CN116777887B - Extraction method, device, equipment, medium and program product for vessel centerline tree - Google Patents
Extraction method, device, equipment, medium and program product for vessel centerline treeInfo
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Abstract
本申请提供了一种血管中心线树的提取方法、装置、设备、介质及程序产品，包括：首先，获取目标主动脉的主动脉血管图像。接下来，将主动脉血管图像，输入预置表面模型生成算法，得到目标主动脉的血管表面模型。然后，将血管表面模型、目标主动脉的血管起始点和目标主动脉的至少一个血管终止点，输入预置中心线提取算法，得到目标主动脉的主动脉血管中心线树。最后，遍历至少一根血管中心线中的每根血管中心线，将符合预置条件的血管中心线的点坐标更新为基准中心线的点坐标，并且，更新主动脉血管中心线树。以避免多根血管中心线的重叠位置存在偏差。
The present application provides a method, apparatus, device, medium and program product for extracting a vascular centerline tree, including: first, obtaining an aortic vascular image of the target aorta. Next, inputting the aortic vascular image into a preset surface model generation algorithm to obtain a vascular surface model of the target aorta. Then, inputting the vascular surface model, the vascular starting point of the target aorta and at least one vascular ending point of the target aorta into a preset centerline extraction algorithm to obtain an aortic vascular centerline tree of the target aorta. Finally, traversing each of the at least one vascular centerline, updating the point coordinates of the vascular centerline that meets the preset conditions to the point coordinates of the baseline centerline, and updating the aortic vascular centerline tree. This is to avoid deviations in the overlapping positions of multiple vascular centerlines.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method, a device, equipment, a medium and a program product for extracting a blood vessel central line tree.
Background
At present, aortic vascular diseases (such as aortic aneurysms and aortic dissection) have an increasing trend year by year, and once vascular rupture occurs, the diseases are converted into critical diseases, and the clinical mortality rate is very high. The physician can obtain results of the patient's lesion analysis (e.g., whether lesions are present, extent of lesions, location of lesions) and interventional therapy based on the aortic centerline tree. Therefore, how to extract the aortic vessel centerline tree rapidly and accurately has important clinical value and practical significance in diagnosis and treatment of aortic vessel diseases.
The aortic centerline tree refers to a characteristic curve of an aortic vessel obtained from an aortic vessel image. Aortic vessel images refer to three-dimensional vessel images obtained by imaging the aorta of a patient with an electronic computed tomography angiography (CTA, computed Tomography angiography) imaging device.
In the related art, firstly, an aortic vessel image (binary image) is input into a surface model generating function provided by a vessel modeling tool (Vascular Modeling Toolkit, VMTK) to obtain a surface model of an aorta, and then the surface model and an aortic vessel end point (including a starting point and an ending point of an aortic vessel) are input into a vessel centerline extracting function provided by VMTK to obtain an aortic vessel centerline tree. However, the aortic vessel centerline tree includes a plurality of vessel centerlines, each vessel centerline being independent, with vessel centerlines corresponding one-to-one to termination points. Thus, there may be deviations in the overlapping positions of the plurality of vessel centerlines, which in turn may lead to inaccurate lesion analysis results or interventional treatment protocols based on the aortic vessel centerline tree.
Disclosure of Invention
The application provides a method, a device, equipment, a medium and a program product for extracting a blood vessel central line tree, so as to avoid deviation of overlapping positions of a plurality of blood vessel central lines.
In a first aspect, a method for extracting a vessel centerline tree is provided, including first, acquiring an aortic vessel image of a target aorta. And inputting the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta. And then, inputting the vessel surface model, the vessel starting point of the target aorta and at least one vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic vessel central line tree of the target aorta. And finally, traversing each vessel center line in at least one vessel center line, updating the point coordinates of the vessel center line meeting the preset condition into the point coordinates of the reference center line, and updating the aortic vessel center line tree.
The aortic blood vessel image is a binary image obtained by dividing an electronic Computed Tomography Angiography (CTA) image. The CTA image is a slice image including the target aorta. The at least one vessel termination point includes a left iliac termination point or a right iliac termination point. The aortic vessel centerline tree includes at least one vessel centerline. At least one vessel centerline corresponds one-to-one with at least one vessel termination point. The extraction preset condition of the vessel centerline tree is that the position deviation value is smaller than the preset deviation threshold value compared with the reference centerline. The reference centerline refers to the vessel centerline corresponding to the left or right iliac termination point.
In a second aspect, an extraction device of a blood vessel central line tree is provided, which comprises an image acquisition module, a patch acquisition module, a line tree extraction module and a line tree update module.
The image acquisition module is used for acquiring an aortic blood vessel image of a target aorta, wherein the aortic blood vessel image is a binary image obtained by dividing an electronic Computer Tomography Angiography (CTA) image, and the CTA image is a slice image comprising the target aorta;
The surface patch acquisition module is used for inputting the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta;
The line tree extraction module is used for inputting a blood vessel surface model, a blood vessel starting point of a target aorta and at least one blood vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic blood vessel central line tree of the target aorta, wherein the at least one blood vessel ending point comprises a left iliac ending point or a right iliac ending point;
The line tree updating module is used for traversing each vessel center line in at least one vessel center line, updating the point coordinates of the vessel center line meeting the preset condition into the point coordinates of the reference center line and updating the aortic vessel center line tree, wherein the preset condition is that the position deviation value is smaller than a preset deviation threshold value compared with the reference center line, and the reference center line refers to the vessel center line corresponding to the left iliac ending point or the right iliac ending point.
