CN111145160A - Method, device, server and medium for determining coronary artery branch where calcified area is located - Google Patents

Method, device, server and medium for determining coronary artery branch where calcified area is located Download PDF

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CN111145160A
CN111145160A CN201911384603.5A CN201911384603A CN111145160A CN 111145160 A CN111145160 A CN 111145160A CN 201911384603 A CN201911384603 A CN 201911384603A CN 111145160 A CN111145160 A CN 111145160A
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image
heart
determining
calcified
processed
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CN111145160B (en
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王晓东
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Shanghai United Imaging Healthcare Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G06T7/344—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods involving models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10081—Computed x-ray tomography [CT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20092—Interactive image processing based on input by user
    • G06T2207/20104—Interactive definition of region of interest [ROI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30048—Heart; Cardiac
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The embodiment of the invention discloses a method, a device, a server and a storage medium for determining coronary artery branches where calcified regions are located, wherein the method comprises the following steps: acquiring an image to be processed, and registering a heart model obtained in advance on the image to be processed to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; obtaining a heart chamber model corresponding to the image to be processed based on the first pre-processing image; and determining the target coronary artery branch of the calcified region according to the target chamber of the calcified region in the heart chamber model and the probability value of the calcified region on at least one coronary artery branch to be selected in the target chamber. The technical scheme of the embodiment of the invention solves the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists, and realizes the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs.

Description

Method, device, server and medium for determining coronary artery branch where calcified area is located
Technical Field
The embodiment of the invention relates to the technical field of medical treatment, in particular to a method, a device, a server and a storage medium for determining coronary artery branches where calcified regions are located.
Background
Coronary artery disease has become the most mortality disease in the world. With the development of medical imaging technology, cardiac CT imaging is increasingly used for the detection of coronary artery disease. There are two main types of cardiac CT scanning, namely, enhanced scanning and non-enhanced scanning. Non-enhanced scans, also known as scout scans, require the injection of contrast agents, and the blood flow containing the contrast agents exhibits high intensity in CT, which is primarily used to view vascular lumen, heart chamber and myocardial perfusion. But is not sensitive enough to vessel calcification because the CT values of the vessel lumen and calcified plaque overlap each other and cannot be completely distinguished in coronary enhancement scanning. Therefore, CT scout images are commonly used to view coronary calcification. The same applies to the calculation of calcium scores based on the detection of calcified plaques on CT scout images. However, this brings a new problem that the lumen of the blood vessel is not visualized on the CT flat scan image, and is difficult to distinguish from the muscle tissue, and it is impossible to determine on which coronary artery the detected calcified plaque is located, and it is necessary for the doctor to check and recognize the coronary artery in turn based on experience.
In the prior art, a worker mainly determines which coronary artery a calcification point is located on according to experience, and certain errors exist.
Disclosure of Invention
The embodiment of the invention provides a method, a device, a server and a medium for determining coronary artery branches where calcified regions are located, so as to achieve the technical effect of quickly and accurately determining target coronary arteries where calcified points belong.
In a first aspect, an embodiment of the present invention provides a method for determining a coronary branch where a calcified region is located, where the method includes:
acquiring an image to be processed, and registering the image to be processed with a heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area;
processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed;
calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model;
and determining the target coronary artery associated with the calcified area according to the probability value.
In a second aspect, an embodiment of the present invention further provides an apparatus for determining a coronary branch where a calcified region is located, where the apparatus includes:
an image preprocessing module, configured to acquire an image to be processed, register the image with a heart standard model to obtain a first preprocessed image, where the image to be processed includes a calcified region
A heart model determining module, configured to process the heart standard model based on the first preprocessed image, and obtain a heart model corresponding to the image to be processed;
a probability value calculating module, configured to calculate a probability value of at least one coronary artery to be candidate in the heart model of the calcified region;
and the coronary artery branch determining module is used for determining the target coronary artery associated with the calcified area according to the probability value.
In a third aspect, an embodiment of the present invention further provides a server, where the server includes:
one or more processors;
a storage device 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 determining coronary branches where calcified regions are located according to any of the embodiments of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for determining coronary branches where calcified regions are located according to any of the embodiments of the present invention.
