Intracranial and extracranial vascular reconstruction methods accompanying cerebral aneurysm surgery

Abstract

The present invention relates to the field of image analysis technology, and in particular to a method for reconstructing intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, the method comprising: obtaining a target three-dimensional image corresponding to the intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, and identifying each target blood vessel in each slice in the target three-dimensional image; performing a thickness analysis on each target blood vessel; determining a local difference index corresponding to each target blood vessel; determining a grayscale prominence corresponding to each pixel point in each target blood vessel; determining a grayscale change difference corresponding to each target blood vessel; and performing MIP reconstruction according to the local complex index and grayscale change difference corresponding to all target blood vessels in all slices, and the grayscale values ​​corresponding to all pixels therein. The present invention quantifies the local complex index and grayscale change difference of the target blood vessel by analyzing the target three-dimensional image, thereby realizing blood vessel reconstruction through MIP reconstruction and improving the effect of blood vessel reconstruction.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis, and in particular to a method for intracranial and extracranial blood vessel reconstruction accompanying cerebral aneurysm surgery. Background Art

[0002] With the development of science and technology, the application of MIP (Maximum Intensity Projection) reconstruction is becoming more and more widespread. For example, it can be used for intracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery. MIP projects the scanned three-dimensional image in a certain direction (such as vertical or horizontal direction) to obtain the strongest signal or image intensity in all slices in that direction to form a reconstructed image. In general, the image grayscale value can be used to represent the image signal or image intensity.

[0003] However, when obtaining the maximum grayscale value in all slices of the three-dimensional image corresponding to the intracranial and extracranial blood vessels obtained by scanning during brain aneurysm surgery to form a vascular reconstruction image, the following technical problems often occur:

[0004] Since the crossing complexity of different blood vessels is often different, the degree to which they need to be highlighted is often different. Therefore, directly and uniformly obtaining the maximum grayscale value from all slices in the scanned three-dimensional image may result in some blood vessels with a small grayscale difference from the surrounding area but a large crossing complexity not being highlighted, resulting in poor vascular reconstruction effect. Summary of the invention

[0005] In order to solve the technical problem of poor vascular reconstruction effect, the present invention proposes an intracranial and extracranial vascular reconstruction method accompanying cerebral aneurysm surgery.

[0006] In a first aspect, the present invention provides a method for intracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery, the method comprising:

[0007] Acquire a target three-dimensional image corresponding to intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, and identify each target blood vessel in each slice of the target three-dimensional image;

[0008] According to the length and area of ​​each target blood vessel, each target blood vessel is analyzed and processed to obtain a thickness representative index corresponding to each target blood vessel;

[0009] Determine the local complexity index corresponding to each target blood vessel according to the thickness representative index corresponding to each target blood vessel and the grayscale difference between each target blood vessel and its intersecting target blood vessel;

[0010] Determine the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel;

[0011] According to the grayscale change in each target blood vessel, the grayscale prominence degree corresponding to each pixel point in each target blood vessel is determined;

[0012] Determine the grayscale change difference corresponding to each target blood vessel according to the local difference index corresponding to each target blood vessel and the grayscale prominence degree corresponding to the pixel point therein;

[0013] MIP reconstruction is performed based on the local complex indexes and grayscale change differences corresponding to all target blood vessels in all slices of the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixels therein.

[0014] In combination with the first aspect, in a possible implementation, the thickness analysis of each target blood vessel is performed according to the length and area of ​​each target blood vessel to obtain a thickness representative index corresponding to each target blood vessel, including:

[0015] The number of pixels on the long side of each target blood vessel is determined as the target length corresponding to each target blood vessel;

[0016] The number of pixels in each target blood vessel is determined as the target area corresponding to each target blood vessel;

[0017] The ratio of the target area to the target length corresponding to each target blood vessel is determined as the initial thickness factor corresponding to each target blood vessel;

[0018] According to the initial thickness factor corresponding to each target blood vessel, a thickness representative index corresponding to each target blood vessel is determined, wherein the initial thickness factor is positively correlated with the thickness representative index.

[0019] In combination with the first aspect, in a possible implementation, determining the local complexity index corresponding to each target vessel according to the thickness representative index corresponding to each target vessel and the grayscale difference between each target vessel and its intersecting target vessel includes:

[0020] The average of the grayscale values ​​corresponding to all the pixels in each target blood vessel is determined as the grayscale representative factor corresponding to each target blood vessel;

[0021] Determine any slice in the target three-dimensional image as a marked slice, determine any target blood vessel in the marked slice as a marked blood vessel, and determine each target blood vessel in the marked slice except the marked blood vessel as a reference blood vessel;

[0022] Determine each reference blood vessel intersecting the marked blood vessel as a candidate blood vessel, and obtain a candidate blood vessel set corresponding to the marked blood vessel;

[0023] Determine the number of candidate blood vessels in the candidate blood vessel set corresponding to the marked blood vessel as the number of intersections corresponding to the marked blood vessel;

[0024] Determining a projection conspicuous factor corresponding to the marked blood vessel according to a difference between a grayscale representative factor corresponding to the marked blood vessel and a grayscale representative factor corresponding to the candidate blood vessel;

[0025] The local complexity index corresponding to the marked blood vessel is determined according to the projection obvious factor, the number of intersections and the thickness representative index corresponding to the marked blood vessel, wherein the number of intersections and the thickness representative index are both positively correlated with the local complexity index; the projection obvious factor is negatively correlated with the local complexity index.

[0026] In combination with the first aspect, in a possible implementation manner, determining the projection conspicuous factor corresponding to the marked blood vessel according to the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to the candidate blood vessel includes:

[0027] determining the absolute value of the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to each candidate blood vessel as the target grayscale difference between the marked blood vessel and each candidate blood vessel;

[0028] The mean value of the target grayscale difference between the marked blood vessel and all candidate blood vessels in the corresponding candidate blood vessel set is determined as the projection conspicuous factor corresponding to the marked blood vessel.

