KR20250070931A - Method, appatus, and computer program for creating artificial intelligence models to predict blood flow data - Google Patents

Abstract

In a server according to an embodiment of the present invention, a method for generating an artificial intelligence model for predicting blood flow data is characterized by including the steps of: setting a structure of a blood flow prediction model; generating blood vessel data in the form of a point cloud corresponding to a major blood vessel from a three-dimensional medical image, specifying a boundary area of the blood vessel data, obtaining a blood flow value of the boundary area, and providing a location of a point cloud for an arbitrary area and a blood flow value of the boundary area as learning data of the blood flow prediction model; and setting a blood flow value of an area other than the boundary area as a label or mask to learn the blood flow prediction model.

Description

METHOD, APPATUS, AND COMPUTER PROGRAM FOR CREATING ARTIFICIAL INTELLIGENCE MODELS TO PREDICT BLOOD FLOW DATA

The present invention relates to a method, device and computer program for generating an artificial intelligence model for predicting blood flow data.

In modern medicine, medical images are very important tools for effective disease diagnosis and patient treatment. In addition, with the development of imaging technology, more sophisticated medical image data has been generated. Accordingly, the amount of data is becoming increasingly large, making it difficult to analyze medical image data by relying on human vision. Accordingly, clinical decision support systems and computer-aided interpretation systems have played an essential role in automatic medical image analysis over the past decade.

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

For example, in the 'Method and device for generating 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 analysis target object 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 a computed tomography device, an ultrasound imaging device, etc.

Recently, artificial intelligence (AI) technology based on machine learning such as deep learning has made great strides in the field of analyzing and processing medical images. Deep learning technology in medical images is applied to various tasks such as image analysis, diagnosis, treatment, and prediction. For example, deep learning-based auxiliary diagnosis systems are being applied to diagnosing cerebrovascular diseases such as cerebral aneurysms.

The present invention aims to provide a method for generating an artificial intelligence model for predicting blood flow data. Furthermore, the present invention aims to provide a system for predicting blood flow data using a blood vessel graph and the artificial intelligence model, and a method for executing the same. In addition, the present invention aims to provide a method for generating a blood vessel graph including coordinates and structural information for major blood vessels from a three-dimensional blood vessel image.

The purposes of the present invention are not limited to the purposes mentioned above, and other purposes and advantages of the present invention which are not mentioned can be understood by the following description, and will be more clearly understood by the embodiments of the present invention. In addition, it will be easily understood that the purposes and advantages of the present invention can be realized by the means and combinations thereof indicated in the claims.

In a server according to an embodiment of the present invention, a method for generating an artificial intelligence model for predicting blood flow data is characterized by including the steps of: setting a structure of a blood flow prediction model; generating blood vessel data in the form of a point cloud corresponding to a major blood vessel from a three-dimensional medical image, specifying a boundary area of the blood vessel data, obtaining a blood flow value of the boundary area, and providing a location of a point cloud for an arbitrary area and a blood flow value of the boundary area as learning data of the blood flow prediction model; and setting a blood flow value of an area other than the boundary area as a label or mask to learn the blood flow prediction model.

According to the present invention as described above, an artificial intelligence model for predicting blood flow data can be generated. Furthermore, according to the present invention, a blood vessel graph including coordinates and structural information for major blood vessels can be generated from a three-dimensional blood vessel image, and blood flow data can be calculated using the blood vessel graph and the blood flow prediction artificial intelligence model.

