CN111986205B

CN111986205B - Vessel tree generation and lesion recognition method, apparatus, device and readable storage medium

Info

Publication number: CN111986205B
Application number: CN201910425253.6A
Authority: CN
Inventors: 梁红霞; 赵丽俊; 刘盛华; 董爱莲
Original assignee: Individual
Current assignee: Individual
Priority date: 2019-05-21
Filing date: 2019-05-21
Publication date: 2024-04-02
Anticipated expiration: 2039-05-21
Also published as: CN111986205A

Abstract

The invention discloses a method, a device, equipment and a readable storage medium for generating a blood vessel tree and identifying lesions. The method comprises the following steps: acquiring a medical image, the medical image comprising a plurality of voxels; segmenting a plurality of vascular regions from the medical image, the vascular regions comprising: a blood vessel and voxels having similar characteristics to the blood vessel; establishing an incidence matrix of the plurality of blood vessel areas according to the adjacent relation among the plurality of blood vessel areas; respectively acquiring a blood vessel section and a blood vessel bifurcation point of each blood vessel region; according to the incidence matrix, constructing the vessel segments, the vessel bifurcation points and the connection relations among the vessel bifurcation points which are respectively contained in the vessel regions into vessel graphs of the vessel regions; and establishing a plurality of corresponding vessel trees based on the plurality of connected subgraphs in the vessel graph. The method can analyze the morphology, structure and characteristics of the blood vessel image and the connection relation among blood vessels, and construct an accurate blood vessel tree.

Description

Vessel tree generation and lesion recognition method, apparatus, device and readable storage medium

Technical Field

The invention relates to the technical field of medical image processing and application, in particular to a blood vessel tree generation method, a lesion recognition method, a lesion research and judgment method, a device, equipment and a readable storage medium.

Background

In the field of medical imaging, the automatic extraction, modeling and analysis of blood vessel images, structures and forms by a computer are a basic problem for realizing intelligent medical treatment. For example, in the field of CT (Computed Tomography, electronic computed tomography), effective information abstraction, extraction and operation on vascular structures in an output medical image can greatly improve the automatic recognition rate of related lesions.

Currently, research on blood vessel images in medical images is mainly focused on the following aspects: firstly, adopting an image processing method to strengthen and display a vascular structure in an original image, thereby achieving the aim of assisting medical staff in diagnosis; secondly, the blood vessel image is separated from the medical image and extracted by an image and graphics method, so that input which is more beneficial to identification is provided for an intelligent diagnosis algorithm.

For the first of these, the effect is mainly to enhance the display of the vessel image, but the vessel structure is not processed. In the second aspect, the blood vessel image is simply extracted, but the morphology, structure, characteristics and connection relation between the blood vessel segments are not analyzed and modeled.

The above information disclosed in the background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.

Disclosure of Invention

In view of this, the present invention provides a blood vessel tree generation method, a lesion recognition method, a lesion research method, a device, an apparatus, and a readable storage medium.

Other features and advantages of the invention will be apparent from the following detailed description, or may be learned by the practice of the invention.

According to an aspect of the present invention, there is provided a medical image-based vessel tree generation method, including: acquiring a medical image, the medical image comprising a plurality of voxels; segmenting a plurality of vascular regions from the medical image, the vascular regions comprising: a blood vessel and voxels having similar characteristics to the blood vessel; establishing an incidence matrix of the plurality of blood vessel areas according to the adjacent relation among the plurality of blood vessel areas; respectively acquiring a blood vessel section and a blood vessel bifurcation point of each blood vessel region; according to the incidence matrix, constructing the blood vessel segments, the blood vessel bifurcation points and the connection relations among the blood vessel segments, the blood vessel bifurcation points which are respectively contained in each blood vessel region into a blood vessel graph taking the blood vessel segments and the blood vessel bifurcation points as nodes; establishing a plurality of corresponding vessel trees based on a plurality of connected subgraphs in the vessel graph; the nodes in the vessel tree are the vessel segments contained in the connected subgraph corresponding to the nodes.

According to an embodiment of the present invention, establishing an association matrix of the plurality of blood vessel regions according to a proximity relation between the plurality of blood vessel regions includes: for each layer of image of the medical image: taking the areas covered by the plurality of blood vessel areas as nodes, and inserting a spatial index structure; expanding adjacent voxels for each blood vessel region to obtain an expanded region of the blood vessel region; searching an overlapped blood vessel region overlapped with the blood vessel region in the spatial index structure by taking the expansion region of the blood vessel region as a spatial query condition; assigning values to corresponding elements in the incidence matrix according to the expansion times of each blood vessel region; and obtaining the correlation matrix for the plurality of vessel regions.

According to an embodiment of the present invention, acquiring the blood vessel segment and the blood vessel bifurcation point of each of the blood vessel areas respectively includes: for each of the vascular regions: generating a plurality of corresponding three-dimensional skeletons for the vascular region by calculating fractal; respectively obtaining the blood vessel bifurcation points from each three-dimensional skeleton, wherein the blood vessel bifurcation points are voxels with 2 or more than 2 adjacent voxels around; and for each of the three-dimensional skeletons: removing the blood vessel bifurcation points to obtain a plurality of blood vessel segment skeletons in the three-dimensional skeleton; performing region growing on the plurality of vessel segment skeletons respectively to obtain a plurality of vessel segments respectively corresponding to the plurality of vessel segment skeletons; and determining attributes of the plurality of vessel segments, respectively, the attributes comprising: the length, radius, direction, voxel set of the vessel segment.

According to an embodiment of the invention, each of the vessel trees comprises a trunk node and a branch node; based on the plurality of connected subgraphs in the vessel graph, establishing a corresponding plurality of vessel trees includes: selecting the blood vessel segment with the closest distance to the heart, the largest radius and only one blood vessel bifurcation point adjacent to the blood vessel segment as the root node of the blood vessel tree aiming at each connected subgraph; processing all nodes in the connected subgraph in turn from the root node, including: for each adjacent vessel bifurcation of the node: acquiring adjacent nodes of the bifurcation point of the blood vessel; selecting one of the adjacent nodes as the trunk node, and marking other adjacent nodes as the branch nodes; connecting the node with the trunk node to form a trunk edge; and connecting the node with the branch node to form a branch edge.

