KR20230150761A - Method for providing information of vascula ultrasound and device usinng the same - Google Patents

Abstract

The present invention is a method of providing information on vascular ultrasound implemented by a processor, comprising the steps of receiving a vascular ultrasound image for an object, classifying the vascular ultrasound image, and using the same. Provides a device.

Description

Method for providing information on vascular ultrasound and a device for providing information on vascular ultrasound using the same {METHOD FOR PROVIDING INFORMATION OF VASCULA ULTRASOUND AND DEVICE USINNG THE SAME}

It relates to a method for providing information on vascular ultrasound measurement and a device for providing information on vascular ultrasound using the same.

Peripheral artery disease (PAD) is a disease in which ischemia of the lower extremities occurs due to atherosclerosis, which is the cause of progressive stenosis or atheroembolization of the lower extremity arteries. In particular, peripheral artery disease is closely associated with adverse cardiovascular events such as myocardial infarction and stroke, and its prevalence has recently been steadily increasing, emerging as a global public health problem.

Atherosclerosis, the representative cause of peripheral arterial disease, is caused by plaque buildup within blood vessels that interferes with complete blood circulation. In clinical practice, vascular ultrasound is often used as a primary imaging test to diagnose the disease or determine treatment methods. It is used.

However, vascular ultrasound imaging is highly dependent on the examiner and has a large degree of variability depending on the examiner, so obtaining unified images from an experienced examiner is essential to minimize the variability for each image. However, in actual clinical settings, variability between collected images is inevitable due to a lack of skilled personnel.

In addition, due to the nature of blood vessels distributed in similar structures (thin and long structures) without distinct features in the body, inter-observer variability increases in classifying which blood vessel the vascular ultrasound image was captured from. , other examiners or clinicians have a lot of difficulty identifying the test site or disease area using only vascular ultrasound images.

Meanwhile, among ultrasound modes, Doppler mode allows measurement of the velocity of blood flow within blood vessels. In particular, the blood flow measurement method based on the Doppler mode of ultrasound has the feature of being able to measure blood flow velocity in real time non-invasively, and is widely used in modern medical diagnosis.

At this time, Doppler ultrasound images can provide blood flow information for various cross-sectional views.

Therefore, by using these Doppler ultrasound images to provide information related to blood flow, it is possible to classify which region of blood vessel the captured vascular ultrasound image was taken from, and it is possible to evaluate vascular function in the corresponding blood vessel through information such as blood flow speed. do.

Therefore, it is not limited to specific blood vessels, but automatically classifies all images taken in the transverse or long axis direction from all major blood vessels existing in the lower extremities and classifies which blood vessel the image was taken from by performing Doppler segmentation within the classified vascular Doppler images. is possible, and the development of a method to evaluate the function of major blood vessels is essential.

The technology behind the invention has been written to facilitate easier understanding of the invention. It should not be understood as an admission that matters described in the technology underlying the invention exist as prior art.

Meanwhile, the quality of ultrasound images is important for classification of vascular ultrasound images, but the quality of ultrasound images may be reduced due to vessel motion artifacts and image noise present in ultrasound images.

Generalized additional processing is necessary to remove reduction factors that reduce image quality present in these images, but the above-mentioned reduction factors may vary depending on the patient and equipment, so there may be difficulties in generalizing them.

Furthermore, the present inventors paid further attention to the fact that, unlike CT images, the image quality varies depending on the clinical specialist, and the similarity between each obtained vascular ultrasound image is high, making it difficult to classify cross-sectional views.

Meanwhile, as a way to overcome the above-mentioned limitations, the inventors of the present invention found that the neural network algorithm of DNN (Deep neural networks) is applied to medical image analysis, and in particular, CNN (convolutional neural networks) shows excellent performance. I paid attention.

More specifically, the inventors of the present invention noted that CNN can extract meaningful features from images, is more robust to noise than conventional methods, and can obtain a generalized network model through learning.

As a result, the inventors of the present invention were able to build a DNN-based classification model for vascular ultrasound images and apply it to a new information provision system.