According to the technical scheme of the embodiment of the invention, the image to be processed is obtained and is registered with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed; calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model; the target coronary artery associated with the calcified area is determined according to the probability value, the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists is solved, and the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs is achieved.
Drawings
In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, a brief description is given below of the drawings used in describing the embodiments. It should be clear that the described figures are only views of some of the embodiments of the invention to be described, not all, and that for a person skilled in the art, other figures can be derived from these figures without inventive effort.
Fig. 1 is a schematic flow chart of a method for determining coronary artery branches where calcified regions are located according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a method for determining coronary artery branches where calcified regions are located according to a second embodiment of the present invention;
fig. 3 is a schematic flow chart illustrating a method for determining coronary artery branches where calcified regions are located according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for determining coronary artery branches where calcified regions are located according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of a server according to a fifth 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 schematic flow chart of a method for determining coronary artery branches located in calcified regions according to an embodiment of the present invention, which may be implemented by a device for determining coronary artery branches located in calcified regions, where the device may be implemented in software and/or hardware.
As shown in fig. 1, the method of this embodiment includes:
s110, acquiring an image to be processed, and registering the image to be processed with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area.
For clarity of describing the technical solution of the present embodiment, the scan target region may be a heart.
Wherein, the original image obtained after scanning the part to be scanned is taken as the image to be processed. The scanned site may be the heart or other site, etc. Correspondingly, the heart model is obtained by acquiring a plurality of historical heart images in advance and processing the plurality of historical heart images. The heart model is used for processing the image to be processed mentioned in the embodiment, that is, the heart model is a model for performing preliminary processing on the image to be processed. The image processed by the heart model can realize the segmentation of each chamber. The image after the preliminary processing by the heart model can be used as a first pre-processed image.
Specifically, the image to be processed is obtained, a pre-trained heart standard model may be registered to the image to be processed by using methods such as registration or generalized hough transform, so as to obtain a registered image, and the registered image is used as a first pre-processed image.
It should be noted that before the standard model of the heart is configured to the image to be processed, the image to be processed is acquired. Optionally, a flat scanning image corresponding to the target scanning part is obtained; determining a calcified area in the flat scanning image according to a preset condition; and taking the scanning image for determining the calcified area as an image to be processed.
It should be noted that the image to be processed is a non-enhanced scout image, and since there is no bone structure in the heart region range, the pixel points with CT values greater than 130HU can be regarded as calcifications. Dividing the calcifications, and removing noise areas with very small volume, wherein the rest areas are calcifications.
The target scanning region may be understood as a focal region, i.e., a heart region. The preset condition may be whether the pixel value in the panning image is higher than 130 HU. If the pixel point is higher than 130HU, the pixel point is taken as a calcification point, and correspondingly, the area where the calcification point is located is a calcification area. The image to be processed is an image including a calcified region.
Specifically, a sweep image of the heart region is acquired as a sweep image in a sweep mode. Determining the pixel value of each pixel point in the flat scanning image, taking the pixel point with the pixel value higher than a preset value as a calcification point, and taking the area where the calcification point is located as a calcification area. If the scanned image comprises a calcified area, taking the image comprising the calcified area as an image to be processed; if the scanned image does not include calcified regions, the panned image may not be processed.
And S120, processing the heart standard model based on the first preprocessing image, and acquiring a heart model corresponding to the image to be processed.
The first preprocessing image comprises a left ventricle, a right ventricle and a calcified area positioned in the first preprocessing image. The first preprocessed image may be processed using an active contour algorithm to obtain a heart chamber model corresponding to the image to be processed. A heart chamber model may be understood as a model corresponding to each chamber in the heart.
Specifically, the first preprocessed image may be processed by using an active contour algorithm to obtain a heart model corresponding to the image to be processed.