[0029] In combination with the first aspect, in a possible implementation, determining the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel, includes:

[0030] The absolute value of the difference between the grayscale value corresponding to each edge pixel point of each target blood vessel and the grayscale value corresponding to each pixel point in the corresponding preset window is determined as the reference grayscale difference between each edge pixel point of each target blood vessel and each pixel point in the corresponding preset window;

[0031] The maximum reference grayscale difference is selected from the reference grayscale differences between each edge pixel point of each target blood vessel and all the pixels in the corresponding preset window as the edge grayscale difference corresponding to each edge pixel point of each target blood vessel;

[0032] According to the edge grayscale differences corresponding to all edge pixels of each target blood vessel and the chain code differences between adjacent edge pixels in each target blood vessel, the local difference index corresponding to each target blood vessel is determined.

[0033] In combination with the first aspect, in a possible implementation, determining the local difference index corresponding to each target blood vessel according to the edge grayscale difference corresponding to all edge pixels of each target blood vessel and the chain code difference between adjacent edge pixels in each target blood vessel includes:

[0034] The average value of the edge grayscale differences corresponding to all edge pixels of each target blood vessel is determined as the overall grayscale difference corresponding to each target blood vessel;

[0035] The absolute value of the difference between the chain code values ​​corresponding to the two adjacent edge pixels at each position is determined as the target chain code difference between the two adjacent edge pixels at each position;

[0036] The average of the target chain code differences between all adjacent edge pixel points in each target blood vessel is determined as the overall chain code difference corresponding to each target blood vessel;

[0037] According to the overall grayscale difference and overall chain code difference corresponding to each target blood vessel, the local difference index corresponding to each target blood vessel is determined, wherein the overall grayscale difference is negatively correlated with the local difference index, and the overall chain code difference is positively correlated with the local difference index.

[0038] In combination with the first aspect above, in a possible implementation, determining the grayscale prominence level corresponding to each pixel point in each target blood vessel according to the grayscale change in each target blood vessel includes:

[0039] Determine any slice in the target three-dimensional image as a marked slice, determine any target blood vessel in the marked slice as a marked blood vessel, and determine any pixel point in the marked blood vessel as a marked pixel point;

[0040] The number of pixel points whose corresponding grayscale values ​​in the marked blood vessel are equal to the grayscale value corresponding to the marked pixel point is determined as the reference grayscale number corresponding to the marked pixel point;

[0041] Determine the average of the grayscale values ​​corresponding to all the pixels in the marked blood vessel as the grayscale representative factor corresponding to the marked blood vessel;

[0042] Determine the absolute value of the difference between the grayscale value corresponding to the marked pixel point and the grayscale representative factor corresponding to the marked blood vessel as the initial protrusion factor corresponding to the marked pixel point;

[0043] The grayscale prominence degree corresponding to the marked pixel point is determined according to the initial prominence factor and the reference grayscale quantity corresponding to the marked pixel point, wherein the initial prominence factor is positively correlated with the grayscale prominence degree, and the reference grayscale quantity is negatively correlated with the grayscale prominence degree.

[0044] In combination with the first aspect above, in a possible implementation manner, the formula corresponding to the grayscale change difference corresponding to the target blood vessel is:

[0045] ;

[0046] in, The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels; i is the serial number of the slice in the target 3D image; j It is i The serial number of the target blood vessel in each slice; The target 3D image i Slice j The local difference index corresponding to the target blood vessel; The target 3D image i Slice j The number of pixels in the target blood vessel; h The target 3D image i Slice j The serial number of the pixel point in the target blood vessel; It is the absolute value function; The target 3D image i Slice j Article Target blood vessels, h The gray level prominence corresponding to each pixel; The target 3D image i Slice j The mean value of the grayscale prominence of all pixels in the target blood vessel.

[0047] In combination with the first aspect, in a possible implementation, performing MIP reconstruction according to the local complex indexes and grayscale change differences corresponding to all target blood vessels in all slices in the target three-dimensional image, and the grayscale values ​​corresponding to all pixels therein, includes:

[0048] Determine the grayscale correction weight corresponding to each target blood vessel according to the grayscale change difference corresponding to each target blood vessel and the average value of the local complexity indexes corresponding to all target blood vessels in the slice to which each target blood vessel belongs;

[0049] According to the grayscale correction weight corresponding to each target blood vessel and the grayscale value corresponding to each pixel in each target blood vessel, a target grayscale index corresponding to each pixel in each target blood vessel is determined, wherein the grayscale correction weight and the grayscale value are both positively correlated with the target grayscale index;

[0050] Determining a target grayscale index corresponding to each pixel point in each slice of the target three-dimensional image except the target blood vessel according to a grayscale value corresponding to each pixel point in each slice of the target three-dimensional image except the target blood vessel, wherein the grayscale value is positively correlated with the target grayscale index;

[0051] The grayscale value corresponding to each pixel in all slices of the target three-dimensional image is updated to its corresponding target grayscale index, and blood vessel reconstruction is achieved through MIP reconstruction based on the updated grayscale values ​​corresponding to all pixels in all slices of the target three-dimensional image.

[0052] In combination with the first aspect above, in a possible implementation, the formula corresponding to the grayscale correction weight corresponding to the target blood vessel is:

[0053] ;

[0054] in, The target 3D image i Slice j Grayscale correction weight corresponding to the target blood vessel; i is the serial number of the slice in the target 3D image; j It is i The serial number of the target blood vessel in each slice; is the normalization function; The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels; The target 3D image i The mean of the local complexity indexes corresponding to all target vessels in a slice.