In a third aspect, there is provided an electronic device comprising a processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory for performing the method as in the first aspect or implementations thereof.
In a fourth aspect, a computer-readable storage medium is provided for storing a computer program for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a fifth aspect, a computer program product is provided comprising computer program instructions for causing a computer to perform the method as in the first aspect or in various implementations thereof.
In a sixth aspect, a computer program is provided, the computer program causing a computer to perform the method as in the first aspect or in various implementations thereof.
Through the technical scheme provided by the application, firstly, the electronic equipment can acquire the aortic vessel image of the target aorta. Next, the electronic device inputs the extracted aortic blood vessel image of the blood vessel centerline tree into a preset surface model generation algorithm to obtain a blood vessel surface model of the extraction target aorta of the blood vessel centerline tree. Then, the electronic equipment inputs a blood vessel surface model of the blood vessel centerline tree, a blood vessel starting point of the extraction target aorta of the blood vessel centerline tree and at least one blood vessel ending point of the extraction target aorta of the blood vessel centerline tree into a preset centerline extraction algorithm to obtain an aortic blood vessel centerline tree of the extraction target aorta of the blood vessel centerline tree. And finally, traversing the blood vessel central line tree by the electronic equipment to extract each blood vessel central line in at least one blood vessel central line, updating the point coordinates of the blood vessel central line meeting the preset condition to the point coordinates of the reference central line, and updating the extracted aortic blood vessel central line tree of the blood vessel central line tree. In the above process, the electronic device may have the same point coordinates of the blood vessel center line meeting the preset condition in each blood vessel center line as the point coordinates of the reference center line, that is, the point coordinates corresponding to the overlapping positions in the reference center line and the other blood vessel center lines, so that the deviation of the overlapping positions of the plurality of blood vessel center lines may be avoided, and further, the accuracy of the lesion analysis result or the interventional treatment scheme obtained based on the aortic blood vessel center line tree may be improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is an application scenario diagram provided in an embodiment of the present application;
FIG. 2 is a flowchart of a method for extracting a vessel centerline tree according to an embodiment of the present application;
FIG. 3 is a schematic illustration of an aortic blood vessel image according to an embodiment of the present application;
FIG. 4 is a schematic view of an aortic vessel centerline tree according to an embodiment of the present application;
FIG. 5 is a flowchart of another method for extracting a vessel centerline tree according to an embodiment of the present application;
fig. 6 is a schematic diagram of an extraction device 600 for a vessel centerline tree according to an embodiment of the present application;
Fig. 7 is a schematic block diagram of an electronic device 700 provided by an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
As described above, in the related art, first, an aortic vessel image (binary image) is input to a surface model generating function to obtain a surface model of an aorta, and then the surface model and an aortic vessel end point (including a start point and an end point of an aortic vessel) are input to a vessel centerline extracting function to obtain an aortic vessel centerline tree. The aortic vessel centerline tree thus obtained comprises a plurality of vessel centerlines, each vessel centerline being independent, in one-to-one correspondence with termination points. Thus, there may be a deviation in the overlapping positions of the plurality of vessel centerlines, which may lead to inaccurate lesion analysis results or interventional treatment protocols based on the aortic vessel centerline tree.
In order to solve the technical problems, the application is characterized in that the electronic equipment can set the point coordinates corresponding to the overlapping positions of the central lines of the multiple blood vessels to be the same point coordinates, so that deviation of the overlapping positions of the central lines of the multiple blood vessels is avoided, and the accuracy of lesion analysis results or interventional treatment schemes obtained based on aortic vessel central line trees is improved.
It should be understood that the technical solution of the present application can be applied to the following scenarios, but is not limited to:
In some implementations, fig. 1 is an application scenario diagram provided in an embodiment of the present application, where, as shown in fig. 1, an application scenario may include an electronic device 110 and a network device 120. The electronic device 110 may establish a connection with the network device 120 through a wired network or a wireless network.
By way of example, the electronic device 110 may be, but is not limited to, a desktop computer, a notebook computer, a tablet computer, and the like. The network device 120 may be a terminal device or a server, but is not limited thereto. In one embodiment of the present application, the electronic device 110 may send a request message to the network device 120, where the request message may be used to request acquisition of an aortic blood vessel image, a blood vessel start point, and a blood vessel end point, and further, the electronic device 110 may receive a response message sent by the network device 120, where the response message includes the aortic blood vessel image, the blood vessel start point, and the blood vessel end point.
Furthermore, fig. 1 illustrates one electronic device and one network device, and may actually include other numbers of electronic devices and network devices, which the present application is not limited to.
In other implementations, the technical solution of the present application may be performed by the electronic device 110, or the technical solution of the present application may be performed by the network device 120, which is not limited by the present application.
After the application scenario of the embodiment of the present application is introduced, the following details of the technical solution of the present application will be described:
Fig. 2 is a flowchart of a method for extracting a vessel centerline tree according to an embodiment of the present application, which may be performed by the electronic device 110 shown in fig. 1, but is not limited thereto. As shown in fig. 2, the method may include the steps of:
s210, the electronic device acquires an aortic blood vessel image of the target aorta.
