CN111862038A - Plaque detection method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the invention discloses a plaque detection method, a plaque detection device, plaque detection equipment and a plaque detection medium. The method comprises the following steps: acquiring a coronary artery image sequence to be detected; generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected; inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected; and determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected. The technical scheme of the embodiment of the invention solves the problems of high detection difficulty and inaccurate detection of the existing coronary artery plaque detection technology, and achieves the effects of reducing the detection difficulty of the coronary artery plaque and improving the plaque detection efficiency and accuracy.
Description
Technical Field
The embodiment of the invention relates to a medical image technology, in particular to a plaque detection method, a device, equipment and a medium.
Background
Coronary arteries are arteries responsible for supplying blood to the heart, and when atherosclerotic lesions occur in coronary arteries, the arteries can cause stenosis or obstruction of blood vessels, and further myocardial ischemia, hypoxia or necrosis is caused, so that the coronary heart disease is commonly called.
Coronary Computed Tomography Angiography (CCTA) is a means of routine Coronary examination, and determines the location, nature and extent of Coronary atherosclerosis in a patient from CCTA data. Coronary atherosclerosis is classified into calcified plaque, non-calcified plaque, and mixed plaque having both calcified and non-calcified properties.
The currently commonly used method for judging the property of coronary artery plaque is as follows: firstly, extracting coronary artery and separating the coronary artery based on CCTA data, then reconstructing the separated coronary artery one by utilizing a reconstruction technology and judging the stenosis position and the plaque property. The coronary artery reconstruction effect depends on the extraction and separation effect of the coronary artery, the extraction and separation difficulty of the small branch coronary artery is high, time is consumed, especially when the stenosis rate of a certain coronary artery reaches 100%, the coronary artery is completely blocked, the result displayed on the image is that one coronary artery is cut into two parts, at this time, the second cut coronary artery cannot be extracted, and the subsequent coronary artery reconstruction also fails. Therefore, the existing method for judging the property of the coronary plaque has the problems of great difficulty and low efficiency.
Disclosure of Invention
The embodiment of the invention provides a plaque detection method, a device, equipment and a medium, which are used for achieving the effects of reducing the difficulty of coronary plaque detection and improving the plaque detection efficiency.
In a first aspect, an embodiment of the present invention provides a plaque detection method, where the method includes:
acquiring a coronary artery image sequence to be detected;
generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected;
inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected;
and determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
In a second aspect, an embodiment of the present invention further provides a plaque detection apparatus, including:
the image sequence acquisition module is used for acquiring a coronary artery image sequence to be detected;
the image combination generating module is used for generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, and the coronary artery image combination to be detected comprises at least two coronary artery images to be detected;
The middle plaque detection result acquisition module is used for inputting the combination of the coronary artery images to be detected into a trained plaque detection model to obtain the middle plaque detection result of each coronary artery image to be detected;
and the final plaque detection result determining module is used for determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
In a third aspect, an embodiment of the present invention further provides an apparatus, where the apparatus 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 plaque detection method as provided by any of the embodiments of the invention.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements the plaque detection method provided in any embodiment of the present invention.
The method comprises the steps of obtaining a coronary artery image sequence to be detected; generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected; inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result aiming at the coronary artery image combination to be detected; and determining a final plaque detection result based on the intermediate plaque detection result, solving the problems of high detection difficulty and inaccurate detection of the existing coronary artery plaque detection technology, and realizing the effects of reducing the detection difficulty of the coronary artery plaque and improving the plaque detection efficiency and accuracy.
Drawings
FIG. 1 is a flow chart of a plaque detection method according to one embodiment of the present invention;
FIG. 2 is a schematic diagram of plaque detection and vessel segmentation in accordance with a first embodiment of the present invention;
FIG. 3 is a flowchart of a plaque detection method according to a second embodiment of the present invention;
FIG. 4 is a block diagram of a plaque detection apparatus in a third embodiment of the invention;
fig. 5 is a schematic structural diagram of an apparatus 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 plaque detection method according to an embodiment of the present invention, where the embodiment is applicable to a plaque detection situation, and the method may be executed by a plaque detection apparatus, and specifically includes the following steps:
and S110, acquiring a coronary artery image sequence to be detected.