[0055] In a second aspect, the present invention provides an intracranial and extracranial vascular reconstruction system accompanying cerebral aneurysm surgery, the system comprising:

[0056] A three-dimensional image acquisition and recognition module is used to acquire a target three-dimensional image corresponding to intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, and to recognize each target blood vessel in each slice of the target three-dimensional image;

[0057] A thickness analysis and processing module is used to analyze and process the thickness of each target blood vessel according to the length and area of ​​each target blood vessel, and obtain a thickness representative index corresponding to each target blood vessel;

[0058] A local complex index determination module, used to determine the local complex index corresponding to each target blood vessel according to the thickness representative index corresponding to each target blood vessel and the grayscale difference between each target blood vessel and its intersecting target blood vessel;

[0059] A local difference index determination module is used to determine the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel;

[0060] A grayscale prominence determination module is used to determine the grayscale prominence corresponding to each pixel point in each target blood vessel according to the grayscale change in each target blood vessel;

[0061] A grayscale change difference determination module is used to determine the grayscale change difference corresponding to each target blood vessel according to the local difference index corresponding to each target blood vessel and the grayscale prominence degree corresponding to the pixel points therein;

[0062] The MIP reconstruction module is used to perform MIP reconstruction based on the local complex indexes and grayscale change differences corresponding to all target blood vessels in all slices in the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixel points therein.

[0063] In a third aspect, a server is provided, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes the method in the first aspect or any possible implementation of the first aspect.

[0064] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0065] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect.

[0066] The present invention has the following beneficial effects:

[0067] The intracranial and extracranial vascular reconstruction method accompanying cerebral aneurysm surgery of the present invention analyzes the target three-dimensional image, quantifies the local complex index and grayscale change difference of the target blood vessel, and thus realizes vascular reconstruction through MIP reconstruction, solves the technical problem of poor vascular reconstruction effect, and improves the vascular reconstruction effect. Compared with directly and uniformly obtaining the maximum grayscale value from all slices in the target three-dimensional image to form a vascular reconstruction image, the present invention comprehensively considers multiple features related to vascular display, such as thickness representative index, local complex index, local difference index, grayscale prominence degree and grayscale change difference, and performs MIP reconstruction based on the local complex index and grayscale change difference corresponding to all target blood vessels in all slices in the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixels therein, adaptively realizes the prominent display of each target blood vessel, thereby realizing vascular reconstruction and improving the vascular reconstruction effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0069] Figure 1 A flowchart of the intracranial and extracranial vascular reconstruction method accompanying cerebral aneurysm surgery of the present invention;

[0070] Figure 2 It is a schematic diagram of the composition structure of the intracranial and extracranial vascular reconstruction system accompanying cerebral aneurysm surgery of the present invention;

[0071] Figure 3 The figure is a schematic diagram of the structure of a computer device of the present invention. DETAILED DESCRIPTION

[0072] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation methods, structures, features and effects of the technical solutions proposed by the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0073] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0074] When performing intracranial and extracranial vascular reconstruction for cerebral aneurysm surgery, it is necessary to reconstruct the blood vessels in the patient's brain based on the medical imaging data of the patient to be tested. After reconstruction, more accurate vascular contours and paths can be obtained. Doctors can more intuitively understand the shape and size of the aneurysm and its relationship with surrounding blood vessels, which helps to evaluate the hemodynamic performance of the aneurysm and thus the risk of rupture; through precise vascular reconstruction, doctors can better avoid possible risks during surgery and improve the safety and effectiveness of surgery.

[0075] refer to Figure 1 , showing the process of some embodiments of the intracranial and extracranial vascular reconstruction method accompanying cerebral aneurysm surgery according to the present invention. The intracranial and extracranial vascular reconstruction method accompanying cerebral aneurysm surgery includes the following steps:

[0076] Step S1, obtaining a target three-dimensional image corresponding to intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, and identifying each target blood vessel in each slice of the target three-dimensional image.

[0077] Among them, brain aneurysm surgery is a surgery to treat aneurysms in brain arteries. Rupture of an aneurysm may cause severe cerebral hemorrhage and even be life-threatening. Therefore, when a brain aneurysm is diagnosed, surgical treatment is usually required, especially when the aneurysm is large or there is a risk of rupture. Intracranial and extracranial blood vessels refer to the vascular system that supplies blood to the brain and head area, which are divided into two categories: intracranial and extracranial blood vessels. Intracranial blood vessels are located inside the skull and are responsible for supplying blood to the brain. Extracranial blood vessels are located outside the skull and supply other structures of the head, such as the face, neck, and scalp. The target three-dimensional image can be a three-dimensional image of intracranial and extracranial blood vessels. Slices, also known as slice images, are two-dimensional images that show cross-sectional information of the detection site and are used to form a three-dimensional image. A target blood vessel can represent the area where a blood vessel is located.

[0078] As an example, MRI (Magnetic Resonance Imaging) scanning equipment can be used to collect three-dimensional images of intracranial and extracranial blood vessels as target three-dimensional images, and the centerline of the blood vessels in each slice can be extracted through the Voronoi diagram technology to obtain the vascular bifurcation topology, and then the blood vessels in each slice can be identified as target blood vessels. Among them, the Voronoi diagram, also known as the Voronoi diagram, is a space segmentation algorithm.

[0079] It should be noted that the vascular imaging data obtained by MRI scanning equipment is usually stored in DICOM (Digital Imaging and Communications in Medicine) format. The dcm2niix tool can often convert DICOM format data into NIfTI (Neuroimaging Informatics Technology Initiative) format. NIfTI (Neuroimaging Informatics Technology Initiative) format is often more suitable for subsequent image processing and analysis.

[0080] Step S2, analyzing and processing the thickness of each target blood vessel according to the length and area of ​​each target blood vessel, and obtaining a thickness representative index corresponding to each target blood vessel.

[0081] As an example, this step may include the following steps:

[0082] In the first step, the number of pixels on the long side of each target blood vessel is determined as the target length corresponding to each target blood vessel.

[0083] In the second step, the number of pixels in each target blood vessel is determined as the target area corresponding to each target blood vessel.

[0084] In the third step, the ratio of the target area to the target length corresponding to each target blood vessel is determined as the initial thickness factor corresponding to each target blood vessel.

[0085] For example, the formula for determining the initial thickness factor corresponding to the target blood vessel may be:

[0086] ;

[0087] in, The target 3D image i Slice j The initial thickness factor corresponding to the target blood vessel. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. The target 3D image i Slice j The target length corresponding to the target vessel. The target 3D image i Slice j The target area corresponding to the target blood vessel.