Accordingly, the problem to be solved by the present invention is to receive vascular ultrasound images of an object, classify the vascular ultrasound images using a classification model, and obtain information related to blood vessels through technology for dividing the Doppler region within the image. The goal is to provide an information provision method and device configured to provide information by evaluating the function of major blood vessels and classifying which blood vessel the image was taken from.

The problems of the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

In order to solve the problems described above, a method for providing information on vascular ultrasound according to an embodiment of the present invention is provided.

The information providing method is implemented by a processor and includes receiving a vascular ultrasound image for an object, classifying the received vascular ultrasound image using a classification model configured to classify the vascular ultrasound image using the vascular ultrasound image as an input. It includes determining and providing the classification result.

At this time, if there is a Doppler pattern in the vascular ultrasound image, a step of classifying the Doppler ultrasound image using a Doppler pattern segmentation model may be further included.

In addition, the step of segmenting the signal in the image may be further included after the step of performing the Doppler segmentation. After the step of segmenting, the maximum velocity (Vmax), VTI, and DT (deceleration) of blood flow in the image having the Doppler pattern may be further included. time), PHT (pressure half time), AT (acceleration time), EDV (end-diastolic velocity), and dP/dt (the rate of pressure change using the 4V2 formula over time during isovolumic contraction). The step of determining one or more values may be further included.

In order to solve the problems described above, a device for providing information on vascular ultrasound according to another embodiment of the present invention is provided. The information provision device includes a communication unit configured to receive a vascular ultrasound image of an object, and a processor functionally connected to the communication unit. At this time, the processor is configured to determine the cross-sectional view of the received blood vessel ultrasound image using a classification model configured to classify the cross-sectional view of the blood vessel ultrasound image using the blood vessel ultrasound image as input.

The processor may be further configured to classify the Doppler ultrasound image using a Doppler pattern segmentation model when there is a Doppler pattern in the vascular ultrasound image.

In addition, the processor may be further configured to perform the Doppler segmentation and segment a signal in the image, and after the segmentation, the maximum velocity of blood flow (Vmax), VTI, deceleration time (DT), etc. in the image with the Doppler pattern. One or more values selected from the group consisting of PHT (pressure half time), AT (acceleration time), EDV (end-diastolic velocity), and dP/dt (the rate of pressure change using the 4V2 formula over time during isovolumic contraction) It may be further configured to determine and provide.

Specific details of other embodiments are included in the detailed description and drawings.

The present invention provides a system for providing information on vascular ultrasound images, especially Doppler mode images, based on an artificial neural network network configured to classify ultrasound images using vascular ultrasound images, thereby classifying which blood vessel the ultrasound image was captured in. is possible and provides information by evaluating the function of major blood vessels, so it can provide information for highly reliable vascular ultrasound measurement.

The present invention can overcome the limitations of conventional information provision systems by providing a new information provision system.

The effects according to the present invention are not limited to the contents exemplified above, and further various effects are included in the present specification.

Figure 1 illustrates a system for providing information on a cross-sectional view of blood vessel ultrasound using a device for providing information on blood vessel ultrasound according to an embodiment of the present invention.
Figure 2a is a block diagram showing the configuration of a medical staff device according to an embodiment of the present invention.
Figure 2b is a block diagram showing the configuration of an information providing server according to an embodiment of the present invention.
Figure 3 exemplarily illustrates the procedure of a method for providing information on vascular ultrasound according to an embodiment of the present invention.
Figures 4 and 5 exemplarily show data sets of classification models used in information provision methods according to various embodiments of the present invention.
Figure 6 exemplarily shows the structure of a classification model used in an information provision method according to various embodiments of the present invention.
FIG. 7 exemplarily shows bidirectional sorting by a classification model used in an information provision method according to various embodiments of the present invention.
8 and 9 exemplarily show a user interface for providing information determined according to various embodiments of the present invention.
Figure 10 exemplarily illustrates the procedure of a method for providing information on vascular ultrasound according to an embodiment of the present invention.

The advantages of the invention and how to achieve them will become clear by referring to the embodiments described in detail below along with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below and will be implemented in various different forms. The present embodiments only serve to ensure that the disclosure of the present invention is complete and that common knowledge in the technical field to which the present invention pertains is not limited. It is provided to fully inform those who have the scope of the invention, and the present invention is only defined by the scope of the claims.