The method comprises the steps of obtaining an image sequence obtained after a medical imaging device scans coronary arteries, carrying out windowing pretreatment on the obtained coronary artery image sequence, compressing pixel values of the images within the range of 0-255, enabling only information needed by the coronary arteries and the like to be displayed in the image sequence, avoiding interference of other useless information on coronary artery plaque detection, improving the accuracy of plaque detection, compressing the pixel value range of the images within a preset range, avoiding the inaccurate plaque detection of a plaque detection model due to the fact that the pixel value range is too large, and reducing the accuracy of the plaque detection.
S120, generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected.
Combining a preset number of adjacent images in the coronary artery image sequence to be detected into at least one coronary artery image combination to be detected, wherein the preset number is at least two. At least one image combination is input into the plaque detection model. Optionally, generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected includes: selecting a preset number of adjacent coronary artery images to be detected as a first coronary artery image combination to be detected from a first coronary artery image to be detected in the coronary artery image sequence to be detected; selecting a preset number of adjacent coronary artery images to be detected as a second coronary artery image combination from a second coronary artery image to be detected in the coronary artery image sequence to be detected; and repeating the operation until each coronary artery image to be detected in the sequence of the coronary artery images to be detected is traversed. Illustratively, the obtained sequence of the coronary artery images to be detected comprises 20 images, and the 1 st to 3 th coronary artery images to be detected are selected in sequence from the first coronary artery image to be detected for the first time to obtain a first coronary artery image combination to be detected; selecting 2-4 coronary artery images to be detected in sequence from the second coronary artery image to be detected for the second time to obtain a second coronary artery image combination to be detected; repeating the above operations, selecting 3 adjacent coronary artery images to be detected each time to obtain a coronary artery image combination to be detected, and traversing each coronary artery image to be detected in the coronary artery image sequence to obtain all image combinations of the coronary artery image sequence to be detected, as shown in fig. 2(a), a generated image combination. The image generation image combination in the coronary artery image sequence to be detected is input into the plaque detection model, so that the detection model can obtain more image information, the detection of the plaque in the image by the detection model is facilitated, and the plaque detection is more accurate.
And S130, combining and inputting the coronary artery images to be detected into the trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected.
And inputting the coronary artery image combination to be detected into the plaque detection model to obtain the plaque detection result of the middle sequence of the coronary artery images to be detected in the coronary artery image combination to be detected. In order to enable the plaque detection model to obtain more information of the coronary artery image, the coronary artery image to be detected is generated into the coronary artery image to be detected and is input to the plaque detection model in a combined mode, and the obtained plaque detection result is more accurate.
The plaque detection model is trained in advance and is used for detecting the plaque property and the plaque position of the coronary artery image to be detected; because a large number of focuses are concentrated on some thick branches such as a front descending branch and a circumflex branch, and the number of focus samples on a fine branch is relatively small, when a plaque detection model is trained, data enhancement needs to be carried out on the fine branch focus by means of translation, rotation and the like, the number proportion of the fine branch focus samples in the training samples is increased, and therefore the detection accuracy of the plaque detection model on the focuses on the fine branches is improved.
Plaque properties include: calcified plaque, non-calcified plaque and mixed plaque. Since the number of non-calcified plaque samples is small, the learning difficulty of the plaque detection model is high, and the proportion of the non-calcified plaque in the training sample needs to be improved. The plaque detection model with short training time is used, and due to the fact that the training time is short, a large amount of non-calcified plaque false positive data can be generated by the plaque detection model, and the non-calcified plaque false positive data are used as input data to be added into a training data set of the plaque detection model, so that the plaque detection model is optimized, and the accuracy rate of the plaque detection model for detecting non-calcified plaques is improved.
Because the morphology of different types of blood vessels is different, the plaque morphology in different morphologies of blood vessels is also different. Because the shapes of individual blood vessels, such as the far end in the anterior descending branch and the left posterior ventricular branch, are very similar under certain conditions, the cavity convolution is used in the design of the plaque detection model, on the premise of not changing the depth of the model, the receptive field of the model is increased, the shapes of the blood vessels are more easily distinguished, and the plaque detection model can more accurately detect plaques of the blood vessels of different types.