Based on the first pre-processing image, obtaining a heart model corresponding to the image to be processed, including: gridding the first preprocessing image, and acquiring at least one grid point to be adjusted on the edge of the chamber of the heart standard model corresponding to the first preprocessing image; inputting the grid points to be adjusted into a pre-trained chamber edge classifier to obtain the probability value of each grid point to be adjusted at the preset chamber edge position on the image to be processed; and deforming the chamber edge corresponding to each chamber according to the probability value to obtain a heart model corresponding to the image to be processed. The gridding processing refers to dividing the first preprocessed image into at least one grid according to a preset rule. And taking the intersection points of the grid lines as grid points, and taking the grid points corresponding to the edges of each chamber in the heart standard model as grid points to be adjusted. Since the chamber edge of the heart is formed by lines, a plurality of grid points can be included, and the grid points on the chamber edge line are used as the grid points to be adjusted. The number of the at least one grid point to be adjusted may be one, two or more, and the number of the specific grid points is related to the preset partition rule and the edge line of the chamber, which is not limited herein. The chamber edge classifier is obtained by pre-training and is used for determining the probability value of the grid point to be adjusted at a certain chamber edge position and configuring an image to be processed.
Specifically, a preset division rule is adopted to divide the first preprocessed image into at least one grid, and each grid point on the edge of each chamber is determined to be used as a grid point to be adjusted. And inputting the grid points to be adjusted into a pre-trained chamber edge classifier, so as to determine probability values of the grid points at the edge positions of the preset chamber. Iteratively adjusting the cavity edge to the corresponding edge according to the obtained probability value, realizing the segmentation of each cavity under the flat scanning image, and obtaining the heart model corresponding to the image to be processed.
And S130, calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model.
Wherein, the coronary artery to be candidate can be understood as the branch of the coronary artery where the calcified area is possibly positioned.
Specifically, since a heart model corresponding to the image to be processed is obtained, and the heart model includes chambers, according to the heart model and the calcified regions, probability values of the calcified regions on the coronary branches to be selected can be determined.
And S140, determining the target coronary artery associated with the calcified area according to the probability value.
Wherein the coronary artery where the calcified area is located is taken as the target coronary artery.
On the basis of the step S130, a probability value that the calcification spots are located in the respective coronary arteries can be determined. And taking the coronary artery corresponding to the highest probability value as the target coronary artery.
Optionally, the probability values of the calcified regions in the coronary artery branches to be selected are respectively calculated, and the coronary artery branch to be selected with the highest probability value is used as the target coronary artery branch.
That is, probability values of the calcified region on each coronary branch to be selected in the heart chamber are respectively determined, and the coronary branch to be selected with the highest probability value is taken as the target coronary branch.
According to the technical scheme of the embodiment of the invention, the image to be processed is obtained and is registered with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed; calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model; the target coronary artery associated with the calcified area is determined according to the probability value, the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists is solved, and the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs is achieved.
Example two
Before determining which coronary artery branch the calcified area in the image to be processed is located in, a heart standard model and chamber classifiers corresponding to the chambers are further required to be determined, so that the image to be processed is registered based on the heart standard model to obtain a heart model, and a target coronary artery to which the calcified area belongs is determined according to the heart model. Fig. 2 is a schematic flow chart of a method for determining coronary artery branches where calcified regions are located according to a second embodiment of the present invention.
As shown in fig. 2, the method includes:
s210, acquiring a plurality of historical heart images as a plurality of current registration sample images, and marking key information in the current registration samples, wherein the key information comprises each actual chamber edge of the heart.
The plurality of historical heart images not only include the image of the patient, but also include the image of a non-patient, namely, the image of the coronary artery branch to which the calcified area belongs is determined, and the image of the non-calcified area is included. And taking a plurality of historical heart images as sample images to be trained, namely current registration sample images. In order to improve the accuracy of the heart model obtained by final training, the sample images to be trained can be acquired as many as possible. Key information in the current registration sample includes: the actual chamber edges of the chambers of the heart, and the coronary branches in the chambers.
Specifically, a plurality of historical heart images are obtained, the actual chamber edge of each chamber in the historical heart images and the coronary artery branch of each chamber are marked, and the image obtained at the moment is used as a current registration sample image.
