CN111738982A - Vascular cavity concentration gradient extraction method and device and readable storage medium - Google Patents

Vascular cavity concentration gradient extraction method and device and readable storage medium Download PDF

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CN111738982A
CN111738982A CN202010469329.8A CN202010469329A CN111738982A CN 111738982 A CN111738982 A CN 111738982A CN 202010469329 A CN202010469329 A CN 202010469329A CN 111738982 A CN111738982 A CN 111738982A
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blood vessel
image
specific
specific blood
concentration gradient
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韦建雍
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Shukun Beijing Network Technology Co Ltd
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Shukun Beijing Network Technology 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
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/66—Analysis of geometric attributes of image moments or centre of gravity
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular

Abstract

The invention discloses a method and a device for extracting a concentration gradient of a blood vessel cavity and a readable storage medium, wherein the method comprises the following steps: acquiring a specific blood vessel image for a specific body part; extracting a central line of the specific blood vessel according to the extracted specific blood vessel image; determining a vessel cross-section image of the specific vessel according to the determined central line; determining a concentration gradient of the specific blood vessel according to the determined blood vessel cross-section image. Therefore, through the steps, the extraction of the concentration gradient can be automatically completed, the error between the measurement result and the real result caused by manual measurement is reduced, and meanwhile, the working efficiency is also improved.

Description

Vascular cavity concentration gradient extraction method and device and readable storage medium
Technical Field
The invention relates to the technical field of angiography, in particular to a method and a device for extracting a concentration gradient of a blood vessel cavity and a readable storage medium.
Background
The concentration gradient of the blood vessel cavity is a functional parameter reflecting the hemodynamic change, the concentration gradient of the blood vessel cavity is calculated by a doctor according to self experience at present, and due to manual operation, the extraction result of the concentration gradient of the blood vessel cavity has larger deviation from the real result, and misdiagnosis is easily caused.
Disclosure of Invention
The embodiment of the invention provides a method and a device for extracting a concentration gradient of a blood vessel cavity and a readable storage medium, which have the technical effect of reducing errors between a measurement result and a real result caused by manual measurement.
The invention provides a method for extracting a concentration gradient of a blood vessel cavity, which comprises the following steps: acquiring a specific blood vessel image for a specific body part; extracting a central line of the specific blood vessel according to the extracted specific blood vessel image; determining a vessel cross-section image of the specific vessel according to the determined central line; determining a concentration gradient of the specific blood vessel according to the determined blood vessel cross-section image.
In an embodiment, the acquiring a specific blood vessel image for a specific body part includes: acquiring an angiographic image of the specified body part; acquiring the specific blood vessel image according to the acquired angiography image.
In an embodiment, the acquiring the specific blood vessel image according to the acquired angiography image includes at least one of: training the angiography image as the input of a specific three-dimensional neural network segmentation model to obtain the specific blood vessel image; and carrying out edge detection on a specific blood vessel in the angiography image by using an active contour snake model to obtain the specific blood vessel image.
In an embodiment, the extracting, according to the extracted specific blood vessel image, a center line of the specific blood vessel includes: and processing the specific blood vessel image by using a skeleton algorithm or an image reduction algorithm, and extracting the central line of the specific blood vessel.
In an embodiment, the determining a vessel cross-sectional image of the specific vessel according to the determined center line includes: determining a plurality of central points of the central lines according to set intervals; acquiring a normal vector aiming at each central point according to the plurality of determined central points; and determining a blood vessel cross-section image of the specific blood vessel according to the acquired normal vector.
In one embodiment, the specific blood vessel image is a CT angiography image; correspondingly, the determining the concentration gradient of the specific blood vessel according to the determined cross-section image of the blood vessel comprises: obtaining body tissue density CT values corresponding to each pixel point in each blood vessel cross section; calculating an average CT value of each blood vessel cross section; generating a linear fitting curve of the CT value changing along with the change of the specific blood vessel diameter according to the calculated plurality of average CT values; determining a concentration gradient of the particular blood vessel from the generated linearly fitted curve.
In an embodiment, in determining the concentration gradient of the specific blood vessel according to the determined cross-sectional image of the blood vessel, the method further comprises: judging whether the CT value is within a preset range; and if the CT value is judged to be out of the preset range, smoothing the CT value so as to enable the CT value to be within the preset range.