Optionally, inputting the combination of the images of the coronary artery to be detected into the trained plaque detection model, including: normalizing the spatial coordinate values of the coronary artery images to be detected in the middle sequence in the coronary artery image combination to be detected; and inputting the spatial coordinate value after the combination and normalization of the coronary artery images to be detected into a trained plaque detection model. Due to individual differences of people, the coronary artery images of different individuals shot by the image equipment have different resolutions, so that the coronary artery images of different individuals have larger difference of space coordinate values, and the plaque detection model is not favorable for positioning the plaque in the coronary artery images, so the space coordinate values of the images need to be normalized, the numerical range of the image is 0-1, and the accuracy rate of the plaque detection model for detecting the position of the plaque is improved. The detection result output by the plaque detection model is the plaque detection result of the middle sequence of the to-be-detected coronary artery images in the input to-be-detected coronary artery image combination, so that only the space coordinate value of the middle sequence of the to-be-detected coronary artery images in the to-be-detected coronary artery image combination is calculated, the space coordinate value of the middle sequence of the to-be-detected coronary artery image combination and the middle sequence of the coronary artery images in each image combination is input into the plaque detection model, and the middle plaque detection result of the middle sequence of the coronary artery images in each to-be-detected coronary artery image combination is obtained. Illustratively, as shown in fig. 2(b), the obtained intermediate plaque detection result is shown, the position of the box in the figure is the plaque position, RCA _ D indicates that the lesion position is the distal end of the right coronary artery, and ncP indicates that the plaque property is non-calcified plaque.
And S140, determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
The intermediate plaque detection result output by the plaque detection model is the detection result of the middle sequential coronary artery images to be detected in each coronary artery image combination to be detected, and is the plaque detection result of the two-dimensional image. And determining a final plaque detection result according to the detection results of the coronary artery images to be detected in the middle sequence in each coronary artery image combination to be detected.
Optionally, determining a final plaque detection result based on the intermediate plaque detection result for each to-be-detected coronary artery image includes: determining the intersection ratio between the areas of the plaques in each two coronary artery images to be detected; clustering the first plaque position in the coronary artery image to be detected, of which the intersection ratio exceeds a preset threshold value, so as to obtain a final plaque position in the coronary artery image to be detected; wherein the intermediate plaque detection result comprises: a first plaque location, the final plaque detection result comprising a final plaque location. The first plaque position is a two-dimensional plaque position, and the final plaque position is a three-dimensional plaque position. And calculating the intersection and union of the plaque position of each coronary artery image to be detected, namely the image area corresponding to the first plaque position, and the image areas corresponding to the plaque positions of other coronary artery images to be detected in the intermediate plaque detection result, and calculating the ratio of the intersection to the union. Illustratively, 3 coronary artery image combinations to be detected are generated according to a coronary artery image sequence to be detected, which are respectively a combination A, a combination B and a combination C, and a plaque detection result of a middle sequential image in each combination is obtained through a plaque detection model, wherein plaque positions respectively corresponding to the combination A, the combination B and the combination C are a position 1, a position 2 and a position 3, intersection and union calculation is performed on image areas respectively corresponding to the position 1 and the position 2 and the position 3 to obtain an intersection 12, an intersection 13, a union 12 and a union 13, the intersection and the union correspond to one, a ratio of the intersection 12 to the union 12 and a ratio of the intersection 13 to the union 13 are calculated, if the ratio of the intersection 12 to the union 12 exceeds a preset threshold, the position 1 and the position 2 are clustered, and if the ratio of the intersection 13 to the union 13 exceeds the preset threshold, the position 1 and the position 3 are clustered, since position 1 and position 2 have already been clustered, position 1, position 2 and position 3 are clustered; if the ratio of the intersection 13 to the union 13 does not exceed the preset threshold, then location 3 cannot be clustered with location 1 and location 2. As shown in fig. 2(c), the three-dimensional plaque positions obtained by clustering the two-dimensional plaque positions in the intermediate plaque detection result are the final plaque positions.