Illustratively, 20 historical heart images are acquired, and the left atrium, the right atrium, the coronary arteries in the left atrium, and the coronary arteries in the right atrium in the historical heart images are marked, and the marked 20 historical heart images are used as current registration sample images.
S220, selecting one current registration sample image from a plurality of current registration sample images as a reference sample image, using the rest current registration sample images as sample images to be registered, and registering the reference sample image and each sample image to be registered according to the key information to generate at least one preliminary registration sample image.
Specifically, one of the multiple current registration sample images is selected as a reference sample image, and the other images in the current registration sample image are used as sample images to be registered. And respectively registering the reference sample image and the sample image to be registered according to the key information in the reference sample image and the key information in the sample image to be registered to generate at least one preliminarily registered sample image.
Illustratively, 20 current registration sample images are respectively marked, an image with the number of 1 can be selected as a reference sample image, and an image with the number of 2-20 can be selected as a sample image to be registered. And respectively registering the actual edge lines of all chambers in the reference sample image with the number of 1 with the sample image to be registered with the number of 2-20 to obtain 20 initial registered sample images after registration.
And S230, performing gridding processing on each actual chamber edge in the preliminary registration sample image to obtain at least one actual chamber edge grid point, and determining an average chamber edge of the preliminary registration sample image according to coordinates of each actual chamber edge grid point of the reference sample image and the sample image to be registered in the preliminary registration sample image.
Specifically, a preset algorithm is adopted to conduct gridding processing on each actual chamber edge in the preliminary registration sample image to obtain at least one grid point. Accordingly, actual chamber edge grid points corresponding to the labeled individual chambers may also be obtained. And determining the average chamber edge grid of the preliminary sample image according to the coordinates of each actual edge grid point of the reference sample image and the sample image to be registered in the preliminary registration sample image. That is, the grid points of the chamber edges in each preliminary sample image are averaged to obtain the averaged grid of the chamber edges of the heart.
Illustratively, gridding processing is performed on the preliminary registration sample image to obtain grid points of the edge of each actual chamber, and the average processing is performed on the coordinates of the edge grid points of the actual cavity corresponding to the grid points of the edge of each chamber in the reference sample image to obtain an average chamber edge grid of the preliminary sample image.
And S240, taking the plurality of preliminary registration sample images as a plurality of current registration sample images, repeatedly executing the operation of selecting the reference sample image for image registration to obtain the average chamber edge until the average chamber edge of the target registration sample image is determined, and determining a heart standard model according to the average chamber edge of the target registration sample image.
The reference image is used as a target registration sample image, that is, the number of the current reference image is several, and the corresponding target registration sample image is several. The standard model of the heart may be understood as the image obtained by processing the edges of the chambers of the target registration sample image.
Illustratively, the image with the number 2 is used as a reference image, the rest images are used as current registration sample images, S210 to S230 are repeatedly executed to obtain at least one preliminary registration sample image, and then the at least one preliminary registration sample image is averaged to obtain an average chamber edge grid of the preliminary sample image corresponding to the number 2. Using the images numbered 3 and 4 … 20 as reference images in sequence and the remaining images as current registration sample images, an average chamber edge grid for the preliminary sample image corresponding to number 3, an average chamber edge grid … for the preliminary sample image corresponding to number 4, and an average chamber edge grid for the preliminary sample image corresponding to number 20 are generated, respectively. And processing the average chamber edge mesh of all the obtained primary sample images to obtain a heart standard model.
In this embodiment, determining a cardiac normative model from the mean chamber margins of the target registration sample images includes: determining coronary branches of the target registration sample image according to the positions of the marked coronary branches relative to the heart model in the target registration sample image; determining a heart model from the coronary branches and the mean chamber margin of the target registration sample image.
After obtaining the heart model, the position and distribution area of each coronary artery in the heart model of each registered image, namely the position and distribution area of each marked coronary artery branch in the preliminary registration sample image relative to the heart model, are calculated, and the coronary artery branches existing in the distance near the edge grid point of the heart chamber are recorded.
