Vascular stenosis detection method and device, electronic equipment and storage medium
By acquiring PCCT images of stents, locating the stent position, and performing boundary recognition and region segmentation, and using a generative network model to output corrected vascular images, the problem of difficult detection of vascular stenosis within stents is solved, and the image accuracy and detection precision are improved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHAOXING PEOPLES HOSPITAL
- Filing Date
- 2026-03-11
- Publication Date
- 2026-06-19
AI Technical Summary
Existing technologies are insufficient to effectively determine the degree of stenosis within stents, and stents can interfere with the detection of stenosis.
By acquiring PCCT images of the stent, locating the stent position, performing stent boundary recognition and image region segmentation, and using a generative network model to output corrected vascular images, the vascular stenosis lesion is finally identified.
It alleviates the difficulty in detecting in-stent vascular stenosis, reduces the interference of stents on stenosis detection results, and improves imaging accuracy and detection precision.
Smart Images
Figure CN122243900A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of medical image processing and intelligent diagnostic technology, and in particular to a method, device, electronic device and storage medium for detecting vascular stenosis. Background Technology
[0002] Currently, during the diagnostic process, conventional CT images are difficult to determine the degree of stenosis in blood vessels within a stent. The advent of Photon-Counting CT (PCCT) has made this possible, but because it assesses the condition within a stent, the stent itself can still negatively impact the determination of stenosis. Summary of the Invention
[0003] The purpose of this invention is to provide a method, device, electronic device and storage medium for detecting vascular stenosis, which can at least alleviate the problem of difficulty in detecting vascular stenosis in existing stents and help reduce the degree of interference of stents on stenosis detection results.
[0004] To address the aforementioned technical problems, in a first aspect, the present invention provides a method for detecting vascular stenosis, comprising at least:
[0005] Acquire PCCT images of stents in any patient and locate the stent position in the images;
[0006] Stent boundary identification is performed based on the stent location in the stent PCCT image to obtain at least a boundary fitting curve;
[0007] Image region segmentation is performed at least based on the boundary fitting curve to obtain at least the vascular region features;
[0008] The vascular region features and the stent PCCT images are at least input into the pre-trained generative network model to output corrected vascular images;
[0009] At least the vascular stenosis detection procedure is performed based on the modified vascular image to at least identify the vascular stenosis lesion.
[0010] Optionally, the step of performing image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features specifically includes:
[0011] Image region segmentation is performed at least based on the boundary fitting curve to obtain the vascular region features and stent region features.
[0012] Optionally, the step of inputting at least the vascular region features and the stent PCCT image into the pre-trained generative network model to output a corrected vascular image specifically includes at least:
[0013] The vascular region features, the stent region features, and the stent PCCT image are input into the pre-trained generative network model to output the corrected vascular image.
[0014] Optionally, the step of performing vascular stenosis detection at least based on the modified vascular image to at least identify vascular stenosis lesions specifically includes:
[0015] The vascular stenosis detection operation is performed using a preset stenosis detection network based at least on the modified vascular image, so as to at least identify the vascular stenosis lesion.
[0016] Based on the same concept, in a second aspect, the present invention also provides a vascular stenosis detection device for performing the vascular stenosis detection method described in any one of the first aspects;
[0017] The vascular stenosis detection device includes at least:
[0018] The stent positioning module is used to acquire stent PCCT images of any patient and locate the stent position in the images.
[0019] A boundary recognition module is used to perform stent boundary recognition based on the stent location in the stent PCCT image, so as to obtain at least a boundary fitting curve;
[0020] A region segmentation module is used to perform image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features;
[0021] The image output module is used to input at least the vascular region features and the stent PCCT image into the pre-trained generative network model to output the corrected vascular image.
[0022] The stenosis determination module is used to perform a vascular stenosis detection operation based at least on the modified vascular image, so as to at least determine the vascular stenosis lesion.
[0023] Optionally, the region segmentation module is specifically used for at least:
[0024] Image region segmentation is performed at least based on the boundary fitting curve to obtain the vascular region features and stent region features.