Optionally, determining a final plaque detection result based on the intermediate plaque detection result for each to-be-detected coronary artery image, further includes: counting the number of plaques of each type of first property in the coronary artery image to be detected, wherein the intersection ratio of the plaques exceeds a preset threshold value; determining final properties of the plaque according to the number of the plaque of each type of the first properties; wherein, the intermediate plaque detection result comprises: a first property of plaque, the final plaque detection result comprising a final property of plaque. And classifying the plaque properties corresponding to the positions of the clustered plaques, counting the quantity of the plaque properties of each type, and determining the final properties of the clustered plaques according to the quantity of the plaque properties of each type.
Optionally, the number of patches of each type of first property determines the final property of the patches, including: sorting the number of the patches with different properties in a descending order; if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is larger than a preset ratio, determining that the final property of the plaques is a mixed plaque; and if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is smaller than a preset ratio, determining the plaque property with the maximum number as the final property of the plaque. Illustratively, the preset ratio is 0.4, 6 plaque positions are clustered together, wherein 4 calcified plaques, 2 mixed plaques and 0 non-calcified plaque are present, the two categories with the minimum number are mixed plaques and non-calcified plaques, the sum of the two categories is 2, the total number is 0.33, and 0.33 is less than 0.4, so the final plaque property of the clustered three-dimensional plaque positions is calcified plaque. If 6 clustered plaque positions comprise 3 calcified plaques, 2 mixed plaques and 1 non-calcified plaque, the two categories with the minimum number are mixed plaques and non-calcified plaques, the sum of the two categories is 3, the total number is 0.50, and 0.50 is more than 0.4, and then the final plaque property of the clustered three-dimensional plaque positions is the mixed plaque.
The plaque detection model can directly carry out plaque detection on an original coronary artery image, the position and the property of the plaque are judged through the reconstructed blood vessel after the blood vessel separation and reconstruction are carried out on the coronary artery image, the time cost and the calculation cost required by three-dimensional reconstruction are reduced, meanwhile, the interference of reconstruction noise on a detection result can be effectively avoided, and the efficiency and the accuracy of the plaque detection are improved.
According to the technical scheme of the embodiment, a coronary artery image sequence to be detected is obtained; generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected; inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result aiming at the coronary artery image combination to be detected; and determining a final plaque detection result based on the intermediate plaque detection result, solving the problems of high detection difficulty and inaccurate detection of the existing coronary artery plaque detection technology, and realizing the effects of reducing the detection difficulty of the coronary artery plaque and improving the plaque detection efficiency and accuracy.
Example two
Fig. 3 is a flowchart of a plaque detection method according to a second embodiment of the present invention, which is a further optimization of the previous embodiment, and the plaque detection method further includes: performing image segmentation on the coronary artery image to be detected according to the final plaque position included in the final plaque detection result so as to extract the coronary artery blood vessel where the plaque is located; and calculating the blood vessel stenosis rate according to the extracted coronary artery blood vessels. After the plaque detection is carried out on the coronary artery image, the image segmentation is carried out on the coronary artery image with the plaque to extract the coronary artery blood vessel where the plaque is located and calculate the blood vessel stenosis rate, the image segmentation is avoided being carried out on the coronary artery image without the plaque, and the time and the difficulty of blood vessel extraction are effectively reduced.
As shown in fig. 3, the method comprises the following steps:
s210, acquiring a coronary artery image sequence to be detected.
S220, generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected.
And S230, combining and inputting the coronary artery images to be detected into the trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected.
S240, determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
And S250, carrying out image segmentation on the coronary artery image to be detected according to the final plaque position included in the final plaque detection result so as to extract the coronary artery blood vessel where the plaque is located.