In another aspect, the present invention provides a blood vessel lumen concentration gradient extraction device, including: a specific blood vessel image acquisition module for acquiring a specific blood vessel image for a specific body part; the blood vessel central line acquisition module is used for extracting the central line of the specific blood vessel according to the extracted specific blood vessel image; a cross-section image acquisition module for determining a blood vessel cross-section image of the specific blood vessel according to the determined central line; and the concentration gradient determining module is used for determining the concentration gradient of the specific blood vessel according to the determined blood vessel cross section image.
In an embodiment, the specific blood vessel image obtaining module is specifically configured to: acquiring an angiographic image of the specified body part; acquiring the specific blood vessel image according to the acquired angiography image.
In another aspect, the present invention provides a computer-readable storage medium comprising a set of computer-executable instructions, which when executed, perform any one of the vascular lumen concentration gradient extraction methods described above.
In the embodiment of the invention, the extraction of the concentration gradient can be automatically completed through the steps, so that the error between the measurement result and the real result caused by manual measurement is reduced, and the working efficiency is improved.
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The above and other objects, features and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description read in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings and in which:
in the drawings, the same or corresponding reference numerals indicate the same or corresponding parts.
Fig. 1 is a schematic flow chart of an implementation of a method for extracting a concentration gradient of a vascular lumen according to an embodiment of the present invention;
FIG. 2 is a schematic overall flow chart of a method for extracting a concentration gradient of a vascular lumen according to an embodiment of the present invention;
fig. 3 is a schematic structural composition diagram of a vascular lumen concentration gradient extraction device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of an implementation of a vascular lumen concentration gradient extraction method according to an embodiment of the present invention.
As shown in fig. 1, in one aspect, the present invention provides a method for extracting a concentration gradient in a vascular lumen, the method including:
step 101, acquiring a specific blood vessel image aiming at a specific body part;
step 102, extracting a central line of a specific blood vessel according to the extracted specific blood vessel image;
103, determining a cross-section image of the blood vessel of the specific blood vessel according to the determined central line;
and step 104, determining the concentration gradient of a specific blood vessel according to the determined cross section image of the blood vessel.
In the present embodiment, a specific blood vessel image is first acquired for a specific body part, wherein the specific body part mainly includes a part of a blood vessel, such as a heart part including a coronary artery tube, a leg part including an aorta, and the like. The specific blood vessel image is specifically a blood vessel portion of the concentration gradient to be extracted, such as left and right branch blood vessels in coronary arteries.
Then extracting the central line of the specific blood vessel according to the extracted specific blood vessel image; where the centerline is the axis of a particular vessel and is perpendicular to the vessel cross-section.
Then, according to the determined central line, a blood vessel cross section image of the specific blood vessel is determined, and finally, according to the determined blood vessel cross section image, the concentration gradient of the specific blood vessel is determined.
Therefore, through the steps, the extraction of the concentration gradient can be completed, the error between the measurement result and the real result caused by manual measurement is reduced, and meanwhile, the working efficiency is improved.
In one embodiment, acquiring a specific blood vessel image for a specific body part includes:
acquiring an angiographic image of a specified body part;
from the acquired angiographic image, a specific blood vessel image is acquired.
In this embodiment, the specific process of step 101 is as follows: the specified body part of the human body may be acquired by acquiring an angiographic image of the specified body part, which may be obtained by scanning the specified body part of the human body by at least one of a CT angiography (CTA) technique, a Magnetic Resonance Angiography (MRA) technique, or a Digital subtraction technique (DSA).
Since there is a region of no interest in the angiographic image, it is necessary to acquire a specific blood vessel image from the acquired angiographic image.
In one embodiment, a specific blood vessel image is acquired based on the acquired angiography image, and the specific blood vessel image includes at least one of the following images:
taking the angiography image as the input of a specific three-dimensional neural network segmentation model for training to obtain a specific blood vessel image;
and carrying out edge detection on a specific blood vessel in the angiography image by using an active contour snake model to obtain a specific blood vessel image.