FIG. 1 is a diagram for explaining a blood flow data prediction system according to one embodiment of the present invention.
FIG. 2 is a schematic diagram illustrating an operation of a blood flow data prediction system according to one embodiment of the present invention.
FIG. 3 is a diagram for explaining an operation of a blood flow data prediction system according to one embodiment of the present invention.
FIG. 4 is a flowchart illustrating one embodiment of a three-dimensional medical image processing method according to one embodiment of the present invention.
FIGS. 5 to 8 are exemplary diagrams for explaining the operation of a three-dimensional medical image processing device according to one embodiment of the present invention.
FIG. 9 is a flowchart illustrating one embodiment of a method for generating a blood vessel graph according to one embodiment of the present invention.
Figures 10 to 14 are exemplary diagrams for explaining the operation of a blood vessel graph generation device according to one embodiment of the present invention.
FIG. 15 is a diagram for explaining a method for generating an artificial intelligence model for blood flow prediction according to one embodiment of the present invention.
Figures 16 and 17 are drawings for explaining the structure and operation of an artificial intelligence model for blood flow prediction according to one embodiment of the present invention.
FIG. 18 is a block diagram illustrating the configuration of a server that generates an artificial intelligence model for predicting blood flow according to an embodiment of the present invention.

The above-mentioned objects, features and advantages will be described in detail below with reference to the attached drawings, so that those with ordinary skill in the art to which the present invention pertains can easily practice the technical idea of the present invention. In describing the present invention, if it is judged that a detailed description of a known technology related to the present invention may unnecessarily obscure the gist of the present invention, a detailed description thereof will be omitted. Hereinafter, a preferred embodiment according to the present invention will be described in detail with reference to the attached drawings. In the drawings, the same reference numerals are used to indicate the same or similar components.

FIG. 1 is a drawing for explaining a blood flow prediction system according to one embodiment of the present invention.

Referring to FIG. 1, the blood flow prediction system includes a three-dimensional medical image processing device (100), a blood vessel graph generation device (200), and a blood flow prediction device (300). Although the blood flow prediction system is illustrated as including a plurality of devices in FIG. 1, this is for convenience of explanation and the present invention cannot be interpreted as being limited thereto. That is, the blood flow prediction system according to an embodiment of the present invention may be executed in a single or multiple devices for each function, or may be executed in a virtualized environment.

A 3D medical image processing device (100) can generate a 3D blood vessel image of a major blood vessel from a 3D medical image.

First, when the 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 any pixel value in the 3D medical image is greater than a preset threshold value, the 3D medical image processing device (100) can determine that it corresponds to a blood vessel, specify a blood vessel pixel point, and extract coordinate information of the blood vessel pixel point.

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 or equal to a predetermined threshold value, and can 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.

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.

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

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.

Thereafter, the 3D medical image processing device (100) can perform first filtering to compare the number of blood vessel pixel points included in any cluster with a threshold value and delete clusters that are less than the threshold value. Filtering can be performed by removing pixel points corresponding to clusters that have a number of blood vessel points less than the threshold value from the blood vessel coordinate system. Through this, noise around major blood vessels can be removed.

A 3D medical image processing device (100) can group blood vessel pixel points into clusters by re-clustering the first filtered blood vessel pixel points based on at least one of their distance, density, and attribute information.

Thereafter, the 3D image processing device (100) can perform a second filtering operation to delete the remaining clusters except for the blood vessel pixel points included in any 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.

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.

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. Another example is to form clusters using a similarity matrix for blood vessel pixel points. Afterwards, filtering for noise and microvessels around blood vessels can be performed by modifying or removing clusters.

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 fine blood vessels removed are formed in a blood vessel coordinate system, and through this, the 3D medical image processing device (100) can generate a 3D blood vessel image.

A blood vessel graph generation device (200) can generate a blood vessel graph based on a three-dimensional blood vessel image generated by a three-dimensional medical image processing device (100).

The blood vessel graph generation device (200) can divide a three-dimensional blood vessel image 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.

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 can correspond to a cross-section of an actual blood vessel.

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

Thereafter, the blood vessel graph generation device (200) can generate connection information (connectivity) of blood vessel 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 blood vessel pixel points of an arbitrary layer and a second group of blood vessel pixel points of another layer adjacent to the layer. 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 because the actual blood vessel is connected.

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

The blood flow prediction device (300) can predict blood flow at an arbitrary point based on a blood vessel graph. To this end, according to an embodiment of the present invention, the blood flow prediction device (300) can generate a blood flow prediction model that predicts the pressure of an arbitrary node by using the location and radius information of each node of the blood vessel graph of the region of interest. A method for generating a blood flow prediction model according to an embodiment of the present invention will be described below with reference to the attached drawings.