[0088] It should be noted that when The larger the i Slice j The target blood vessel is larger in area and shorter in length, which often indicates i Slice j The thicker the target blood vessel.

[0089] The fourth step is to determine the thickness representative index corresponding to each target blood vessel according to the initial thickness factor corresponding to each target blood vessel.

[0090] Among them, the initial thickness factor can be positively correlated with the thickness representative index.

[0091] For example, the formula for determining the thickness representative index corresponding to the target blood vessel may be:

[0092] ;

[0093] in, The target 3D image i Slice j The thickness of the target blood vessels represents the index. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. The target 3D image i Slice j The initial thickness factor corresponding to the target blood vessel. The target 3D image i The maximum value of the initial thickness factors corresponding to all target vessels in a slice.

[0094] It should be noted that, in actual situations, blood vessels with cerebral aneurysms are often thicker than normal blood vessels. In order to facilitate doctors to observe the development of cerebral aneurysms, it is often necessary to highlight the cerebral aneurysms and adjust their corresponding grayscale values ​​to enhance the display. The larger the i Slice j The thicker the target blood vessel. The larger the i Slice j The target blood vessels are relatively thick, which often indicates that j The more likely the target blood vessel is a cerebral aneurysm, the more likely it is that the first j The grayscale value corresponding to the target blood vessel.

[0095] Step S3, determining a local complexity index corresponding to each target blood vessel according to the thickness representative index corresponding to each target blood vessel and the grayscale difference between each target blood vessel and its intersecting target blood vessel.

[0096] As an example, this step may include the following steps:

[0097] In the first step, the mean of the grayscale values ​​corresponding to all pixels in each target blood vessel is determined as the grayscale representative factor corresponding to each target blood vessel.

[0098] In the second step, any slice in the target three-dimensional image is determined as a marked slice, and any target blood vessel in the marked slice is determined as a marked blood vessel, and each target blood vessel in the marked slice except the marked blood vessel is determined as a reference blood vessel.

[0099] In the third step, each reference blood vessel intersecting the marked blood vessel is determined as a candidate blood vessel, and a candidate blood vessel set corresponding to the marked blood vessel is obtained.

[0100] The intersection of two blood vessels indicates that the two blood vessels intersect. The candidate blood vessel set corresponding to the marked blood vessel may include: all reference blood vessels that intersect with the marked blood vessel.

[0101] In the fourth step, the number of candidate blood vessels in the candidate blood vessel set corresponding to the marked blood vessel is determined as the number of intersections corresponding to the marked blood vessel.

[0102] The fifth step, according to the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to the candidate blood vessel, determines the projection obvious factor corresponding to the marked blood vessel, which may include the following sub-steps:

[0103] In the first sub-step, the absolute value of the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to each candidate blood vessel is determined as the target grayscale difference between the marked blood vessel and each candidate blood vessel.

[0104] In the second sub-step, the average of the target grayscale differences between the marked blood vessel and all candidate blood vessels in the corresponding candidate blood vessel set is determined as the projection conspicuous factor corresponding to the marked blood vessel.

[0105] Step 6: Determine the local complexity index corresponding to the marked blood vessels according to the projection obvious factor, the number of intersections and the thickness representative index corresponding to the marked blood vessels.

[0106] Among them, the cross number and thickness representative index can be positively correlated with the local complexity index. The projection obvious factor can be negatively correlated with the local complexity index.

[0107] For example, the formula for determining the local complexity index corresponding to the target blood vessel may be:

[0108] ;

[0109] ;

[0110] in, The target 3D image i Slice j The local complexity index corresponding to the target blood vessel. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. The target 3D image i Slice j The thickness of the target blood vessels represents the index. The target 3D image i Slice j The number of crosses corresponding to the target blood vessels, that is, j The number of candidate blood vessels in the candidate blood vessel set corresponding to the target blood vessel. The target 3D image i In the slice, j The sequence number of the candidate blood vessel in the candidate blood vessel set corresponding to the target blood vessel. is a natural exponential function. The target 3D image i Slice j The projection conspicuous factor corresponding to the target vessel. It is the absolute value function. The target 3D image i Slice j The grayscale representative factor corresponding to the target blood vessel is j The mean grayscale value of all pixels in the target blood vessel. The target 3D image i Slice j In the candidate blood vessel set corresponding to the target blood vessel, a The grayscale representative factor corresponding to each candidate blood vessel. Indicates the target grayscale difference.

[0111] It should be noted that when The larger the value, the more it needs to be adjusted. i Slice j The gray value corresponding to the target blood vessel. The larger the iThe slice j The more blood vessels intersecting the target blood vessels, the more likely it is that i Slice j The more complex the environment of the target blood vessel is, the more likely it is that i The slice is j The more blood vessels directly affected by the target blood vessel, the more likely it is that the i The slice is j The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The gray value corresponding to the target blood vessel. The larger the i Slice j The greater the grayscale difference between the target vessel and its intersecting vessel, the i Slice j The more the target blood vessel is highlighted, the less adjustment is needed. i Slice j The grayscale value corresponding to the target blood vessel. The larger the value, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel.

[0112] Step S4, determining the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in its corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel.

[0113] The edge pixel point may be a pixel point on the edge of the target blood vessel. The preset window may be a preset rectangular window. For example, the preset window may be a 3×3 window. The edge pixel point may be located at the center of the preset window corresponding to the edge pixel point. The chain code value corresponding to the edge pixel point may be obtained by the eight-chain code algorithm.

[0114] As an example, this step may include the following steps:

[0115] In the first step, the absolute value of the difference between the grayscale value corresponding to each edge pixel point of each target blood vessel and the grayscale value corresponding to each pixel point in its corresponding preset window is determined as the reference grayscale difference between each edge pixel point of each target blood vessel and each pixel point in its corresponding preset window.

