CN116030273B - Data processing method and device suitable for microwave ablation analysis system - Google Patents
Data processing method and device suitable for microwave ablation analysis system Download PDFInfo
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Abstract
The application provides a data processing method and device suitable for a microwave ablation analysis system. According to the method, a scanning image sequence of a target object in a target area in a preset period is obtained, then, the characteristic outline of a target part in each CT image is determined according to each CT image in the scanning image sequence and a preset characteristic outline extraction model, and the characteristic point of the target part in each CT image is determined according to each CT image in the scanning image sequence and a preset characteristic point extraction model, so that a characteristic data sequence to be evaluated corresponding to the target part in the preset period is generated according to the characteristic outline and the characteristic point corresponding to each CT image, and then, the characteristic curve of the target part in the preset period is determined according to the characteristic data sequence and a preset microwave ablation evaluation model, so that the size dynamic change condition of the target part in the preset period after microwave ablation treatment can be automatically monitored.
Description
Technical Field
The present disclosure relates to data processing technologies, and in particular, to a data processing method and apparatus suitable for a microwave ablation analysis system.
Background
Microwave ablation refers to the process of destroying a nodular focus by utilizing microwave energy to cause necrosis and carbonization of the focus, and gradually absorbing the carbonized focus by a body after a period of time until the focus disappears. Currently, microwave ablation is often used to treat nodular lesions of solid organs such as thyroid, breast, liver, etc.
After microwave ablation, regular CT scan review is usually required in a predetermined period, and the result of each review needs to be output to a specific doctor, so that the doctor can manually judge the condition of the ablation region, which is low in efficiency, and a method for automatically processing the CT scan result of the microwave ablation part and realizing automatic monitoring is needed.
Disclosure of Invention
The application provides a data processing method and device suitable for a microwave ablation analysis system, which are used for solving the technical problems that automatic data processing cannot be carried out on CT scanning results of microwave ablation parts and automatic monitoring is realized.
In a first aspect, the present application provides a data processing method suitable for a microwave ablation analysis system, comprising:
acquiring a scanning image sequence of a target area of a target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period;
Determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model, and determining the characteristic point of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model so as to generate a characteristic data sequence to be evaluated corresponding to the target part in the preset period according to the characteristic contour and the characteristic point corresponding to each CT image;
and determining a characteristic curve of the target part in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, wherein the characteristic curve is used for representing the dimensional change of the target part in the preset period.
Optionally, the determining, according to the feature data sequence and a preset microwave ablation evaluation model, a feature curve of the target portion in the preset period includes:
setting N demarcation points on the characteristic profile under a preset coordinate system to divide the characteristic profile into N equal parts, wherein N is a positive integer greater than 10000;
determining a feature size according to coordinate values of the feature points under the preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated,
is the abscissa of the feature point under the preset coordinate system,
is the ordinate of the feature point in the preset coordinate system,
is the abscissa of the nth demarcation point under the preset coordinate system,
is the ordinate of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
and generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period.
Optionally, after the generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period, the method further includes:
displaying the characteristic curve, wherein the characteristic curve is displayed with data trigger points corresponding to all time points;
and responding to a first trigger instruction acted on the first data trigger point, and displaying the CT image corresponding to the first data trigger point.
Optionally, after the displaying the characteristic curve, the method further includes:
responding to a second trigger instruction acting on a second data trigger point and a third data trigger point, and displaying a CT image corresponding to the second data trigger point and a CT image corresponding to the third data trigger point, wherein the second trigger instruction comprises: and the display instruction is used for determining and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point.
Optionally, after displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point, the method further includes:
and responding to a contrast triggering instruction acting on a first characteristic point of the second CT image and a second characteristic point of the third CT image, and generating an overlapped display contrast image after the second CT image and the third CT image are subjected to the transparency processing, wherein the transparency degree of one CT image with an earlier corresponding time node in the second CT image and the third CT image is higher than that of the other CT image, and the first characteristic point and the second characteristic point are overlapped in the overlapped display image.
Optionally, the determining the feature profile of the target portion in each CT image according to each CT image in the scan image sequence and a preset feature profile extraction model includes:
performing filtering processing on each CT image in the scanning image sequence to generate a CT image to be extracted, wherein the filtering processing is used for filtering out pixel points of which the pixel value is smaller than a preset pixel threshold value in the CT image to be extracted;
determining edge characteristic values of each pixel point according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
the pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3:
Determining that the pixel points with the edge characteristic values smaller than a preset characteristic threshold value are edge contour points, and generating an edge contour point set according to the determined edge contour points;
and determining the characteristic contour of the target part according to each edge contour point in the edge contour point set.