When the blood flow prediction model is prepared, the blood flow prediction device (300) can specify a blood vessel graph of the region of interest. The blood flow prediction device (300) can distinguish a boundary node located at the end of the blood vessel graph of the region of interest and another node located inside, and can obtain a pressure value (p) of the boundary node together with information on the location (x, y, z) and node radius (r) of the boundary node and the other node and input this into the model.

The blood flow prediction model can predict and output the pressure of an arbitrary node (query node), and the blood flow prediction device (300) can predict the blood flow of an arbitrary point.

FIG. 2 is a schematic diagram illustrating an operation of a blood flow data prediction system according to one embodiment of the present invention.

A blood flow prediction system according to one embodiment of the present application can predict blood flow data of an arbitrary area by using an artificial intelligence model (blood flow prediction model) that predicts blood flow.

More specifically, the blood flow prediction system can extract a blood vessel graph from a 3D medical image. The blood vessel graph is in the form of a point cloud and can be configured in the form of a 1D network connecting points (or nodes), and can include coordinate values configured as x-coordinates, y-coordinates, and z-coordinates of nodes constituting the blood vessel, and information on the radius (r) of the node corresponding to the actual blood vessel radius.

Furthermore, the blood flow prediction system can input a blood vessel graph into a blood flow prediction model whose learning has been completed. The learning and structure of the blood flow prediction model are described in more detail later with reference to the attached Figures 15 to 17.

The blood flow prediction system can predict blood flow data such as blood pressure at an arbitrary point through the output value of the blood flow prediction model. For example, when an arbitrary region is specified in a blood vessel graph, the blood flow prediction system can identify the boundary of the region, obtain the location (x, y, z) and node radius (r) information of the boundary node and other nodes in the blood vessel graph, and obtain the pressure value (p) of the boundary node through measurement or calculation and input it into the blood flow prediction model. The blood flow prediction model can predict the pressure of an arbitrary node (query node) and output it, and through this, the blood flow prediction system can predict blood flow at an arbitrary point.

FIG. 3 is a diagram for explaining an operation of a blood flow data prediction system according to one embodiment of the present invention.

A blood flow prediction system according to one embodiment of the present application can acquire a three-dimensional medical image, extract blood vessel pixel points, and generate a three-dimensional blood vessel image. Thereafter, a region of interest can be specified through user input, and a blood vessel graph of the region of interest can be generated.

A blood flow prediction system according to one embodiment of the present invention can acquire a three-dimensional medical image, extract blood vessel pixel points to generate a three-dimensional blood vessel image, and generate a blood vessel graph using the three-dimensional blood vessel image. Thereafter, a region of interest can be specified through user input, and the region of interest can be extracted from a pre-generated blood vessel graph.

The blood vessel graph of the region of interest is in the form of a point cloud and can be configured in the form of a 1D network connecting points (or nodes), and can include coordinate values composed of the x-coordinate, y-coordinate, and z-coordinate of the nodes constituting the blood vessel, and information on the radius (r) of the node corresponding to the actual blood vessel radius.

Furthermore, the blood flow prediction system can obtain actual blood flow data (boundary value p in Fig. 3) of the boundary of the region of interest. The actual blood flow data of the boundary of the region of interest can be, for example, blood pressure data of the boundary of the region of interest, which can be a measured value or a predicted value calculated by an algorithm.

The blood flow prediction system can predict blood flow data for an arbitrary region by inputting a region of interest blood vessel graph and boundary values into a blood flow prediction model for which learning has been completed. For example, the blood flow prediction system can input the boundary value (p) together with the location of the boundary node and other nodes and the node radius (x, y, z, r) information in the region of interest blood vessel graph into the blood flow prediction model. If an arbitrary point in the region of interest is specified as a query node, the blood flow prediction model can predict the blood flow data (P) of the corresponding query node, and through this, the blood flow prediction system can predict blood flow, such as pressure, blood flow velocity, and flow rate, of an arbitrary point.

