CN111145173A - Plaque identification method, device, equipment and medium for coronary angiography image - Google Patents
Plaque identification method, device, equipment and medium for coronary angiography image Download PDFInfo
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Abstract
The embodiment of the invention discloses a plaque identification method, a device, equipment and a medium of a coronary angiography image, wherein the method comprises the following steps: acquiring a coronary angiography image to be identified; and inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unmarked coronary angiography image and a marked coronary angiography-free image. The plaque identification method of the coronary angiography image provided by the embodiment of the invention trains the plaque identification model based on the labeled coronary non-angiography image and the unlabeled coronary angiography image, so that the plaque identified by the plaque identification model is more accurate.
Description
Technical Field
The embodiment of the invention relates to the technical field of images, in particular to a plaque identification method, a device, equipment and a medium for a coronary angiography image.
Background
Coronary stenosis is a significant cause of angina, myocardial infarction and sudden cardiac death. Therefore, the detection of coronary stenosis is particularly important. In the process of detecting coronary stenosis, the plaque extraction needs to be carried out on the coronary image. The existing plaque detection is divided into hard plaque detection and soft plaque detection, and in the coronary artery extraction process, because the contrast blood vessel, the stent and the hard plaque also present higher CT values, the stent and the hard plaque can be extracted together during the coronary artery extraction, while the soft plaque presents lower CT values and can not be extracted. Thus, if a true coronary lumen is desired, the hard plaque and stent need to be removed from the coronary. However, the distribution region of the CT values of the hard plaque and the stent is overlapped with the CT value of the contrast blood vessel, so that the threshold value for distinguishing the CT values of the hard plaque, the stent and the contrast blood vessel is difficult to set, and the accuracy of the extracted lumen is low.
Disclosure of Invention
The embodiment of the invention provides a plaque identification method, a device, equipment and a medium of a coronary angiography image, which are used for improving the plaque identification accuracy in the coronary angiography image and further improving the lumen extraction accuracy.
In a first aspect, an embodiment of the present invention provides a plaque identification method for a coronary angiography image, including:
acquiring a coronary angiography image to be identified;
and inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unmarked coronary angiography image and a marked coronary angiography-free image.
In a second aspect, an embodiment of the present invention further provides a plaque identification apparatus for a coronary angiography image, including:
the contrast image acquisition module is used for acquiring a coronary angiography image to be identified;
and the image patch identification module is used for inputting the coronary angiography image to be identified into a completely trained patch identification model and obtaining an identification result output by the patch identification model, wherein a training sample of the patch identification model is generated based on an unlabeled coronary angiography image and an labeled coronary angiography non-contrast image.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for plaque identification of a coronary image as provided by any of the embodiments of the invention.
In a fourth aspect, the embodiments of the present invention further provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements a plaque identification method for a coronary angiography image as provided in any of the embodiments of the present invention.
The embodiment of the invention obtains the coronary angiography image to be identified; inputting the coronary angiography image to be recognized into a completely trained plaque recognition model, and obtaining a recognition result output by the plaque recognition model, wherein a training sample of the plaque recognition model is generated based on an unmarked coronary angiography image and a marked coronary angiography image, and the plaque recognition model is trained based on the marked coronary angiography image and the unmarked coronary angiography image, so that the plaque recognized by the plaque recognition model is more accurate.
Drawings
Fig. 1 is a flowchart of a method for identifying a plaque in a coronary angiography image according to an embodiment of the present invention;
fig. 2 is a flowchart of a plaque identification method for a coronary angiography image according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a plaque identification apparatus for a coronary angiography image according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for identifying a plaque in a coronary angiography image according to an embodiment of the present invention. The present embodiment is applicable to a case when a plaque is recognized in a coronary angiography image. The method may be performed by a plaque identification apparatus of a coronary image, which may be implemented in software and/or hardware, for example, which may be configured in a computer device. As shown in fig. 1, the method includes:
and S110, acquiring a coronary angiography image to be identified.
In this embodiment, the coronary angiography image to be identified may be an angiography image in which the coronary tree and the coronary centerline have been extracted and the naming of the coronary centerline is completed. Optionally, after the coronary angiography image is obtained, the seed points in the coronary angiography image are extracted, the coronary artery tree is extracted from the coronary angiography image based on the selected seed points according to a region growing algorithm or a level set algorithm, after the coronary artery is extracted, the coronary artery center tree is extracted from the coronary artery tree by adopting a skeletonization method or a level set-based center line extraction method on the basis of a coronary artery mask, and then the extracted coronary artery center tree is named by using a model matching method or a deep learning method, so that the coronary angiography image to be identified is obtained.