The shapes, sizes, proportions, angles, numbers, etc. disclosed in the drawings for explaining embodiments of the present invention are illustrative, and the present invention is not limited to the matters shown. Additionally, in describing the present invention, if it is determined that a detailed description of related known technologies may unnecessarily obscure the gist of the present invention, the detailed description will be omitted. When 'includes', 'has', 'consists of', etc. mentioned in this specification are used, other parts may be added unless 'only' is used. In cases where a component is expressed in the singular, the plural is included unless specifically stated otherwise.

When interpreting components, it is interpreted to include the margin of error even if there is no separate explicit description.

Each feature of the various embodiments of the present invention can be partially or fully combined or combined with each other, and as can be fully understood by those skilled in the art, various technical interconnections and operations are possible, and each embodiment may be implemented independently of each other. It may be possible to conduct them together due to a related relationship.

For clarity of interpretation of this specification, terms used in this specification will be defined below.

As used herein, the term “subject” may refer to any subject who wishes to receive information on vascular ultrasound image classification. Meanwhile, the entity disclosed in this specification may be any mammal except human, but is not limited thereto.

The term “ultrasound image” used in this specification may be a vascular ultrasound image.

Meanwhile, the ultrasound image may be a still cut image or a video composed of multiple cuts. For example, the video may be classified into a cross-sectional view of each vascular ultrasound image for each frame of the video according to the method for providing information on the vascular ultrasound image according to an embodiment of the present invention. As a result, the present invention can provide a streaming service by simultaneously receiving vascular ultrasound images from an imaging device and classifying the images, and can also provide vascular ultrasound image information in real time.

Meanwhile, the ultrasound image may be a two-dimensional video, but is not limited thereto and may also be a three-dimensional image.

As used herein, the term “Doppler ultrasound image” may be an image capable of analyzing the velocity of blood flow within a blood vessel in a non-invasive manner among various ultrasound modes. At this time, Doppler ultrasound images can provide blood flow information.

As used herein, the term "classification model" refers to learning images obtained from several major blood vessels, especially in the case of long major blood vessels, images that are precisely divided into several parts and photographed as learning data to learn ultrasound images. It could be classification. At this time, the classification model may be a deep learning-based classification model that classifies ultrasound images by learning vascular ultrasound images and set correct answer data through a pair set.

The term “Doppler pattern segmentation model” used in this specification may be one that learns the Doppler pattern of an ultrasound image in advance and, when a Doppler pattern is present in the classified vascular ultrasound image, performs additional Doppler segmentation.

Hereinafter, with reference to FIGS. 1, 2a, and 2b, a system for providing information on a vascular ultrasound cross-sectional view and a device for providing information on vascular ultrasound using a device for providing information on vascular ultrasound according to an embodiment of the present invention will be described. .

Figure 1 illustrates a system for providing information on a cross-sectional view of blood vessel ultrasound using a device for providing information on blood vessel ultrasound according to an embodiment of the present invention.

Figure 2a is a block diagram showing the configuration of a medical staff device according to an embodiment of the present invention. Figure 2b is a block diagram showing the configuration of an information providing server according to an embodiment of the present invention.

First, referring to FIG. 1 , the information providing system 1000 may be a system configured to provide information about blood vessel ultrasound measurement based on a blood vessel ultrasound image of an object. At this time, the information providing system 1000 includes a medical staff device 100 that receives information on vascular ultrasound measurement, a vascular ultrasound image diagnosis device 200 that provides a vascular ultrasound image, and a blood vessel ultrasound image based on the received vascular ultrasound image. It may be comprised of an information provision server 300 that generates information about ultrasonic measurements.

Next, the medical staff device 100 is an electronic device that provides a user interface for providing information about vascular ultrasound measurement, and includes at least one of a smartphone, a tablet PC (personal computer), a laptop, and/or a PC. can do.

The medical staff device 100 may receive a prediction result associated with a blood vessel ultrasound cross-section for an object from the information provision server 300 and display the received result through a display unit to be described later.

