Blood vessel graph generation system and execution method thereof

Abstract

The present invention is characterized by comprising: a three-dimensional medical image processing apparatus which analyzes a three-dimensional medical image to extract a blood vessel pixel point coordinate corresponding to a blood vessel on the basis of a pixel value, removes noise around a main blood vessel, and generates a three-dimensional blood vessel model; and a blood vessel graph generation device which divides the three-dimensional blood vessel model into a plurality of layers, sets a blood vessel pixel point group for each layer as a blood vessel node, and generates connection information to generate a blood vessel graph.

Description

Vascular graph generation system and execution method thereof

[0001] The present invention relates to a vascular graph generation system and an execution method thereof, and more particularly, to a vascular graph generation system and an execution method thereof for generating a vascular graph including vascular location and structure information from a three-dimensional vascular image.

[0002] In modern medicine, medical imaging is a crucial tool for effective disease diagnosis and patient treatment. Furthermore, advancements in imaging technology have led to the generation of increasingly sophisticated medical image data. Consequently, the volume of data has grown exponentially, making it increasingly difficult to analyze medical image data solely through human visualization. Therefore, over the past decade, clinical decision support systems and computer-aided interpretation systems have played an essential role in the automated analysis of medical images.

[0003] Conventional clinical decision support systems or computer-aided reading systems perform the function of detecting and displaying lesion areas or providing reading information to medical staff or medical workers (hereinafter referred to as users).

[0004] For example, in the 'Method and device for producing disease diagnosis information based on medical images' disclosed in Korean Patent Publication No. 10-2017-0017614, the method includes detecting a region of interest in which an object to be analyzed is photographed, calculating a coefficient of variation, creating a coefficient of variation image, and comparing it with a reference sample, and mentions the effect of diagnosing the degree of a patient's disease by utilizing medical images acquired through CT, CTA, MRI, MRA, DSA, and ultrasound imaging devices.

[0005] Recently, artificial intelligence (AI) technologies based on machine learning, such as deep learning, have been making rapid progress in the field of medical image analysis and processing. Deep learning in medical imaging is being applied to a wide range of tasks, including image analysis, diagnosis, treatment, and prediction. For example, deep learning-based auxiliary diagnostic systems are being used to diagnose cerebrovascular diseases, such as cerebral aneurysms.

[0006] One method for improving the accuracy of image interpretation using deep learning-based models is to input coordinate values ​​for each location within the image. This method involves inputting coordinate values ​​for a target image, which is also a coordinate value, into an AI model trained on the image, and then utilizing the coordinate values ​​for interpretation to improve accuracy.

[0007] The present invention aims to provide a vascular graph generation system and an execution method thereof, which extract pixel points corresponding to blood vessels from a three-dimensional medical image, remove noise and microvessels, and generate a blood vessel graph including blood vessel coordinates and structural information.

[0008] The objectives of the present invention are not limited to those mentioned above. Other objectives and advantages of the present invention not mentioned above can be understood through the following description and will be more clearly understood through the embodiments of the present invention. Furthermore, it will be readily apparent that the objectives and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims.

[0009] A vascular graph generation system according to an embodiment of the present invention is characterized by including a 3D medical image processing device that analyzes a 3D medical image to extract vascular pixel point coordinates corresponding to blood vessels based on pixel values, removes noise around major blood vessels, and generates a 3D blood vessel model; and a vascular graph generation device that divides the 3D blood vessel model into a plurality of layers, sets a group of blood vessel pixel points for each layer as a blood vessel node, and generates connection information to generate a blood vessel graph.

[0010] According to the present invention as described above, a vascular graph containing coordinates and structural information for major blood vessels can be generated from a three-dimensional vascular image. The vascular graph according to the present invention is a visual representation of the vascular structure, and information on characteristics such as branching, connections, size, and location of blood vessels is compressed, and has the effect of providing parameters such as the length, diameter, angle, branching pattern, and shape of blood vessels.

[0011] FIG. 1 is a drawing for explaining a blood vessel graph generation system according to one embodiment of the present invention.

[0012] FIG. 2 is a drawing for explaining a three-dimensional medical image processing device according to one embodiment of the present invention.