In order to accelerate the convergence rate of the coronary artery segmentation model, the coronary artery images corresponding to the clustered plaque positions are subjected to data enhancement operations such as isotropic transformation based on spatial resolution, rotation, scaling and contrast change, and are input into the trained coronary artery segmentation model for image segmentation, and the coronary artery blood vessels where the plaques are located are extracted. Optionally, the training of the coronary artery segmentation model may be: and inputting the sample image into a coronary artery segmentation model to be trained, and obtaining a prediction result output by the detection model, wherein the prediction result comprises a predicted blood vessel segmentation result. The loss function of the coronary artery segmentation model to be trained comprises a cross entropy loss function and a loss function based on the contour, and the accuracy of the contour determines the accuracy of the extracted blood vessel diameter, so that the segmentation module pays more attention to the contour accuracy of the segmentation result, and therefore, in addition to the traditional cross entropy loss function, in the coronary artery segmentation model, the loss function based on the contour is also used, and the accuracy of the coronary artery segmentation model on the blood vessel segmentation is improved. The cross entropy loss function calculates and predicts that the blood vessel segmentation result is different from the standard blood vessel segmentation result pixel by pixel. The contour-based penalty function calculation predicts a vessel segmentation result that is different from the vessel contour of a standard vessel segmentation result. And reversely inputting the loss function into the detection model to be trained, and adjusting the network parameters in the detection model based on a gradient descent method. And iteratively executing the training method until the training for the preset times is finished or the detection precision of the detection model reaches the preset precision, and determining that the training of the coronary artery segmentation model is finished.
And S260, calculating the blood vessel stenosis rate according to the extracted coronary artery blood vessels.
Determining the central line of the coronary artery vessel by a central line extraction technology, measuring the distance from the edge of the vessel to the central line, wherein 2 times of the distance is the caliber of the coronary artery vessel; measuring the real-time caliber of a coronary artery blood vessel, presetting a standard caliber, and calculating the caliber ratio between the real-time caliber and the preset standard caliber; the stenosis rate of the coronary vessels is determined by the ratio of vessel diameters.
On the basis of the above embodiment, the technical solution of the embodiment of the present invention further includes: the functions of the plaque detection model and the coronary artery segmentation model are realized through one deep learning model, the two-dimensional coronary artery image is input into the deep learning model, the plaque position and the plaque property can be output, the segmentation of the blood vessel corresponding to the plaque position is completed at the same time, the operation of clustering the plaque positions to determine the final plaque position and counting the quantity of the properties of each type of plaque to determine the final plaque property is not needed, and the efficiency of coronary artery plaque detection and blood vessel segmentation is improved.
According to the technical scheme of the embodiment, a coronary artery image sequence to be detected is obtained; generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected; inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result aiming at the coronary artery image combination to be detected; determining a final plaque detection result based on the intermediate plaque detection result, and performing image segmentation on a coronary artery image to be detected according to a final plaque position included in the final plaque detection result so as to extract a coronary artery blood vessel where the plaque is located; and calculating the blood vessel stenosis rate according to the extracted coronary artery blood vessels. After the plaque detection is carried out on the coronary artery image, the image segmentation is carried out on the coronary artery image with the plaque to extract the coronary artery blood vessel where the plaque is located and calculate the blood vessel stenosis rate, the image segmentation is avoided being carried out on the coronary artery image without the plaque, and the time and the difficulty of blood vessel extraction are effectively reduced. The method solves the problems of high detection difficulty and inaccurate detection of the existing coronary artery plaque detection technology, and achieves the effects of reducing the detection difficulty of the coronary artery plaque and improving the plaque detection efficiency and accuracy.
EXAMPLE III
Fig. 4 is a structural diagram of a plaque detection apparatus according to a third embodiment of the present invention, where the plaque detection apparatus includes: an image sequence acquisition module 310, an image combination generation module 320, an intermediate patch detection result acquisition module 330, and a final patch detection result determination module 340.
The image sequence acquiring module 310 is configured to acquire a coronary artery image sequence to be detected;
an image combination generating module 320, configured to generate at least one coronary artery image combination to be detected according to the sequence of the coronary artery images to be detected, where the coronary artery image combination to be detected includes at least two coronary artery images to be detected;
an intermediate plaque detection result obtaining module 330, configured to input the to-be-detected coronary artery image combination to the trained plaque detection model, and obtain an intermediate plaque detection result of each to-be-detected coronary artery image;
a final plaque detection result determining module 340, configured to determine a final plaque detection result based on the intermediate plaque detection result of each to-be-detected coronary artery image.