The information provision server 300 includes a general-purpose computer, a laptop, and/or a data server, etc. At this time, the information providing server 300 may be a device for accessing a web server that provides web pages or a mobile web server that provides a mobile web site, but is not limited to this.

More specifically, the information provision server 300 receives a vascular ultrasound image from the vascular ultrasound image diagnosis device 200, classifies an ultrasound cross-section within the received vascular ultrasound image, and provides information on the vascular ultrasound image. You can. At this time, the information provision server 300 may classify the ultrasound cross-section from the vascular ultrasound image using a classification model.

The information provision server 300 may provide the prediction result to the medical staff device 100.

In this way, the information provided from the information providing server 300 may be provided as a web page through a web browser installed on the medical staff device 100, or may be provided in the form of an application or program. In various embodiments, such data may be provided as part of a platform in a client-server environment.

Next, with reference to FIGS. 2A and 2B, the components of the information provision server 300 of the present invention will be described in detail.

First, referring to FIG. 2A , medical staff device 100 may include a memory interface 110, one or more processors 120, and a peripheral interface 130. The various components within medical device 100 may be connected by one or more communication buses or signal lines.

The memory interface 110 is connected to the memory 150 and can transmit various data to the processor 120. Here, the memory 150 is a flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage, and cloud. , may include at least one type of storage medium among blockchain data.

In various embodiments, memory 150 includes operating system 151, communications module 152, graphical user interface module (GUI) 153, sensor processing module 154, telephony module 155, and application module 156. ) can be stored. Specifically, the operating system 151 may include instructions for processing basic system services and instructions for performing hardware tasks. Communication module 152 may communicate with at least one of one or more other devices, computers, and servers. A graphical user interface module (GUI) 153 can handle graphical user interfaces. Sensor processing module 154 may process sensor-related functions (e.g., processing voice input received using one or more microphones 192). The phone module 155 can handle phone-related functions. Application module 156 may perform various functions of a user application, such as electronic messaging, web browsing, media processing, navigation, imaging, and other processing functions. In addition, the medical staff device 100 may store one or more software applications 156-1 and 156-2 (eg, information provision applications) associated with one type of service in the memory 150.

In various embodiments, memory 150 may store a digital assistant client module 157 (hereinafter referred to as the DA client module), thereby storing various user data 158 and instructions for performing the functions of the client side of the digital assistant. can be saved.

Meanwhile, the DA client module 157 uses the user's voice input, text input, touch input, and/or gesture through various user interfaces (e.g., I/O subsystem 140) provided in the medical staff device 100. Input can be obtained.

Additionally, the DA client module 157 can output data in audiovisual and tactile forms. For example, the DA client module 157 may output data consisting of a combination of at least two or more of voice, sound, notification, text message, menu, graphics, video, animation, and vibration. In addition, the DA client module 157 can communicate with a digital assistant server (not shown) using the communication subsystem 180.

In various embodiments, DA client module 157 may collect additional information about the surrounding environment of medical staff device 100 from various sensors, subsystems, and peripheral devices to construct a context associated with user input. . For example, the DA client module 157 may provide context information along with user input to the digital assistant server to infer the user's intent. Here, context information that may accompany the user input may include sensor information, for example, lighting, ambient noise, ambient temperature, images of the surrounding environment, video, etc. As another example, the contextual information may include the physical state of the medical staff device 100 (e.g., device orientation, device location, device temperature, power level, speed, acceleration, motion pattern, cellular signal strength, etc.) . As another example, the context information may include information related to the software state of the medical staff device 100 (e.g., processes running on the medical staff device 100, installed programs, past and current network activity, background services, error logs, resource usage, etc.).

In various embodiments, memory 150 may include added or deleted instructions, and further, medical device 100 may include additional elements other than those shown in FIG. 2A or exclude some elements.

The processor 120 can control the overall operation of the medical staff device 100 and executes various commands to implement an interface that provides information about vascular ultrasound measurement by running an application or program stored in the memory 150. can do.

The processor 120 may correspond to a computing device such as a CPU (Central Processing Unit) or AP (Application Processor). Additionally, the processor 120 may be implemented in the form of an integrated chip (IC) such as a system on chip (SoC) in which various computing devices such as a neural processing unit (NPU) are integrated.