[0013] Figure 3 is a flowchart illustrating one embodiment of a three-dimensional medical image processing method according to the present invention.

[0014] Figures 4 to 7 are exemplary diagrams for explaining the operation of a three-dimensional medical image processing device according to the present invention.

[0015] Figure 8 is a flowchart illustrating one embodiment of a method for generating a blood vessel graph according to the present invention.

[0016] Figures 9 to 13 are exemplary diagrams for explaining the operation of a blood vessel graph generation device according to the present invention.

[0017] FIG. 14 is a block diagram illustrating the configuration of a server that generates a blood vessel graph according to an embodiment of the present invention.

[0018] The above-described objects, features, and advantages will be described in detail below with reference to the accompanying drawings, so that those skilled in the art can easily practice the technical idea of ​​the present invention. In describing the present invention, if it is determined that a detailed description of known technologies related to the present invention may unnecessarily obscure the gist of the present invention, a detailed description thereof will be omitted. Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, the same reference numerals are used to indicate the same or similar components.

[0019]

[0020] FIG. 1 is a drawing for explaining a blood vessel graph generation system according to one embodiment of the present invention.

[0021] Referring to FIG. 1, the vascular graph generation system includes a 3D medical image processing device (100) and a vascular graph generation device (200).

[0022] A 3D medical image processing device (100) can create a 3D blood vessel model for a major blood vessel from a 3D medical image.

[0023] First, when a 3D medical image processing device (100) receives a 3D medical image, it can analyze the pixel values ​​of the 3D medical image to extract blood vessel pixel points and coordinates. For example, when a certain pixel value in the 3D medical image is greater than a preset threshold value, the 3D medical image processing device (100) can determine that the pixel value corresponds to a blood vessel, identify the blood vessel pixel point, and extract coordinate information of the blood vessel pixel point.

[0024] In the above embodiment, the 3D medical image processing device (100) can extract the coordinates of a pixel if the pixel value is greater than a predetermined threshold value and create a blood vessel pixel coordinate table using the pixel coordinates. Thereafter, the 3D medical image processing device (100) can display blood vessel points on a coordinate system based on the blood vessel pixel coordinate table to create a coordinate system of the entire blood vessel.

[0025] A 3D medical image processing device (100) can crop a major blood vessel portion from a blood vessel pixel point displayed on a coordinate system.

[0026] Thereafter, the 3D medical image processing device (100) can perform clustering on blood vessel pixel points and filter out noise and / or microvascular pixels.

[0027] According to an embodiment of the present invention, a three-dimensional medical image processing device (100) can group blood vessel pixel points into clusters by performing clustering based on at least one of distance, density, and / or attribute information of blood vessel pixel points.

[0028] Thereafter, the 3D medical image processing device (100) can perform a first filtering operation to compare the number of blood vessel pixel points included in any cluster with a threshold value and delete clusters whose number of blood vessel points is below the threshold value. The filtering operation can be performed by removing pixel points corresponding to clusters whose number of blood vessel points is below the threshold value from the blood vessel coordinate system. This can remove noise around major blood vessels.

[0029] The 3D medical image processing device (100) can group the blood vessel pixel points into clusters by performing clustering again based on at least one of the distance, density, and attribute information of the first filtered blood vessel pixel points.

[0030] Thereafter, the 3D image processing device (100) can perform a second filtering operation that deletes all clusters except for the blood vessel pixel points included in a random cluster. For example, the filtering operation can be performed by removing pixel points corresponding to clusters other than the largest cluster from the blood vessel coordinate system. This can remove microvessels.

[0031] According to another embodiment of the present invention, a three-dimensional medical image processing device (100) can perform clustering and / or filtering of blood vessel pixel points using various algorithms, and the present invention cannot be interpreted by limiting the clustering and / or filtering algorithms.

[0032] For example, clustering can be performed by grouping blood vessel pixel points into K clusters, calculating the center of each cluster, and assigning the blood vessel pixel points to the cluster with the closest center. Alternatively, clusters can be formed using a similarity matrix for blood vessel pixel points. Subsequently, filtering for noise and microvessels around blood vessels can be performed by modifying or removing clusters.