Optionally, the determining the feature point of the target part in each CT image according to each CT image in the scan image sequence and a preset feature point extraction model includes:
performing contrast enhancement processing on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
the gray value of the target pixel point with the transverse serial number i and the longitudinal serial number j in the CT image after the contrast enhancement treatment, 
Gray values before the contrast enhancement treatment are carried out on the target pixel points in the CT image,
a preset maximum gray value for the feature object,
a preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and a formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained;
the first characteristic constant is positive;
is a second characteristic constant, a positive number；
Is the characteristic dimension between adjacent pixel points in the transverse direction;
for the feature size between adjacent pixels in the longitudinal direction,
as a first component of the rate of contrast change,
as a second component of the rate of contrast change,
as a third component of the rate of contrast change,
as a fourth component of the rate of contrast change,
a fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to a preset comprehensive change rate as the characteristic points of the target part.
In a second aspect, the present application provides a data processing apparatus adapted for use in a microwave ablation analysis system, comprising:
the acquisition module is used for acquiring a scanning image sequence of a target area of a target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period;
The processing module is used for determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model, and determining the characteristic point of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model so as to generate a characteristic data sequence to be evaluated corresponding to the target part in the preset period according to the characteristic contour and the characteristic point corresponding to each CT image;
the determining module is used for determining a characteristic curve of the target part in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, and the characteristic curve is used for representing the dimensional change of the target part in the preset period.
Optionally, the determining module is specifically configured to:
setting N demarcation points on the characteristic profile under a preset coordinate system to divide the characteristic profile into N equal parts, wherein N is a positive integer greater than 10000;
determining a feature size according to coordinate values of the feature points under the preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated,
is the abscissa of the feature point under the preset coordinate system,
is the ordinate of the feature point in the preset coordinate system,
is the abscissa of the nth demarcation point under the preset coordinate system,
is the ordinate of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
and generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period.
Optionally, the apparatus further includes:
the display module is used for displaying the characteristic curve, wherein the characteristic curve is displayed with data trigger points corresponding to all time points;
the display module is also used for responding to a first trigger instruction acted on the first data trigger point and displaying the CT image corresponding to the first data trigger point.
Optionally, the display module is further configured to respond to a second trigger instruction acting on a second data trigger point and a third data trigger point, and display a CT image corresponding to the second data trigger point and a CT image corresponding to the third data trigger point, where the second trigger instruction includes: and the display instruction is used for determining and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point.
Optionally, the display module is further configured to generate an overlapped display contrast image after performing the transparency process on the second CT image and the third CT image in response to a contrast trigger instruction applied to a first feature point of the second CT image and a second feature point of the third CT image, where a transparency degree of a CT image of the second CT image and a corresponding time node in the third CT image is higher than that of another CT image, and the first feature point and the second feature point overlap in the overlapped display image.
Optionally, the processing module is specifically configured to:
performing filtering processing on each CT image in the scanning image sequence to generate a CT image to be extracted, wherein the filtering processing is used for filtering out pixel points of which the pixel value is smaller than a preset pixel threshold value in the CT image to be extracted;
determining edge characteristic values of each pixel point according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
the pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted; 
Taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3:
Determining that the pixel points with the edge characteristic values smaller than a preset characteristic threshold value are edge contour points, and generating an edge contour point set according to the determined edge contour points;
and determining the characteristic contour of the target part according to each edge contour point in the edge contour point set.
Optionally, the processing module is specifically configured to:
performing contrast enhancement processing on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
the gray value of the target pixel point with the transverse serial number i and the longitudinal serial number j in the CT image after the contrast enhancement treatment,
gray values before the contrast enhancement treatment are carried out on the target pixel points in the CT image,
a preset maximum gray value for the feature object,
a preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and a formula 5, wherein the formula 5 is as follows:
wherein ,
The contrast ratio comprehensive change rate of the target pixel point is obtained;
the first characteristic constant is positive;
the second characteristic constant is positive;
is the characteristic dimension between adjacent pixel points in the transverse direction;
for the feature size between adjacent pixels in the longitudinal direction,
as a first component of the rate of contrast change,
as a second component of the rate of contrast change,
as a third component of the rate of contrast change,
as a fourth component of the rate of contrast change,
a fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to a preset comprehensive change rate as the characteristic points of the target part.