According to an embodiment of the present invention, performing region growing on the plurality of vessel segment skeletons, respectively, to obtain a plurality of the vessel segments corresponding to the plurality of vessel segment skeletons, respectively, includes: constructing a voxel correlation matrix A of the blood vessel region, wherein when two voxels are adjacent, the element in the correlation matrix A is 1; when two voxels are not adjacent, the element in the incidence matrix A is 0; constructing a vector b of each vessel segment, and assigning points in the vector b as numbers corresponding to the vessel segment frameworks; repeating the following steps until all elements in the vector b are not 0, thereby obtaining the vessel segment: 1) Let g _ij ＝a _ij b _j Wherein a is _ij B is the element in the incidence matrix A _j Is an element in the vector b; 2) Make b _i ’＝max(g _i1 ,g _i2 ,…,g _in ) The method comprises the steps of carrying out a first treatment on the surface of the 3) If b _j Equal to 0, then b is caused to _j Equal to b _i ’。

According to an aspect of the present invention, there is provided a lesion recognition method based on a blood vessel tree, including: based on the vessel tree generated by any one of the vessel tree generation methods based on the medical image, the lesion recognition is performed according to a predefined lesion recognition rule.

According to an embodiment of the present invention, the lesion recognition rule includes some or all of the following rules: when the number of nodes in the blood vessel tree is smaller than a preset threshold value of the number of nodes, determining that a lesion exists in a region corresponding to the blood vessel tree; when the radius difference value of the node in the vascular tree and the adjacent node is larger than a preset first radius threshold value, determining that a lesion exists in a region corresponding to the node; when traversing each node of the vessel tree by adopting a breadth-first traversing strategy, determining that lesions exist in a region corresponding to a node of which the radius difference value of two nodes accessed before and after is out of a preset radius range; when the radius of a branch node of the blood vessel tree is larger than a preset second radius threshold value, determining that a lesion exists in a region corresponding to the branch node; for the medical image which is an image of a bilateral symmetry organ, when the KL distance of the radius distribution curve of the node in the two largest vessel trees on the left side and the right side of the organ is larger than a preset first distance threshold value, determining that lesions exist in the areas corresponding to the two vessel trees; and for the medical image which is an image of a left-right asymmetric organ, when the KL distance between the radius distribution curve of the node in the vascular tree and the standard radius curve is larger than a preset second distance threshold value, determining that a lesion exists in the region corresponding to the vascular tree.

According to an aspect of the present invention, there is provided a lesion area studying and judging method based on a blood vessel tree, including: and performing lesion research and judgment on the region to be researched according to a predefined lesion research and judgment rule based on the vessel tree generated by any one of the vessel tree generation methods based on the medical image.

According to an embodiment of the present invention, the lesion research rule includes some or all of the following rules: when the difference value between the radius of the region to be ground and the radius of the node of the blood vessel tree where the region to be ground is located is larger than a preset first radius threshold value, determining that the region to be ground is a lesion region; when the node number of the blood vessel tree where the region to be determined is located is smaller than a preset first node number threshold value, determining the region to be determined as a lesion region; when the difference between the radius of the region to be determined and the radius of all adjacent nodes of the blood vessel tree where the region to be determined is located is larger than a preset second radius threshold value, determining the region to be determined as a lesion region; and when the radius of the node of the blood vessel tree where the region to be determined is located is larger than a third radius threshold and the node of the subtree of the node is smaller than a second node number threshold, determining the region to be determined as a lesion region.

According to an aspect of the present invention, there is provided a medical image-based vessel tree generating apparatus, comprising: the image acquisition module is used for acquiring a medical image, and the medical image comprises a plurality of voxels; the region segmentation module is used for segmenting a plurality of blood vessel regions from the medical image, and the blood vessel regions comprise: a blood vessel and voxels having similar characteristics to the blood vessel; the matrix establishing module is used for establishing an incidence matrix of the plurality of blood vessel areas according to the adjacent relation among the plurality of blood vessel areas; the blood vessel section acquisition module is used for respectively acquiring the blood vessel section and the blood vessel bifurcation point of each blood vessel area; the blood vessel map construction module is used for constructing a blood vessel map taking the blood vessel segments and the blood vessel bifurcation points as nodes according to the incidence matrix and the connection relation among the blood vessel segments, the blood vessel bifurcation points and the connection relation among the blood vessel segments and the blood vessel bifurcation points respectively contained in each blood vessel region; the vessel tree building module is used for building a plurality of corresponding vessel trees based on a plurality of connected subgraphs in the vessel graph; wherein each vessel tree comprises a trunk node and a branch node.

According to an aspect of the present invention, there is provided a lesion recognition device based on a blood vessel tree, comprising: and the lesion recognition module is used for recognizing the lesions according to a predefined lesion recognition rule based on the vessel tree generated by any one of the vessel tree generation methods based on the medical images.

According to an aspect of the present invention, there is provided a lesion area studying and judging device based on a blood vessel tree, including: and the lesion research and judgment module is used for conducting lesion research and judgment on the region to be researched according to a predefined lesion research and judgment rule based on the blood vessel tree generated by any one of the blood vessel tree generation methods based on the medical image.

According to an aspect of the present invention, there is provided a computer apparatus comprising: the system comprises a memory, a processor and executable instructions stored in the memory and executable in the processor, wherein the processor implements any one of the methods when executing the executable instructions.

According to an aspect of the present invention there is provided a computer readable storage medium having stored thereon computer executable instructions which when executed by a processor implement a method as any one of the above.

According to the medical image-based vessel tree generation method provided by the embodiment of the invention, the morphology, structure, characteristics and connection relation among vessels of the vessel image can be analyzed aiming at the separated vessel image, and an accurate vessel tree can be constructed. The vessel tree can be used for lesion recognition, and can be used for further researching and judging lesion areas recognized by other modes so as to obtain a more accurate lesion recognition result.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.

Drawings

The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings.

Fig. 1 is a flowchart illustrating a medical image-based vessel tree generation method according to an exemplary embodiment.

Fig. 2 is a cross-sectional view of a vascular region at a layer, according to an example.

Fig. 3 and 4 show a vessel tree generated according to the vessel tree generation method described above based on scanned retinal images and lung images, respectively.