[0033] According to an embodiment of the present invention, when a 3D medical image processing device (100) performs filtering through clustering, blood vessel pixel points with noise and microvessels removed are formed in a blood vessel coordinate system, and through this, the 3D medical image processing device (100) can create a 3D blood vessel model.

[0034] The blood vessel graph generation device (200) can generate a blood vessel graph based on a three-dimensional blood vessel model generated by a three-dimensional medical image processing device (100).

[0035] The blood vessel graph generation device (200) can divide a three-dimensional blood vessel model into multiple layers and check the coordinate values ​​of blood vessel pixel points for each layer. At this time, it is appropriate to generate the layers in two dimensions.

[0036] Thereafter, the blood vessel graph generation device (200) can group blood vessel pixel points for each layer. The blood vessel pixel point group of each layer may correspond to a cross-section of an actual blood vessel.

[0037] Thereafter, the blood vessel graph generation device (200) can calculate the center of gravity of each group. This is for generating nodes of a blood vessel graph, and nodes of a blood vessel graph according to an embodiment of the present invention can be set as the center of gravity of blood vessel pixel point groups for each layer. In a blood vessel graph according to an embodiment of the present invention, nodes can correspond to the centers of actual blood vessels, and it is appropriate to set the radius of nodes of the blood vessel graph to the radius of the actual blood vessel.

[0038] Thereafter, the vascular graph generation device (200) can generate connectivity information of vascular graph nodes. More specifically, connection information can be generated for nodes for the first group and nodes for the second group based on whether there is an intersection between a first group of vascular pixel points of an arbitrary layer and a second group of vascular pixel points of another layer adjacent to the layer. The vascular pixel point group of each layer corresponds to a cross-section of an actual blood vessel, and if there is an intersection with an adjacent layer, it means that the actual blood vessel is connected.

[0039] Through this, the vascular graph generation device (200) can generate node and connection information from a 3D vascular model and generate a vascular graph. Furthermore, the vascular graph generation device (200) can calculate the actual vascular radius from the vascular graph and set it as the node radius. Through this, the vascular graph created according to an embodiment of the present invention can reflect characteristics such as branching, connection, size, and location of the blood vessels. A specific method for this will be described later in the description of the attached drawings.

[0040]

[0041] FIG. 2 is a drawing for explaining the configuration of a three-dimensional medical image processing device according to one embodiment of the present invention.

[0042] Referring to FIG. 2, a 3D medical image processing device (100) includes a pixel coordinate extraction unit (110), a noise removal unit (120), and a 3D blood vessel model generation unit (130).

[0043] The pixel coordinate extraction unit (110) can analyze the pixel values ​​of a 3D medical image to extract blood vessel pixel points and coordinates. For example, if a pixel value in a 3D medical image is greater than or equal to a preset threshold, the pixel coordinate extraction unit (110) can determine that the pixel value corresponds to a blood vessel, specify the blood vessel pixel point, and extract coordinate information of the blood vessel pixel point. Thereafter, the pixel coordinate extraction unit (110) can display the blood vessel point on a coordinate system based on the coordinate information to generate a coordinate system of the entire blood vessel.

[0044] The noise removal unit (120) can remove noise and / or microvascular pixels by filtering the blood vessel pixel points generated by the pixel coordinate extraction unit (110).

[0045] More specifically, the noise removal unit (120) can remove noise and / or fine blood vessel pixels by cropping the main blood vessel portion from the blood vessel pixel points displayed on the coordinate system in the pixel coordinate extraction unit (110) and performing clustering and filtering on the blood vessel pixel points in the crop area.

[0046] For example, the noise removal unit (120) may perform clustering based on at least one of distance, density, and / or attribute information of blood vessel pixel points to group blood vessel pixel points into clusters. Thereafter, filtering may be performed to compare the number of blood vessel points included in any cluster with a threshold value and delete clusters having a number of blood vessel points below the threshold value. Filtering may be performed by removing pixel points corresponding to clusters having a number of blood vessel points below the threshold value from the blood vessel coordinate system. This may remove noise around major blood vessels.