FIG. 4 is a flowchart for explaining an embodiment of a three-dimensional medical image processing method according to an embodiment of the present invention, and FIGS. 5 to 8 are exemplary diagrams for explaining the operation of a three-dimensional medical image processing device according to an embodiment of the present invention.

Referring to FIG. 4, 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 S410).

In one embodiment of step S410, the 3D medical image processing device (100) can analyze the 3D medical image, determine that it is a blood vessel if any pixel value is greater than a predetermined threshold value, specify a blood vessel pixel point, and extract 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.

For example, a 3D medical image processing device (100) can analyze a 3D medical image of (a) of FIG. 5, extract pixel coordinates corresponding to blood vessels based on pixel values as in (b) of FIG. 5, and then generate a pixel coordinate table as in (a) of FIG. 6. A coordinate system of the entire blood vessel can be generated based on the pixel coordinate table as in (b) of FIG. 6. That is, a coordinate system of the entire blood vessel can be generated by forming blood vessel pixel points on the coordinate system.

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 S420). For example, the 3D medical image processing device (100) can specify a major blood vessel portion to be the target of image processing, as in (b) of FIG. 7, from the pixel points of the entire blood vessel displayed on the coordinate system, as in (a) of FIG. 7.

Furthermore, the 3D medical image processing device (100) can perform clustering and filtering on the blood vessel pixel points of the crop area to remove noise and/or fine blood vessel pixels. (Step S430) Through this, a 3D blood vessel image can be generated. (Step S440)

For example, the 3D medical image processing device (100) 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 less than the threshold value. Filtering may be performed by removing pixel points corresponding to clusters with a number of blood vessel points less than the threshold value from the blood vessel coordinate system. Through this, noise around major blood vessels may be removed.

Furthermore, the 3D medical image processing device (100) can group the blood vessel pixel points into clusters by re-clustering the primary filtered blood vessel pixel points based on at least one of their distance, density, and attribute information. Thereafter, filtering can be performed to delete the remaining clusters except for the blood vessel pixel points included in any cluster. For example, the filtering can be performed in a manner of removing pixel points corresponding to clusters other than the largest cluster from the blood vessel coordinate system. Through this, microvessels can be removed.

For example, a 3D medical image processing device (100) can generate a 3D blood vessel image as in (b) of FIG. 8 by removing noise and fine blood vessel pixels from a blood vessel that is a target of image processing as in (a) of FIG. 8.

Fig. 9 is a flowchart for explaining one embodiment of a method for generating a blood vessel graph according to the present invention. Figs. 10 to 14 are exemplary diagrams for explaining the execution process of Fig. 9.

Referring to Fig. 9, the blood vessel graph generation device (200) can divide a three-dimensional blood vessel image 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. (Step S910). For example, the blood vessel graph generation device (200) can divide a three-dimensional blood vessel image into multiple layers as in (a) of Fig. 10, and check the coordinate values of blood vessel pixel points for each layer as in (b) of Fig. 10.

Afterwards, the blood vessel graph generation device (200) can group blood vessel pixel points for each layer (step S920) and calculate the center of gravity of each group. This is for generating nodes of the blood vessel graph, and nodes of the blood vessel graph according to the embodiment of the present invention can be set as the center of gravity of blood vessel pixel point groups for each layer. (step S930)

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. 11. In the example of FIG. 11, the blood vessel pixel point group corresponds to one section of an actual blood vessel, and the center of gravity of the group will correspond to the center of one 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.

Afterwards, the blood vessel graph generation device (200) can generate connection information (connectivity) of blood vessel graph nodes. (Step S940)

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. 12, and if there is an intersection between the groups as shown in FIG. 13, 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 an actual blood vessel is connected.

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 S950)

Referring to Fig. 14, when a 3D blood vessel image is divided into 2D layers, the cross-sectional radius corresponds to the radius (R ₀ ) of the blood vessel pixel point group. That is, since it is not the actual radius of the blood vessel but the radius (R ₀ ) of the blood vessel pixel point group, it is necessary to calculate the actual radius (r) of the blood vessel. To this end, the radius (R ₀ ) of the blood vessel pixel point group can be converted by cosθ, and the actual radius (r) can be calculated by [Mathematical Formula 1] below.