Fig. 5 is a flowchart illustrating another medical image-based vessel tree generation method according to an exemplary embodiment.

Fig. 6 is a flowchart illustrating yet another medical image-based vessel tree generation method according to an exemplary embodiment.

Fig. 7 shows a cross-sectional view of the three-dimensional skeleton generated for the vascular region shown in fig. 2 in one layer.

Fig. 8 is a schematic diagram showing a process of generating a blood vessel segment according to an example.

Fig. 9 is a flowchart illustrating yet another medical image-based vessel tree generation method according to an exemplary embodiment.

Fig. 10 shows a process of generating a vessel tree from the vessel segments as shown in fig. 8.

FIG. 11 is a flowchart illustrating a lesion recognition method based on a vessel tree, according to an exemplary embodiment.

Fig. 12 shows a schematic representation of a lung lesion identified according to the identification rule.

Fig. 13 is a flowchart illustrating a lesion area studying and judging method based on a blood vessel tree according to an exemplary embodiment.

Fig. 14 is a block diagram illustrating a medical image-based vessel tree generating apparatus according to an exemplary embodiment.

Fig. 15 is a block diagram illustrating a lesion recognition device based on a vessel tree according to an exemplary embodiment.

Fig. 16 is a block diagram showing a lesion area studying and judging device based on a blood vessel tree according to an exemplary embodiment.

Fig. 17 is a schematic diagram of an electronic device according to an exemplary embodiment.

Fig. 18 is a schematic diagram of a computer-readable storage medium according to an example embodiment.

Detailed Description

Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments may be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.

Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, etc. In other instances, well-known structures, methods, devices, implementations, or operations are not shown or described in detail to avoid obscuring aspects of the invention.

Furthermore, in the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise. "and/or" describes an association relationship of an associated object, meaning that there may be three relationships, e.g., a and/or B, and that there may be a alone, B alone, and both a and B. The symbol "/" generally indicates that the context-dependent object is an "or" relationship. The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.

Referring to fig. 1, a medical image-based vessel tree generation method 10 includes:

in step S102, a medical image is acquired.

The medical image may include, for example, a CT medical image, an MRI (Magnetic Resonance Imaging, nuclear magnetic resonance) medical image, an ultrasound medical image, or the like; in addition, the organs collected in the medical image can be lung, liver, brain, retina, etc., and the invention is not limited thereto.

Taking a common axial CT scan image as an example, the medical image may be a three-dimensional tensor Q, wherein each element Q _i,j,k Is a non-negative integer and may also be referred to as a voxel or pixel. Where subscript i=0, 1,2,3, … …, row index in a layer for CT scan; subscript j=0, 1,2,3, … …, column index in a layer for CT scan; k=0, 1,2,3, … … is the layer index of the CT scan.

In step S104, a plurality of blood vessel regions are segmented from the medical image.

The medical image is subjected to image preprocessing, and voxel values of a non-blood vessel region (for example, an air region of a lung) and other tissue regions except for a blood vessel region and voxels having similar characteristics to the blood vessel can be set to 0 so as to divide a plurality of blood vessel regions from the medical image.

The vascular region includes: blood vessels and voxels with similar characteristics to blood vessels.

One vascular region is r _x ，r _x E R, where R is the set of vessel regions segmented from the medical image. Each vascular region is a separate three-dimensional region and each vascular region is not adjacent to other vascular regions. That is, the adjacent voxel values of each vessel region are all 0. Fig. 2 is a cross-sectional view of a vascular region at a layer, according to an example. Wherein, the black voxel value is 0, and the white voxel value is greater than 0.

In step S106, an association matrix of a plurality of blood vessel regions is established according to the proximity relation between the plurality of blood vessel regions.

The correlation matrix of the plurality of vessel regions may be denoted as M for representing the neighbor relation between the vessel regions, i.e. which vessel regions are closer. Because the vessel regions that are closer together are likely vessels that are connected together in the image, but these vessels are disconnected due to noise from the CT scan or when decision is made by thresholding. The construction of the correlation matrix helps to establish possible connection relations between the vascular regions.

In step S108, a blood vessel segment and a blood vessel bifurcation point for each blood vessel region are acquired.

For example, the vessel segments and vessel bifurcation points contained in each vessel region may be obtained by calculating fractal (also referred to as calculating morphology).

In step S110, a vessel segment, a vessel bifurcation point, and a connection relationship between the vessel segments and the vessel bifurcation points included in each vessel region are constructed as a vessel map using the vessel segment and the vessel bifurcation point as nodes, based on the correlation matrix.

For example, the vessel segments, the vessel bifurcation points, and the connection relations between the vessel segments and the vessel bifurcation points included in each vessel region may be first constructed as a preliminary vessel map, and then the connection relations between the vessel regions may be supplemented to the preliminary vessel map according to the correlation matrix M to obtain a final vessel map (e.g., may be denoted as G).

In step S112, a corresponding plurality of vessel trees are created based on the plurality of connected subgraphs in the vessel map.

The nodes in each vessel tree are vessel segments contained in the corresponding connected subgraph.

The vessel map may include a plurality of connected sub-maps, each of which has vessel segments connected to each other, and vessels between the connected sub-maps are disconnected from each other. Based on each connected subgraph, corresponding blood vessel trees are established. For example, the blood vessels between the left and right lungs are not connected with each other, so that the blood vessels belong to different connected subgraphs, and different blood vessel trees can be respectively constructed.

According to the medical image-based vessel tree generation method provided by the embodiment of the invention, the morphology, structure, characteristics and connection relation among vessels of the vessel image can be analyzed aiming at the separated vessel image, and an accurate vessel tree can be constructed. The vessel tree can be used for lesion recognition, and can be used for further researching and judging lesion areas recognized by other modes so as to obtain a more accurate lesion recognition result. Fig. 3 and 4 show a vessel tree generated according to the vessel tree generation method described above based on scanned retinal images and lung images, respectively. Wherein fig. 3 (a) and fig. 4 (a) show scanned retinal images and lung images, respectively, and fig. 3 (b) and fig. 4 (b) show vessel trees corresponding to the retinal images and the lung images, respectively. As can be seen from fig. 3 and 4, by this method, an accurate vessel tree can be generated based on the acquired medical images.