[0025] Optionally, the image output module is specifically used for at least:
[0026] The vascular region features, the stent region features, and the stent PCCT image are input into the pre-trained generative network model to output the corrected vascular image.
[0027] Optionally, the narrowing determination module is specifically used for at least:
[0028] The vascular stenosis detection operation is performed using a preset stenosis detection network based at least on the modified vascular image, so as to at least identify the vascular stenosis lesion.
[0029] Based on the same concept, in a third aspect, the present invention also provides an electronic device including a memory and a processor, the memory storing a computer program executable on the processor, the processor executing the program to implement the steps of the vascular stenosis detection method according to any one of the first aspects.
[0030] Based on the same concept, in a fourth aspect, the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vascular stenosis detection method according to any one of the first aspects.
[0031] The technical solution provided by this invention first acquires a PCCT image of a stent from any patient and locates the stent position within the image; further, it performs stent boundary recognition based on the stent position in the PCCT image to obtain at least a boundary fitting curve; further, it performs image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features; further, it inputs at least the vascular region features and the PCCT image of the stent into a pre-trained generative network model to output a corrected vascular image; finally, it performs a vascular stenosis detection operation based at least on the corrected vascular image to at least identify the vascular stenosis lesion. Therefore, this invention provides at least one PCCT-based in-stent vascular stenosis detection process, which can at least alleviate the difficulty of detecting in-stent vascular stenosis in existing methods and help reduce the interference of the stent on the stenosis detection results. Attached Figure Description
[0032] Figure 1 This is a flowchart of a method for detecting vascular stenosis provided in an embodiment of the present invention;
[0033] Figure 2 This is a flowchart of another method for detecting vascular stenosis provided in an embodiment of the present invention;
[0034] Figure 3 This is a schematic diagram of the structure of a vascular stenosis detection device provided in an embodiment of the present invention;
[0035] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation
[0036] To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0037] The terminology used in the embodiments of this application is for the purpose of describing particular embodiments only and is not intended to limit the application. The singular forms “a,” “said,” and “the” used in the embodiments of this application and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.
[0038] It should be understood that the term "and / or" used in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.
[0039] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of this application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, first may also be referred to as second without departing from the scope of the embodiments of this application, and similarly, second may also be referred to as first.
[0040] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrases “if determination” or “if detection (of the stated condition or event)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the stated condition or event)” or “in response to detection (of the stated condition or event).”
[0041] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.
[0042] It should be noted that any symbols and / or numbers present in the specification that are not marked in the accompanying drawings are not reference numerals.
[0043] Figure 1 This is a flowchart of a method for detecting vascular stenosis provided by an embodiment of the present invention. This embodiment is at least applicable to the scenario of detecting the risk of stenosis in in-stent vessels in clinical settings. This method for detecting vascular stenosis can be, but is not limited to, executed by the vascular stenosis detection device in this embodiment of the present invention as the executing entity, which can be implemented in software and / or hardware. For example... Figure 1 As shown, this method for detecting vascular stenosis includes at least the following steps:
[0044] S1. Obtain PCCT images of stents from any patient and locate the stent position in the images.
[0045] Step S1 can be achieved using any existing stent detection network, which can employ neural network models or multi-classification network models, etc.
[0046] In one specific implementation, the stent positioning process can be as follows:
[0047] (1) Obtain the pre-identified region of the stent in the PCCT image of the stent.
[0048] Specifically, the stent pre-identification area can be marked as area A, that is, area A in the stent PCCT image is the stent pre-identification area.
[0049] (2) Perform feature extraction on the pre-identified area of the stent to generate a feature image of a preset size.
[0050] Specifically, the pre-identified region of the support can be imaged to generate a local region image; further, feature extraction can be performed on the local region image to generate a feature image of a preset size.