[0116] For example, the formula corresponding to determining the reference grayscale difference between the edge pixel point of the target blood vessel and the pixel point in the corresponding preset window may be:

[0117] ;

[0118] in, The target 3D image i Slice j Article Target blood vessels, b The edge pixel point and its corresponding preset window d The reference grayscale difference between pixels. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. b It is j The serial number of the edge pixel point inside the target blood vessel. d It is the sequence number of the pixel in the preset window. It is the absolute value function. The target 3D image i Slice j Article Target intravascular b The gray value corresponding to the edge pixel. The target 3D image i Slice j Article Target blood vessels, b The edge pixel point corresponds to the first d The gray value corresponding to each pixel.

[0119] It should be noted that when The larger the b The edge pixel point and its corresponding preset window d The greater the grayscale difference between pixels.

[0120] In the second step, the maximum reference grayscale difference is screened out from the reference grayscale differences between each edge pixel point of each target blood vessel and all pixels in its corresponding preset window as the edge grayscale difference corresponding to each edge pixel point of each target blood vessel.

[0121] It should be noted that when the edge grayscale difference corresponding to the edge pixel point is greater, it often means that the grayscale difference between the edge pixel point and the pixels in its preset window is relatively greater, and it often means that the grayscale difference between the edge pixel point and the surrounding pixels is relatively greater.

[0122] The third step is to determine the local difference index corresponding to each target blood vessel according to the edge grayscale difference corresponding to all edge pixels of each target blood vessel and the chain code difference between adjacent edge pixels in each target blood vessel, which may include the following sub-steps:

[0123] In the first sub-step, the average value of the edge grayscale differences corresponding to all edge pixels of each target blood vessel is determined as the overall grayscale difference corresponding to each target blood vessel.

[0124] In the second sub-step, the absolute value of the difference between the chain code values ​​corresponding to the two adjacent edge pixels at each position is determined as the target chain code difference between the two adjacent edge pixels at each position.

[0125] In the third sub-step, the average of the target chain code differences between all adjacent edge pixels in each target blood vessel is determined as the overall chain code difference corresponding to each target blood vessel.

[0126] In the fourth sub-step, a local difference index corresponding to each target blood vessel is determined according to the overall grayscale difference and the overall chain code difference corresponding to each target blood vessel.

[0127] Among them, the overall grayscale difference can be negatively correlated with the local difference index, and the overall chain code difference can be positively correlated with the local difference index.

[0128] For example, the formula for determining the local difference index corresponding to the target blood vessel can be:

[0129] ;

[0130] in, The target 3D image i Slice j The local difference index corresponding to the target blood vessel. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. is a normalization function. is a natural exponential function. The target 3D image i Slice j The overall grayscale difference corresponding to the target blood vessels, that is, j The mean value of the edge grayscale difference corresponding to all edge pixels of the target blood vessel. The target 3D image i Slice j The overall chain code difference corresponding to the target blood vessel, that is, j The mean of the target chain code differences between all adjacent edge pixels in the target vessel.

[0131] It should be noted that when The larger the i Slice jThe larger the grayscale difference between the edge of the target blood vessel and the surrounding area, the i Slice j The more the target blood vessel is highlighted, the less adjustment is needed. i Slice j The gray value corresponding to the target blood vessel. The larger the i Slice j The greater the difference in chain codes between adjacent pixels on the edge of the target blood vessel, the stronger the i Slice j The greater the fluctuation of the edge path of the target blood vessel, the more i Slice j The larger the amount of information contained in the target blood vessel, the more likely it is that i Slice j The more the target vessel should be highlighted. The larger the i Slice j The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel.

[0132] Step S5, determining the grayscale prominence degree corresponding to each pixel point in each target blood vessel according to the grayscale change in each target blood vessel.

[0133] As an example, this step may include the following steps:

[0134] In the first step, any slice in the target three-dimensional image is determined as a marked slice, any target blood vessel in the marked slice is determined as a marked blood vessel, and any pixel point in the marked blood vessel is determined as a marked pixel point.

[0135] In the second step, the number of pixel points whose grayscale values ​​corresponding to the marked blood vessels are equal to the grayscale values ​​corresponding to the marked pixel points is determined as the reference grayscale number corresponding to the marked pixel points.

[0136] In the third step, the average of the grayscale values ​​corresponding to all the pixels in the marked blood vessels is determined as the grayscale representative factor corresponding to the marked blood vessels.

[0137] In the fourth step, the absolute value of the difference between the grayscale value corresponding to the marked pixel point and the grayscale representative factor corresponding to the marked blood vessel is determined as the initial protrusion factor corresponding to the marked pixel point.

[0138] The fifth step is to determine the grayscale prominence degree corresponding to the marked pixel point according to the initial prominence factor corresponding to the marked pixel point and the reference grayscale quantity.

[0139] The initial prominence factor may be positively correlated with the grayscale prominence degree, and the reference grayscale quantity may be negatively correlated with the grayscale prominence degree.

[0140] For example, the formula for determining the grayscale prominence corresponding to the marked pixel point can be:

[0141] ;

[0142] ;in, It is the grayscale prominence corresponding to the marked pixel. is a normalization function. is the gray value corresponding to the marked pixel. is the grayscale representative factor corresponding to the marked blood vessels. Indicates that the gray value in the marked blood vessel is equal to The proportion of pixels in the marked blood vessels. T is the reference grayscale number corresponding to the marked pixel, that is, the grayscale value corresponding to the marked blood vessel is equal to The number of pixels. is the number of pixels marking the blood vessels. It is the absolute value function. is the initial saliency factor corresponding to the marked pixel.

[0143] It should be noted that, generally speaking, when the gray value of a certain pixel in a blood vessel accounts for a small proportion and the gray value difference between it and other pixels is large, the pixel will often be highlighted. B The smaller the value, the gray value in the marked blood vessel is equal to The smaller the percentage of pixels in the marked blood vessels. The larger the value, the greater the difference between the marked pixel and the overall grayscale of the blood vessel to which it belongs. t The larger the value, the more the marked pixel is highlighted, and the more obvious the marked pixel is in the blood vessel to which it belongs.