[Mathematical Formula 1]

r = R ₀ Хcosθ

R ₀ : Radius of the blood vessel pixel point group,

r: actual radius of the vessel,

cosθ: is calculated by [Mathematical Formula 2],

는 인접 레이어의 연결 정보 방향 () 및 해당 레이어의 혈관 픽셀 포인트 그룹의 반경 방향 ()을 이용하여 계산된다.

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 .

[Mathematical formula 2]

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

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

Returning to the description of FIG. 9, in step S960, the blood vessel graph generation device (200) can generate a blood vessel graph using nodes, connection information, and node radii. This is a visual representation of a blood vessel structure, and information on characteristics such as branching, connection, size, and location of blood vessels are compressed, and parameters such as length, diameter, angle, branching pattern, and shape of blood vessels are included.

FIG. 15 is a drawing for explaining a method for generating an artificial intelligence model for blood flow prediction according to one embodiment of the present invention, and FIGS. 16 and 17 are drawings for explaining the structure and operation of an artificial intelligence model for blood flow prediction according to one embodiment of the present invention.

Referring to Fig. 15, the blood flow prediction system can collect data for learning an artificial intelligence model for blood flow prediction. (Step S1501)

The learning data may be a blood vessel graph for an arbitrary region, as shown in Fig. 16. The blood vessel graph is in the form of a point cloud and may be configured in the form of a 1D network connecting points (or nodes), and may include a boundary node (1601) corresponding to the boundary of the region and other nodes (1602) that are not boundary nodes. Furthermore, the blood vessel graph may include coordinate values composed of the x-coordinate, the y-coordinate, and the z-coordinate of each node, and information on the radius (r) of the node corresponding to the actual blood vessel radius. The learning data includes the pressure (p) of the boundary node (1601), and the pressure (p) of other nodes may be set as a label or mask.

Furthermore, the blood flow prediction system can set an input layer of a blood flow prediction model. (Step S1502)

The input layer of the blood flow prediction model according to the embodiment of the present invention can be set to a first channel, a second channel, and a third channel, as in 1701 of Fig. 17. The first channel can input the coordinate value and radius information (x, y, z, r) of the query node in the blood vessel graph, the second channel can input the coordinate value and radius information (x, y, z, r) of the boundary node in the blood vessel graph, and the third channel can input the pressure value (p) of the boundary node.

Furthermore, the blood flow prediction system can set a feature extraction layer of the blood flow prediction model. (Step S1503)

A feature extraction layer of a blood flow prediction model according to an embodiment of the present invention can be referenced to 1702 of FIG. 17.

In particular, the feature extraction layer (1702) can separate the pressure value (p) of the boundary node input from the third channel into four values corresponding to (x, y, z, r) to reflect the correlation between the coordinate value of the node and the radius information (x, y, z, r). Furthermore, the feature extraction layer 1902 can pass the (x, y, z, r) input from each channel or the pressure (p) separated into four to a fully connected layer (FC1, FC2) that shares weights.

Through this, the feature extraction layer (1702) can form a local feature (1705) reflecting the characteristics of the query node and a boundary feature (1706) reflecting the characteristics of the boundary node. At this time, it is appropriate to apply a max pooling layer (max pooling layer, 1707) to remove the input order information of the boundary node from the boundary feature (1706).

Furthermore, the blood flow prediction system can set a feature stitching layer. (Step S1504)

The feature stitching layer of the blood flow prediction model according to the embodiment of the present invention may have a structure that combines local features and boundary features and passes them to a fully connected layer (FC1, FC2), as in 1703 of Fig. 17. This is for the model to understand global features for all channels.

Furthermore, the blood flow prediction system can set an output layer of the blood flow prediction model. (Step S1505) The output layer of the blood flow prediction model according to the embodiment of the present invention can be set to predict and output the pressure of the query node, as in 1704 of FIG. 17.