And S120, inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on the unmarked coronary angiography image and the marked coronary angiography non-angiography image.
In order to solve the problems of inaccurate plaque identification and the like in the prior art, the embodiment of the invention identifies the coronary angiography image to be identified through a machine learning algorithm to obtain the identification result of the coronary angiography image to be identified. Specifically, the coronary angiography image to be identified is input into a completely trained plaque identification model, and an identification result output by the plaque identification model, namely an identification result of the coronary angiography image to be identified is obtained. Alternatively, the plaque identification model may be constructed based on a neural network. The Neural Network is a module constructed based on an Artificial Neural Network (ANN). The artificial neural network is formed by connecting a large number of nodes (or called neurons) with each other. Each node represents a particular output function, called the excitation function. Each connection between two nodes represents a weighted value, called weight, for the signal passing through the connection. The neural network comprises a data input layer, a middle hiding layer and a data output layer. In this embodiment, the Neural network may be a Convolutional Neural Network (CNN), a Generic Adaptive Network (GAN) or other types of Neural network models.
On the basis of the scheme, the training method of the plaque recognition model comprises the following steps:
acquiring a sample coronary angiography image and an annotated sample coronary non-angiography image;
registering the sample coronary angiography image with the sample coronary non-angiography image to obtain a mark point of the sample coronary angiography image corresponding to the mark point in the sample coronary non-angiography image;
and generating a recognition training sample based on the sample coronary angiography image and the mark points of the sample coronary angiography image, and training a pre-established plaque recognition model by using the recognition training sample to obtain a completely trained plaque recognition model.
Since the contrast blood vessel in the contrast image has high brightness, the CT value distribution region of the hard plaque and the stent coincides with the CT value of the contrast blood vessel in the contrast image, and thus it is difficult to distinguish them. And the blood vessels in the non-contrast image have low brightness, so the CT values of the hard plaque and the stent in the non-contrast image can be obviously distinguished from the CT values of the blood vessels. In the embodiment, the sample coronary non-contrast image is labeled, the labeling point of the sample coronary non-contrast image corresponding to the sample coronary non-contrast image is generated based on the labeling point in the sample coronary non-contrast image, and the labeled sample coronary contrast image is used for training the pre-established plaque identification model, so that the plaque identification model obtained by training is more accurate, and the identification result obtained based on the plaque identification model is more accurate.
Specifically, a sample coronary angiography image and a sample coronary non-angiography image corresponding to the sample coronary angiography image are obtained, a hard plaque and a stent in the sample coronary non-angiography image are manually marked, a marked sample coronary non-angiography image is obtained, then the marked sample coronary non-angiography image and an unmarked sample coronary angiography image are subjected to image registration, a sample coronary angiography image with marked points is obtained, a pre-established plaque identification model is trained by using the sample coronary angiography image with the marked points, and a well-trained plaque identification model is obtained. The method for performing image registration on the labeled sample coronary non-contrast image and the unlabeled sample coronary contrast image is not limited herein.
Illustratively, the image registration may be performed using a gray-scale and template-based image registration method (e.g., a mean absolute difference algorithm, a sum of absolute errors algorithm, a sum of squared errors algorithm, a normalized product correlation algorithm, a sequential similarity detection algorithm, a hadamard transform algorithm, a local gray value coding algorithm, etc.), a feature-based image registration method (e.g., a point feature-based registration method, a line feature-based registration method, a region feature-based registration method, a local feature-based registration method, a global feature-based registration method, etc.), or a domain transform-based method (e.g., a phase correlation algorithm, a walsh transform method, etc.), etc.
In the embodiment, the hard plaque and the stent in the sample coronary non-contrast image are manually marked, and the marking point in the sample contrast image is determined according to the marking point in the sample coronary non-contrast image, so that the problem of inaccuracy of a training sample caused by directly marking the sample coronary non-contrast image is solved, the sample accuracy of the plaque identification model is improved, and the identification accuracy of the plaque identification model is further improved.
The embodiment of the invention obtains the coronary angiography image to be identified; inputting the coronary angiography image to be recognized into a completely trained plaque recognition model, and obtaining a recognition result output by the plaque recognition model, wherein a training sample of the plaque recognition model is generated based on an unmarked coronary angiography image and a marked coronary angiography image, and the plaque recognition model is trained based on the marked coronary angiography image and the unmarked coronary angiography image, so that the plaque recognized by the plaque recognition model is more accurate.