[0047] Furthermore, the noise removal unit (120) can re-cluster the primary filtered blood vessel pixel points based on at least one of their distance, density, and attribute information to group the blood vessel pixel points into clusters. Subsequently, filtering can be performed to exclude blood vessel pixel points included in any cluster and delete the remaining clusters. For example, the filtering can be performed by removing pixel points corresponding to clusters other than the largest cluster from the blood vessel coordinate system. This can remove microvessels.

[0048] The 3D blood vessel model generation unit (130) can form blood vessel pixel points with noise and fine blood vessels removed in a blood vessel coordinate system and generate a 3D blood vessel model.

[0049] Figures 3 to 6 are exemplary diagrams for explaining a three-dimensional medical image processing process according to the present invention.

[0050] Referring to FIG. 3, a 3D medical image processing device (100) can analyze a 3D medical image and extract pixel coordinates corresponding to a blood vessel based on pixel values ​​(step S310).

[0051] In one embodiment of step S310, the 3D medical image processing device (100) analyzes the 3D medical image and determines that a blood vessel exists if any pixel value exceeds a predetermined threshold value, thereby specifying a blood vessel pixel point and extracting coordinate information of the blood vessel pixel point. Thereafter, based on the coordinate information, the blood vessel point can be displayed on a coordinate system to generate a coordinate system of the entire blood vessel.

[0052] For example, a 3D medical image processing device (100) can analyze a 3D medical image of (a) of FIG. 4, extract pixel coordinates corresponding to blood vessels based on pixel values ​​as in (b) of FIG. 4, and then generate a pixel coordinate table as in (a) of FIG. 5. A coordinate system of the entire blood vessel can be generated based on the pixel coordinate table as in (b) of FIG. 5. That is, a coordinate system of the entire blood vessel can be generated by forming blood vessel pixel points on the coordinate system.

[0053] The 3D medical image processing device (100) can crop a major blood vessel portion from the blood vessel pixel points displayed on the coordinate system (step S320). For example, the 3D medical image processing device (100) can specify a major blood vessel portion to be the target of image processing, as shown in (b) of FIG. 6, from the pixel points of the entire blood vessel displayed on the coordinate system, as shown in (a) of FIG. 6.

[0054] Furthermore, the 3D medical image processing device (100) can perform clustering and filtering on blood vessel pixel points in the crop area to remove noise and / or microvascular pixels. (Step S330)

[0055] For example, a 3D medical image processing device (100) can remove noise and microvascular pixels from a blood vessel that is the target of image processing, as shown in (a) of FIG. 7, and generate a 3D blood vessel model, as shown in (b) of FIG. 7.

[0056] Fig. 8 is a flowchart illustrating one embodiment of a method for generating a blood vessel graph according to the present invention. Figs. 9 to 13 are exemplary diagrams illustrating the execution process of Fig. 8.

[0057] Referring to FIG. 8, the blood vessel graph generation device (200) can divide a three-dimensional blood vessel model into multiple layers and check the coordinate values ​​of blood vessel pixel points for each layer. At this time, it is appropriate to create the layers in two dimensions (step S810). For example, the blood vessel graph generation device (200) can divide a three-dimensional blood vessel model into multiple layers as in (a) of FIG. 9 and check the coordinate values ​​of blood vessel pixel points for each layer as in (b) of FIG. 9.

[0058] Thereafter, the vascular graph generation device (200) can group vascular pixel points for each layer (step S820) and calculate the center of gravity of each group. This is for generating nodes of a vascular graph, and nodes of a vascular graph according to an embodiment of the present invention can be set as the center of gravity of a group of vascular pixel points for each layer (step S830).

[0059] For example, the blood vessel graph generation device (200) can group blood vessel pixel points in an arbitrary layer and form the center of gravity of the group as a node of the blood vessel graph, as shown in FIG. 10. In the example of FIG. 10, the blood vessel pixel point group corresponds to a cross-section of an actual blood vessel, and the center of gravity of the group will correspond to the center of the cross-section of the blood vessel. Therefore, it is appropriate to set the radius of the node of the blood vessel graph to the radius of the actual blood vessel.