In the technical solution of the above embodiment, the image combination generating module 320 includes:
an image combination selecting unit, configured to select, from a first to-be-detected coronary artery image of the to-be-detected coronary artery image sequence, adjacent preset number of to-be-detected coronary artery images as a first to-be-detected coronary artery image combination; selecting a preset number of adjacent coronary artery images to be detected as a second coronary artery image combination from a second coronary artery image to be detected in the coronary artery image sequence to be detected; and repeating the operation until each coronary artery image to be detected in the sequence of the coronary artery images to be detected is traversed.
In the technical solution of the foregoing embodiment, the final plaque detection result determining module 340 includes:
the area intersection ratio determining unit is used for determining the intersection ratio between the areas of the plaques in each two coronary artery images to be detected;
the plaque position clustering unit is used for clustering the first plaque position in the coronary artery image to be detected, of which the intersection ratio exceeds a preset threshold value, so as to obtain the final plaque position in the coronary artery image to be detected; wherein the intermediate plaque detection result comprises: a first plaque location, the final plaque detection result comprising a final plaque location.
In the technical solution of the foregoing embodiment, the final plaque detection result determining module 340 further includes:
the plaque number counting unit is used for counting the number of plaques of each type of first property in the coronary artery image to be detected, wherein the intersection ratio of the plaques exceeds a preset threshold value;
a final plaque property determining unit, configured to determine a final property of the plaque according to the number of plaques of each type of the first property; wherein the intermediate plaque detection result comprises: a first property of plaque, the final plaque detection result comprising a final property of plaque.
In the technical solution of the above embodiment, the final plaque property determining unit includes:
The plaque sequencing subunit is used for sequencing the plaque quantity with different properties in a descending order;
the final plaque property determining subunit is used for determining that the final property of the plaque is a mixed plaque if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is larger than a preset ratio; and if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is smaller than a preset ratio, determining the plaque property with the maximum number as the final property of the plaque.
In the technical solution of the above embodiment, the intermediate patch detection result obtaining module 330 includes:
the coordinate value normalization unit is used for normalizing the spatial coordinate values of the coronary artery images to be detected in the middle sequence in the coronary artery image combination to be detected;
and the data input unit is used for inputting the spatial coordinate values of the combination and normalization of the coronary artery images to be detected into the trained plaque detection model.
In the technical solution of the above embodiment, the plaque detection apparatus further includes:
the image segmentation module is used for carrying out image segmentation on the coronary artery image to be detected according to the final plaque position included in the final plaque detection result so as to extract a coronary artery blood vessel where the plaque is located;
And the stenosis rate calculation module is used for calculating the blood vessel stenosis rate according to the extracted coronary artery blood vessel.
According to the technical scheme of the embodiment, a coronary artery image sequence to be detected is obtained; generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected; inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result aiming at the coronary artery image combination to be detected; and determining a final plaque detection result based on the intermediate plaque detection result, solving the problems of high detection difficulty and inaccurate detection of the existing coronary artery plaque detection technology, and realizing the effects of reducing the detection difficulty of the coronary artery plaque and improving the plaque detection efficiency and accuracy.
The plaque detection device provided by the embodiment of the invention can execute the plaque detection method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 5 is a schematic structural diagram of an apparatus according to a fourth embodiment of the present invention, as shown in fig. 5, the apparatus includes a processor 410, a memory 420, an input device 430, and an output device 440; the number of processors 410 in the device may be one or more, and one processor 410 is taken as an example in fig. 5; the processor 410, the memory 420, the input device 430 and the output device 440 in the apparatus may be connected by a bus or other means, for example, in fig. 5.
The memory 420 serves as a computer-readable storage medium, and may be used for storing software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the plaque detection method in the embodiment of the present invention (for example, the image sequence acquisition module 310, the image combination generation module 320, the intermediate plaque detection result acquisition module 330, and the final plaque detection result determination module 340 in the plaque detection apparatus). The processor 410 executes various functional applications of the device and data processing by executing software programs, instructions and modules stored in the memory 420, i.e., implements the plaque detection method described above.