Example two
Fig. 2 is a flowchart of a plaque identification method for a coronary angiography image according to a second embodiment of the present invention. The present embodiment is further optimized on the basis of the above embodiments. As shown in fig. 2, the method includes:
s210, acquiring a coronary angiography image, and extracting a coronary artery tree and a coronary artery central line from the coronary angiography image.
In the present embodiment, the coronary artery tree extraction is embodied. Firstly, a coronary angiography image is obtained, and a coronary artery tree and a coronary artery central line are extracted from the coronary angiography image. Alternatively, the coronary artery tree may be extracted by a region growing method. Wherein, the seed point can be one or more. Considering that the coronary tree is divided into a left coronary artery and a right coronary artery, more than two seed points are selected for extracting the coronary tree. Optionally, a plurality of seed points may be selected along the boundary line of the coronary artery. Optionally, a template matching method may be used to extract seed points from the coronary angiography image. Illustratively, a template image marked with coronary artery is made in advance, the acquired coronary angiography image and the template image are registered, so that the position of the coronary artery in the coronary angiography image can be determined according to the position of the coronary artery in the template image, the opening position of the coronary artery on the aorta is determined, and the seed point is selected based on the opening position. However, the accuracy of the template matching method is low, and in order to improve the accuracy of the selected seed points, a deep learning method may be used to extract the seed points.
In one embodiment of the present invention, the extracting a coronary artery tree from the coronary angiography image includes:
inputting the coronary angiography image into a seed point extraction model with complete training to obtain seed points output by the seed point extraction model; performing image enhancement on the coronary angiography image to obtain a coronary angiography enhanced image of the coronary angiography image; extracting the coronary artery tree from the coronary angiography enhanced image based on the seed points through a region growing algorithm.
In this embodiment, a seed point position (for example, at a coronary artery opening) in a large number of sample coronary angiography images is marked, and a seed point extraction model which is established in advance is trained through a deep learning method, so as to obtain a seed point extraction model with complete training. After acquiring the coronary angiography image, inputting the coronary angiography image into a seed point extraction model with complete training, and acquiring the seed points output by the seed point extraction model to be used as the seed points of the coronary angiography image.
In order to make the extraction of the coronary artery tree more accurate, before the extraction of the coronary artery tree is carried out, the coronary artery contrast image is enhanced, and a coronary artery contrast enhanced image is obtained. The method for enhancing the coronary angiography image is not limited herein. Illustratively, a line enhancement method of a black plug (Hessian) matrix can be adopted to obtain a coronary contrast enhanced image, and a deep learning-based method can also be adopted to obtain the coronary contrast enhanced image. Optionally, obtaining the coronary angiography enhanced image based on the deep learning method may be: and inputting the coronary angiography image into a completely trained enhanced model to obtain a coronary angiography enhanced image output by the enhanced model. The enhancement model is trained on the sample coronary angiography image and the sample coronary angiography enhancement image corresponding to the sample coronary angiography image.
In the present embodiment, the order of the operation of enhancing the coronary angiography image and the operation of extracting the seed point is not limited. Namely, seed points can be extracted from the coronary angiography image, and then the coronary angiography image is enhanced; or the coronary angiography image can be enhanced first, and then the seed points are extracted from the coronary angiography image; the enhancement of the coronary image and the extraction of seed points from the coronary image may also be performed simultaneously.
After obtaining the seed points of the coronary angiography image and obtaining the coronary angiography enhanced image, a certain enhancement threshold value may be set in the coronary angiography enhanced image, region growth may be performed from the positions of the seed points, and after the region growth is completed, the extracted coronary artery tree may be obtained. Or, combining the enhanced image with the original image, that is, setting a certain gray threshold in the coronary angiography image, setting a certain enhanced threshold in the coronary angiography enhanced image, performing region growing from the position of the seed point, and obtaining the extracted coronary artery tree after the region growing is completed. After the coronary artery tree is extracted, the coronary artery central line can be extracted by adopting a skeletonization method or a central line extraction method based on a level set.
S220, naming the coronary artery central line to obtain the coronary artery angiography image to be identified.