In a third aspect, the present application provides an electronic device, comprising:
a processor; the method comprises the steps of,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform any one of the possible methods described in the first aspect via execution of the executable instructions.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions which, when executed by a processor, are adapted to carry out any one of the possible methods described in the first aspect.
According to the data processing method and device suitable for the microwave ablation analysis system, the scanning image sequence of the target object in the target area in the preset period is obtained, then, the characteristic outline of the target part in each CT image is determined according to each CT image in the scanning image sequence and the preset characteristic outline extraction model, and the characteristic point of the target part in each CT image is determined according to each CT image in the scanning image sequence and the preset characteristic point extraction model, so that the characteristic data sequence to be evaluated corresponding to the target part in the preset period is generated according to the characteristic outline and the characteristic point corresponding to each CT image, and then, the characteristic curve of the target part in the preset period is determined according to the characteristic data sequence and the preset microwave ablation evaluation model, so that the size dynamic change condition of the target part in the preset period after microwave ablation treatment can be automatically monitored.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a flow chart illustrating a data processing method suitable for use in a microwave ablation analysis system according to an example embodiment of the present application;
FIG. 2 is a flow chart illustrating a data processing method suitable for use in a microwave ablation analysis system according to another example embodiment of the present application;
FIG. 3 is a schematic diagram of a data processing apparatus suitable for use in a microwave ablation analysis system according to an example embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an example embodiment of the present application.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
Fig. 1 is a flow chart illustrating a data processing method suitable for a microwave ablation analysis system according to an example embodiment of the present application. As shown in fig. 1, the method provided in this embodiment includes:
s101, acquiring a scanning image sequence of a target area of a target object in a preset period.
And acquiring a scanning image sequence of a target area of the target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period. For example, it may be that after microwave ablation of the liver of the target subject, the abdominal region (including the liver region) is CT scanned periodically every month for a period of one year after the ablation to obtain a sequence of scanned images within one year after the ablation.
It should be noted that, when CT scanning is performed at different time nodes in the preset period, the image scale in the CT image may be changed due to reasons such as scanning distance, so that the image scale may be kept unchanged by uniformly adopting uniform scanning parameters for a specific target object during scanning, or scaling may be performed uniformly after the CT image is acquired, and the scanned image sequence is reproduced after the uniform scale.
S102, determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model.
In this step, the feature profile of the target portion in each CT image may be determined according to each CT image in the scan image sequence and a preset feature profile extraction model, where the preset feature profile extraction model may be a model established based on a boundary tracking method, a region growing method, a neural network algorithm, and the like.
In one possible implementation manner, filtering processing may be performed on each CT image in the scan image sequence to generate a CT image to be extracted, where the filtering processing is used to filter out pixel points in the CT image to be extracted, where a pixel value of the pixel point is smaller than a preset pixel threshold. And then determining edge characteristic values of all pixel points according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
the pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
For the following
Define as equation 3:
And determining the pixel points with the edge characteristic values smaller than the preset characteristic threshold as edge contour points, generating an edge contour point set according to the determined edge contour points, and determining the characteristic contour of the target part according to each edge contour point in the edge contour point set. Through the comparison feature judgment of the 8 adjacent pixel points, the edge feature value of each pixel point is determined, the edge contour point is determined by comparing the edge feature value with a preset feature threshold value, and finally, the feature contour is determined, so that the feature contour of the target part can be effectively extracted.
S103, determining characteristic points of target parts in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model.
Specifically, feature points of target parts in each CT image may be determined according to each CT image in the scan image sequence and a preset feature point extraction model, where the corresponding feature points may be bifurcation points or confluence points of portal veins and hepatic veins in the liver, and the preset feature point extraction model may be implemented based on algorithms such as a neural network algorithm and an image texture algorithm. And then, generating a feature data sequence to be evaluated corresponding to the target part in a preset period according to the feature contours and the feature points corresponding to each CT image.
In this step, contrast enhancement processing may be performed on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
the gray value of the target pixel point with the transverse serial number i and the longitudinal serial number j in the CT image after the contrast enhancement treatment,
gray values before the contrast enhancement treatment are carried out for target pixel points in the CT image,
a preset maximum gray value for the feature object,
a preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and the formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained;
the first characteristic constant is positive;
the second characteristic constant is positive;
is the characteristic dimension between adjacent pixel points in the transverse direction;
for the feature size between adjacent pixels in the longitudinal direction,
as a first component of the rate of contrast change,
as a second component of the rate of contrast change,
as a third component of the rate of contrast change,
as a fourth component of the rate of contrast change,
a fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to the preset comprehensive change rate as characteristic points of the target part.