[0060] Afterwards, the blood vessel graph generation device (200) can generate connection information (connectivity) of blood vessel graph nodes. (Step S840)

[0061] For example, the blood vessel graph generation device (200) can check the group of blood vessel pixel points of the i-th layer and the group of blood vessel pixel points of the i+1-th layer adjacent to the i-th layer as shown in FIG. 11, and if there is an intersection between the groups as shown in FIG. 12, connection information can be generated for the nodes. This is because the blood vessel pixel point group of each layer corresponds to a cross-section of an actual blood vessel, and if there is an intersection with an adjacent layer, it is interpreted that the actual blood vessel is connected.

[0062] Furthermore, the blood vessel graph generation device (200) can calculate the radius of an actual blood vessel in the blood vessel graph and set it as a node radius. (Step S850)

[0063] More specifically, this will be explained with reference to Fig. 13.

[0064] When a 3D blood vessel model is divided into 2D layers, the cross-sectional radius corresponds to the radius (R0) of the blood vessel pixel point group. That is, since it is the radius (R0) of the blood vessel pixel point group, not the actual radius of the blood vessel, it is necessary to calculate the actual radius (r) of the blood vessel. To this end, the radius (R0) of the blood vessel pixel point group can be transformed by cosθ, and the actual radius (r) can be calculated by [Mathematical Formula 1] below.

[0065] [Mathematical Formula 1]

[0066] r = R0Х cosθ

[0067] R0: Radius of the blood vessel pixel point group,

[0068] r: actual radius of the vessel,

[0069] cosθ: It is calculated by [Mathematical Formula 2], and θ is the direction of connection information of adjacent layers ( ) and the radial direction of the blood vessel pixel point group of that layer ( ) is calculated using.

[0070] [Equation 2]

[0071]

[0072] : Length direction calculated from connection information, : radial direction of the blood vessel cross-section, or

[0073] : Perpendicular to the length direction calculated from the connection information, : Vertical of the blood vessel pixel point group

[0074]

[0075] Returning to the description of FIG. 8, in step S860, the vascular graph generation device (200) can generate a vascular graph using nodes, connection information, and node radii. This is a visual representation of the vascular structure, and information on characteristics such as branching, connections, size, and location of the vascular is compressed, and parameters such as length, diameter, angle, branching pattern, and shape of the vascular are included.

[0076]

[0077] FIG. 14 is a block diagram illustrating the configuration of a server that generates a blood vessel graph according to an embodiment of the present invention.

[0078] As illustrated in FIG. 14, a server (1400) according to an embodiment of the present invention may include a communication unit (1410), a storage unit (1430), and a control unit (1420), and although not illustrated in FIG. 14, may further include an input unit and a display unit. In FIG. 14, the server (1400) is illustrated as including a communication unit (1410), a storage unit (1430), and a control unit (1420), but each block may be physically separated. For example, the storage unit (1430) may exist in a virtualized data center and be connected to the control unit (1420) of the server (1400) via the communication unit (1410).

[0079] The communication unit (1410) performs data transmission and reception functions for wired and wireless communication of the server (1400), receives data through wired and wireless channels, outputs the data to the control unit (1420), and transmits the data output from the control unit (1420) through a wireless channel. In particular, the communication unit (1410) according to an embodiment of the present invention can perform a function of receiving a 3D medical image.

[0080] The storage unit (1430) serves to store programs and data required for the operation of the server (1400), and may be divided into a program area and a data area. The data area of ​​the storage unit (1430) according to an embodiment of the present invention may store blood vessel pixel point coordinates, a 3D blood vessel model, and a blood vessel graph.

[0081] Furthermore, the program area of ​​the storage unit (1430) according to an embodiment of the present invention can store a computer program that executes a process of generating a blood vessel graph on a server. The computer program can perform a function of analyzing a 3D medical image to extract blood vessel pixel point coordinates corresponding to blood vessels based on pixel values, removing noise around major blood vessels, generating a 3D blood vessel model, and a function of dividing the 3D blood vessel model into a plurality of layers, setting a blood vessel pixel point group for each layer as a blood vessel node, and generating connection information to generate a blood vessel graph.