It should be clearly understood that the present invention describes how to make and use specific examples, but the principles of the present invention are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.

Fig. 5 is a flowchart illustrating another medical image-based vessel tree generation method according to an exemplary embodiment. Unlike the medical image-based vessel tree generation method 10 shown in fig. 1, the medical image-based vessel tree generation method 20 shown in fig. 5 further provides a method of establishing an association matrix of a plurality of vessel regions according to a proximity relation between the plurality of vessel regions.

Referring to fig. 5, a medical image-based vessel tree generation method 20 includes:

in step S202, for each layer of image of the medical image, a plurality of blood vessel regions are processed:

1. multiple vascular regions r _x The space index structure I is inserted into the area covered by the layer as a node _k 。

The spatial index structure I _k For example, it may be an existing rtre structure index, or it may be an existing other spatial index structure.

2. For each vessel region r _x Extending voxels adjacent thereto to obtain an extended region of the vessel region, which can be denoted as r _x ’。

3. With an expanded region r of the vascular region _x ' is a space query condition, in the space index structure I _k Find and the vessel region r _x Overlapping vessel region r _y 。

4. And assigning values to corresponding elements in the association matrix M according to the expansion times.

For example, the number of extensions z=0 is initialized, and r _x ’＝r _x 。

If element M in the association matrix M _xy Equal to 0 or m _xy Greater than z, then let z be assigned to m _xy M _yx 。

After the expansion is finished, if the expansion times z do not reach a preset threshold value (such as 2-6 voxels), the step 2 is skipped, and the execution is repeated.

In step S204, an association matrix of a plurality of blood vessel regions is obtained.

After the above operations are completed for each layer, a final correlation matrix of a plurality of blood vessel regions is established.

Fig. 6 is a flowchart illustrating yet another medical image-based vessel tree generation method according to an exemplary embodiment. Unlike the medical image-based vessel tree generation method 10 shown in fig. 1, the medical image-based vessel tree generation method 30 shown in fig. 6 further provides a method of acquiring a vessel segment and a vessel bifurcation point of each vessel region, respectively.

Referring to fig. 6, a medical image-based vessel tree generation method 30 includes: the following steps are performed separately for each vessel region:

in step S302, a corresponding plurality of three-dimensional skeletons are generated for the vascular region by calculating fractal.

The corresponding three-dimensional skeleton can be generated for each vascular region by adopting the existing skeleton extraction (skeletonization) method in computational fractal. Fig. 7 shows a cross-sectional view of the three-dimensional skeleton generated for the vascular region shown in fig. 2 in one layer.

In step S304, a blood vessel bifurcation point is acquired from each three-dimensional skeleton, respectively.

The vessel bifurcation point is a voxel surrounded by 2 or more adjacent voxels, for example, gray voxels as in fig. 7.

In a specific implementation, for example, the voxel value of the bifurcation point of the blood vessel may be set to-1, and marked as a virtual node, and added to the virtual node set S.

In step S306, for each three-dimensional skeleton: removing the bifurcation points of the blood vessels to obtain a plurality of blood vessel segment skeletons in the three-dimensional skeleton; performing region growing on the plurality of vessel segment skeletons respectively to obtain a plurality of vessel segments respectively corresponding to the plurality of vessel segment skeletons; and determining attributes of the plurality of vessel segments, respectively.

Fig. 8 is a schematic diagram showing a process of generating a blood vessel segment according to an example. Fig. 8 (a) shows the respective vessel bifurcation points s of the skeleton.

After each vessel bifurcation s was removed, each vessel segment skeleton shown in fig. 8 b was obtained by a labeling (labeling) method of the connected region. The vessel segment skeleton is actually the centerline of the vessel segment without any vessel branches.

Each vessel segment skeleton is subjected to region growing and restored to the vessel segment as shown in fig. 8 (c). Specifically, voxels in adjacent vessel regions are sequentially expanded circumferentially for each vessel segment until no vessel region voxels can be expanded.

The algorithm scheme may include, for example: and (5) carrying out algorithm design based on a Matrix-Vector operation framework.

A voxel correlation matrix a of the vessel region is defined, wherein when two voxels are adjacent, the element in the corresponding matrix a is 1, otherwise 0. The vessel segments of these voxels are assigned a vector b. Since vessel segment skeletons are known and labeled with numbers greater than 0, points therein are assigned on vector b as the number of vessel segment skeletons.

The following operations were repeated:

1. let g _ij ＝a _ij b _j Representing the passage of voxel i through edge a _ij Can reach the vascular skeleton b _j ；

2. Make b _i ’＝max(g _i1 ,g _i2 ,…,g _in ) Which determines the final reachable vessel segment skeleton of voxel i;

3. if b _i Equal to 0, then b is caused to _i ＝b _i ’。

Until all elements in vector b are not 0, an assignment is obtained, thereby completing the region growing from the vessel segment skeleton to the vessel segment.

The set of vessel segments may be denoted as V, for example, with each vessel segment V e V.

The attributes of each vessel segment are then calculated separately, and the vessel segment attributes may include: the length, radius, direction, voxel set, etc. of the vessel segment may be denoted as { l, r, θ, C }, respectively.

Wherein the set of voxels corresponding to each vessel segment v is denoted C.

The radius of the vessel segment v can be calculated by the formula (1):

wherein the function dist (P, P _c ) Representing the point P belonging to O to the skeleton P corresponding to the vessel segment v _c Is a distance of (3).

Calculate the direction θ for each vessel segment v: the main distribution direction of all voxels C of a vessel segment is taken as the direction of the vessel segment v. The voxel set C of the vessel segment is represented as a 3 XN voxel point coordinate matrix, and each three-dimensional column vector C epsilon C is one of the voxel coordinates. Thus, the direction of voxel point distribution, i.e. the direction of the voxel point distribution, can be obtained by principal component analysis (Principal Component Analysis, PCA)

Wherein the method comprises the steps ofIs the average coordinates of the voxels in C.

The length l of the vessel segment v can be calculated by the formula (2):

wherein T represents the transpose.