And S104, determining a characteristic curve of the target part in a preset period according to the characteristic data sequence and a preset microwave ablation evaluation model.
And determining a characteristic curve of the target part in a preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, wherein the characteristic curve is used for representing the dimensional change of the target part in the preset period.
Specifically, N demarcation points may be set on the feature profile under a preset coordinate system to divide the feature profile into N equal parts, where N is a positive integer greater than 10000.
Then, determining the feature size according to the coordinate value of each feature point under a preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated,
is the abscissa of the feature point under the preset coordinate system,
is the ordinate of the feature point in the preset coordinate system,
is the abscissa of the nth demarcation point under the preset coordinate system,
is the ordinate of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
and finally, generating a characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period.
In this embodiment, a scan image sequence of a target object in a target area within a preset period is obtained, then, a feature contour of a target part in each CT image is determined according to each CT image in the scan image sequence and a preset feature contour extraction model, and feature points of the target part in each CT image are determined according to each CT image in the scan image sequence and a preset feature point extraction model, so as to generate a feature data sequence to be evaluated corresponding to the target part within the preset period according to the feature contour and the feature points corresponding to each CT image, and then, a feature curve of the target part within the preset period is determined according to the feature data sequence and a preset microwave ablation evaluation model, so that the size dynamic change condition of the target part within the preset period after microwave ablation processing can be automatically monitored.
Fig. 2 is a flow chart illustrating a data processing method suitable for use in a microwave ablation analysis system according to another example embodiment of the present application. As shown in fig. 2, the data processing method suitable for the microwave ablation analysis system provided in this embodiment includes:
s201, acquiring a scanning image sequence of a target area of a target object in a preset period.
And acquiring a scanning image sequence of a target area of the target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period. For example, it may be that after microwave ablation of the liver of the target subject, the abdominal region (including the liver region) is CT scanned periodically every month for a period of one year after the ablation to obtain a sequence of scanned images within one year after the ablation.
It should be noted that, when CT scanning is performed at different time nodes in the preset period, the image scale in the CT image may be changed due to reasons such as scanning distance, so that the image scale may be kept unchanged by uniformly adopting uniform scanning parameters for a specific target object during scanning, or scaling may be performed uniformly after the CT image is acquired, and the scanned image sequence is reproduced after the uniform scale.
S202, determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model.
In this step, the feature profile of the target portion in each CT image may be determined according to each CT image in the scan image sequence and a preset feature profile extraction model, where the preset feature profile extraction model may be a model established based on a boundary tracking method, a region growing method, a neural network algorithm, and the like.
In one possible implementation manner, filtering processing may be performed on each CT image in the scan image sequence to generate a CT image to be extracted, where the filtering processing is used to filter out pixel points in the CT image to be extracted, where a pixel value of the pixel point is smaller than a preset pixel threshold. And then determining edge characteristic values of all pixel points according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
the pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3:
And determining the pixel points with the edge characteristic values smaller than the preset characteristic threshold as edge contour points, generating an edge contour point set according to the determined edge contour points, and determining the characteristic contour of the target part according to each edge contour point in the edge contour point set. Through the comparison feature judgment of the 8 adjacent pixel points, the edge feature value of each pixel point is determined, the edge contour point is determined by comparing the edge feature value with a preset feature threshold value, and finally, the feature contour is determined, so that the feature contour of the target part can be effectively extracted.
S203, determining characteristic points of target parts in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model.
Specifically, feature points of target parts in each CT image may be determined according to each CT image in the scan image sequence and a preset feature point extraction model, where the corresponding feature points may be bifurcation points or confluence points of portal veins and hepatic veins in the liver, and the preset feature point extraction model may be implemented based on algorithms such as a neural network algorithm and an image texture algorithm. And then, generating a feature data sequence to be evaluated corresponding to the target part in a preset period according to the feature contours and the feature points corresponding to each CT image.