In this embodiment, after the coronary artery central line is extracted, the coronary artery central line is named, so that the detection result is easy to locate during subsequent plaque identification or coronary artery stenosis detection. On the basis of the coronary artery central line tree, the names of all the coronary artery central lines can be named, and the main names of the coronary artery central lines comprise a left coronary artery trunk, a left anterior descending branch, a left circumflex branch, a left diagonal branch, a left blunt edge branch, a right coronary artery, a right descending branch and the like. Alternatively, the coronary centerline naming can adopt a model matching method. Illustratively, according to sample data, the names of the coronary artery central lines are marked manually, the central lines on the sample are averaged, a central line average model is manufactured, then the model is matched to the image where the coronary artery central lines are located through methods such as point registration, and the name of each coronary artery central line is named as the name of the central line on the model close to the model. In order to reduce the error rate, besides model matching, a machine learning method can be adopted, and the accuracy rate is further improved.
In one embodiment of the present invention, the naming of the coronary centerline includes:
inputting the coronary artery central line into a completely trained central line naming model to obtain a naming result output by the central line naming model.
Preferably, the machine learning method is used for naming the coronary artery central line, and the machine learning method is used for naming the coronary artery central line, so that the naming of the coronary artery central line is more accurate. Specifically, the coronary artery central line is input into a completely trained central line naming model, a naming result output by the central line naming model is obtained, and the coronary artery central line is named according to the output naming result.
On the basis of the scheme, the method for training the centerline naming model comprises the following steps:
obtaining a center line parameter in a center line model and a center line name corresponding to the center line model;
and generating a naming training sample based on the centerline parameter and the centerline name corresponding to the centerline model, and training a pre-established centerline naming model by using the naming training sample to obtain a well-trained centerline naming model.
Optionally, the centerline parameter may be a characteristic parameter of each coronary centerline in the coronary centerline model, such as a length of the coronary centerline, an angle of the coronary centerline, and the like, and the centerline parameter and the centerline model are used to train the pre-established centerline naming model, so as to obtain a completely trained centerline naming model.
And S230, inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on the unmarked coronary angiography image and the marked coronary angiography non-angiography image.
S240, generating a to-be-detected coronary angiography image based on the to-be-identified coronary angiography image and the identification result.
In this embodiment, after the plaque and the stent in the coronary angiography image to be identified are identified, the plaque and the stent in the coronary angiography image to be identified are removed, and the coronary angiography image to be detected which only includes the blood vessel lumen is obtained.
And S250, carrying out stenosis detection on the coronary angiography image to be detected, and outputting a detection result.
And after obtaining the coronary angiography image to be detected, carrying out stenosis detection on the coronary angiography image to be detected. Optionally, the cross-sectional profile, area, length and diameter (or diameter) of each coronary artery may be calculated, fitting is performed, and whether the coronary artery is narrow or not is determined according to a fitting result. Optionally, if the vessel diameter of a coronary artery is smaller than the vessel diameters of the coronary artery upstream and downstream, and a certain proportion threshold is reached, then a stenosis is considered to exist at the coronary artery.
In another embodiment of the present invention, the stenosis detection can also be performed on the coronary angiography image to be detected by a machine learning method. Optionally, the coronary angiography image to be detected is input into a stenosis detection model with complete training, and a detection result output by the stenosis detection model is obtained. The well-trained stenosis detection model is trained based on a sample coronary angiography image and a detection result corresponding to the sample coronary angiography image.
On the basis of the scheme, after the narrow detection is carried out on the tube cavity, the detection result can be output. In the embodiment, the operations of coronary artery segmentation, centerline extraction, centerline naming, plaque and stent detection and stenosis detection are automatically completed, and the results corresponding to the steps can be displayed on the same interface, so that the user can edit inaccurate places in the detection process without switching the interface or performing steps during coronary artery stenosis detection. After receiving the editing operation of the user, the result of the subsequent affected step can be automatically updated according to the editing operation of the user.