Fig. 9 is a flowchart illustrating yet another medical image-based vessel tree generation method according to an exemplary embodiment. Unlike the medical image-based vessel tree generation method 10 shown in fig. 1, the medical image-based vessel tree generation method 40 shown in fig. 9 further provides a method of establishing a corresponding plurality of vessel trees based on a plurality of connected subgraphs in the vessel map.

Referring to fig. 9, a medical image-based vessel tree generation method 40 includes:

in step S402, for each connected subgraph, a vessel segment having the closest distance to the heart, the largest radius, and only one vessel bifurcation point adjacent thereto is selected as the root node of the vessel tree.

For example, the root node may be noted as v _c . Fig. 10 shows a process of generating a vessel tree from the vessel segments as shown in fig. 8. Wherein fig. 10 (a) is a schematic view of a plurality of vessel segments and vessel bifurcation points, and fig. 10 (b) is a schematic view of one connected sub-graph in the vessel graph constructed from the vessel segments and vessel bifurcation points in fig. 10 (a). Fig. 10 (c) shows a schematic diagram of a vessel tree generated from the connected subgraph shown in fig. 10 (b).

As shown in fig. 10 (b), a blood vessel segment v ₀ Meets the selection rule of the root node of the blood vessel tree.

In step S404, starting from the root node, all nodes in the connected subgraph are sequentially processed, including: for each adjacent vessel bifurcation of the node:

1. acquiring adjacent nodes of the bifurcation point of the blood vessel.

To process the root node v as in fig. 10 (b) ₀ For example, the vessel bifurcation point s ₀ Adjacent nodes of (a) are respectively blood vessel segments v ₁ And v ₄ ,。

2. One of the adjacent nodes is selected as a trunk node of the vessel tree, and other adjacent nodes are marked as branch nodes.

Selecting one of the adjacent nodes as a trunk node of the vessel tree may include, for example: selecting a trunk node according to a formula (3):

wherein v is _τ Representing the backbone node of the network, Representing a set of adjacent nodes of the vessel bifurcation. Sigma=2 to 5 voxels representing the upper limit of the radius difference between the vessel nodes

3. The node is connected with the trunk node, and is formed and marked as trunk side tau.

4. The node is connected with the branch node, and is formed and marked as a branch edge b.

The constructed vessel tree is shown in fig. 10 (c).

Referring to fig. 11, a lesion recognition method 50 based on a vessel tree includes:

in step S502, lesion recognition is performed according to a predefined lesion recognition rule based on the vessel tree generated according to any one of the above-described medical image-based vessel tree generation methods 10 to 40.

In some embodiments, the lesion recognition rules include some or all of the following rules:

and when the number of nodes in the blood vessel tree is smaller than a preset threshold value of the number of nodes, determining that a lesion exists in a region corresponding to the blood vessel tree.

For example, if the vessel tree has only isolated root nodes, or very few nodes, i.e., the tree root depth is small, it is determined that there is a lesion in the region corresponding to the vessel tree. The threshold value of the number of the nodes can be set according to actual requirements when the node is applied.

And when the radius difference value between the node in the vessel tree and the adjacent node is larger than a preset first radius threshold value, determining that the region corresponding to the node has lesions.

The first radius threshold may be set to 1 to 5 pixel numbers, for example, but the present invention is not limited thereto.

When traversing each node of the blood vessel tree by adopting the breadth-first traversing strategy, determining that lesions exist in the region corresponding to the node of which the radius difference value of the two nodes accessed before and after is out of the preset radius range.

The radius range may be set to [ -1,2], for example, but the invention is not limited thereto.

When the radius of the branch node of the blood vessel tree is larger than a preset second radius threshold value, determining that a lesion exists in the region corresponding to the branch node.

The second radius threshold may be set to 1 to 5, for example, but the present invention is not limited thereto.

For the medical image which is an image of a bilateral symmetry organ, when the KL distance of the radius distribution curves of the nodes in the two vessel trees with the largest left and right sides of the organ is larger than a preset first distance threshold, determining that lesions exist in the areas corresponding to the two vessel trees.

The KL distance is the abbreviation of the Kullback-Leibler difference (Kullback-Leibler Divergence), also known as Relative Entropy (Relative Entropy). It measures the difference between two probability distributions in the same event space.

Specifically, assuming that the radius distribution vectors of the nodes in the two vessel trees are p1 and p2, the KL distances thereof are calculated according to formula (4):

wherein, p1 and p2 are normalized vectors, p1 (x) and p2 (x) are radial distribution functions of nodes in two vessel trees respectively, that is, the number proportion of vessel segments with the radius of x is p1 (x) and p2 (x) respectively.

Fig. 12 shows a schematic representation of a lung lesion identified according to the identification rule. As shown in fig. 12 (a), KL distances between the distribution curves of the radii of the nodes in the corresponding two vessel trees of the left and right lungs are larger than the first distance threshold, the CT scan of each layer is correspondingly taken out, a large lesion as shown in fig. 12 (b) is found, and the lesion can be seen in many layers.

For the medical image which is an image of a left-right asymmetric organ, when the KL distance between the radius distribution curve of the node in the blood vessel tree and the standard radius curve is larger than a preset second distance threshold value, determining that a lesion exists in the region corresponding to the blood vessel tree.

The standard radius curve can be obtained, for example, by counting the distribution curve of the node radius of all normal populations in the normalized vessel tree.

The normalization method is applied to (x, y, z) ∈Q _l The following transformation is performed to obtain the product:

x’＝(x-x_min)/(x_max-x_min)

y’＝(y-y_min)/(y_max-x_min)

z’＝(z-z_min)/(z_max-z_min)

Ql represents the organ segmented region tensor of the CT image three-dimensional tensor Q, and x_min, x_max represent the minimum abscissa and the maximum abscissa of the non-0 voxel coordinates in Ql. The other coordinates are the same.

The first distance threshold and the second distance threshold can be set according to actual requirements when the first distance threshold and the second distance threshold are applied.

According to the lesion recognition method based on the blood vessel tree, based on the constructed blood vessel tree, lesions in medical images can be accurately recognized according to the predefined lesion recognition rules.

Referring to fig. 13, the lesion area judging method 60 based on a blood vessel tree includes:

in step S602, lesion research and judgment are performed on the region to be researched according to a predefined lesion research and judgment rule based on the blood vessel tree generated according to any one of the above-described medical image-based blood vessel tree generation methods 10 to 40.