The peripheral interface 130 is connected to various sensors, subsystems, and peripheral devices, and can provide data so that the medical staff device 100 can perform various functions. Here, what function the medical staff device 100 performs may be understood as being performed by the processor 120.

The peripheral interface 130 may receive data from the motion sensor 160, the light sensor (light sensor) 161, and the proximity sensor 162, through which the medical staff device 100 can detect orientation, light, and proximity. It can perform detection functions, etc. For another example, the peripheral interface 130 may receive data from other sensors 163 (positioning system-GPS receiver, temperature sensor, biometric sensor), through which the medical staff device 100 can use other sensors. (163) It is possible to perform functions related to .

In various embodiments, the medical staff device 100 may include a camera subsystem 170 connected to the peripheral interface 130 and an optical sensor 171 connected thereto, through which the medical staff device 100 can take pictures and video. You can perform various shooting functions such as clip recording.

In various embodiments, medical staff device 100 may include a communication subsystem 180 coupled with peripheral interface 130 . The communication subsystem 180 is comprised of one or more wired/wireless networks and may include various communication ports, radio frequency transceivers, and optical transceivers.

In various embodiments, medical staff device 100 includes an audio subsystem 190 coupled with a peripheral interface 130, which includes one or more speakers 191 and one or more microphones 192. By including this, clinician device 100 can perform voice-activated functions, such as voice recognition, voice replication, digital recording, and telephony functions.

In various embodiments, medical staff device 100 may include an I/O subsystem 140 coupled with a peripheral interface 130 . For example, the I/O subsystem 140 may control the touch screen 143 included in the medical staff device 100 through the touch screen controller 141. As an example, the touch screen controller 141 uses any one of a plurality of touch sensing technologies such as capacitive, resistive, infrared, surface acoustic wave technology, proximity sensor array, etc. to sense the user's contact and movement or contact. and cessation of movement can be detected. As another example, I/O subsystem 140 may control other input/control devices 144 included in medical staff device 100 through other input controller(s) 142. As an example, other input controller(s) 142 may control one or more buttons, rocker switches, thumb-wheels, infrared ports, USB ports, and pointer devices such as a stylus.

Next, referring to FIG. 2B, the information providing server 300 may include a communication interface 310, a memory 320, an I/O interface 330, and a processor 340, each component being one. They can communicate with each other through one or more communication buses or signal lines.

The communication interface 310 can be connected to the medical staff device 100 and the vascular ultrasound imaging device 200 through a wired/wireless communication network to exchange data. For example, the communication interface 310 may receive a vascular ultrasound image from the vascular ultrasound imaging device 200 and transmit the determined information to the medical staff device 100.

Meanwhile, the communication interface 310 that enables transmission and reception of such data includes a communication pod 311 and a wireless circuit 312, where the wired communication port 311 is connected to one or more wired interfaces, for example, Ethernet, May include Universal Serial Bus (USB), Firewire, etc. Additionally, the wireless circuit 312 can transmit and receive data with an external device through RF signals or optical signals. Additionally, wireless communications may use at least one of a plurality of communication standards, protocols and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol.

The memory 320 can store various data used in the information provision server 300. For example, the memory 320 may store vascular ultrasound images or models learned to classify vascular ultrasound images.

In various embodiments, memory 320 may include volatile or non-volatile recording media capable of storing various data, instructions, and information. For example, the memory 320 may be a flash memory type, hard disk type, multimedia card micro type, card type memory (e.g. SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage storage. , cloud, and blockchain data may include at least one type of storage medium.

In various embodiments, memory 320 may store configuration of at least one of operating system 321, communication module 322, user interface module 323, and one or more applications 324.

Operating system 321 (e.g., embedded operating system such as LINUX, UNIX, MAC OS, WINDOWS, VxWorks, etc.) controls and manages general system operations (e.g., memory management, storage device control, power management, etc.) It may include various software components and drivers to do so, and may support communication between various hardware, firmware, and software components.

The communication module 323 may support communication with other devices through the communication interface 310. The communication module 320 may include various software components for processing data received by the wired communication port 311 or the wireless circuit 312 of the communication interface 310.