In this step, contrast enhancement processing may be performed on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
the gray value of the target pixel point with the transverse serial number i and the longitudinal serial number j in the CT image after the contrast enhancement treatment,
gray values before the contrast enhancement treatment are carried out for target pixel points in the CT image,
a preset maximum gray value for the feature object, 
A preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and the formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained;
the first characteristic constant is positive;
the second characteristic constant is positive;
is the characteristic dimension between adjacent pixel points in the transverse direction;
for the feature size between adjacent pixels in the longitudinal direction,
as a first component of the rate of contrast change,
as a second component of the rate of contrast change,
as a third component of the rate of contrast change,
as a fourth component of the rate of contrast change,
a fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to the preset comprehensive change rate as characteristic points of the target part.
S204, determining a characteristic curve of the target part in a preset period according to the characteristic data sequence and a preset microwave ablation evaluation model.
And determining a characteristic curve of the target part in a preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, wherein the characteristic curve is used for representing the dimensional change of the target part in the preset period.
Specifically, N demarcation points may be set on the feature profile under a preset coordinate system to divide the feature profile into N equal parts, where N is a positive integer greater than 10000.
Then, determining the feature size according to the coordinate value of each feature point under a preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated,
is the abscissa of the feature point under the preset coordinate system,
is the ordinate of the feature point in the preset coordinate system,
is the abscissa of the nth demarcation point under the preset coordinate system,
is the ordinate of the nth demarcation point under the preset coordinate system,
for characteristic points and eachA minimum distance between demarcation points;
and finally, generating a characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period.
S205, displaying a characteristic curve.
And determining a characteristic curve of the target part in a preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, and displaying the characteristic curve, wherein the characteristic curve is displayed with data trigger points corresponding to all time points. It is understood that the characteristic curve may be displayed in a predetermined rectangular coordinate system, wherein the abscissa is time, the ordinate is feature size, and the data trigger point may be an identifier displayed on the characteristic curve.
S206, responding to the first trigger instruction acted on the first data trigger point, and displaying the CT image corresponding to the first data trigger point.
And responding to the first trigger instruction acted on the first data trigger point, and displaying the CT image corresponding to the first data trigger point. The patient or doctor can trigger and display the CT image corresponding to the first data trigger point by clicking the first data trigger point, wherein the first data trigger point can be any one data trigger point on the characteristic curve, so that the technical effect of rapidly acquiring specific CT scanning results corresponding to all points from the monitored characteristic curve is realized, and particularly, the high-efficiency data viewing can be realized by the rapid clicking mode aiming at the points with abnormal changes on the characteristic curve.
S207, responding to a second trigger instruction acting on the second data trigger point and the third data trigger point, and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point.
Responding to a second trigger instruction acting on a second data trigger point and a third data trigger point, and displaying a CT image corresponding to the second data trigger point and a CT image corresponding to the third data trigger point, wherein the second trigger instruction comprises: and the display instruction is used for determining and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point. Specifically, the patient or doctor can select at least two data trigger points and then select to check specific results of the two data points, so that the requirement of comparing the two results at different time points is met.
S208, generating an overlapped display contrast image after the second CT image and the third CT image are subjected to the transparency processing in response to the contrast triggering instruction acted on the first feature point of the second CT image and the second feature point of the third CT image.
And responding to a contrast triggering instruction acting on the first characteristic point of the second CT image and the second characteristic point of the third CT image, generating an overlapped display contrast image after the second CT image and the third CT image are subjected to the transparency processing, wherein the transparency degree of one CT image with an earlier corresponding time node in the second CT image and the third CT image is higher than that of the other CT image, and the first characteristic point and the second characteristic point are overlapped in the overlapped display image. Specifically, the first feature point of the second CT image may be a bifurcation point or a junction point of a portal vein and a hepatic vein of a hepatic blood vessel, and the second feature point of the third CT image may be a bifurcation point or a junction point of a portal vein and a hepatic vein of a hepatic blood vessel, which corresponds to the same hepatic blood vessel, and may be a junction point of a main vein selected as the feature point. Then, triggering a comparison instruction, namely, generating an overlapped display comparison image after the second CT image and the third CT image are subjected to transparency processing, so that the visual comparison requirement of two results at different time points is met.