In some embodiments, the lesion development rule includes some or all of the following rules:

and when the difference value between the radius of the region to be determined and the radius of the node of the blood vessel tree where the region to be determined is greater than a preset first radius threshold value, determining the region to be determined as a lesion region.

The first radius threshold may be set to 1 to 5, for example, but the present invention is not limited thereto.

And when the node number of the blood vessel tree where the region to be determined is located is smaller than a preset first node number threshold value, determining the region to be determined as a lesion region.

And when the difference between the radius of the region to be determined and the radius of all adjacent nodes of the blood vessel tree where the region to be determined is located is larger than a preset second radius threshold value, determining the region to be determined as a lesion region.

And when the radius of the node of the blood vessel tree where the region to be determined is located is larger than a third radius threshold (for example, 0.5-2), and the node of the subtree of the node is smaller than a second node number threshold, determining the region to be determined as a lesion region.

It should be noted that, when the above thresholds are applied, they may be set according to actual requirements, and the present invention is not limited thereto.

According to the lesion area research and judgment method based on the blood vessel tree, based on the constructed blood vessel tree, the identified lesion area can be further subjected to research and judgment according to the predefined lesion research and judgment rule, so that the accuracy of lesion identification is improved.

Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as a computer program executed by a CPU. When executed by a CPU, performs the functions defined by the above-described method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic disk or an optical disk, etc.

Furthermore, it should be noted that the above-described figures are merely illustrative of the processes involved in the method according to the exemplary embodiment of the present invention, and are not intended to be limiting. It will be readily appreciated that the processes shown in the above figures do not indicate or limit the temporal order of these processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, for example, among a plurality of modules.

The following are examples of the apparatus of the present invention that may be used to perform the method embodiments of the present invention. For details not disclosed in the embodiments of the apparatus of the present invention, please refer to the embodiments of the method of the present invention.

Referring to fig. 14, a medical image-based vessel tree generating apparatus 70 includes: an image acquisition module 702, a region segmentation module 704, a matrix establishment module 706, a vessel segment acquisition module 708, a vessel map construction module 710, and a vessel tree establishment module 712.

The image acquisition module 702 is configured to acquire a medical image, where the medical image includes a plurality of voxels.

The region segmentation module 704 is configured to segment a plurality of blood vessel regions from the medical image, where the blood vessel regions include: blood vessels and voxels with similar characteristics to blood vessels.

The matrix establishing module 706 is configured to establish an association matrix of the plurality of blood vessel regions according to a proximity relation between the plurality of blood vessel regions.

The vessel segment acquisition module 708 is configured to acquire a vessel segment and a vessel bifurcation point of each vessel region.

The vessel map construction module 710 is configured to construct a vessel map with vessel segments and vessel bifurcation points as nodes according to the connection relations between the vessel segments and the vessel bifurcation points included in each vessel region according to the correlation matrix.

The vessel tree establishment module 712 is configured to establish a corresponding plurality of vessel trees based on the plurality of connected subgraphs in the vessel map.

Wherein each vessel tree comprises a trunk node and a branch node.

In some embodiments, matrix establishment module 706 includes: a first setup unit and a second setup unit. Wherein the first establishing unit is used for aiming at each layer of image of the medical image: taking the areas covered by the plurality of blood vessel areas as nodes, and inserting a spatial index structure; for each vessel region, expanding adjacent voxels to obtain an expanded region of the vessel region; searching an overlapped blood vessel region overlapped with the blood vessel region in a spatial index structure by taking the expansion region of the blood vessel region as a spatial query condition; and assigning values to corresponding elements in the association matrix according to the expansion times of each blood vessel region. The second establishing unit is used for obtaining the incidence matrixes of a plurality of blood vessel areas.

In some embodiments, vessel segment acquisition module 708 includes: skeleton generation unit, bifurcation point removal unit and blood vessel segment acquisition unit. The skeleton generation unit is used for generating a plurality of corresponding three-dimensional skeletons for the vascular region through calculation of fractal. The bifurcation point removing unit is used for respectively obtaining a blood vessel bifurcation point from each three-dimensional skeleton, wherein the blood vessel bifurcation point is a voxel with 2 or more than 2 adjacent voxels around. The vessel segment obtaining unit is configured to, for each three-dimensional skeleton: removing the bifurcation points of the blood vessels to obtain a plurality of blood vessel segment skeletons in the three-dimensional skeleton; performing region growing on the plurality of vessel segment skeletons respectively to obtain a plurality of vessel segments respectively corresponding to the plurality of vessel segment skeletons; and determining attributes of the plurality of vessel segments, respectively, the attributes comprising: length, radius, direction, voxel set of vessel segments.

In some embodiments, the vessel segment obtaining unit further comprises: matrix construction subunit, vector construction subunit, and region growing subunit. The matrix construction subunit is used for constructing a voxel association matrix A of the blood vessel region, wherein when two voxels are adjacent, the element in the association matrix A is 1; when two voxels are not adjacent, the element in the incidence matrix A is 0; the vector construction subunit is used for constructing a vector b of each vessel segment, and points in the vector b are assigned to numbers corresponding to the vessel segment frameworks; the region growing subunit is configured to repeat the following steps until all elements in the vector b are not 0, thereby obtaining the vessel segment:

1) Let g _ij ＝a _ij b _j Wherein a is _ij B is the element in the incidence matrix A _j Is an element in the vector b;

2) Make b _i ’＝max(g _i1 ,g _i2 ,…,g _in )；

3) If b _j Equal to 0, then b is caused to _j Equal to b _i ’。

In some embodiments, each vessel tree includes a trunk node and a branch node. The vessel tree creation module 712 includes: the first establishing unit is used for selecting a blood vessel segment which is closest to the heart, has the largest radius and has only one blood vessel bifurcation point adjacent to the blood vessel segment as a root node of the blood vessel tree aiming at each connected subgraph; processing all nodes in the connected subgraph in turn from the root node, including: for each adjacent vessel bifurcation of the node: acquiring adjacent nodes of the bifurcation point of the blood vessel; selecting one of adjacent nodes as a trunk node, and marking other adjacent nodes as branch nodes; connecting the node with a trunk node to form a trunk edge; and connecting the node with the branch node to form a branch edge.