The memory 420 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, memory 420 may further include memory located remotely from processor 410, which may be connected to devices through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input means 430 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the apparatus. The output device 440 may include a display device such as a display screen.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, perform a plaque detection method, the method including:
acquiring a coronary artery image sequence to be detected;
generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected;
inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected;
and determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the method operations described above, and may also perform related operations in the plaque detection method provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the plaque detection apparatus, the included units and modules are merely divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
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 plaque detection method, comprising:
acquiring a coronary artery image sequence to be detected;
generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, wherein the coronary artery image combination to be detected comprises at least two coronary artery images to be detected;
inputting the coronary artery image combination to be detected into a trained plaque detection model to obtain a middle plaque detection result of each coronary artery image to be detected;
and determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
2. The method according to claim 1, wherein generating at least one coronary artery image combination to be detected from the sequence of coronary artery images to be detected comprises:
selecting a preset number of adjacent coronary artery images to be detected as a first coronary artery image combination to be detected from a first coronary artery image to be detected in the coronary artery image sequence to be detected;
selecting a preset number of adjacent coronary artery images to be detected as a second coronary artery image combination from a second coronary artery image to be detected in the coronary artery image sequence to be detected;
and repeating the operation until each coronary artery image to be detected in the sequence of the coronary artery images to be detected is traversed.
3. The method of claim 1, wherein determining a final plaque detection result based on the intermediate plaque detection results for each coronary artery image to be detected comprises:
determining the intersection ratio between the areas of the plaques in each two coronary artery images to be detected;
clustering the first plaque position in the coronary artery image to be detected, of which the intersection ratio exceeds a preset threshold value, so as to obtain a final plaque position in the coronary artery image to be detected;
Wherein the intermediate plaque detection result comprises: a first plaque location, the final plaque detection result comprising a final plaque location.
4. The method of claim 3, wherein determining a final plaque detection result based on the intermediate plaque detection results for each coronary artery image to be detected further comprises:
counting the number of plaques of each type of first property in the coronary artery image to be detected, wherein the intersection ratio of the plaques exceeds a preset threshold value;
determining the final property of the plaque according to the number of the plaque of each type of first property;
wherein the intermediate plaque detection result comprises: a first property of plaque, the final plaque detection result comprising a final property of plaque.
5. The method of claim 4, wherein determining the final property of the plaque from the number of plaques of each type of first property comprises:
sorting the number of the patches with different properties in a descending order;
if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is larger than a preset ratio, determining that the final property of the plaques is a mixed plaque;
and if the ratio of the number of the two types of plaques with the minimum number to the total number of the plaques with different properties is smaller than a preset ratio, determining the plaque property with the maximum number as the final property of the plaque.
6. The method according to claim 1, wherein the inputting the combination of the coronary artery images to be detected into the trained plaque detection model comprises:
normalizing the space coordinate values of the middle sequence coronary artery images to be detected in the coronary artery image combination to be detected;
and inputting the spatial coordinate value after the coronary artery image to be detected is combined and normalized to a trained plaque detection model.
7. The method of claim 1, further comprising, after said determining a final plaque detection result based on the intermediate plaque detection result for each coronary artery image to be detected:
performing image segmentation on the coronary artery image to be detected according to the final plaque position included in the final plaque detection result so as to extract a coronary artery blood vessel where the plaque is located;
and calculating the blood vessel stenosis rate according to the extracted coronary artery blood vessels.
8. A plaque detection apparatus comprising:
the image sequence acquisition module is used for acquiring a coronary artery image sequence to be detected;
the image combination generating module is used for generating at least one coronary artery image combination to be detected according to the coronary artery image sequence to be detected, and the coronary artery image combination to be detected comprises at least two coronary artery images to be detected;
The middle plaque detection result acquisition module is used for inputting the combination of the coronary artery images to be detected into a trained plaque detection model to obtain the middle plaque detection result of each coronary artery image to be detected;
and the final plaque detection result determining module is used for determining a final plaque detection result based on the intermediate plaque detection result of each coronary artery image to be detected.
9. An apparatus, characterized in that the apparatus comprises:
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 the plaque detection method of 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 the plaque detection method according to any one of claims 1 to 7.
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