Fig. 3 is a schematic structural view of an apparatus according to an exemplary embodiment of the present application. As shown in fig. 3, the apparatus 300 provided in this embodiment includes:
the acquisition module 301 is configured to acquire a scan image sequence of a target area of a target object in a preset period, where the target area includes a target portion that is subjected to microwave ablation before the preset period, and the scan image sequence includes CT images acquired by a plurality of time nodes in the preset period;
the processing module 302 is configured to determine a feature contour of the target portion in each CT image according to each CT image in the scan image sequence and a preset feature contour extraction model, and determine a feature point of the target portion in each CT image according to each CT image in the scan image sequence and a preset feature point extraction model, so as to generate a feature data sequence to be evaluated corresponding to the target portion in the preset period according to the feature contour and the feature point corresponding to each CT image;
the determining module 303 is configured to determine a characteristic curve of the target portion in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, where the characteristic curve is used to characterize a dimensional change of the target portion in the preset period.
Optionally, the determining module 303 is specifically configured to:
setting N demarcation points on the characteristic profile under a preset coordinate system to divide the characteristic profile into N equal parts, wherein N is a positive integer greater than 10000;
determining a feature size according to coordinate values of the feature points under the preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated,
is the abscissa of the feature point under the preset coordinate system,
is the ordinate of the feature point in the preset coordinate system,
at the preset sitting position for the nth demarcation pointThe abscissa under the scale of the system of symbols,
is the ordinate of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
and generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period.
Optionally, the apparatus 300 further includes:
the display module 304 is configured to display the characteristic curve, where the characteristic curve displays data trigger points corresponding to each time point;
the display module 304 is further configured to display a CT image corresponding to the first data trigger point in response to a first trigger instruction acting on the first data trigger point.
Optionally, the display module 304 is further configured to display a CT image corresponding to the second data trigger point and a CT image corresponding to the third data trigger point in response to a second trigger instruction acting on the second data trigger point and the third data trigger point, where the second trigger instruction includes: and the display instruction is used for determining and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point.
Optionally, the display module 304 is further configured to generate, in response to a contrast trigger instruction applied to a first feature point of the second CT image and a second feature point of the third CT image, an overlapping display contrast image after performing a transparency process on the second CT image and the third CT image, where a transparency degree of a CT image of the second CT image and a corresponding time node in the third CT image is higher than that of another CT image, and the first feature point overlaps with the second feature point in the overlapping display image.
Optionally, the processing module 302 is specifically configured to:
Performing filtering processing on each CT image in the scanning image sequence to generate a CT image to be extracted, wherein the filtering processing is used for filtering out pixel points of which the pixel value is smaller than a preset pixel threshold value in the CT image to be extracted;
determining edge characteristic values of each pixel point according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
the pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted;
taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3:
Determining that the pixel points with the edge characteristic values smaller than a preset characteristic threshold value are edge contour points, and generating an edge contour point set according to the determined edge contour points;
and determining the characteristic contour of the target part according to each edge contour point in the edge contour point set.
Optionally, the processing module 302 is specifically configured to:
performing contrast enhancement processing on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
the gray value of the target pixel point with the transverse serial number i and the longitudinal serial number j in the CT image after the contrast enhancement treatment,
gray values before the contrast enhancement treatment are carried out on the target pixel points in the CT image,
a preset maximum gray value for the feature object,
a preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and a formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained;
the first characteristic constant is positive;
the second characteristic constant is positive;
is the characteristic dimension between adjacent pixel points in the transverse direction;
for the feature size between adjacent pixels in the longitudinal direction,
as a first component of the rate of contrast change,
as a second component of the rate of contrast change,
as a third component of the rate of contrast change,
as a fourth component of the rate of contrast change,
a fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to a preset comprehensive change rate as the characteristic points of the target part.
Fig. 4 is a schematic structural diagram of an electronic device according to an example embodiment of the present application. As shown in fig. 4, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein:
A memory 402 for storing a computer program, which memory may also be a flash memory.
A processor 401 for executing the execution instructions stored in the memory to implement the steps in the above method. Reference may be made in particular to the description of the embodiments of the method described above.
Alternatively, the memory 402 may be separate or integrated with the processor 401.
When the memory 402 is a device separate from the processor 401, the electronic apparatus 400 may further include:
a bus 403 for connecting the memory 402 and the processor 401.
The present embodiment also provides a readable storage medium having a computer program stored therein, which when executed by at least one processor of an electronic device, performs the methods provided by the various embodiments described above.