According to the vascular tree generating device based on the medical image, provided by the embodiment of the invention, the morphology, the structure and the characteristics of the vascular image and the connection relation among blood vessels can be analyzed aiming at the segmented vascular image, and an accurate vascular tree can be constructed. The vessel tree can be used for lesion recognition, and can be used for further researching and judging lesion areas recognized by other modes so as to obtain a more accurate lesion recognition result.

Referring to fig. 15, a lesion recognition device 80 based on a blood vessel tree includes: a lesion recognition module 802, configured to perform lesion recognition according to a predefined lesion recognition rule based on the vessel tree generated according to any one of the above-mentioned vessel tree generation methods 10 to 40 based on medical images.

when the difference value between the radius of the region to be determined and the radius of the node of the blood vessel tree where the region to be determined is located is larger than a preset first radius threshold value, determining the region to be determined as a lesion region;

when the node number of the blood vessel tree where the region to be researched and judged is located is smaller than a preset first node number threshold value, determining the region to be researched and judged as a lesion region;

when the difference between the radius of the region to be determined and the radius of all adjacent nodes of the blood vessel tree where the region to be determined is located is larger than a preset second radius threshold value, determining the region to be determined as a lesion region;

and when the radius of the node of the blood vessel tree where the region to be determined is located is larger than a third radius threshold value and the node of the subtree of the node is smaller than a second node number threshold value, determining the region to be determined as a lesion region.

According to the lesion recognition device based on the blood vessel tree, provided by the embodiment of the invention, based on the constructed blood vessel tree, the lesions in the medical image can be accurately recognized according to the predefined lesion recognition rule.

Referring to fig. 16, a lesion area studying and judging device 90 based on a blood vessel tree includes: a lesion research module 902, configured to perform a lesion research on a region to be researched according to a predefined lesion research rule based on the vessel tree generated according to any one of the above-mentioned vessel tree generation methods 10 to 40 based on the medical image.

According to the lesion area research and judgment device based on the blood vessel tree, based on the constructed blood vessel tree, according to the predefined lesion research and judgment rule, the identified lesion area can be further subjected to research and judgment, so that the accuracy of lesion identification is improved.

It should be noted that the block diagrams shown in the above figures are functional entities and do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor devices and/or microcontroller devices.

Fig. 17 is a schematic diagram of an electronic device according to an exemplary embodiment. It should be noted that the electronic device shown in fig. 17 is only an example, and should not impose any limitation on the functions and the application scope of the embodiments of the present invention.

As shown in fig. 17, the electronic device 800 is embodied in the form of a general purpose computer device. The components of the electronic device 800 include: at least one Central Processing Unit (CPU) 801 that can perform various appropriate actions and processes according to program code stored in a Read Only Memory (ROM) 802 or program code loaded from at least one storage unit 808 into a Random Access Memory (RAM) 803.

In particular, according to an embodiment of the present invention, the program code may be executed by the central processing unit 801, such that the central processing unit 801 performs the steps according to various exemplary embodiments of the present invention described in the method embodiment section above in this specification. For example, the central processing unit 801 may perform the steps as shown in fig. 1, 5, 6, 9, 11, or 14.

In the RAM 803, various programs and data required for the operation of the electronic device 800 are also stored. The CPU 801, ROM 802, and RAM 803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to the bus 804.

The following components are connected to the I/O interface 805: an input unit 806 including a keyboard, a mouse, and the like; an output unit 807 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and a speaker; a storage unit 808 including a hard disk or the like; and a communication unit 809 including a network interface card such as a LAN card, modem, or the like. The communication unit 809 performs communication processing via a network such as the internet. The drive 810 is also connected to the I/O interface 805 as needed. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on the drive 810, so that a computer program read out therefrom is installed into the storage unit 808 as needed.

Referring to fig. 18, a program product 900 according to an embodiment of the present invention configured to implement the above-described method is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer-readable medium carries one or more programs which, when executed by one of the devices, cause the computer-readable medium to implement the functions as shown in fig. 1, 5, 6, 9, 11, or 14.

The exemplary embodiments of the present invention have been particularly shown and described above. It is to be understood that this invention is not limited to the precise arrangements, instrumentalities and instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

Claims

1. A medical image-based vessel tree generation method, comprising:

acquiring a medical image, the medical image comprising a plurality of voxels;

segmenting a plurality of vascular regions from the medical image, the vascular regions comprising: a blood vessel and voxels having similar characteristics to the blood vessel;

establishing an incidence matrix of the plurality of blood vessel areas according to the adjacent relation among the plurality of blood vessel areas;

respectively acquiring a blood vessel section and a blood vessel bifurcation point of each blood vessel region;

according to the incidence matrix, constructing the blood vessel segments, the blood vessel bifurcation points and the connection relations among the blood vessel segments, the blood vessel bifurcation points which are respectively contained in each blood vessel region into a blood vessel graph taking the blood vessel segments and the blood vessel bifurcation points as nodes; and

establishing a plurality of corresponding vessel trees based on a plurality of connected subgraphs in the vessel graph;

the nodes in the vessel tree are the vessel segments contained in the connected subgraph corresponding to the nodes.

2. The method of claim 1, wherein establishing an association matrix for the plurality of vessel regions based on proximity relations between the plurality of vessel regions comprises:

for each layer of image of the medical image: taking the areas covered by the plurality of blood vessel areas as nodes, and inserting a spatial index structure; expanding adjacent voxels for each blood vessel region to obtain an expanded region of the blood vessel region; searching an overlapped blood vessel region overlapped with the blood vessel region in the spatial index structure by taking the expansion region of the blood vessel region as a spatial query condition; assigning values to corresponding elements in the incidence matrix according to the expansion times of each blood vessel region; and

The correlation matrix of the plurality of vessel regions is obtained.