According to the technical scheme of the embodiment of the invention, the accuracy of coronary artery extraction, center line naming, plaque detection and stenosis detection is improved, so that the detection result can be obtained without user intervention in the coronary artery stenosis detection process, and the results of all the steps are directly presented to the user on the same interface, so that the technical effect of directly modifying the final detection result without checking and modifying the steps of the user under the condition that the automatic extraction cannot meet the requirement or the user needs to modify part of parameters is achieved.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a plaque identification apparatus for a coronary angiography image according to a third embodiment of the present invention. The means for identifying plaque in a coronary image may be implemented in software and/or hardware, for example, the means for identifying plaque in a coronary image may be configured in a computer device. As shown in fig. 3, the apparatus includes a contrast image acquisition module 310 and an image blob identifying module 320, wherein:
a contrast image acquisition module 310, configured to acquire a coronary angiography image to be identified;
an image patch identification module 320, configured to input the coronary angiography image to be identified into a completely trained patch identification model, and obtain an identification result output by the patch identification model, where a training sample of the patch identification model is generated based on an unlabeled coronary angiography image and an labeled coronary angiography-free image.
According to the embodiment of the invention, a coronary angiography image to be identified is acquired through an angiography image acquisition module; the image plaque identification module inputs the coronary angiography image to be identified into a completely trained plaque identification model, and obtains an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unlabeled coronary angiography image and an labeled coronary angiography image, and the plaque identification model is trained based on the labeled coronary angiography image and the unlabeled coronary angiography image, so that the plaque identified by the plaque identification model is more accurate.
On the basis of the above scheme, the apparatus further includes a recognition model training module, configured to:
acquiring a sample coronary angiography image and an annotated sample coronary non-angiography image;
registering the sample coronary angiography image with the sample coronary non-angiography image to obtain a mark point of the sample coronary angiography image corresponding to the mark point in the sample coronary non-angiography image;
and generating a recognition training sample based on the sample coronary angiography image and the mark points of the sample coronary angiography image, and training a pre-established plaque recognition model by using the recognition training sample to obtain a completely trained plaque recognition model.
On the basis of the above scheme, the contrast image acquisition module 310 includes:
a coronary artery extraction unit, which is used for acquiring a coronary angiography image and extracting a coronary artery tree and a coronary artery central line from the coronary angiography image;
and the central line naming unit is used for naming the coronary artery central line to obtain the coronary angiography image to be identified.
On the basis of the above scheme, the coronary artery extraction unit is specifically configured to:
inputting the coronary angiography image into a seed point extraction model with complete training to obtain seed points output by the seed point extraction model;
performing image enhancement on the coronary angiography image to obtain a coronary angiography enhanced image of the coronary angiography image;
extracting the coronary artery tree from the coronary angiography enhanced image based on the seed points through a region growing algorithm.
On the basis of the above scheme, the centerline naming unit is specifically configured to:
inputting the coronary artery central line into a completely trained central line naming model to obtain a naming result output by the central line naming model.
On the basis of the scheme, the device further comprises a naming model training module, which is used for:
obtaining a center line parameter in a center line model and a center line name corresponding to the center line model;
and generating a naming training sample based on the centerline parameter and the centerline name corresponding to the centerline model, and training a pre-established centerline naming model by using the naming training sample to obtain a well-trained centerline naming model.
On the basis of the scheme, the device further comprises a coronary stenosis detection module, which is used for:
generating a to-be-detected coronary angiography image based on the to-be-identified coronary angiography image and the identification result;
and carrying out stenosis detection on the coronary angiography image to be detected, and outputting a detection result.
The plaque identification device of the coronary angiography image provided by the embodiment of the invention can execute the plaque identification method of the coronary angiography image provided by any embodiment, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary computer device 412 suitable for use in implementing embodiments of the present invention. The computer device 412 shown in FIG. 4 is only one example and should not impose any limitations on the functionality or scope of use of embodiments of the present invention.
As shown in FIG. 4, computer device 412 is in the form of a general purpose computing device. Components of computer device 412 may include, but are not limited to: one or more processors 416, a system memory 428, and a bus 418 that couples the various system components (including the system memory 428 and the processors 416).
Computer device 412 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 412 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 428 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)430 and/or cache memory 432. The computer device 412 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage 434 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 418 by one or more data media interfaces. Memory 428 can include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 440 having a set (at least one) of program modules 442 may be stored, for instance, in memory 428, such program modules 442 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. The program modules 442 generally perform the functions and/or methodologies of the described embodiments of the invention.
The computer device 412 may also communicate with one or more external devices 414 (e.g., keyboard, pointing device, display 424, etc.), with one or more devices that enable a user to interact with the computer device 412, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 412 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 422. Also, computer device 412 may 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) through network adapter 420. As shown, network adapter 420 communicates with the other modules of computer device 412 over bus 418. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 412, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processor 416 executes programs stored in the system memory 428 to perform various functional applications and data processing, such as implementing a plaque identification method for a coronary angiography image provided by an embodiment of the present invention, the method including:
acquiring a coronary angiography image to be identified;
and inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unmarked coronary angiography image and a marked coronary angiography-free image.