As shown in fig. 3, the aortic blood vessel image is a binary image obtained by segmentation from an electronic computed tomography angiography CTA image. The CTA image is a slice image including the target aorta. The CTA image refers to a 3-dimensional image obtained by scanning a cross section of a target aorta around a position where the target aorta is located one by one with a precisely collimated X-ray beam, gamma rays, ultrasonic waves, and the like after intravenous injection of a contrast agent.
Specifically, the CTA image includes a plurality of slice images, the size of the CTA image may be M x N x L,
Wherein M, N is the length and width of each slice image, e.g., M is 512, N is 512, L is the number of slice images in the CTA image, i.e., the number of slice images (typically greater than 500). Typically, the slice images of adjacent layers are axially spaced between 0.5mm and 2mm, with axial referring to the Z-axis direction of CT tomography, which is the direction from foot to head of the human body.
In some embodiments, the electronic device may use NNUNet models to segment the CTA image to obtain an aortic blood vessel image, and may also obtain an aortic blood vessel image from another electronic device, which is not limited in the embodiment of the present application.
In some embodiments, for a bifurcation of the connection that may exist in the aortic vessel image, the electronic device may have an extraction error in extracting the aortic vessel centerline tree, which may result in terminating the extraction process. In order to avoid the problem in the early termination process, the electronic device may perform defect repair by using morphological closing operation.
Specifically, the electronic device can perform expansion corrosion treatment on the aortic vessel image according to the preset expansion nuclear radius and the preset corrosion nuclear radius to obtain an aortic vessel image after defect repair.
It will be appreciated that, first, the electronic device performs an inflation process on the aortic vessel image according to the preset inflation core radius, and fills the vessel connection defect. Then, the electronic equipment performs corrosion treatment on the expanded image according to the preset corrosion nuclear radius, so that the treated image is restored to the original image size. Illustratively, the preset dilation kernel radius may be 5 (number of pixels in the vicinity), as well as the preset erosion kernel radius is 5 (number of pixels in the vicinity).
Therefore, the defect of connection bifurcation of the aortic vessel image can be repaired through the expansion corrosion operation, so that extraction errors are avoided in the process of extracting the aortic vessel centerline tree, and further the extraction process is prevented from being stopped.
S220, the electronic equipment inputs the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta.
The preset surface model generation algorithm may be an isosurface extraction algorithm, and is specifically used for extracting a discrete triangular patch from a three-dimensional discrete data field (aortic blood vessel image) to obtain a blood vessel surface model.
In an embodiment of the application, the vessel surface model comprises at least two vessel patches. In practice, the number of patches in the vessel surface model is large in order to improve the consistency of the vessel surface model with the aortic vessel image, especially in order to represent thin vessels. Therefore, in the subsequent process of extracting the aortic vessel centerline tree, a large number of operations need to be correspondingly performed, resulting in extremely low efficiency of extracting the aortic vessel centerline tree. Therefore, the electronic equipment can extract part of the blood vessel patches in the blood vessel surface model so as to reduce the calculation amount for extracting the aortic blood vessel centerline tree, improve the efficiency for extracting the aortic blood vessel centerline tree and simultaneously not influence the accuracy of the aortic blood vessel centerline tree.
Specifically, the electronic device may perform a patch extraction operation on the blood vessel surface model to update the blood vessel surface model. It will be appreciated that the patch extraction operation is used to attenuate the number of patches. The patch extraction operation can reduce the number of patches in a uniform attenuation manner. By setting the extraction coefficient, the attenuation rate of the blood vessel patch is controlled. Wherein the attenuation rate can be 30%, 50%, 90%
Illustratively, the vessel surface model includes 100 vessel patches, assuming an extraction factor of 2, then the first vessel patch in the vessel surface model is extracted first, then the third vessel patch in the vessel surface model is extracted, and so on, 50 vessel patches are extracted from the 100 vessel patches, with a 50% attenuation rate.
Illustratively, the vessel surface model includes 100 vessel patches, assuming an extraction factor of 5, then the first vessel patch in the vessel surface model is extracted first, then the sixth vessel patch in the vessel surface model is extracted, and so on, 20 vessel patches are extracted from the 100 vessel patches, with an attenuation rate of 80%.
Therefore, by extracting part of the blood vessel patches in the at least two blood vessel patches, the calculation amount for extracting the aortic vessel centerline tree can be reduced and the efficiency for extracting the aortic vessel centerline tree can be improved on the premise that the accuracy of the aortic vessel centerline tree is not affected.
In other embodiments, due to the difference in pixel density corresponding to different vessel patches in the vessel surface model, the surface of the vessel image corresponding to the vessel surface model is rugged, which may result in poor extraction effect of the aortic vessel centerline tree. The electronic device may also smooth the vessel surface model according to a preset smoothness threshold based on a preset smoothing algorithm. If the surface model of the blood vessel is not subjected to the patch extraction operation, the blood vessel surface model is smoothed after the blood vessel surface model is obtained, and if the surface model of the blood vessel is subjected to the patch extraction operation, the blood vessel surface model is smoothed after the patch extraction operation.
Specifically, the preset smoothing algorithm may be Taubin smoothing algorithm, and the smoothing rate and the number of iterations may be modified in the preset smoothing algorithm so that the smoothness parameter value belongs to a preset range, for example, 0.1-0.2.