After the heart model is obtained, it is also necessary to determine the heart chamber classifiers corresponding to the respective chambers in the heart model. Optionally, acquiring a plurality of historical heart images as a plurality of training samples, and marking edges of each actual chamber of the heart in the training samples; inputting a plurality of marked training samples into a pre-established machine learning model to obtain a chamber edge classifier corresponding to each actual chamber; the heart chamber edge classifier is used for determining the probability value of each chamber edge in the image to be tested and processing each chamber edge according to the probability value.
It can be understood that: machine learning can be adopted for training according to the marked heart chamber edges in the preliminary sample images to obtain classifiers of the heart chamber edges, and the classifiers can identify the probability that pixel points at each position are located at the edge of a certain chamber on unmarked images. The benefit of determining the individual chamber classifiers is that the image to be processed can be registered with the standard model of the heart.
According to the technical scheme of the embodiment of the invention, the image to be processed is obtained and is registered with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed; calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model; the target coronary artery associated with the calcified area is determined according to the probability value, the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists is solved, and the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs is achieved.
EXAMPLE III
As a preferred embodiment of the foregoing embodiment, fig. 3 is a flowchart illustrating a method for determining coronary artery branches where calcified regions are located according to an embodiment of the present invention.
As shown in fig. 3, the method of this embodiment includes:
s301, reading training data.
A certain amount of training data, optionally thousands or tens of thousands, etc., is selected. The training data may be understood as cardiac scan images.
It should be noted that, in order to obtain the heart model, the size and resolution of the heart image need to be consistent.
Specifically, a preset number of training sample images are acquired to train the heart standard model.
S302, marking a heart chamber and a coronary artery.
Before training the data, the left ventricle, right ventricle, left atrium, right atrium, pulmonary artery root, aorta, etc. structures of the heart may be labeled on each heart image. Simultaneously, marking coronary artery, mainly comprising left coronary artery main trunk, anterior descending branch, circumflex branch, blunt edge branch and diagonal branch; right coronary artery, anterior descending branch, acute marginal branch, etc.
It should be noted that, in this embodiment, only the heart chambers and the coronary arteries that need to be marked are listed, but not limited to the above list.
S303, generating an average model of the heart chamber.
The mean model of the heart chamber can be understood as a standard model of the heart.
Specifically, the collected cardiac images are registered to obtain at least one registered image. And gridding the marked heart chamber edge on each registered image, and averaging points on each grid according to each configuration image to obtain an averaged heart chamber edge grid, thereby generating an average model of the heart chamber.
Illustratively, the number of cardiac images is twenty, and the twenty registered images are obtained by first registering the second image to the twentieth image on the first image, respectively, with reference to the first image. And respectively carrying out gridding treatment on the edges of the chambers of the twenty registered images to obtain edge grid points corresponding to the edges of the chambers. And determining coordinates of the edge grid points in the space rectangular coordinate system, optionally, respectively determining coordinates of each edge grid point in the twenty images, and averaging the coordinates of the edge grid points at the same position to obtain averaged edge grid points of each chamber of the heart.
In order to obtain an average model of the heart, the second image is taken as a reference, the third image is respectively registered to the second image to obtain twenty registered images, the twenty registered images are divided into at least one grid according to the same mode, and the grids of the same position point are averaged to obtain an average heart chamber edge grid point. By analogy, twenty groups of averaged edge grid points of each chamber of the heart are obtained. That is, twenty images can be respectively taken as a reference, and the images are registered to obtain the edge grids of each chamber of the heart after the twenty images are averaged. And averaging twenty averaged edge grids of each chamber of the heart to obtain a heart standard model.
And S304, marking a coronary artery region on the cavity model.
Marking the area of coronary artery on the heart standard model, calculating the position and distribution area of the marked coronary artery area corresponding to the heart standard model, and recording the coronary artery branch existing near the grid point of the edge of each heart chamber.
S305, training classifiers according to the chamber boundaries to obtain each chamber classifier.