[0051] It is understood that the preset size can refer to a fixed length or a fixed dimension. Specifically, region image extraction can be performed on the aforementioned region A to generate a first local region image, a second local region image, and a third local region image; further, a neural network is used to extract features from the first local region image to generate a first feature image of fixed length; further, a neural network is used to extract features from the second local region image to generate a second feature image of fixed length; further, a neural network is used to extract features from the third local region image to generate a third feature image of fixed length.
[0052] It is understandable that the first feature image, the second feature image, and the third feature image can have the same length. The reason for this setting is that, from the perspective of matrix representation, only feature images with the same size or length can be combined.
[0053] (3) Combine the features of the feature images to generate a pre-identified scaffold image.
[0054] Specifically, feature combination can be performed on the generated fixed-length first feature image, second feature image, and third feature image to generate a pre-identified scaffold image.
[0055] (4) Perform stent detection on the pre-identified stent image to locate the stent position in the stent PCCT image.
[0056] Specifically, stent detection can be performed on pre-identified stent images. If a pre-identified stent image is detected as a stent, the detection result of the stent in the stent PCCT image can be obtained, and the stent position in the stent PCCT image can be located.
[0057] S2. Perform stent boundary identification based on stent location in stent PCCT images to obtain at least a boundary fitting curve.
[0058] Since vascular stents are mostly tubular mesh structures, the stent boundaries in stent PCCT images may appear as "discontinuous highlights." Therefore, based on the stent location determined in step S1, pixels above a preset CT threshold can be selected from the stent PCCT image, and the outer and inner contours of the stent can be fitted accordingly. In another specific implementation, the boundary fitting curve refers at least to the aforementioned fitted inner contour of the stent.
[0059] S3. Perform image region segmentation based at least on the boundary fitting curve to obtain at least the vascular region features.
[0060] One approach is to segment vascular region features using image region segmentation methods based on deep learning algorithms, which may include the following steps:
[0061] Data acquisition and cleaning: Select the original PCCT images of the stent, acquire the selected original PCCT images of the stent and desensitize them, clean the data according to the preset exclusion criteria, remove case images that affect identification and annotation, eliminate potential image interference in the images, and integrate them into a research dataset for subsequent annotation, training and validation.
[0062] Data annotation: Desensitized image data is manually annotated using strategies such as semi-automatic iterative annotation and hierarchical annotation to reduce the annotation workload of doctors and improve the efficiency of building the image region segmentation database. Contour annotation follows a coarse-to-fine principle. First, the vascular region features are manually coarsely annotated to obtain the coarsely annotated vascular region features. Based on the coarsely annotated vascular region features, a preliminary image region segmentation model is built. Subsequently, the vascular region contours are automatically generated based on this model, manually annotated and modified, and the model is repeatedly trained and tested using subsequent batches of finely annotated data to iteratively optimize the model accuracy. This is the semi-automatic iterative annotation method.
[0063] Data partitioning: The labeled stent PCCT image atlas is divided into training and test sets according to a certain ratio. For example, the training set can be divided into 10 equal-sized folds, and after 10 cross-validations, the optimal model hyperparameters are selected through grid search. The model hyperparameters are then fine-tuned using 10x cross-validation on the training set. In this process, the training data is divided into roughly equal-sized subsets; training uses a subset, while validation is performed on the remaining subset. The optimized model then enters the testing phase, with the test set independent of the training set.
[0064] Blood vessel region segmentation: Based on fully convolutional neural networks, models including but not limited to BASNet, UNet, and Mask R-CNN can be used. The training loss function is adjusted, replacing the original model's fusion loss with Focal Loss. This simplifies the loss function while improving edge detection capabilities and training stability. A small amount of data is used as a validation set during training to verify the model's performance. By observing changes in the loss metrics of the training and validation sets, training is terminated promptly to prevent overfitting.
[0065] Test data: Input the test set, load the image region segmentation model, output the 3-value map of the blood vessel region segmentation (i.e. the aforementioned blood vessel region features), and save the output results.
[0066] S4. At least the vascular region features and stent PCCT images are input into the pre-trained generative network model to output corrected vascular images.