Therefore, the electronic equipment performs smoothing treatment on the blood vessel surface model according to the preset smoothness threshold based on the preset smoothing algorithm, so that the blood vessel image surface corresponding to the blood vessel surface model is smoother, and the extraction effect of the aortic blood vessel centerline tree is improved.
S230, the electronic equipment inputs the blood vessel surface model, the blood vessel starting point of the target aorta and at least one blood vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic blood vessel central line tree of the target aorta.
Wherein the at least one vessel termination point comprises a left iliac termination point or a right iliac termination point and the aortic vessel centerline tree comprises at least one vessel centerline. At least one vessel centerline corresponds one-to-one with at least one vessel termination point.
It is understood that the vessel starting point may be the starting end point of the ascending aorta of the target aorta, i.e. the aortic sinus end point. The at least one vessel endpoint may be a branch vessel endpoint of the target aorta, such as a left iliac endpoint, a right iliac endpoint, a left collarbone endpoint, a left cervical endpoint, a right collarbone endpoint, a left kidney endpoint, and a right kidney endpoint.
In the embodiment of the application, for the blood vessel starting point and at least one blood vessel ending point, the electronic device can be acquired according to the input of a user, the electronic device can also be acquired according to the transmission of other electronic devices, and the electronic device can also be acquired from the aortic blood vessel image according to the blood vessel end point extraction algorithm, and the sources of the blood vessel starting point and the at least one blood vessel ending point are not limited in the embodiment of the application.
In the embodiment of the present application, the preset centerline extraction algorithm may be a weighted shortest path algorithm. Specifically, a vessel centerline is generated by tracking a vessel start point to any one of the at least one vessel end points. At least one vessel centerline included in the aortic vessel centerline tree, i.e., a set of centerline sequences, corresponding to the at least one vessel endpoint, each vessel centerline in the set of centerline sequences being arranged in the order of the at least one vessel endpoint.
Illustratively, individual vessel centerlines of the at least one vessel centerline are superimposed on one another, as shown in FIG. 4, resulting in an aortic vessel centerline tree.
In some embodiments, the electronic device may further smooth and uniformly process each vessel centerline in the aortic vessel centerline tree according to the preset point distance step.
Specifically, all pixel points on a blood vessel central line obtained by a preset central line extraction algorithm are uneven. The electronic device can set a preset point distance step (such as 2 mm), and spline curves are performed on the central line of each blood vessel, so that the pixel points on the central line of the blood vessel are smooth and evenly distributed.
Thus, after each vessel center line in the aortic vessel center line tree is processed smoothly and uniformly, if operations such as enlarging, reducing, local manual adjustment and the like are performed, the aortic vessel center line tree is not affected, so that the accuracy of lesion analysis results or interventional treatment schemes obtained based on the aortic vessel center line tree is improved.
And S240, traversing each of the at least one blood vessel center line by the electronic equipment, updating the point coordinates of the blood vessel center line meeting the preset condition into the point coordinates of the reference center line, and updating the aortic blood vessel center line tree.
The preset condition is that the position deviation value is smaller than a preset deviation threshold value compared with a reference center line, wherein the reference center line refers to a blood vessel center line corresponding to a left iliac ending point or a right iliac ending point.
In an embodiment of the application, the aortic vessel centerline tree includes at least one vessel centerline. Each vessel centerline is independent of each other from a vessel start point to one vessel end point position of the at least one vessel end point. Due to the structural features of the aorta, any two vessel centerlines of the at least one vessel centerline have overlapping positions which are independent of each other on a per vessel centerline basis, and therefore, the overlapping positions of the vessel centerlines may have deviations. The electronic device may avoid deviations in the overlapping locations by merging the overlapping locations into a shared representation.
Illustratively, the center line of the blood vessel corresponding to the left iliac ending point starts from the blood vessel starting point (aortic sinus end point) and passes through the bifurcation point corresponding to the left collarbone artery, the bifurcation point corresponding to the left carotid artery, the bifurcation point corresponding to the right collarbone artery, the bifurcation point corresponding to the left renal artery, and the bifurcation point corresponding to the right renal artery, and reaches the bifurcation point corresponding to the left iliac ending point of the left kidney. The center line of the blood vessel corresponding to the left renal ending point starts from the blood vessel starting point (aortic sinus end point), passes through the bifurcation point corresponding to the left collarbone artery, the bifurcation point corresponding to the left carotid artery, the bifurcation point corresponding to the right carotid artery and the bifurcation point corresponding to the right collarbone artery, and reaches the bifurcation point corresponding to the left renal artery until reaching the left renal ending point. Therefore, the positions of the left renal termination point and the left iliac termination point at which the blood vessel centerlines overlap each other start from the blood vessel start point (aortic sinus end point), and pass through the bifurcation point corresponding to the left collarbone artery, the bifurcation point corresponding to the left carotid artery, the bifurcation point corresponding to the right carotid artery, and the bifurcation point corresponding to the right collarbone artery, to the bifurcation point corresponding to the left renal artery.
In some implementations, as shown in fig. 5, the step S240 may include:
S510, the electronic equipment acquires central line dictionary data corresponding to the aortic vessel central line tree according to a preset data conversion function.