In this embodiment, the angiographic image and the specific blood vessel image are both three-dimensional images. The specific three-dimensional neural network segmentation model is used for extracting a specific blood vessel image from an angiography image, the model needs to be trained in advance, and during training, training samples of the model are input into the angiography image and the angiography image with the labeled blood vessel position and output into the corresponding blood vessel image.
The edge detection processing can be carried out on a specific blood vessel in the angiography image through the existing active contour model, and then the specific blood vessel image is extracted from the angiography image.
In one embodiment, extracting a centerline of a specific blood vessel from the extracted specific blood vessel image includes:
and processing the specific blood vessel image by using a skeleton algorithm or an image reduction algorithm, and extracting the central line of the specific blood vessel.
In this embodiment, the skeleton algorithm or the image reduction algorithm mainly refines the contour of the blood vessel image toward the center thereof, and finally obtains the center line of the specific blood vessel. The skeleton algorithm specifically comprises a K3M algorithm, a Zhang-Suen algorithm and the like; the image reduction algorithm specifically includes a nearest neighbor interpolation method, a bilinear interpolation method, a cubic convolution method, and the like.
In one embodiment, determining a vessel cross-sectional image of a specific vessel from the determined centerline comprises:
determining a plurality of central points of the central line according to set intervals;
acquiring a normal vector for each central point according to the determined central points;
and determining a blood vessel cross-section image of the specific blood vessel according to the acquired normal vector.
In this embodiment, the specific process of step 103 is:
the center lines are determined to be a plurality of center points according to set intervals, wherein the set intervals can be equal intervals or unequal intervals, and are preferably equal intervals and are preferably spaced at a distance of 5mm in the embodiment.
Then, a normal vector for each center point is obtained according to the tangent direction of each center point along the center line.
And (4) carrying out image interception on the specific blood vessel image by using the plane where the normal vector is positioned, and determining the blood vessel cross section image of the specific blood vessel.
In one embodiment, the specific blood vessel image is a CT angiography image;
correspondingly, according to the determined cross section image of the blood vessel, the concentration gradient of the specific blood vessel is determined, and the method comprises the following steps:
acquiring a body tissue density CT value corresponding to each pixel point in each blood vessel cross section;
calculating the average CT value of each blood vessel cross section;
generating a linear fitting curve of which the CT value changes along with the change of the diameter of the specific blood vessel according to the calculated average CT values;
from the generated linearly fitted curve, the concentration gradient of the specific blood vessel is determined.
In this embodiment, the angiographic image is preferably a CT angiographic image, the CT angiographic image includes spatial coordinate information and a CT value corresponding to the coordinate, the spatial coordinate information and the CT value corresponding to the coordinate may be stored in a first specific file in advance, and similarly, after the specific blood vessel image is obtained, the spatial coordinate information of the specific blood vessel image in the angiographic image is stored in a second specific file.
Thus, the specific process of step 104 is:
the method comprises the steps of firstly finding space coordinate information of each pixel point in each blood vessel cross section through a second appointed file, and then inquiring a corresponding CT value from a first appointed file according to the space coordinate information.
Calculating the number of pixel points of each blood vessel cross section and the CT value of each pixel point, calculating to obtain the average CT value of each blood vessel cross section, expressing the CT value of each blood vessel cross section by using the average CT value, calculating to obtain the blood vessel diameter according to the blood vessel cross section area, generating an original curve of which the CT value changes along with the change of the blood vessel diameter in a two-dimensional coordinate system, performing linear fitting processing on the original curve to obtain a linear fitting curve of which the CT value changes along with the change of the specific blood vessel diameter, and finally obtaining the slope of the linear fitting curve through calculation to obtain the concentration gradient of the specific blood vessel.
In an embodiment, in determining the concentration gradient of the specific blood vessel according to the determined cross-sectional image of the blood vessel, the method further comprises:
judging whether the CT value is within a preset range;
and if the CT value is judged to be out of the preset range, smoothing the CT value so as to enable the CT value to be within the preset range.
In this embodiment, for some patients with vascular diseases, calcified plaque, non-calcified plaque or mixed plaque may be contained in a specific blood vessel, and the presence of plaque may affect the acquisition of CT value, and thus the detection result of the final concentration gradient.