[0144] Step S6, determining the grayscale change difference corresponding to each target blood vessel according to the local difference index corresponding to each target blood vessel and the grayscale prominence degree corresponding to the pixel points therein,

[0145] As an example, the formula for determining the grayscale change difference corresponding to the target blood vessel may be:

[0146] ;

[0147] in, The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. The target 3D image i Slice j The local difference index corresponding to the target blood vessel. The target 3D image i Slice j The number of pixels in the target blood vessel. h The target 3D image i Slice j The serial number of the pixel point in the target blood vessel. It is the absolute value function. The target 3D image i Slice j Article Target blood vessels, h The grayscale prominence corresponding to each pixel. The target 3D image i Slice j The mean value of the grayscale prominence of all pixels in the target blood vessel.

[0148] It should be noted that when The larger the i Slice j The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The gray value corresponding to the target blood vessel. The larger the i Slice j The more inconsistent the degree of highlighting of different pixels in the target blood vessel, the more likely it is that i Slice j The greater the fluctuation of grayscale changes in the target blood vessels, the stronger the i Slice j The more information a target blood vessel contains, the more likely it is that i Slice j The more the target vessel should be highlighted. The larger the i Slice jThe more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel.

[0149] Step S7, performing MIP reconstruction according to the local complex indexes and grayscale variation differences corresponding to all target blood vessels in all slices in the target three-dimensional image, and the grayscale values ​​corresponding to all pixel points therein.

[0150] Among them, MIP (Maximum Intensity Projection) is a medical image processing technology, which is mainly used for post-processing of three-dimensional images, especially in CT and MRI imaging.

[0151] As an example, this step may include the following steps:

[0152] In the first step, the grayscale correction weight corresponding to each target vessel is determined according to the grayscale change difference corresponding to each target vessel and the mean value of the local complexity indexes corresponding to all target vessels in the slice to which each target vessel belongs.

[0153] For example, the formula for determining the grayscale correction weight corresponding to the target blood vessel may be:

[0154] ;

[0155] in, The target 3D image i Slice j Grayscale correction weight corresponding to the target blood vessel. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. is a normalization function. The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels. The target 3D image i The mean of the local complexity indexes corresponding to all target vessels in a slice.

[0156] It should be noted that Can characterize the j The overall environment of the target blood vessel. The larger the value, the better. j The more complex the environment of the target vessel is, the less obvious the grayscale difference between it and the crossing vessel is, and the greater the possibility that it is a cerebral aneurysm. This often means that the more you need to adjust the j The gray value corresponding to the target blood vessel. The larger the i Slice j The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel. The larger the i Slice j The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel.

[0157] In the second step, the target grayscale index corresponding to each pixel in each target blood vessel is determined according to the grayscale correction weight corresponding to each target blood vessel and the grayscale value corresponding to each pixel in each target blood vessel.

[0158] Among them, both the grayscale correction weight and the grayscale value can be positively correlated with the target grayscale index.

[0159] For example, the formula for determining the target grayscale index corresponding to the pixel point in the target blood vessel may be:

[0160] ;

[0161] in, The target 3D image i Slice j Article Target blood vessels, h The target grayscale index corresponding to each pixel. i is the sequence number of the slice in the target 3D image. j It is i The serial number of the target vessel in each slice. h The target 3D image i Slice j The serial number of the pixel point in the target blood vessel. is a normalization function. The target 3D image i Slice j Article Target blood vessels, h The grayscale value corresponding to the pixel is the grayscale value before updating. The target 3D image i Slice j Grayscale correction weight corresponding to the target blood vessel.

[0162] It should be noted that when The larger the i Slicej The more the target blood vessel is highlighted, the more it needs to be adjusted. i Slice j The grayscale value corresponding to the target blood vessel. And since the grayscale value range is [0, 255], Can characterize the i Slice j Article Target intravascular h The gray value of each pixel after adjustment.

[0163] The third step is to determine the target grayscale index corresponding to each pixel point except the target blood vessel in each slice of the target three-dimensional image according to the grayscale value corresponding to each pixel point except the target blood vessel in each slice of the target three-dimensional image.

[0164] Among them, the grayscale value can be positively correlated with the target grayscale index.

[0165] For example, the formula for determining the target grayscale index corresponding to the pixel points other than the target blood vessel in the slice of the target three-dimensional image may be:

[0166] ;

[0167] in, The target 3D image i In the slices except the target vessel m The target grayscale index corresponding to each pixel. i is the sequence number of the slice in the target 3D image. m It is i The sequence number of the pixel points in the slice except the target blood vessel. is a normalization function. The target 3D image i In the slices except the target vessel m The gray value corresponding to each pixel.

[0168] In the fourth step, the grayscale value corresponding to each pixel in all slices of the target three-dimensional image is updated to its corresponding target grayscale index, and blood vessel reconstruction is achieved through MIP reconstruction based on the updated grayscale values ​​corresponding to all pixels in all slices of the target three-dimensional image.

[0169] For example, MIP reconstruction can be used to obtain the updated maximum grayscale value in all slices in the target three-dimensional image, thereby forming a final vascular reconstruction image and achieving vascular reconstruction.

[0170] It should be noted that, in the embodiment of the present invention, different weights for highlighting are adaptively set for different target blood vessels during MIP reconstruction, thereby adaptively adjusting the grayscale value corresponding to each pixel in each target blood vessel, thereby improving the effect of subsequent MIP reconstruction.

[0171] In actual situations, after obtaining the post-processed image of MIP, volume rendering (VR) and multi-plane reconstruction (MPR) can also be performed to determine the reconstructed image of the current brain tumor blood vessels. After obtaining the vascular reconstruction image, the orientation, angle, pseudo color, contrast and local magnification of the reconstructed image can be appropriately adjusted to obtain an ideal clinical diagnosis and treatment image. Among them, the post-processed image of MIP can be an image composed of the updated maximum grayscale value in all slices in the target three-dimensional image. When performing intracranial and extracranial surgery, doctors often perform vascular reconstruction to more accurately evaluate the condition of brain tumor blood vessels and provide important reference information for further treatment.