The central line dictionary data comprises a dictionary identifier, an identity identifier ID array and a point coordinate array, wherein the dictionary identifier is used for marking the central line sequence of the blood vessel central line, the identity identifier ID array comprises a point sequence set of all pixel points contained in the blood vessel central line marked by the dictionary identifier, the point coordinate array comprises three-dimensional coordinate data of all pixel points contained in the blood vessel central line marked by the dictionary identifier, and the pixel points corresponding to the point coordinate array start from a blood vessel starting point to a blood vessel ending point of the blood vessel central line marked by the dictionary identifier.
In an embodiment of the application, the aortic vessel centerline tree is vtkPolyData model data consisting of dotted lines and planes. An aortic vessel centerline tree (vtkPolyData model data) is converted into centerline dictionary data (point coordinate data) by presetting a data conversion function.
By way of example, assuming that the aortic vessel centerline tree includes 10 vessel centerlines, for a first vessel centerline (351 in number of pixels), a corresponding dictionary identification may be set to "001", and an identification (Identity Document, ID) array may be set [1,2,., 351]. The corresponding dictionary identification may be set to "002" and the ID array may be set for the second vessel centerline (240 pixels) in sequential consecutive ranks according to the vessel centerline sequence [352,353, 591]. And so on, the third to tenth vessel centerlines are set.
Illustratively, based on the previous example, the coordinate array is stored in association with the ID number, and includes all the point coordinates. For the first blood vessel center line (the number of the pixel points is 351), the point coordinate array comprises 351 coordinate elements, each coordinate element corresponds to one pixel point, each coordinate element comprises three coordinate values of x, y and z, and the electronic equipment can identify the first pixel point in the first blood vessel center line by adopting (001,1). Similarly, the electronic device may employ (002,353) to identify a second pixel in the second vessel centerline. Therefore, the electronic equipment can search the coordinate value of any pixel point on the aortic vessel centerline tree.
S520, the electronic equipment determines the blood vessel center line corresponding to the left iliac termination point or the right iliac termination point as a reference center line.
The reference center line refers to a blood vessel center line corresponding to the left iliac termination point or the right iliac termination point. Since the aorta is raised to lowered to the iliac artery branch vessels, the reference centerline is the longest vessel centerline in the aortic centerline tree. This ensures that all possible overlapping positions are on the reference centre line.
It will be appreciated that after the electronic device obtains at least one vessel termination point, either the left iliac termination point or the right iliac termination point may be set as the first vessel termination point.
And S530, traversing each blood vessel center line in at least one blood vessel center line by the electronic equipment according to the sequence of dictionary identification, comparing the point coordinate array corresponding to each blood vessel center line with the point coordinate array corresponding to the reference center line, and calculating the position deviation value of the pixel point pair from the blood vessel starting point.
In the embodiment of the application, the point coordinate arrays corresponding to the blood vessel center lines are compared with the point coordinate arrays corresponding to the reference center lines each time according to the dictionary identification sequence, namely, the arrangement of the blood vessel center lines in at least one blood vessel center line is smooth.
By way of example, assume that the aortic vessel centerline tree includes 10 vessel centerlines. The first vessel centerline is the reference centerline. The first vessel centerline is compared to the second vessel centerline. Extracting the point coordinate corresponding to the first pixel point from the point coordinate array corresponding to the first blood vessel center line, extracting the point coordinate corresponding to the first pixel point from the point coordinate array corresponding to the second blood vessel center line, and forming a pixel point pair by the two corresponding pixel points. And respectively comparing the three pairs of coordinate values of x, y and z to determine the position deviation value. The positional deviation values may include three differences corresponding to three pairs of coordinate values.
S540, if the position deviation value is smaller than the preset deviation threshold value, the electronic equipment acquires the datum point coordinates and the datum point sequence of the pixel points belonging to the datum center line in the pixel point pair corresponding to the position deviation value, replaces the point coordinates of the pixel points not belonging to the datum center line in the pixel point pair corresponding to the position deviation value with the datum point coordinates, and replaces the point sequence of the pixel points not belonging to the datum center line in the pixel point pair corresponding to the position deviation value with the datum point sequence.
In the embodiment of the present application, the position deviation value is smaller than the preset deviation threshold, which can be understood that three differences corresponding to three pairs of coordinate values are smaller than the preset deviation threshold (2 mm).
Illustratively, assuming that the aortic vessel centerline tree includes 10 vessel centerlines, for a reference centerline and a second vessel centerline, if the position deviation values corresponding to 100 pixel points are all less than a preset deviation threshold, reference point coordinates and reference point sequences of 100 pixel points in the reference centerline, an ID array [1,2,..100 ], and 100 reference coordinate elements in the corresponding point coordinate array are obtained. The ID array [352,353, ], 591 corresponding to the second vessel centerline is then replaced with [1,2, ], 100,453, ], 591], and the 100 pending coordinate elements in the corresponding point coordinate array are replaced with 100 reference coordinate elements.
And if the position deviation value is not smaller than the preset deviation threshold, judging the position deviation value corresponding to the next blood vessel central line until each blood vessel central line in at least one blood vessel central line is traversed.
S550, the electronic equipment resets the point sequence except the reference point sequence in the ID array corresponding to each blood vessel center line according to the point sequence step length among different pixel points in the reference center line.
