set of the target object includes a sub-contour feature point set corresponding to each target part; The step of obtaining, based on the spatial position information of each contour feature point, a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object comprises: Acquire all sub-contour feature point sets corresponding to the same target part, and the spatial position information corresponding to each sub-contour feature point set; Based on each sub-contour feature point set and the spatial position information, obtaining a sub-projection point of each sub-contour feature point on the sub-standard three-dimensional model corresponding to the target part; The step of calibrating the standard three-dimensional model based on the spatial position information of each contour feature point and the target projection point to obtain a target three-dimensional model of the target object includes: Based on the spatial position information of each sub-contour feature point corresponding to the target part and the sub-projection point, calibrate the sub-standard three-dimensional model corresponding to the target part to obtain a sub-three-dimensional model of each target part; The target three-dimensional model of the target object is obtained based on the sub-three-dimensional model of each target part.

6. The generation method according to claim 5, characterized in that The entire target organ or the entire target tissue includes a heart; The target sites include the left atrium, left ventricle, right atrium, right ventricle, aorta, pulmonary artery, and superior vena cava.

7. A model generation system, characterized in that: The generation system includes an acquisition module, an extraction module, a mapping module, a projection module and a generation module; The acquisition module is used to acquire a plurality of ultrasonic images of the target object and corresponding searchlight position information; The extraction module is used to extract a contour feature point set of the target object in each of the ultrasound images, wherein the contour feature point set includes a plurality of contour feature points; The mapping module is used to obtain the spatial position information of each contour feature point based on the contour feature point set and the searchlight position information corresponding to each ultrasound image; The projection module is used to obtain a target projection point of each contour feature point on the standard three-dimensional model corresponding to the target object according to the spatial position information of each contour feature point; The generating module is used to calibrate the standard three-dimensional model based on the spatial position information of each of the contour feature points and the target projection point to obtain a target three-dimensional model of the target object; The projection module includes an acquisition unit, an alignment unit and a mapping unit; The acquisition unit is used to acquire a set of intersection points between the contour feature point set corresponding to each of the ultrasound images and the standard three-dimensional model; The alignment unit is configured to align each of the contour feature point sets with the corresponding intersection point set using a preset alignment method based on the spatial position information; The mapping unit is used to map each of the aligned contour feature points in each contour feature point set to the standard three-dimensional model to obtain the target projection point corresponding to each contour feature point.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and configured to run on the processor, wherein: When the processor executes the computer program, the generation method according to any one of claims 1 to 6 is implemented.

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

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the generating method according to any one of claims 1 to 6 is implemented.

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