[0082] The control unit (1420) controls the overall operation of each component of the server (1400). In particular, the control unit (1420) according to an embodiment of the present invention analyzes a three-dimensional medical image to extract blood vessel pixel point coordinates corresponding to blood vessels based on pixel values, removes noise around major blood vessels, generates a three-dimensional blood vessel model, divides the three-dimensional blood vessel model into a plurality of layers, sets a group of blood vessel pixel points for each layer as blood vessel nodes, and generates connection information to generate a blood vessel graph, and can control the communication unit (1410) to provide the blood vessel graph.

[0083]

[0084] While described with reference to limited embodiments and drawings, the present invention is not limited to the above-described embodiments, and various modifications and variations are possible based on this disclosure by those skilled in the art. Accordingly, the scope of the present invention should be understood solely by the scope of the claims set forth below, and all equivalent or equivalent modifications thereof are deemed to fall within the scope of the present invention.

Claims

1. A 3D medical image processing device that analyzes a 3D medical image to extract blood vessel pixel point coordinates corresponding to the blood vessel based on pixel values, removes noise around major blood vessels, and generates a 3D blood vessel model; and A blood vessel graph generating device characterized by comprising a device for generating a blood vessel graph by dividing the three-dimensional blood vessel model into a plurality of layers, setting a group of blood vessel pixel points for each layer as a blood vessel node, and generating connection information. Vascular graph generation system.

2. In paragraph 1, The above 3D medical image processing device The method is characterized by analyzing the above 3D medical image, determining that it is a blood vessel if the pixel value is greater than a predetermined threshold value, specifying a blood vessel pixel point, and extracting the coordinates of the blood vessel pixel point. Vascular graph generation system.

3. In paragraph 2, The above 3D medical image processing device A method characterized in that clustering is performed based on at least one of distance, density and / or attribute information of the blood vessel pixel points, and the noise is removed by filtering pixel points corresponding to clusters having a number of blood vessel points less than a threshold value. Vascular graph generation system.

4. In paragraph 1, The above blood vessel graph generating device The above 3D blood vessel model is divided into multiple layers, the blood vessel pixel points are grouped for each layer, the center of gravity of each group is calculated, and the center of gravity is set to the blood vessel node of the corresponding layer. Vascular graph generation system.

5. In paragraph 4, The above blood vessel graph generating device A method characterized in that connection information is generated for a node for a first group and a node for a second group based on whether there is an intersection between a first group of blood vessel pixel points of an arbitrary layer and a second group of blood vessel pixel points of another layer adjacent to the layer. Vascular graph generation system 6. In paragraph 5, The above blood vessel graph generating device The method is characterized in that the actual blood vessel radius is calculated using the direction of the above connection information and the radius direction of the node, and the actual blood vessel radius is set as the node radius of the blood vessel graph. Vascular graph generation system.

7. A step of analyzing a 3D medical image to extract blood vessel pixel point coordinates corresponding to the blood vessel based on pixel values, removing noise around the main blood vessel, and generating a 3D blood vessel model; and A method for generating a blood vessel graph, comprising: dividing the above 3D blood vessel model into multiple layers, setting a group of blood vessel pixel points for each layer as a blood vessel node, and generating connection information. How to create a blood vessel graph.

8. A pixel coordinate extraction unit that analyzes a 3D medical image and extracts blood vessel pixel point coordinates corresponding to the blood vessel based on pixel values; A noise removal unit for removing noise and / or fine blood vessel pixels by filtering the above blood vessel pixel points; and It is characterized by including a 3D blood vessel model generation unit that forms blood vessel pixel points from which the noise and / or microvessels are removed in a blood vessel coordinate system and generates a 3D blood vessel model. 3D medical image processing device 9. In a computer program stored in a medium for performing processing to generate a blood vessel graph, The function of analyzing a 3D medical image to extract the blood vessel pixel point coordinates corresponding to the blood vessel based on the pixel value, removing noise around the main blood vessel, and generating a 3D blood vessel model; and The above 3D blood vessel model is divided into multiple layers, and the blood vessel pixel point group for each layer is set as a blood vessel node and connection information is generated to perform the function of generating a blood vessel graph. A computer program for generating vascular graphs.

Images