[0067] Among them, generative network models can be selected from GAN (Generative Adversarial Network), VAE (Variational Autoencoder), Flow (Flow Model), Diffusion (Diffusion Model), and AR (Autoregressive Model), etc.
[0068] For example, the basic idea of the Diffusion Model is to view the data generation process as a Markov chain. Starting with the target data, each step approaches random noise until a state of pure noise is reached. Then, through the reverse process, the data is gradually restored from pure noise to the target data. This process is typically described by a series of conditional probability distributions.
[0069] More specifically, the pre-training process of a Diffusion Model can include:
[0070] (1) Forward process: Starting from real data, noise is gradually added until a state of pure noise is reached. In this process, the noise level at each step needs to be calculated and saved.
[0071] (2) Reverse process: Starting from pure noise, the noise is gradually removed until the target data is recovered. In this process, a neural network (usually a U-Net structure) is used to predict the noise level at each step and generate data accordingly.
[0072] (3) Optimization: The model is trained by minimizing the difference between the real data and the generated data. Commonly used loss functions include MSE (mean squared error) and BCE (binary cross-entropy).
[0073] S5. Perform vascular stenosis detection procedures based at least on modified vascular images to at least identify vascular stenosis lesions.
[0074] The standard for detecting vascular stenosis can adopt the internationally used grading standard for the degree of coronary artery stenosis as follows: if the stenosis rate is <25%, it is considered as mild stenosis; if the stenosis rate is between 25% and 49%, it is considered as mild stenosis; if the stenosis rate is between 50% and 69%, it is considered as moderate stenosis; if the stenosis rate is ≥70%, it is considered as severe stenosis.
[0075] In another specific implementation, step S5 may optionally include at least: using a preset stenosis detection network (which may be part of a generative network model) to perform a vascular stenosis detection operation based at least on the modified vascular image, so as to at least identify vascular stenosis lesions.
[0076] The technical solution provided in this embodiment first acquires a PCCT image of a stent from any patient and locates the stent position within the image; further, it performs stent boundary recognition based on the stent position in the PCCT image to obtain at least a boundary fitting curve; further, it performs image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features; further, it inputs at least the vascular region features and the PCCT image of the stent into a pre-trained generative network model to output a corrected vascular image; finally, it performs a vascular stenosis detection operation based at least on the corrected vascular image to identify at least the vascular stenosis lesion. Therefore, this embodiment provides at least one PCCT-based in-stent vascular stenosis detection process, which can at least alleviate the difficulty of detecting in-stent vascular stenosis in existing methods and help reduce the interference of the stent on the stenosis detection results.
[0077] It should be noted that, in addition to the identification error caused by the tubular mesh structure of the vascular stent, the identification error caused by the volume effect of PCCT images should also be considered.
[0078] In view of this, in another specific implementation, optionally, image region segmentation is performed at least based on the boundary fitting curve to obtain at least vascular region features, specifically including:
[0079] Image region segmentation is performed at least based on the boundary fitting curve to obtain vascular region features and stent region features.
[0080] In another specific implementation, optionally, at least the vascular region features and stent PCCT images are input into the pre-trained generative network model to output corrected vascular images, specifically including at least:
[0081] The vascular region features, stent region features, and stent PCCT images are input into the pre-trained generative network model to output corrected vascular images.
[0082] Based on the technical solutions of the above embodiments, Figure 2 This is a flowchart of another method for detecting vascular stenosis provided in an embodiment of the present invention, such as... Figure 2 As shown, this method for detecting vascular stenosis includes at least the following steps:
[0083] S1. Obtain PCCT images of stents from any patient and locate the stent position in the images.
[0084] S2. Perform stent boundary identification based on stent location in stent PCCT images to obtain at least a boundary fitting curve.
[0085] S6. Perform image region segmentation based at least on the boundary fitting curve to obtain vascular region features and stent region features.
[0086] S7. Input the vascular region features, stent region features, and stent PCCT images into the pre-trained generative network model to output corrected vascular images.