For example, the ID array [1,2, ], 100,453, ], 591] corresponding to the replaced second blood vessel centerline is reset according to the point order step size of 1 between [1,2, ], 100], and the ID array [1,2, ], 351] corresponding to the reference centerline, and the ID array [1,2, ], 100,352,453, ], 490] corresponding to the second blood vessel centerline is reset. Similarly, the pixel point sequence of the non-overlapping position of the ID array corresponding to the third blood vessel center line is continuously compiled from 491 until all the ID arrays corresponding to the blood vessel center lines are reset.
S560, the electronic equipment updates the aortic vessel centerline tree according to the replaced point coordinates, the replaced point sequence and the reset point sequence.
In the embodiment of the application, after the replaced point coordinates, the replaced point sequences and the reset point sequences, the overlapping positions corresponding to different blood vessel central lines have the same ID numbers and the same point coordinates, and the ID numbers corresponding to the non-overlapping positions are numbered continuously, so that the deviation of the overlapping positions of a plurality of blood vessel central lines can be avoided.
In some embodiments, after determining the reference centreline, the electronic device may also reestablish centreline dictionary data for each vessel centreline, i.e. the updated ID numbers are restored, and the replaced point coordinates, the replaced point sequence, and the reset point sequence are directly obtained.
Therefore, through the replaced point coordinates, the replaced point sequence and the reset point sequence, so that the overlapped positions have the same point coordinates and point sequences, deviation of the overlapped positions of the central lines of a plurality of blood vessels can be avoided, and further accuracy of lesion analysis results or interventional treatment schemes obtained based on the central line tree of the blood vessels of the aorta is improved.
In the above process, the electronic device may have the same point coordinates of the blood vessel center line meeting the preset condition in each blood vessel center line as the point coordinates of the reference center line, that is, the point coordinates corresponding to the overlapping positions in the reference center line and the other blood vessel center lines, so that the deviation of the overlapping positions of the plurality of blood vessel center lines may be avoided, and further, the accuracy of the lesion analysis result or the interventional treatment scheme obtained based on the aortic blood vessel center line tree may be improved.
Fig. 6 is a schematic diagram of an extraction device 600 for a vessel centerline tree according to an embodiment of the present application. As shown in fig. 6, the apparatus 600 includes an image acquisition module 610, a patch acquisition module 620, a line tree extraction module 630, and a line tree update module 640.
The image acquisition module 610 is configured to acquire an aortic blood vessel image of a target aorta, where the aortic blood vessel image is a binary image obtained by segmentation according to an electronic computed tomography angiography CTA image;
The patch obtaining module 620 is configured to input the aortic blood vessel image into a preset surface model generating algorithm to obtain a blood vessel surface model of the target aorta;
The line tree extraction module 630 is configured to input a vessel surface model, a vessel starting point of a target aorta and at least one vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic vessel central line tree of the target aorta, where the at least one vessel ending point includes a left iliac ending point or a right iliac ending point;
The line tree updating module 640 is configured to traverse each of the at least one vessel centerline, update a point coordinate of the vessel centerline that meets a preset condition to a point coordinate of a reference centerline, and update the aortic vessel centerline tree, where the preset condition is that a position deviation value is smaller than a preset deviation threshold value compared with the reference centerline, and the reference centerline refers to a vessel centerline corresponding to a left iliac termination point or a right iliac termination point.
In some implementations, a line tree updating module 640 is specifically configured to obtain centerline dictionary data corresponding to an aortic centerline tree according to a preset data transfer function, where the centerline dictionary data includes dictionary identifications, identity ID arrays and point coordinate arrays, the dictionary identifications are configured to mark a centerline line sequence of the vessel centerline, the identity ID arrays include a set of point sequences of all pixel points included in the vessel centerline of the dictionary identification mark, the point coordinate arrays include three-dimensional coordinate data of all pixel points included in the vessel centerline of the dictionary identification mark, the pixel points corresponding to the point coordinate arrays start from a vessel start point to a vessel termination point of the vessel centerline of the dictionary identification mark, the vessel centerline corresponding to a left iliac termination point or a right iliac termination point is determined as a reference centerline, each vessel centerline of at least one vessel centerline is traversed according to an order of the dictionary identifications, the point coordinate arrays corresponding to each vessel centerline are compared with the point coordinate arrays corresponding to the reference centerline, if the position offset value is smaller than a preset offset threshold, the pixel points corresponding to the reference centerline of the pixel point pair are obtained, the pixel points corresponding to the reference centerline of the position offset value and the reference centerline of the reference centerline pair belong to a reference centerline of the reference centerline are not replaced pixel points according to a replacement reference pixel value, and the position offset value of the reference point is not equal to the reference centerline of the reference centerline is determined to the reference centerline of the reference centerline pair, and the reference point is not the reference centerline of the reference centerline is the reference centerline of the reference centerline, and the reference centerline is the reference centerline of the reference centerline is the reference line, and the reference line is the reference line of the reference line, and the reference line is the reference line, resetting the point sequence except the reference point sequence in the ID array corresponding to each blood vessel center line, and updating the aortic blood vessel center line tree according to the replaced point coordinate, the replaced point sequence and the reset point sequence.
In some implementations, as shown in FIG. 6, the apparatus further includes an image processing module 650.
The image processing module 650 is configured to perform dilation and erosion processing on the aortic blood vessel image according to the preset dilation nucleus radius and the preset erosion nucleus radius after obtaining the aortic blood vessel image of the target aorta, so as to obtain an aortic blood vessel image after defect repair.