The user interface module 323 may receive a user's request or input from a keyboard, touch screen, microphone, etc. through the I/O interface 330 and provide a user interface on the display.

Applications 324 may include programs or modules configured to be executed by one or more processors 330 . Here, an application for providing information on vascular ultrasound measurement may be implemented on a server farm.

The I/O interface 330 may connect at least one of input/output devices (not shown) of the information providing server 300, such as a display, keyboard, touch screen, and microphone, to the user interface module 323. The I/O interface 330 may receive user input (eg, voice input, keyboard input, touch input, etc.) together with the user interface module 323 and process commands according to the received input.

The processor 340 is connected to the communication interface 310, the memory 320, and the I/O interface 330 to control the overall operation of the information server 300, and operates the application or The program can execute various commands to provide information.

The processor 340 may correspond to a computing device such as a CPU (Central Processing Unit) or AP (Application Processor). Additionally, the processor 340 may be implemented in the form of an integrated chip (IC) such as a system on chip (SoC) in which various computing devices are integrated. Alternatively, the processor 340 may include a module for calculating an artificial neural network model, such as a Neural Processing Unit (NPU).

In various embodiments, the processor 340 may be configured to determine and provide an ultrasound cross-section within a vascular ultrasound image using classification models.

Hereinafter, a method of providing information on vascular ultrasound according to an embodiment of the present invention will be described with reference to FIGS. 3 to 5.

Figure 3 exemplarily illustrates the procedure of a method for providing information on vascular ultrasound according to an embodiment of the present invention. 4A and 4B exemplarily illustrate a data set of a classification model used in an information provision method according to various embodiments of the present invention.

First, the information provision procedure according to an embodiment of the present invention is as follows. Real-time vascular ultrasound images are received from the object, and view identification is performed considering the probe direction. At this time, view identification can be performed by a classification model, and the position of each frame of the ultrasound image can be classified by the classification model.

At this time, a large amount of training data is required to train a DNN-based classification model, and it may be particularly important to set the target area well so that the DNN can make meaningful inferences from the image.

Referring to FIG. 4, the vascular ultrasound image that can be obtained at a specific location of the blood vessel may vary depending on whether the linear probe scanning the blood vessel captures the blood vessel in the short axis or the long axis.

Therefore, for classification of vascular ultrasound images, the classification model used in various embodiments of the present invention is a learning image obtained from several major blood vessels, especially in the case of long major blood vessels, images that are precisely divided into several parts. can be used as learning data.

Meanwhile, in order to classify a vascular ultrasound image, the correct answer data of the target image must be set, and a deep learning-based classification model can be learned through a pair set of the vascular ultrasound image and the set correct answer data.

More specifically, referring to Figures 5a and 5b, the vascular ultrasound image for learning consists of 38 target regions (classes) under the condition of scanning the blood vessel in the short axis and 51 target regions (classes) under the condition of scanning the blood vessel in the long axis. It can be composed of .

Additionally, in the case of vascular ultrasound, vascular Doppler ultrasound is mainly used to evaluate the function of blood vessels. To this end, if there is a Doppler pattern in the vascular ultrasound image classified based on the previous model, Doppler segmentation may be performed based on an additional Doppler pattern segmentation model.

5A to 5F show an example of an image processing process according to an embodiment of the present invention. FIGS. 5A to 5F illustrate results obtained from each of the processing steps for analysis of a medical image representing time-varying information, according to various embodiments.

FIG. 5A shows an example of a raw image 510. In the raw image 510, the horizontal axis represents time and the vertical axis represents blood flow speed.

That is, the raw image 510 lists time-varying blood flow speeds on the time axis. For example, when measuring blood flow velocity using Doppler ultrasound technology, the image can change in real time by adding new data and excluding past data over time, as shown in the raw image 510 of FIG. 5A. ) can be understood as capturing an image that changes in real time at a specific point in time. As shown in Figure 5a, the waveform has a certain pattern, and the pattern has periodicity. Hereinafter, a pattern with consistency is referred to as a 'signal'.