Because the CT values of the pixel points where some plaques are located are higher or lower than the normal CT values, in the process of determining the concentration gradient of a specific blood vessel according to the determined cross-sectional image of the blood vessel, it is necessary to determine whether the CT values are within a preset range, where the step of determining may be after querying the corresponding CT values from the first specified file, or after making an original curve, and the preset range may be set according to an actual situation.
If the CT value is judged to be out of the preset range, namely the CT value is higher or lower, Gaussian smoothing is carried out on the blood vessel cross section image or the specific blood vessel image to remove noise, or smoothing is carried out on the CT value on the original curve or the CT value is deleted, so that the CT value is in the preset range.
FIG. 2 is a schematic overall flow chart of a method for extracting a concentration gradient of a vascular lumen according to an embodiment of the present invention;
taking the extraction of coronary artery concentration as an example, as shown in fig. 2, firstly, obtaining a coronary artery CT angiography image, and simultaneously recording the coordinate position of each pixel point in the image in space and a corresponding CT value;
then, segmenting a blood vessel image from the coronary artery CT angiography image by utilizing a three-dimensional neural network segmentation model or an active contour snake model, and recording the coordinate position of each pixel point in the blood vessel image;
then extracting the center line of the blood vessel by using a skeleton algorithm or an image reduction algorithm;
then selecting a plurality of central points at intervals on the central line and obtaining corresponding normal vectors according to the tangent direction of the central points on the central line, wherein the intersection of the plane where the normal vectors are located and the blood vessel image is the cross-section image;
calculating to obtain the mean value of the CT value of each cross section according to the number of pixel points in each blood vessel cross section image and the CT value of each pixel point;
outputting an original curve of the CT value changing along with the diameter of the blood vessel in a two-dimensional coordinate system;
detecting whether a plaque exists or not by the CT value, and if the plaque exists, performing smoothing treatment on the CT value at the corresponding position in the original curve to remove the plaque;
and then carrying out linear fitting treatment on the original curve to obtain a linear fitting curve, and calculating the slope of the linear fitting curve to obtain a coronary concentration gradient result.
Fig. 3 is a schematic structural composition diagram of a vascular lumen concentration gradient extraction device according to an embodiment of the present invention.
As shown in fig. 3, another aspect of the present invention provides a vascular lumen concentration gradient extraction device, including:
a specific blood vessel image acquisition module 201 for acquiring a specific blood vessel image for a specific body part;
a blood vessel centerline obtaining module 202, configured to extract a centerline of the specific blood vessel according to the extracted specific blood vessel image;
a cross-section image obtaining module 203, configured to determine a blood vessel cross-section image of the specific blood vessel according to the determined center line;
a concentration gradient determining module 204, configured to determine a concentration gradient of the specific blood vessel according to the determined blood vessel cross-section image.
In this embodiment, a specific blood vessel image for a specific body part, which mainly includes a blood vessel part, such as a heart part including a coronary artery tube, a leg part including an aorta, etc., is first acquired by the specific blood vessel image acquisition module 201. The specific blood vessel image is specifically a blood vessel portion of the concentration gradient to be extracted, such as left and right branch blood vessels in coronary arteries.
Then, the center line of the specific blood vessel is extracted according to the extracted specific blood vessel image through the blood vessel center line acquisition module 202; where the centerline is the axis of a particular vessel and is perpendicular to the vessel cross-section.
Then, a cross-section image acquisition module 203 determines a cross-section image of the blood vessel of the specific blood vessel according to the determined central line, and finally, a concentration gradient determination module 204 determines a concentration gradient of the specific blood vessel according to the determined cross-section image of the blood vessel.
Therefore, through the steps, the extraction of the concentration gradient can be completed, the error between the measurement result and the real result caused by manual measurement is reduced, and meanwhile, the working efficiency is improved.
In an embodiment, the specific blood vessel image obtaining module 201 is specifically configured to:
acquiring an angiographic image of the specified body part;
acquiring the specific blood vessel image according to the acquired angiography image.
In this embodiment, the specific blood vessel image acquisition module is specifically configured to: the specified body part of the human body may be acquired by acquiring an angiographic image of the specified body part, which may be obtained by scanning the specified body part of the human body by at least one of a CT angiography (CTA) technique, a Magnetic Resonance Angiography (MRA) technique, or a Digital subtraction technique (DSA).