And training the heart chamber edges marked on the images by adopting machine learning to obtain chamber edge classifiers corresponding to the chambers. And each chamber classifier can be used for identifying the probability value of each pixel point at the edge of a certain chamber on the unmarked image.
And S306, inputting the data after the heart model is registered into a chamber classifier, and determining the distribution region of each branch coronary artery.
And acquiring an image to be tested, and optionally acquiring a heart image of a certain user. And registering the heart image to a heart model obtained by pre-training to obtain a registered image. And gridding the registered image to obtain at least one grid. Wherein at least one grid includes an edge grid point for each chamber. Inputting the images after gridding processing into a cavity classifier obtained by pre-training, obtaining the maximum probability value of the grids on the edge line of each cavity in the normal direction, and deforming the registered images according to the probability value to obtain the cavity edge grids attached to the actual image edges.
S307, determining the coronary artery where the calcification points are located.
After the iterative deformation of the chamber is complete, the region of coronary distribution can be determined.
It should be noted that, in the practical application process, there may be a case where the calcified region is located on two different coronary artery distribution regions, and it is impossible to accurately determine which coronary artery the calcified region is located on. Therefore, the probability values of calcified regions respectively located in a certain coronary artery can be determined, and the coronary artery corresponding to the high probability value is taken as the target coronary artery, namely, the calcified regions are located on the coronary artery.
According to the technical scheme of the embodiment of the invention, the image to be processed is obtained and is registered with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed; calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model; the target coronary artery associated with the calcified area is determined according to the probability value, the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists is solved, and the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs is achieved.
Example four
Fig. 4 is a fourth embodiment of the present invention, which provides an apparatus for determining coronary artery branches where calcified regions are located, the apparatus includes: an image preprocessing module 410, a cardiac model determination module 420, a probability value calculation module 430, and a coronary branch determination module 440.
The image preprocessing module 410 is configured to acquire an image to be processed, and perform registration with a heart standard model to obtain a first preprocessed image, where the image to be processed includes a calcified region; a heart model determining module 420, configured to process the heart standard model based on the first processed image, and obtain a heart model corresponding to the image to be processed; a probability value calculating module 430, configured to calculate a probability value of at least one coronary artery to be candidate in the heart model of the calcified region; a coronary branch determining module 440, configured to determine a target coronary associated with the calcified region according to the probability value.
On the basis of the above technical solution, the image preprocessing module further includes:
the gridding unit is used for gridding the first preprocessing image and acquiring at least one grid point to be adjusted of the chamber edge of the heart standard model corresponding to the first preprocessing image;
a probability value determining unit, configured to input the grid points to be adjusted into a pre-trained chamber edge classifier, and obtain probability values of the grid points to be adjusted at preset chamber edge positions on the image to be processed;
and the heart chamber determining unit is used for deforming the chamber edge corresponding to each chamber according to the probability value to obtain a heart model corresponding to the image to be processed.
On the basis of the above technical solutions, the probability value determining unit is further configured to:
and respectively calculating the probability value of the calcified area in each coronary artery to be selected, and taking the corresponding coronary artery to be selected when the probability value is highest as the target coronary artery.
On the basis of the above technical solutions, before the image preprocessing module is configured to acquire the image to be processed, the image preprocessing module is further configured to:
acquiring a flat scanning image corresponding to a target scanning part;
determining a calcified area in the flat scanning image according to a preset condition;
and taking the scanning image for determining the calcified area as the image to be processed.
On the basis of the above technical solutions, the apparatus further includes:
acquiring a plurality of historical heart images as a plurality of current registration sample images, and marking key information in the current registration samples, wherein the key information comprises the edges of each actual chamber of the heart;
selecting one current registration sample image from a plurality of current registration sample images as a reference sample image, taking the rest current registration sample images as sample images to be registered, and registering the reference sample image and each sample image to be registered according to the key information to generate at least one preliminary registration sample image;
gridding the edges of each actual chamber in the preliminary registration sample image to obtain at least one actual chamber edge grid point, and determining the average chamber edge of the preliminary registration sample image according to the coordinates of the actual chamber edge grid points of the reference sample image and the sample image to be registered in the preliminary registration sample image;
and taking the plurality of preliminary registration sample images as a plurality of current registration sample images, repeatedly executing the operation of selecting the reference sample image for image registration to obtain the average chamber edge until the average chamber edge of the target registration sample image is determined, and determining a heart chamber standard model according to the average chamber edge of the target registration sample image.