When the learning data of the blood flow prediction model is prepared and the model structure setting is completed, the blood flow prediction system can learn the blood flow prediction model. (Step S1506)

Once the generation of the blood flow prediction model is completed, the blood flow prediction system can specify a blood vessel graph of the region of interest, distinguish a boundary node located at the end of the specified blood vessel graph and other nodes located inside, and obtain the pressure value (p) of the boundary node together with the location (x, y, z) and node radius (r) information of the boundary node and other nodes and input them into the model. The blood flow prediction model can predict the pressure of an arbitrary node (query node) and output the pressure, through which the blood flow prediction system can predict blood flow at an arbitrary point.

FIG. 18 is a block diagram illustrating the configuration of a server that generates an artificial intelligence model for predicting blood flow according to an embodiment of the present invention.

As illustrated in FIG. 18, a server (1800) according to an embodiment of the present invention may include a communication unit (1810), a storage unit (1830), and a control unit (1820), and may further include an input unit and a display unit, although not illustrated in FIG. 18. In FIG. 18, the server (1800) is illustrated as including a communication unit (1810), a storage unit (1830), and a control unit (1820), but each block may be physically separated. For example, the storage unit (1830) may exist in a virtualized data center and may be connected to the control unit (1820) of the server (1800) through the communication unit (1810).

The communication unit (1810) performs a data transmission and reception function for wired and wireless communication of the server (1800), receives data through a wired and wireless channel, outputs it to the control unit (1820), and transmits data output from the control unit (1820) through a wireless channel. In particular, the communication unit (1810) according to an embodiment of the present invention can perform a function of receiving a 3D medical image.

The storage unit (1830) serves to store programs and data required for the operation of the server (1800), and may be divided into a program area and a data area. The data area of the storage unit (1830) according to an embodiment of the present invention may store blood vessel pixel point coordinates, 3D blood vessel images and blood vessel graphs, blood flow values at any point of the blood vessel, for example, pressure values.

Furthermore, the program area of the storage unit (1830) according to the embodiment of the present invention can store an artificial intelligence model that predicts blood flow at an arbitrary point in a blood vessel image. The artificial intelligence model can perform a function of predicting pressure at an arbitrary node by using the location and radius information of each node of the blood vessel graph of the region of interest.

Furthermore, the program area of the storage unit (1830) according to the 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 three-dimensional medical image to extract blood vessel pixel point coordinates corresponding to blood vessels based on pixel values, removing noise around major blood vessels, and generating a three-dimensional blood vessel image, and a function of dividing the three-dimensional blood vessel image 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.

The control unit (1820) controls the overall operation of each component of the server (1800). In particular, the control unit (1820) according to the embodiment of the present invention can set the structure of a blood flow prediction model and generate data for learning the model.

The control unit (1820) can generate blood vessel data in the form of a point cloud corresponding to a major blood vessel from a three-dimensional medical image, specify a boundary area of the blood vessel data, obtain a blood flow value of the boundary area, and generate learning data for a model by specifying the location of the point cloud for an arbitrary area and the blood flow value of the boundary area.

The control unit (1820) can set the structure of the model, and the model can include an input layer, a feature extraction layer, an output layer, etc. The control unit (1820) can set the input layer of the blood flow prediction model to include a channel into which location and radius information of a boundary node in a blood vessel graph of a region of interest are input, a channel into which location and radius information of other nodes are input, and a channel into which blood flow values of boundary nodes are input. Furthermore, a feature extraction layer can be set to form a boundary feature that reflects local features that reflect characteristics of other nodes and characteristics of boundary nodes and from which input order information of the boundary nodes is removed.

When learning data of a blood flow prediction model is prepared and the structure of the model is set, the control unit (1820) can train the blood flow prediction model.

In addition, the control unit (1820) analyzes a 3D 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 3D blood vessel image, divides the 3D blood vessel image into multiple layers, sets blood vessel pixel point groups for each layer as blood vessel nodes, and generates connection information to generate a blood vessel graph, and can control the communication unit (1810) to provide the blood vessel graph.