[0087] S5. Perform vascular stenosis detection procedures based at least on modified vascular images to at least identify vascular stenosis lesions.
[0088] Compared to the previous embodiment, this embodiment obtains both vascular region features and stent region features through image region segmentation. It is understood that this embodiment, by inputting vascular region features, stent region features, and stent PCCT images into the pre-trained generative network model, produces a corrected vascular image that takes into account the recognition error caused by the volume effect of PCCT images, compared to the corrected vascular image output by the pre-trained generative network model in the previous embodiment, which only inputs vascular region features and stent PCCT images. This results in higher image accuracy and helps improve the precision of stenosis detection.
[0089] Figure 3 This is a schematic diagram of a vascular stenosis detection device provided in an embodiment of the present invention. This embodiment is at least applicable to the scenario of detecting the risk of stenosis in in-stent vessels in clinical settings. This vascular stenosis detection device can be implemented using software and / or hardware. Figure 3 As shown, the vascular stenosis detection device is used to perform the vascular stenosis detection method of any of the foregoing embodiments or implementations.
[0090] A vascular stenosis detection device includes at least:
[0091] The stent positioning module 110 is used to acquire stent PCCT images of any patient and locate the stent position in the images.
[0092] The boundary recognition module 120 is used to perform stent boundary recognition based on stent location in stent PCCT images to obtain at least a boundary fitting curve;
[0093] Region segmentation module 130 is used to perform image region segmentation based at least on boundary fitting curves to obtain at least vascular region features;
[0094] The image output module 140 is used to input at least the vascular region features and stent PCCT images into the pre-trained generative network model to output corrected vascular images.
[0095] The stenosis determination module 150 is used to perform a vascular stenosis detection operation based at least on the modified vascular image, so as to at least determine the vascular stenosis lesion.
[0096] Optionally, the region segmentation module 130 is specifically used for at least:
[0097] Image region segmentation is performed at least based on the boundary fitting curve to obtain vascular region features and stent region features.
[0098] Optionally, the image output module 140 is specifically used for at least:
[0099] The vascular region features, stent region features, and stent PCCT images are input into the pre-trained generative network model to output corrected vascular images.
[0100] Optionally, the narrowing determination module 150 is specifically used for at least:
[0101] Using a pre-defined stenosis detection network, vascular stenosis detection is performed at least based on modified vascular images to identify at least the stenotic lesions.
[0102] The technical solution provided in this embodiment first acquires PCCT images of stents from any patient using a stent positioning module and locates the stent position within the images. Further, a boundary recognition module performs stent boundary recognition in the PCCT images based on the stent position to obtain at least a boundary fitting curve. Further, a region segmentation module performs image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features. Further, an image output module inputs at least the vascular region features and the PCCT images of the stents into a pre-trained generative network model to output a corrected vascular image. Finally, a stenosis determination module performs vascular stenosis detection based at least on the corrected vascular image to determine at least the vascular stenosis lesion. Therefore, this embodiment provides at least one PCCT-based in-stent vascular stenosis detection process, which can alleviate the difficulty of detecting in-stent vascular stenosis in existing methods and reduce the interference of stents on stenosis detection results.
[0103] This embodiment provides an electronic device. Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. See also: Figure 4 The electronic device 1000 includes a processor 1001 and a memory 1002. The memory 1002 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 1001, the steps in any of the above methods for detecting vascular stenosis are performed. Through the above technical solution, the processor 1001 and the memory 1002 are interconnected and communicate with each other through a communication bus and / or other forms of connection mechanism (not shown). The memory 1002 stores a computer program executable by the processor. When the electronic device 1000 is running, the processor 1001 executes the computer program to perform the vascular stenosis detection method in any optional implementation of the above embodiments, so as to achieve at least the following functions: acquiring stent PCCT images of any patient and locating the stent position in the images; performing stent boundary recognition based on the stent position in the stent PCCT images to obtain at least a boundary fitting curve; performing image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features; inputting at least the vascular region features and the stent PCCT images into a pre-trained generative network model to output a corrected vascular image; and performing a vascular stenosis detection operation based at least on the corrected vascular image to determine at least the vascular stenosis lesion.