On the basis of the above technical solutions, the determining a heart standard model according to the mean chamber edge of the target registration sample image includes:
determining coronary branches of the target registration sample image according to the positions of the marked coronary branches relative to a heart standard model in the target registration sample image;
determining a heart standard model according to each coronary branch of the target registration sample image and the mean chamber edge.
On the basis of the above technical solutions, the apparatus further includes:
the training sample acquisition unit is used for acquiring a plurality of historical heart images as a plurality of training samples and marking the edges of each actual chamber of the heart in the training samples;
the chamber edge classifier unit is used for inputting a plurality of marked training samples into a pre-established machine learning model to obtain a chamber edge classifier corresponding to each actual chamber;
the heart chamber edge classifier is used for registering the image to be processed.
According to the technical scheme of the embodiment of the invention, the image to be processed is obtained and is registered with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area; processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed; calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model; the target coronary artery associated with the calcified area is determined according to the probability value, the technical problem that in the prior art, a worker needs to determine which coronary artery the calcified point is located on according to experience, and a certain error exists is solved, and the technical effect of quickly and accurately determining the target coronary artery to which the calcified point belongs is achieved.
The device for determining coronary artery branches where the calcified regions are located, provided by the embodiment of the invention, can execute the method for determining coronary artery branches where the calcified regions are located, provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
It should be noted that, the units and modules included in the apparatus 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 embodiment of the invention.
EXAMPLE five
Fig. 5 is a schematic structural diagram of a server according to a fifth embodiment of the present invention. FIG. 5 illustrates a block diagram of an exemplary server 50 suitable for use in implementing embodiments of the present invention. The server 50 shown in fig. 5 is only an example, and should not bring any limitation to the function and the scope of use of the embodiment of the present invention.
As shown in fig. 5, the server 50 is in the form of a general purpose computing server. The components of server 50 may include, but are not limited to: one or more processors or processing units 501, a system memory 502, and a bus 503 that couples the various system components (including the system memory 502 and the processing unit 501).
Bus 503 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, micro-channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
The server 50 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by server 50 and includes both volatile and nonvolatile media, removable and non-removable media.
The system memory 502 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)504 and/or cache memory 505. The server 50 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 506 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard drive"). Although not shown in FIG. 5, 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 the bus 503 by one or more data media interfaces. Memory 502 may 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 508 having a set (at least one) of program modules 507 may be stored, for instance, in memory 502, such program modules 507 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. Program modules 507 generally perform the functions and/or methodologies of embodiments of the invention as described herein.
The server 50 may also communicate with one or more external servers 509 (e.g., keyboard, pointing server, display 510, etc.), with one or more servers that enable a user to interact with the server 40, and/or with any servers (e.g., network card, modem, etc.) that enable the server 50 to communicate with one or more other computing servers. Such communication may occur via input/output (I/O) interfaces 511. Also, the server 50 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) via a network adapter 512. As shown, the network adapter 512 communicates with the other modules of the server 50 over a bus 503. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with the server 50, including but not limited to: microcode, server drives, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
The processing unit 501 executes a program stored in the system memory 502, thereby executing various functional applications and data processing, for example, implementing the method for determining coronary branches where calcified regions are located according to the embodiment of the present invention.
EXAMPLE five
Embodiments of the present invention also provide a storage medium containing computer-executable instructions that, when executed by a computer processor, perform a method for determining coronary branches where calcified regions are located.