The present embodiment also provides a program product comprising a computer program stored in a readable storage medium. The computer program may be read from a readable storage medium by at least one processor of an electronic device, and executed by the at least one processor, causes the electronic device to implement the methods provided by the various embodiments described above.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (7)
1. A data processing method suitable for use in a microwave ablation analysis system, comprising:
acquiring a scanning image sequence of a target area of a target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period;
Determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model, and determining the characteristic point of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model so as to generate a characteristic data sequence to be evaluated corresponding to the target part in the preset period according to the characteristic contour and the characteristic point corresponding to each CT image;
determining a characteristic curve of the target part in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, wherein the characteristic curve is used for representing the dimensional change of the target part in the preset period;
the determining the characteristic curve of the target part in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model comprises the following steps:
setting N demarcation points on the characteristic profile under a preset coordinate system to divide the characteristic profile into N equal parts, wherein N is a positive integer greater than 10000;
determining a feature size according to coordinate values of the feature points under the preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated, < >>
Is the abscissa of the feature point under said preset coordinate system,/->
Is the ordinate of the feature point under the preset coordinate system,/->
For the abscissa of the nth demarcation point under said preset coordinate system, +.>
Is the abscissa of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period;
the determining the feature profile of the target part in each CT image according to each CT image in the scanning image sequence and a preset feature profile extraction model comprises the following steps:
performing filtering processing on each CT image in the scanning image sequence to generate a CT image to be extracted, wherein the filtering processing is used for filtering out pixel points of which the pixel value is smaller than a preset pixel threshold value in the CT image to be extracted;
determining edge characteristic values of each pixel point according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted; / >
The pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted; />
Is a constant value, and is used for the treatment of the skin,the value is positively correlated with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3: />
Determining that the pixel points with the edge characteristic values smaller than a preset characteristic threshold value are edge contour points, and generating an edge contour point set according to the determined edge contour points;
determining the characteristic contour of the target part according to each edge contour point in the edge contour point set;
the determining the feature point of the target part in each CT image according to each CT image in the scanned image sequence and a preset feature point extraction model comprises the following steps:
performing contrast enhancement processing on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
gray value after contrast enhancement treatment is carried out on target pixel point with transverse serial number i and longitudinal serial number j in CT image, < >>
Gray value before contrast enhancement treatment is carried out on the target pixel point in the CT image, and the gray value is +.>
Maximum gray value preset for the feature object, < > >
A preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and a formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained; />
The first characteristic constant is positive; />
The second characteristic constant is positive; />
Is the characteristic dimension between adjacent pixel points in the transverse direction; />
Is the feature size between adjacent pixels in the longitudinal direction, < >>
For the first component of the contrast ratio of change, +.>
For the second component of the contrast ratio, +.>
For the third component of the contrast ratio, +.>
For the fourth component of the contrast ratio, +.>
A fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to a preset comprehensive change rate as the characteristic points of the target part.
2. The method for processing data applicable to a microwave ablation analysis system according to claim 1, further comprising, after the generating the characteristic curve according to the characteristic dimensions corresponding to each time node in the preset period:
displaying the characteristic curve, wherein the characteristic curve is displayed with data trigger points corresponding to all time points;
And responding to a first trigger instruction acted on the first data trigger point, and displaying the CT image corresponding to the first data trigger point.
3. The method for processing data suitable for use in a microwave ablation analysis system according to claim 2, further comprising, after said displaying the characteristic curve:
responding to a second trigger instruction acting on a second data trigger point and a third data trigger point, and displaying a CT image corresponding to the second data trigger point and a CT image corresponding to the third data trigger point, wherein the second trigger instruction comprises: and the display instruction is used for determining and displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point.
4. The method of claim 3, further comprising, after said displaying the CT image corresponding to the second data trigger point and the CT image corresponding to the third data trigger point:
and responding to a contrast triggering instruction acting on a first characteristic point of a second CT image and a second characteristic point of a third CT image, generating an overlapped display contrast image after the second CT image and the third CT image are subjected to the transparency processing, wherein the transparency degree of one CT image with an earlier corresponding time node in the second CT image and the third CT image is higher than that of the other CT image, and the first characteristic point and the second characteristic point are overlapped in the overlapped display image.