In some implementations, the vessel surface model includes at least two vessel patches, as shown in FIG. 7, the apparatus further includes a patch extraction module 660.
The patch extraction module 660 is configured to input the aortic blood vessel image into a preset surface model generation algorithm, obtain a blood vessel surface model of the target aortic, perform patch extraction operation on the blood vessel surface model, and update the blood vessel surface model, where the patch extraction operation is used to attenuate the number of patches.
In some implementations, as shown in FIG. 6, the apparatus further includes a surface treatment module 670.
The surface processing module 670 is configured to input the aortic blood vessel image into a preset surface model generation algorithm, obtain a blood vessel surface model of the target aorta, and then smooth the blood vessel surface model according to a preset smoothness threshold based on a preset smoothing algorithm.
In some implementations, as shown in FIG. 6, the apparatus further includes a line processing module 680.
The line processing module 680 is configured to perform smooth and uniform processing on each vessel centerline in the aortic vessel centerline tree according to the preset point distance step after updating the aortic vessel centerline tree.
It should be understood that the embodiment of the extraction device of the vessel centerline tree and the embodiment of the extraction method of the vessel centerline tree may correspond to each other, and similar descriptions may refer to the embodiment of the extraction method of the vessel centerline tree. To avoid repetition, no further description is provided here. Specifically, the apparatus 600 shown in fig. 6 may perform the above-mentioned embodiment of the method for extracting a vessel centerline tree, and the foregoing and other operations and/or functions of each module in the apparatus 600 are respectively for implementing the corresponding flow in the above-mentioned method for extracting a vessel centerline tree, which is not described herein for brevity.
The apparatus 600 of the embodiment of the present application is described above in terms of functional modules in conjunction with the accompanying drawings. It should be understood that the functional module may be implemented in hardware, or may be implemented by instructions in software, or may be implemented by a combination of hardware and software modules. Specifically, each step of the embodiment of the extraction method of the blood vessel centerline tree in the embodiment of the present application may be completed by an integrated logic circuit of hardware in a processor and/or an instruction in a software form, and the steps of the extraction method of the blood vessel centerline tree disclosed in connection with the embodiment of the present application may be directly embodied and executed by a hardware decoding processor or may be completed by a combination of hardware and software modules in the decoding processor. Alternatively, the software modules may be located in a well-established storage medium in the art such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, and the like. The storage medium is located in the memory, and the processor reads the information in the memory, and the steps in the embodiment of the medical team configuration method are completed by combining the hardware of the processor.
Fig. 7 is a schematic block diagram of an electronic device 700 provided by an embodiment of the present application.
As shown in fig. 7, the electronic device 700 may include:
A memory 710 and a processor 720, the memory 710 being configured to store a computer program and to transfer the program code to the processor 720. In other words, the processor 720 may call and run a computer program from the memory 710 to implement the method in the embodiment of the present application.
For example, the processor 720 may be configured to perform the above-described method embodiments according to instructions in the computer program.
In some embodiments of the application, the processor 720 may include, but is not limited to:
A general purpose Processor, digital signal Processor (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), field programmable gate array (Field Programmable GATE ARRAY, FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like.
In some embodiments of the application, the memory 710 includes, but is not limited to:
Volatile memory and/or nonvolatile memory. The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable EPROM (EEPROM), or a flash Memory. The volatile memory may be random access memory (Random Access Memory, RAM) which acts as external cache memory. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (STATIC RAM, SRAM), dynamic random access memory (DYNAMIC RAM, DRAM), synchronous Dynamic Random Access Memory (SDRAM), double data rate Synchronous dynamic random access memory (Double DATA RATE SDRAM, DDR SDRAM), enhanced Synchronous dynamic random access memory (ENHANCED SDRAM, ESDRAM), synchronous link dynamic random access memory (SYNCH LINK DRAM, SLDRAM), and Direct memory bus RAM (DR RAM).
In some embodiments of the application, the computer program may be partitioned into one or more modules that are stored in the memory 710 and executed by the processor 720 to perform the methods provided by the application. The one or more modules may be a series of computer program instruction segments capable of performing the specified functions, which are used to describe the execution of the computer program in the electronic device.
As shown in fig. 7, the electronic device may further include:
a transceiver 730, the transceiver 730 being connectable to the processor 720 or the memory 710.
The processor 720 may control the transceiver 730 to communicate with other devices, and in particular, may send information or data to other devices or receive information or data sent by other devices. Transceiver 730 may include a transmitter and a receiver. Transceiver 730 may further include antennas, the number of which may be one or more.
It will be appreciated that the various components in the electronic device are connected by a bus system that includes, in addition to a data bus, a power bus, a control bus, and a status signal bus.
The present application also provides a computer storage medium having stored thereon a computer program which, when executed by a computer, enables the computer to perform the method of the above-described method embodiments. Alternatively, embodiments of the present application also provide a computer program product comprising instructions which, when executed by a computer, cause the computer to perform the method of the method embodiments described above.
When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a Solid State Drive (SSD)), or the like.