The method comprises the following steps:
acquiring an image to be processed, and registering the image to be processed with a heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area;
processing the heart standard model based on the first processing image to obtain a heart model corresponding to the image to be processed;
calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model;
and determining the target coronary artery associated with the calcified area according to the probability value. 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 embodiments 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 determining coronary branches where calcified regions are located, comprising:
acquiring an image to be processed, and registering the image to be processed with a heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area;
processing the heart standard model based on the first preprocessing image to obtain a heart model corresponding to the image to be processed;
calculating the probability value of the calcified area in at least one coronary artery to be candidate in the heart model;
and determining the target coronary artery associated with the calcified area according to the probability value.
2. The method according to claim 1, wherein the processing the standard model of the heart based on the first preprocessed image to obtain a model of the heart corresponding to the image to be processed comprises:
gridding the first preprocessing image, and acquiring at least one grid point to be adjusted on the edge of a chamber of the heart standard model corresponding to the first preprocessing image;
inputting the grid points to be adjusted into a pre-trained chamber edge classifier to obtain the probability value of each grid point to be adjusted at the preset chamber edge position on the image to be processed;
and deforming the chamber edge corresponding to each chamber according to the probability value to obtain a heart model corresponding to the image to be processed.
3. The method of claim 1, wherein the determining a target coronary artery associated with the calcified region according to the probability value comprises:
and respectively calculating the probability value of the calcified area in each coronary artery to be selected, and taking the corresponding coronary artery to be selected when the probability value is highest as the target coronary artery.
4. The method of claim 1, wherein prior to acquiring the image to be processed, further comprising:
acquiring a flat scanning image corresponding to a target scanning part;
determining a calcified area in the flat scanning image according to a preset condition;
and taking the scanning image for determining the calcified area as the image to be processed.
5. The method of claim 1, further comprising:
acquiring a plurality of historical heart images as a plurality of current registration sample images, and marking key information in the current registration samples, wherein the key information comprises the edges of each actual chamber of the heart;
selecting one current registration sample image from a plurality of current registration sample images as a reference sample image, taking the rest current registration sample images as sample images to be registered, and registering the reference sample image and each sample image to be registered according to the key information to generate at least one preliminary registration sample image;
gridding the edges of each actual chamber in the preliminary registration sample image to obtain at least one actual chamber edge grid point, and determining the average chamber edge of the preliminary registration sample image according to the coordinates of the actual chamber edge grid points of the reference sample image and the sample image to be registered in the preliminary registration sample image;
and taking the plurality of preliminary registration sample images as a plurality of current registration sample images, repeatedly executing the operation of selecting the reference sample image for image registration to obtain the average chamber edge until the average chamber edge of the target registration sample image is determined, and determining a heart standard model according to the average chamber edge of the target registration sample image.
6. The method of claim 5, wherein the key information includes coronary branches of the heart;
determining a heart standard model from the mean chamber edge of the target registration sample image, comprising:
determining coronary branches of the target registration sample image according to the positions of the marked coronary branches relative to a heart standard model in the target registration sample image;
determining a heart standard model according to each coronary branch of the target registration sample image and the mean chamber edge.
7. The method of claim 2, further comprising:
acquiring a plurality of historical heart images as a plurality of training samples, and marking the edge of each actual chamber of the heart in the training samples;
inputting a plurality of marked training samples into a pre-established machine learning model to obtain a chamber edge classifier corresponding to each actual chamber;
the heart chamber edge classifier is used for registering the image to be processed.
8. An apparatus for determining coronary branches where calcified regions are located, comprising:
the image preprocessing module is used for acquiring an image to be processed, and registering the image to be processed with the heart standard model to obtain a first preprocessed image, wherein the image to be processed comprises a calcified area;
a heart model determining module, configured to process the heart standard model based on the first preprocessed image, and obtain a heart model corresponding to the image to be processed;
a probability value calculating module, configured to calculate a probability value of at least one coronary artery to be candidate in the heart model of the calcified region;
and the coronary artery branch determining module is used for determining the target coronary artery associated with the calcified area according to the probability value.
9. A server, characterized in that the server comprises:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of determining coronary branches where calcified regions are located as claimed in any one of claims 1 to 7.
10. A storage medium containing computer executable instructions which, when executed by a computer processor, are for performing a method of determining a coronary branch in which a calcified region as claimed in any one of claims 1 to 7 is located.
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