Although the invention has been described by way of limited embodiments and drawings, it is not limited to the above embodiments, and various modifications and variations are possible from this description by those skilled in the art to which the invention pertains. Accordingly, the spirit of the invention should be understood only by the scope of the claims set forth below, and all equivalent or equivalent variations thereof will be deemed to fall within the scope of the spirit of the invention.

100: 3D medical image processing device
200: Vascular Graph Generation Device
300 : Blood Flow Prediction Device
1800 : Server
1810 : Department of Communications
1820 : Control Unit
1830 : Storage Department

Claims (7)

In a method for generating an artificial intelligence model for predicting blood flow data on a server,
Step of setting up the structure of the blood flow prediction model;
A step of generating blood vessel data in the form of a point cloud corresponding to a major blood vessel from a three-dimensional medical image, specifying a boundary area of the blood vessel data, obtaining a blood flow value of the boundary area, and providing the location of the point cloud for an arbitrary area and the blood flow value of the boundary area as learning data for the blood flow prediction model; and
A method for generating an artificial intelligence model, characterized by including a step of training the blood flow prediction model by setting blood flow values in an area other than the above-mentioned boundary area as a label or mask. In the first paragraph, the step of setting the structure of the blood flow prediction model is:
A method for predicting blood flow, comprising: setting an input layer of the blood flow prediction model to include a first channel into which location and radius information of a boundary node in a blood vessel graph for an arbitrary region are input, a second channel into which location and radius information of another node in the blood vessel graph are input, and a third channel into which blood flow values of the boundary nodes are input.
How to create an artificial intelligence model. In the first paragraph, the step of setting the structure of the blood flow prediction model is:
A method characterized by comprising the step of setting a feature extraction layer of the blood flow prediction model to form a local feature reflecting the characteristics of the other node and a boundary feature reflecting the characteristics of the boundary node.
How to create an artificial intelligence model. In the third paragraph, the step of setting the feature extraction layer of the blood flow prediction model is:
characterized by including a step of forming the boundary feature by removing the input order information of the boundary node.
How to create an artificial intelligence model. A 3D medical image processing device that analyzes a 3D medical image and extracts blood vessel pixel point coordinates corresponding to the blood vessel based on pixel values to generate a 3D blood vessel image; and
A blood vessel graph generation device that generates a blood vessel graph from the three-dimensional blood vessel image by using the blood vessel pixel point group as a node; and
A blood flow prediction model is generated by setting a structure of a blood flow prediction model, specifying a boundary region of the blood vessel graph, obtaining a blood flow value of the boundary region, providing a location of an arbitrary region and a blood flow value of the boundary region as learning data of the blood flow prediction model, and setting a blood flow value of a region other than the boundary region as a label or mask.
Artificial intelligence model generation system. In a server that generates an artificial intelligence model that predicts blood flow data, the server,
A communication unit for receiving three-dimensional medical images; and
A blood flow prediction model is set up, and a control unit is included to generate blood flow prediction model by setting up a structure of a blood flow prediction model, generating blood vessel data in the form of a point cloud corresponding to a major blood vessel from the three-dimensional medical image, specifying a boundary area of the blood vessel data, obtaining a blood flow value of the boundary area, providing a location of a point cloud for an arbitrary area and a blood flow value of the boundary area as learning data of the blood flow prediction model, and setting a blood flow value of an area other than the boundary area as a label or mask.
Artificial intelligence model generation server. In a computer program stored in a medium for performing processing to generate an artificial intelligence model that predicts blood flow data,
Ability to set the structure of the blood flow prediction model;
A function of generating vascular data in the form of a point cloud corresponding to a major blood vessel from a 3D medical image, specifying a boundary area of the vascular data, obtaining a blood flow value of the boundary area, and providing the location of the point cloud for an arbitrary area and the blood flow value of the boundary area as learning data for the blood flow prediction model; and
A computer program characterized by performing a function of learning the blood flow prediction model by setting blood flow values in an area other than the above boundary area as a label or mask.

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