Those of ordinary skill in the art will appreciate that the various illustrative modules and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. For example, functional modules in various embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily appreciate variations or alternatives within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of extracting a vessel centerline tree, comprising:
Acquiring an aortic blood vessel image of a target aorta, wherein the aortic blood vessel image is a binary image obtained by dividing an electronic Computer Tomography Angiography (CTA) image, and the CTA image is a slice image comprising the target aorta;
inputting the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta;
Inputting the blood vessel surface model, the blood vessel starting point of the target aorta and at least one blood vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic blood vessel central line tree of the target aorta, wherein the at least one blood vessel ending point comprises a left iliac ending point or a right iliac ending point;
Traversing each vessel center line in the at least one vessel center line, updating the point coordinates of the vessel center line meeting preset conditions into the point coordinates of a reference center line, and updating the aortic vessel center line tree, wherein the preset conditions are that compared with the reference center line, the position deviation value is smaller than a preset deviation threshold value, and the reference center line refers to the vessel center line corresponding to the left iliac ending point or the right iliac ending point.
2. The method of claim 1, wherein traversing each of the at least one vessel centerline updates the point coordinates of the vessel centerline that meet the preset condition to the point coordinates of the reference centerline, and updating the aortic vessel centerline tree comprises:
The method comprises the steps of obtaining central line dictionary data corresponding to an aortic vessel central line tree according to a preset data conversion function, wherein the central line dictionary data comprise dictionary identifications, identity Identification (ID) arrays and point coordinate arrays, the dictionary identifications are used for marking central line orders of vessel central lines, the identity Identification (ID) arrays comprise point order sets of all pixel points contained in the vessel central lines marked by the dictionary identifications, the point coordinate arrays comprise three-dimensional coordinate data of all pixel points contained in the vessel central lines marked by the dictionary identifications, and the pixel points corresponding to the point coordinate arrays start from a vessel starting point to a vessel ending point of the vessel central lines marked by the dictionary identifications;
determining a vessel centerline corresponding to the left or right iliac termination point as the reference centerline;
traversing each blood vessel center line in the at least one blood vessel center line according to the sequence of the dictionary identifications, comparing the point coordinate array corresponding to each blood vessel center line with the point coordinate array corresponding to the reference center line, and calculating the position deviation value of the pixel point pair from the blood vessel starting point;
If the position deviation value is smaller than a preset deviation threshold value, acquiring a datum point coordinate and a datum point sequence of the pixel points which belong to the datum center line in the pixel point pair corresponding to the position deviation value, replacing the point coordinate of the pixel point which does not belong to the datum center line in the pixel point pair corresponding to the position deviation value with the datum point coordinate, and simultaneously replacing the point sequence of the pixel point which does not belong to the datum center line in the pixel point pair corresponding to the position deviation value with the datum point sequence;
Resetting the point sequence except the reference point sequence in the ID array corresponding to each blood vessel center line according to the point sequence step length among different pixel points in the reference center line;
And updating the aortic vessel centerline tree according to the replaced point coordinates, the replaced point sequence and the reset point sequence.
3. The method of claim 1, wherein after the acquiring the aortic vessel image of the target aorta, the method further comprises:
And performing expansion corrosion treatment on the aortic vessel image according to the preset expansion nuclear radius and the preset corrosion nuclear radius to obtain an aortic vessel image after defect repair.
4. The method of claim 1, wherein the vessel surface model comprises at least two vessel patches, and wherein after inputting the aortic vessel image into a preset surface model generation algorithm to obtain the vessel surface model of the target aorta, the method further comprises:
and performing patch extraction operation on the blood vessel surface model to update the blood vessel surface model, wherein the patch extraction operation is used for attenuating the number of patches.
5. The method of claim 1, wherein after inputting the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta, the method further comprises:
and carrying out smoothing treatment on the blood vessel surface model according to a preset smoothness threshold value based on a preset smoothing algorithm.
6. The method of any one of claims 1 to 5, wherein after said updating the aortic vessel centerline tree, the method further comprises:
And carrying out smooth and uniform treatment on each vessel center line in the aortic vessel center line tree according to the preset point distance step length.
7. An extraction device for a vessel centerline tree, comprising:
The image acquisition module is used for acquiring an aortic blood vessel image of a target aorta, wherein the aortic blood vessel image is a binary image obtained by dividing an electronic Computer Tomography Angiography (CTA) image, and the CTA image is a slice image comprising the target aorta;
The surface patch acquisition module is used for inputting the aortic blood vessel image into a preset surface model generation algorithm to obtain a blood vessel surface model of the target aorta;
The line tree extraction module is used for inputting the blood vessel surface model, the blood vessel starting point of the target aorta and at least one blood vessel ending point of the target aorta into a preset central line extraction algorithm to obtain an aortic blood vessel central line tree of the target aorta, wherein the at least one blood vessel ending point comprises a left iliac ending point or a right iliac ending point;
The line tree updating module is used for traversing each vessel center line in the at least one vessel center line, updating the point coordinates of the vessel center line meeting the preset condition into the point coordinates of a reference center line and updating the aortic vessel center line tree, wherein the preset condition is that the position deviation value is smaller than a preset deviation threshold value compared with the reference center line, and the reference center line refers to the vessel center line corresponding to the left iliac ending point or the right iliac ending point.
8. An electronic device, comprising:
A processor and a memory for storing a computer program, the processor being for invoking and running the computer program stored in the memory to perform the method of any of claims 1-6.
9. A computer readable storage medium storing a computer program for causing a computer to perform the method of any one of claims 1-6.
10. A computer program product comprising instructions which, when run on an electronic device, cause the electronic device to perform the method of any of claims 1-6.
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