3. The method of claim 1, wherein separately acquiring the vessel segment and the vessel bifurcation of each of the vessel areas comprises: for each of the vascular regions:

generating a plurality of corresponding three-dimensional skeletons for the vascular region by calculating fractal;

respectively obtaining the blood vessel bifurcation points from each three-dimensional skeleton, wherein the blood vessel bifurcation points are voxels with 2 or more than 2 adjacent voxels around; and

for each of the three-dimensional skeletons: removing the blood vessel bifurcation points to obtain a plurality of blood vessel segment skeletons in the three-dimensional skeleton; performing region growing on the plurality of vessel segment skeletons respectively to obtain a plurality of vessel segments respectively corresponding to the plurality of vessel segment skeletons; and determining attributes of the plurality of vessel segments, respectively, the attributes comprising: the length, radius, direction, voxel set of the vessel segment.

4. The method of claim 3, wherein separately performing region growing on the plurality of vessel segment skeletons to obtain a plurality of the vessel segments respectively corresponding to the plurality of vessel segment skeletons comprises:

Constructing a voxel correlation matrix A of the blood vessel region, wherein when two voxels are adjacent, the element in the correlation matrix A is 1; when two voxels are not adjacent, the element in the incidence matrix A is 0;

constructing a vector b of each vessel segment, and assigning points in the vector b as numbers corresponding to the vessel segment frameworks;

repeating the following steps until all elements in the vector b are not 0, thereby obtaining the vessel segment:

2) Make b _i ’＝max(g _i1 ,g _i2 ,…,g _in )；

3) If b _j Equal to 0, then b is caused to _j Equal to b _i ’。

5. The method of claim 1, wherein each of the vessel trees includes a trunk node and a branch node; based on the plurality of connected subgraphs in the vessel graph, establishing a corresponding plurality of vessel trees includes: selecting the blood vessel segment with the closest distance to the heart, the largest radius and only one blood vessel bifurcation point adjacent to the blood vessel segment as the root node of the blood vessel tree aiming at each connected subgraph; processing all nodes in the connected subgraph in turn from the root node, including:

for each adjacent vessel bifurcation of the node: acquiring adjacent nodes of the bifurcation point of the blood vessel; selecting one of the adjacent nodes as the trunk node, and marking other adjacent nodes as the branch nodes; connecting the node with the trunk node to form a trunk edge; and connecting the node with the branch node to form a branch edge.

6. A lesion recognition method based on a vessel tree, comprising:

lesion recognition is performed according to predefined lesion recognition rules based on a vessel tree generated by the medical image based vessel tree generation method according to any of claims 1-5.

7. The method of claim 6, wherein the lesion recognition rule comprises some or all of the following rules:

when the number of nodes in the blood vessel tree is smaller than a preset threshold value of the number of nodes, determining that a lesion exists in a region corresponding to the blood vessel tree;

when the radius difference value of the node in the vascular tree and the adjacent node is larger than a preset first radius threshold value, determining that a lesion exists in a region corresponding to the node;

when traversing each node of the vessel tree by adopting a breadth-first traversing strategy, determining that lesions exist in a region corresponding to a node of which the radius difference value of two nodes accessed before and after is out of a preset radius range;

when the radius of a branch node of the blood vessel tree is larger than a preset second radius threshold value, determining that a lesion exists in a region corresponding to the branch node;

for the medical image which is an image of a bilateral symmetry organ, when the KL distance of the radius distribution curve of the node in the two largest vessel trees on the left side and the right side of the organ is larger than a preset first distance threshold value, determining that lesions exist in the areas corresponding to the two vessel trees;

And for the medical image which is an image of a left-right asymmetric organ, when the KL distance between the radius distribution curve of the node in the vascular tree and the standard radius curve is larger than a preset second distance threshold value, determining that a lesion exists in the region corresponding to the vascular tree.

8. The lesion area studying and judging method based on the blood vessel tree is characterized by comprising the following steps of:

lesion research is performed on the region to be research according to predefined lesion research rules based on a vessel tree generated by the medical image-based vessel tree generation method according to any one of claims 1-5.

9. The method of claim 8, wherein the lesion development rule comprises some or all of the following rules:

when the difference value between the radius of the region to be ground and the radius of the node of the blood vessel tree where the region to be ground is located is larger than a preset first radius threshold value, determining that the region to be ground is a lesion region;

when the node number of the blood vessel tree where the region to be determined is located is smaller than a preset first node number threshold value, determining the region to be determined as a lesion region;

And when the radius of the node of the blood vessel tree where the region to be determined is located is larger than a third radius threshold and the node of the subtree of the node is smaller than a second node number threshold, determining the region to be determined as a lesion region.

10. A medical image-based vessel tree generation device, comprising:

the image acquisition module is used for acquiring a medical image, and the medical image comprises a plurality of voxels;

the region segmentation module is used for segmenting a plurality of blood vessel regions from the medical image, and the blood vessel regions comprise: a blood vessel and voxels having similar characteristics to the blood vessel;

the matrix establishing module is used for establishing an incidence matrix of the plurality of blood vessel areas according to the adjacent relation among the plurality of blood vessel areas;

the blood vessel section acquisition module is used for respectively acquiring the blood vessel section and the blood vessel bifurcation point of each blood vessel area;

the blood vessel map construction module is used for constructing the blood vessel map which takes the blood vessel segments and the blood vessel bifurcation points as nodes according to the blood vessel segments, the blood vessel bifurcation points and the connection relations among the blood vessel segments and the blood vessel bifurcation points which are respectively contained in each blood vessel region; and

the vessel tree building module is used for building a plurality of corresponding vessel trees based on a plurality of connected subgraphs in the vessel graph;

Wherein each vessel tree comprises a trunk node and a branch node.

11. A lesion recognition device based on a vascular tree, comprising:

a lesion recognition module for performing lesion recognition according to predefined lesion recognition rules based on a vessel tree generated by the medical image based vessel tree generation method according to any of claims 1-5.

12. The utility model provides a pathological change region is ground and is judged device based on vessel tree which characterized in that includes:

a lesion research module, configured to perform a lesion research on a region to be researched according to a predefined lesion research rule based on a vessel tree generated by the medical image-based vessel tree generation method according to any one of claims 1 to 5.

13. A computer device, comprising: memory, a processor and executable instructions stored in the memory and executable in the processor, wherein the processor implements the method of any of claims 1-9 when executing the executable instructions.

14. A computer readable storage medium having stored thereon computer executable instructions, which when executed by a processor implement the method of any of claims 1-9.