[0172] refer to Figure 2 Based on the same inventive concept as the above method embodiment, the present invention provides a system for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery, the system comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the above computer program is executed by the processor, the steps of a method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery are implemented, which may specifically include:

[0173] The three-dimensional image acquisition and recognition module 201 is used to acquire a target three-dimensional image corresponding to the intracranial and extracranial blood vessels accompanying the cerebral aneurysm surgery, and recognize each target blood vessel in each slice of the target three-dimensional image;

[0174] A thickness analysis processing module 202 is used to perform thickness analysis processing on each target blood vessel according to the length and area of ​​each target blood vessel to obtain a thickness representative index corresponding to each target blood vessel;

[0175] A local complexity index determination module 203, for determining a local complexity index corresponding to each target blood vessel according to a thickness representative index corresponding to each target blood vessel and a grayscale difference between each target blood vessel and its intersecting target blood vessel;

[0176] A local difference index determination module 204 is used to determine the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel;

[0177] A grayscale prominence determination module 205 is used to determine the grayscale prominence corresponding to each pixel point in each target blood vessel according to the grayscale change in each target blood vessel;

[0178] A grayscale change difference determination module 206 is used to determine the grayscale change difference corresponding to each target blood vessel according to the local difference index corresponding to each target blood vessel and the grayscale prominence degree corresponding to the pixel points therein;

[0179] The MIP reconstruction module 207 is used to perform MIP reconstruction according to the local complex indexes and grayscale variation differences corresponding to all target blood vessels in all slices in the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixel points therein.

[0180] Figure 3 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Figure 3 As shown, the computer device 300 includes: a memory 301, a processor 302, and a computer program 303 stored in the memory 301 and running on the processor 302, wherein when the processor 302 executes the computer program 303, the computer device can execute any one of the above-mentioned intracranial and extracranial vascular reconstruction methods accompanying cerebral aneurysm surgery.

[0181] Based on the same inventive concept as the above method embodiment, the present invention provides a server, including a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the device executes any of the above extracranial and extracranial vascular reconstruction methods accompanying cerebral aneurysm surgery.

[0182] Based on the same inventive concept as the above method embodiment, the present invention provides a computer program product, which includes: computer program code, when the computer program code runs on a computer, the computer executes any one of the above-mentioned extracranial and extracranial vascular reconstruction methods accompanying cerebral aneurysm surgery.

[0183] Based on the same inventive concept as the above-mentioned method embodiment, the present invention provides a computer-readable storage medium, which stores a computer program code. When the computer program code runs on a computer, the computer executes any one of the above-mentioned extracranial and extracranial vascular reconstruction methods accompanying cerebral aneurysm surgery.

[0184] In summary, compared with directly and uniformly obtaining the maximum grayscale value from all slices in the target three-dimensional image to form a blood vessel reconstruction image, the present invention comprehensively considers multiple features related to blood vessel display, such as thickness representative index, local complexity index, local difference index, grayscale prominence degree and grayscale change difference, and performs MIP reconstruction according to the local complexity index and grayscale change difference corresponding to all target blood vessels in all slices in the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixels therein, so as to adaptively realize the highlighting display of each target blood vessel, thereby realizing blood vessel reconstruction and improving the blood vessel reconstruction effect.

[0185] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments can still be modified, or some of the technical features can be replaced by equivalents. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for intracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery, characterized in that: The following steps are involved: Acquire a target three-dimensional image corresponding to intracranial and extracranial blood vessels accompanying cerebral aneurysm surgery, and identify each target blood vessel in each slice of the target three-dimensional image; According to the length and area of ​​each target blood vessel, each target blood vessel is analyzed and processed to obtain a thickness representative index corresponding to each target blood vessel; Determine the local complexity index corresponding to each target blood vessel according to the thickness representative index corresponding to each target blood vessel and the grayscale difference between each target blood vessel and its intersecting target blood vessel; Determine the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel; According to the grayscale change in each target blood vessel, the grayscale prominence degree corresponding to each pixel point in each target blood vessel is determined; Determine the grayscale change difference corresponding to each target blood vessel according to the local difference index corresponding to each target blood vessel and the grayscale prominence degree corresponding to the pixel point therein; MIP reconstruction is performed based on the local complex indexes and grayscale change differences corresponding to all target blood vessels in all slices of the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixels therein.

2. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: According to the length and area of ​​each target blood vessel, each target blood vessel is analyzed and processed to obtain a thickness representative index corresponding to each target blood vessel, including: The number of pixels on the long side of each target blood vessel is determined as the target length corresponding to each target blood vessel; The number of pixels in each target blood vessel is determined as the target area corresponding to each target blood vessel; The ratio of the target area to the target length corresponding to each target blood vessel is determined as the initial thickness factor corresponding to each target blood vessel; According to the initial thickness factor corresponding to each target blood vessel, a thickness representative index corresponding to each target blood vessel is determined, wherein the initial thickness factor is positively correlated with the thickness representative index.

3. The method for intracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: The method of determining the local complexity index corresponding to each target blood vessel according to the thickness representative index corresponding to each target blood vessel and the grayscale difference between each target blood vessel and its intersecting target blood vessel includes: The average of the grayscale values ​​corresponding to all the pixels in each target blood vessel is determined as the grayscale representative factor corresponding to each target blood vessel; Determine any slice in the target three-dimensional image as a marked slice, determine any target blood vessel in the marked slice as a marked blood vessel, and determine each target blood vessel in the marked slice except the marked blood vessel as a reference blood vessel; Determine each reference blood vessel intersecting the marked blood vessel as a candidate blood vessel, and obtain a candidate blood vessel set corresponding to the marked blood vessel; Determine the number of candidate blood vessels in the candidate blood vessel set corresponding to the marked blood vessel as the number of intersections corresponding to the marked blood vessel; Determining a projection conspicuous factor corresponding to the marked blood vessel according to a difference between a grayscale representative factor corresponding to the marked blood vessel and a grayscale representative factor corresponding to the candidate blood vessel; The local complexity index corresponding to the marked blood vessel is determined according to the projection obvious factor, the number of intersections and the thickness representative index corresponding to the marked blood vessel, wherein the number of intersections and the thickness representative index are both positively correlated with the local complexity index; the projection obvious factor is negatively correlated with the local complexity index.

4. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 3, characterized in that: The step of determining the projection conspicuous factor corresponding to the marked blood vessel according to the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to the candidate blood vessel comprises: determining the absolute value of the difference between the grayscale representative factor corresponding to the marked blood vessel and the grayscale representative factor corresponding to each candidate blood vessel as the target grayscale difference between the marked blood vessel and each candidate blood vessel; The mean value of the target grayscale difference between the marked blood vessel and all candidate blood vessels in the corresponding candidate blood vessel set is determined as the projection conspicuous factor corresponding to the marked blood vessel.

5. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: Determining the local difference index corresponding to each target blood vessel according to the grayscale difference between the edge pixel point of each target blood vessel and the pixel point in the corresponding preset window, and the chain code difference between adjacent edge pixel points in each target blood vessel, includes: The absolute value of the difference between the grayscale value corresponding to each edge pixel point of each target blood vessel and the grayscale value corresponding to each pixel point in the corresponding preset window is determined as the reference grayscale difference between each edge pixel point of each target blood vessel and each pixel point in the corresponding preset window; The maximum reference grayscale difference is selected from the reference grayscale differences between each edge pixel point of each target blood vessel and all the pixels in the corresponding preset window as the edge grayscale difference corresponding to each edge pixel point of each target blood vessel; According to the edge grayscale differences corresponding to all edge pixels of each target blood vessel and the chain code differences between adjacent edge pixels in each target blood vessel, the local difference index corresponding to each target blood vessel is determined.

6. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 5, characterized in that: Determining the local difference index corresponding to each target blood vessel according to the edge grayscale difference corresponding to all edge pixels of each target blood vessel and the chain code difference between adjacent edge pixels in each target blood vessel includes: The average value of the edge grayscale differences corresponding to all edge pixels of each target blood vessel is determined as the overall grayscale difference corresponding to each target blood vessel; The absolute value of the difference between the chain code values ​​corresponding to the two adjacent edge pixels at each position is determined as the target chain code difference between the two adjacent edge pixels at each position; The average of the target chain code differences between all adjacent edge pixel points in each target blood vessel is determined as the overall chain code difference corresponding to each target blood vessel; According to the overall grayscale difference and overall chain code difference corresponding to each target blood vessel, the local difference index corresponding to each target blood vessel is determined, wherein the overall grayscale difference is negatively correlated with the local difference index, and the overall chain code difference is positively correlated with the local difference index.

7. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: Determining the grayscale prominence level corresponding to each pixel point in each target blood vessel according to the grayscale change in each target blood vessel includes: Determine any slice in the target three-dimensional image as a marked slice, determine any target blood vessel in the marked slice as a marked blood vessel, and determine any pixel point in the marked blood vessel as a marked pixel point; The number of pixel points whose corresponding grayscale values ​​in the marked blood vessel are equal to the grayscale value corresponding to the marked pixel point is determined as the reference grayscale number corresponding to the marked pixel point; Determine the average of the grayscale values ​​corresponding to all the pixels in the marked blood vessel as the grayscale representative factor corresponding to the marked blood vessel; Determine the absolute value of the difference between the grayscale value corresponding to the marked pixel point and the grayscale representative factor corresponding to the marked blood vessel as the initial protrusion factor corresponding to the marked pixel point; The grayscale prominence degree corresponding to the marked pixel point is determined according to the initial prominence factor and the reference grayscale quantity corresponding to the marked pixel point, wherein the initial prominence factor is positively correlated with the grayscale prominence degree, and the reference grayscale quantity is negatively correlated with the grayscale prominence degree.

8. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: The formula corresponding to the grayscale change difference of the target blood vessel is: ； in, The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels; i is the serial number of the slice in the target 3D image; j It is i The serial number of the target vessel in each slice; The target 3D image i Slice j The local difference index corresponding to the target blood vessel; The target 3D image i Slice j The number of pixels in the target blood vessel; h The target 3D image i Slice j The serial number of the pixel point in the target blood vessel; It is the absolute value function; The target 3D image i Slice j Article intravascular, h The gray level prominence corresponding to each pixel; The target 3D image i Slice j The mean value of the grayscale prominence of all pixels in the target blood vessel.

9. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 1, characterized in that: The MIP reconstruction is performed according to the local complex indexes and grayscale change differences corresponding to all target blood vessels in all slices in the target three-dimensional image, as well as the grayscale values ​​corresponding to all pixel points therein, including: Determine the grayscale correction weight corresponding to each target blood vessel according to the grayscale change difference corresponding to each target blood vessel and the average value of the local complexity indexes corresponding to all target blood vessels in the slice to which each target blood vessel belongs; According to the grayscale correction weight corresponding to each target blood vessel and the grayscale value corresponding to each pixel in each target blood vessel, a target grayscale index corresponding to each pixel in each target blood vessel is determined, wherein the grayscale correction weight and the grayscale value are both positively correlated with the target grayscale index; Determining a target grayscale index corresponding to each pixel point in each slice of the target three-dimensional image except the target blood vessel according to a grayscale value corresponding to each pixel point in each slice of the target three-dimensional image except the target blood vessel, wherein the grayscale value is positively correlated with the target grayscale index; The grayscale value corresponding to each pixel in all slices of the target three-dimensional image is updated to its corresponding target grayscale index, and blood vessel reconstruction is achieved through MIP reconstruction based on the updated grayscale values ​​corresponding to all pixels in all slices of the target three-dimensional image.

10. The method for extracranial and extracranial vascular reconstruction accompanying cerebral aneurysm surgery according to claim 9, characterized in that: The formula corresponding to the grayscale correction weight of the target blood vessel is: ； in, The target 3D image i Slice j Grayscale correction weight corresponding to the target blood vessel; i is the serial number of the slice in the target 3D image; j It is i The serial number of the target vessel in each slice; is the normalization function; The target 3D image i Slice j The grayscale change difference corresponding to the target blood vessels; The target 3D image i The mean of the local complexity indexes corresponding to all target vessels in a slice.

Images