Since there is a region of no interest in the angiographic image, it is necessary to acquire a specific blood vessel image from the acquired angiographic image.
In another aspect, the present invention provides a computer-readable storage medium comprising a set of computer-executable instructions which, when executed, perform any of the vascular lumen concentration gradient extraction methods described above.
In an embodiment of the present invention, a computer-readable storage medium comprises a set of computer-executable instructions that, when executed, are operable to acquire a particular blood vessel image for a particular body part; extracting a central line of the specific blood vessel according to the extracted specific blood vessel image; determining a vessel cross-section image of the specific vessel according to the determined central line; and determining the concentration gradient of the specific blood vessel according to the determined blood vessel cross section image.
Therefore, through the steps, the extraction of the concentration gradient can be automatically completed, the error between the measurement result and the real result caused by manual measurement is reduced, and meanwhile, the working efficiency is also improved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A vascular lumen concentration gradient extraction method, the method comprising:
acquiring a specific blood vessel image for a specific body part;
extracting a central line of the specific blood vessel according to the extracted specific blood vessel image;
determining a vessel cross-section image of the specific vessel according to the determined central line;
determining a concentration gradient of the specific blood vessel according to the determined blood vessel cross-section image.
2. The method of claim 1, wherein the acquiring a particular blood vessel image for a particular body part comprises:
acquiring an angiographic image of the specified body part;
acquiring the specific blood vessel image according to the acquired angiography image.
3. The method of claim 2, wherein the obtaining the vessel-specific image from the obtained angiographic image comprises at least one of:
training the angiography image as the input of a specific three-dimensional neural network segmentation model to obtain the specific blood vessel image;
and carrying out edge detection on a specific blood vessel in the angiography image by using an active contour snake model to obtain the specific blood vessel image.
4. The method according to claim 1, wherein the extracting a center line of the specific blood vessel from the extracted specific blood vessel image comprises:
and processing the specific blood vessel image by using a skeleton algorithm or an image reduction algorithm, and extracting the central line of the specific blood vessel.
5. The method according to claim 1 or 4, wherein said determining a vessel cross-sectional image of the specific vessel from the determined centerline comprises:
determining a plurality of central points of the central lines according to set intervals;
acquiring a normal vector aiming at each central point according to the plurality of determined central points;
and determining a blood vessel cross-section image of the specific blood vessel according to the acquired normal vector.
6. The method according to claim 1, wherein the specific vessel image is a CT angiography image;
correspondingly, the determining the concentration gradient of the specific blood vessel according to the determined cross-section image of the blood vessel comprises:
obtaining body tissue density CT values corresponding to each pixel point in each blood vessel cross section;
calculating an average CT value of each blood vessel cross section;
generating a linear fitting curve of the CT value changing along with the change of the specific blood vessel diameter according to the calculated plurality of average CT values;
determining a concentration gradient of the particular blood vessel from the generated linearly fitted curve.
7. The method according to claim 6, wherein in determining the concentration gradient of the specific blood vessel from the determined cross-sectional image of the blood vessel, the method further comprises:
judging whether the CT value is within a preset range;
and if the CT value is judged to be out of the preset range, smoothing the CT value so as to enable the CT value to be within the preset range.
8. A vascular lumen concentration gradient extraction device, the device comprising:
a specific blood vessel image acquisition module for acquiring a specific blood vessel image for a specific body part;
the blood vessel central line acquisition module is used for extracting the central line of the specific blood vessel according to the extracted specific blood vessel image;
a cross-section image acquisition module for determining a blood vessel cross-section image of the specific blood vessel according to the determined central line;
and the concentration gradient determining module is used for determining the concentration gradient of the specific blood vessel according to the determined blood vessel cross section image.
9. The apparatus of claim 8, wherein the vessel-specific image acquisition module is specifically configured to:
acquiring an angiographic image of the specified body part;
acquiring the specific blood vessel image according to the acquired angiography image.
10. A computer-readable storage medium comprising a set of computer-executable instructions that, when executed, perform the vascular lumen concentration gradient extraction method of any of claims 1-7.
CN202010469329.8A 2020-05-28 2020-05-28 Vascular cavity concentration gradient extraction method and device and readable storage medium Pending CN111738982A (en)

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