[0104] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the vascular stenosis detection method as provided in all embodiments of this application: acquiring a stent PCCT image of any patient and locating the stent position in the image; performing stent boundary recognition based on the stent position in the stent PCCT image to obtain at least a boundary fitting curve; performing image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features; inputting at least the vascular region features and the stent PCCT image into a pre-trained generative network model to output a corrected vascular image; and performing a vascular stenosis detection operation based at least on the corrected vascular image to determine at least the vascular stenosis lesion.
[0105] Any combination of one or more computer-readable media may be used. A computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium can be, for example—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.
[0106] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.
[0107] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0108] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0109] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.
Claims
1. A method of detecting a vascular stenosis, characterized in that, At least including: Acquire PCCT images of stents in any patient and locate the stent position in the images; Stent boundary identification is performed based on the stent location in the stent PCCT image to obtain at least a boundary fitting curve; Image region segmentation is performed at least based on the boundary fitting curve to obtain at least the vascular region features; The vascular region features and the stent PCCT images are at least input into the pre-trained generative network model to output corrected vascular images; At least the vascular stenosis detection procedure is performed based on the modified vascular image to at least identify the vascular stenosis lesion.
2. The method of claim 1, wherein, The step of performing image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features specifically includes: Image region segmentation is performed at least based on the boundary fitting curve to obtain the vascular region features and stent region features.
3. The method of claim 2, wherein the step of detecting a stenosis comprises the step of: The step of inputting at least the vascular region features and the stent PCCT image into the pre-trained generative network model to output a corrected vascular image specifically includes at least the following: The vascular region features, the stent region features, and the stent PCCT image are input into the pre-trained generative network model to output the corrected vascular image.
4. The method of claim 1, wherein the step of detecting a stenosis comprises the step of: The step of performing vascular stenosis detection based at least on the modified vascular image to at least identify vascular stenosis lesions specifically includes at least: The vascular stenosis detection operation is performed using a preset stenosis detection network based at least on the modified vascular image, so as to at least identify the vascular stenosis lesion.
5. A blood vessel stenosis detection apparatus characterized by comprising: Used to perform the method for detecting vascular stenosis according to any one of claims 1-4; The vascular stenosis detection device includes at least: The stent positioning module is used to acquire stent PCCT images of any patient and locate the stent position in the images. A boundary recognition module is used to perform stent boundary recognition based on the stent location in the stent PCCT image, so as to obtain at least a boundary fitting curve; A region segmentation module is used to perform image region segmentation based at least on the boundary fitting curve to obtain at least vascular region features; The image output module is used to input at least the vascular region features and the stent PCCT image into the pre-trained generative network model to output the corrected vascular image. The stenosis determination module is used to perform a vascular stenosis detection operation based at least on the modified vascular image, so as to at least determine the vascular stenosis lesion.
6. The vascular stenosis detection apparatus of claim 5, wherein The region segmentation module is specifically used for at least: Image region segmentation is performed at least based on the boundary fitting curve to obtain the vascular region features and stent region features.
7. The vascular stenosis detection apparatus of claim 6, wherein The image output module is specifically used for at least: The vascular region features, the stent region features, and the stent PCCT image are input into the pre-trained generative network model to output the corrected vascular image.
8. The vascular stenosis detection apparatus of claim 5, wherein The narrowing determination module is specifically used for at least: The vascular stenosis detection operation is performed using a preset stenosis detection network based at least on the modified vascular image, so as to at least identify the vascular stenosis lesion.
9. An electronic device comprising a memory and a processor, said memory storing a computer program operable on said processor, characterized in that, When the processor executes the program, it implements the steps in the vascular stenosis detection method according to any one of claims 1 to 4.
10. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps in the method for detecting vascular stenosis according to any one of claims 1 to 4.