5. A data processing apparatus adapted for use in a microwave ablation analysis system, comprising:
the acquisition module is used for acquiring a scanning image sequence of a target area of a target object in a preset period, wherein the target area comprises a target part subjected to microwave ablation before the preset period, and the scanning image sequence comprises CT images acquired by a plurality of time nodes in the preset period;
the processing module is used for determining the characteristic contour of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic contour extraction model, and determining the characteristic point of the target part in each CT image according to each CT image in the scanning image sequence and a preset characteristic point extraction model so as to generate a characteristic data sequence to be evaluated corresponding to the target part in the preset period according to the characteristic contour and the characteristic point corresponding to each CT image;
the determining module is used for determining a characteristic curve of the target part in the preset period according to the characteristic data sequence and a preset microwave ablation evaluation model, wherein the characteristic curve is used for representing the dimensional change of the target part in the preset period;
The determining module is specifically configured to:
setting N demarcation points on the characteristic profile under a preset coordinate system to divide the characteristic profile into N equal parts, wherein N is a positive integer greater than 10000;
determining a feature size according to coordinate values of the feature points under the preset coordinate system and a formula 1, wherein the formula 1 is as follows:
wherein ,
for the feature size, T is the total number of feature points in the feature data sequence to be evaluated, < >>
Is the abscissa of the feature point under said preset coordinate system,/->
Is the ordinate of the feature point under the preset coordinate system,/->
For the abscissa of the nth demarcation point under said preset coordinate system, +.>
Is the abscissa of the nth demarcation point under the preset coordinate system,
the minimum distance between the feature point and each demarcation point;
generating the characteristic curve according to the characteristic sizes corresponding to the time nodes in the preset period;
the processing module is specifically configured to:
performing filtering processing on each CT image in the scanning image sequence to generate a CT image to be extracted, wherein the filtering processing is used for filtering out pixel points of which the pixel value is smaller than a preset pixel threshold value in the CT image to be extracted;
Determining edge characteristic values of each pixel point according to the CT image to be extracted and a formula 2, wherein the formula 2 is as follows:
wherein ,
the edge characteristic value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted; />
The pixel value of the pixel point with the transverse serial number i and the longitudinal serial number j in the CT image to be extracted; />
Taking the value as a constant and forming positive correlation with the total number of pixel points in the CT image to be extracted;
for the following
Define as equation 3:
Determining that the pixel points with the edge characteristic values smaller than a preset characteristic threshold value are edge contour points, and generating an edge contour point set according to the determined edge contour points;
determining the characteristic contour of the target part according to each edge contour point in the edge contour point set;
the processing module is specifically configured to:
performing contrast enhancement processing on each CT image in the scan image sequence according to formula 4 to generate an enhanced image corresponding to each CT image, where formula 4 is:
wherein ,
gray value after contrast enhancement treatment is carried out on target pixel point with transverse serial number i and longitudinal serial number j in CT image, < > >
Gray value before contrast enhancement treatment is carried out on the target pixel point in the CT image, and the gray value is +.>
Maximum gray value preset for the feature object, < >>
A preset minimum gray value for the feature object;
determining the contrast ratio comprehensive change rate of each pixel point in the enhanced image according to the enhanced image and a formula 5, wherein the formula 5 is as follows:
wherein ,
the contrast ratio comprehensive change rate of the target pixel point is obtained; />
The first characteristic constant is positive; />
The second characteristic constant is positive; />
Is the characteristic dimension between adjacent pixel points in the transverse direction; />
Is the feature size between adjacent pixels in the longitudinal direction, < >>
For the first component of the contrast ratio of change, +.>
For the second component of the contrast ratio, +.>
For the third component of the contrast ratio, +.>
For the fourth component of the contrast ratio, +.>
A fifth component of the contrast ratio;
and determining the pixel points with the contrast ratio comprehensive change rate larger than or equal to a preset comprehensive change rate as the characteristic points of the target part.
6. An electronic device, comprising:
a processor; the method comprises the steps of,
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the method of any one of claims 1 to 4 via execution of the executable instructions.
7. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 4.
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| CN113855235A (en) * | 2021-08-02 | 2021-12-31 | 应葵 | Magnetic resonance navigation method and device for microwave thermal ablation operation of liver part |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113855235A (en) * | 2021-08-02 | 2021-12-31 | 应葵 | Magnetic resonance navigation method and device for microwave thermal ablation operation of liver part |
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Non-Patent Citations (2)
| Title |
|---|
| 3.0 T MRI与CT对肝脏肿瘤射频消融术后局部疗效评价的比较;尹娜;郭震;付金凤;张磊;王梦茹;沈文荣;张晋;;现代医学(第03期);全文 * |
| 非小细胞肺癌微波消融术后血清Caspase-4变化及意义;王笑;李鑫;杨学刚;何清;;中华肺部疾病杂志(电子版)(第02期);全文 * |
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