Of course, those skilled in the art can understand that the processor may also implement the technical solution of the method for identifying a plaque in a coronary angiography image according to any embodiment of the present invention.
EXAMPLE five
An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for identifying a plaque in a coronary angiography image, where the method includes:
acquiring a coronary angiography image to be identified;
and inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unmarked coronary angiography image and a marked coronary angiography-free image.
Of course, the computer program stored on the computer-readable storage medium provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in the method for identifying a plaque in a coronary angiography image provided by any embodiments of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for identifying a plaque in a coronary angiography image, comprising:
acquiring a coronary angiography image to be identified;
and inputting the coronary angiography image to be identified into a completely trained plaque identification model, and obtaining an identification result output by the plaque identification model, wherein a training sample of the plaque identification model is generated based on an unmarked coronary angiography image and a marked coronary angiography-free image.
2. The method of claim 1, wherein the training of the blob identification model comprises:
acquiring a sample coronary angiography image and an annotated sample coronary non-angiography image;
registering the sample coronary angiography image with the sample coronary non-angiography image to obtain a mark point of the sample coronary angiography image corresponding to the mark point in the sample coronary non-angiography image;
and generating a recognition training sample based on the sample coronary angiography image and the mark points of the sample coronary angiography image, and training a pre-established plaque recognition model by using the recognition training sample to obtain a completely trained plaque recognition model.
3. The method of claim 1, wherein the acquiring a coronary angiographic image to be identified comprises:
acquiring a coronary angiography image, and extracting a coronary artery tree and a coronary artery central line from the coronary angiography image;
naming the coronary artery central line to obtain the coronary artery angiography image to be identified.
4. The method of claim 3, wherein said extracting a coronary tree from said coronary angiography image comprises:
inputting the coronary angiography image into a seed point extraction model with complete training to obtain seed points output by the seed point extraction model;
performing image enhancement on the coronary angiography image to obtain a coronary angiography enhanced image of the coronary angiography image;
extracting the coronary artery tree from the coronary angiography enhanced image based on the seed points through a region growing algorithm.
5. The method of claim 3, wherein the naming the coronary centerline comprises:
inputting the coronary artery central line into a completely trained central line naming model to obtain a naming result output by the central line naming model.
6. The method of claim 5, wherein the method of training the centerline naming model comprises:
obtaining a center line parameter in a center line model and a center line name corresponding to the center line model;
and generating a naming training sample based on the centerline parameter and the centerline name corresponding to the centerline model, and training a pre-established centerline naming model by using the naming training sample to obtain a well-trained centerline naming model.
7. The method of claim 1, after obtaining the identification result output by the plaque identification model, further comprising:
generating a to-be-detected coronary angiography image based on the to-be-identified coronary angiography image and the identification result;
and carrying out stenosis detection on the coronary angiography image to be detected, and outputting a detection result.
8. A plaque identification apparatus for a coronary angiography image, comprising:
the contrast image acquisition module is used for acquiring a coronary angiography image to be identified;
and the image patch identification module is used for inputting the coronary angiography image to be identified into a completely trained patch identification model and obtaining an identification result output by the patch identification model, wherein a training sample of the patch identification model is generated based on an unlabeled coronary angiography image and an labeled coronary angiography non-contrast image.
9. A computer device, the device comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement a method for plaque identification of a coronary image as recited in any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a method for plaque identification of a coronary image as claimed in any one of claims 1 to 7.
Priority Applications (4)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911423770.6A CN111145173B (en) | 2019-12-31 | Plaque identification method, device, equipment and medium of coronary angiography image | |
| EP20910740.8A EP4066207A4 (en) | 2019-12-31 | 2020-12-29 | Systems and methods for image processing |
| PCT/CN2020/141089 WO2021136304A1 (en) | 2019-12-31 | 2020-12-29 | Systems and methods for image processing |
| US17/810,322 US20220335613A1 (en) | 2019-12-31 | 2022-06-30 | Systems and methods for image processing |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911423770.6A CN111145173B (en) | 2019-12-31 | Plaque identification method, device, equipment and medium of coronary angiography image |
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| CN111145173A true CN111145173A (en) | 2020-05-12 |
| CN111145173B CN111145173B (en) | 2024-04-26 |
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