Figure 5b shows an example of a segmentation result for a signal. Referring to FIG. 5B, segmentation results 521, 522, and 523 for the three signals expressed in the image are generated. Segments 521, 522, and 523 include at least a portion of the envelope for the signals.

Figure 5c shows an example of detection results of maximum speed values 531a, 532a, 533a and time values 531b, 532b, 533b. Maximum speed values 531a, 532a, 533a and time values 531b, 532b, 533b are detected based on the segmentation results 521, 522, 523 illustrated in FIG. 5B. That is, in the segmentation results 521, 522, or 523 of each signal, the points with the largest size are selected as the maximum speed values 531a, 532a, and 533a, and the end points of each signal are selected as the maximum visual values 531b, 532b, and 533b. ) is selected as.

Figure 5d shows an example of the results of calculating various measurement values 541, 542, and 543. Referring to Figure 5d, for each of the three signals, maximum velocity (Vmax), VTI, deceleration time (DT), pressure half time (PHT), acceleration time (AT), end-diastolic velocity (EDV), dP/ Measured values 541, 542, 543, including dt (the rate of pressure change using the 4V2 formula over time during isovolumic contraction), etc. may be determined.

Figure 5E shows an example of entropies 551, 552, and 553 for segmentation. Entropy expresses the probability value for the segmentation area created in the process of performing segmentation. It can be understood that the clearer the entropy and the clearer the distinction from other areas, the higher the accuracy of the derived segmentation. In the case of FIG. 5E, the first entropy 551 and the second entropy 552 on the right are confirmed to be relatively clearer than the third entropy 553 on the left.

Figure 5F shows examples of classification results 561, 562, and 563 for true and false signals. True signals and false signals can be classified based on entropies 551, 552, and 553 as shown in FIG. 5E. When segmentation is performed on part of the signal, the clarity is lower than the other entropies 551 and 552, such as the third entropy 553 in FIG. 5E. Accordingly, true signals and false signals can be determined based on the sharpness or blurring of the entropy.

In the procedure described with reference to FIGS. 5A to 5F, segmentation is performed on the signal. Here, segmentation can be performed using an artificial intelligence model, and the artificial intelligence model for segmentation can be defined in various ways. According to one embodiment, the artificial intelligence model shown in FIGS. 6 and 7 below or an artificial intelligence model similar thereto may be used for segmentation of signals in an image.

In particular, referring to FIG. 8, the information providing method and device according to various embodiments of the present invention, when receiving a selection for a vascular ultrasound image from a user, selects the corresponding probe scanning direction (for example, short axis or long axis). ), a user interface for the scanned position of the cross-sectional view (e.g., Right CF or Left CFA) can be provided through the output unit. Furthermore, further information about the patient can be provided.

Referring further to FIG. 9, the information providing method and device according to various embodiments of the present invention further provides a user interface that performs cross-sectional view classification for the target image and sorts images corresponding to each cross-sectional view of the vascular ultrasound image. You may.

Accordingly, it may be possible to classify the position of each frame of a vascular ultrasound image regardless of the skill level of the medical staff.

Figure 10 exemplarily illustrates the procedure of a method for providing information on vascular ultrasound according to an embodiment of the present invention.

Referring to FIG. 10, the method of providing information on vascular ultrasound of the present invention includes receiving a vascular ultrasound image for an object, and using a classification model (FIG. 4) configured to classify the vascular ultrasound image using the vascular ultrasound image as an input. Thus, the method may include classifying and determining a received vascular ultrasound image and providing the classification result.

At this time, referring to FIGS. 5A to 5F together, in the step of classifying and determining the received vascular ultrasound image, the classified ultrasound image may be about the position of each frame.

In addition, if there is a Doppler pattern in the classified blood vessel ultrasound image in the step of classifying and determining the received blood vessel ultrasound image, a step of performing Doppler segmentation using an additional Doppler pattern segmentation model may be further included. In addition, the step of segmenting the signal in the image may be further included after the step of performing the Doppler segmentation. After the step of segmenting, the maximum velocity (Vmax), VTI, and DT (deceleration) of blood flow in the image having the Doppler pattern may be further included. time), PHT (pressure half time), AT (acceleration time), EDV (end-diastolic velocity), and dP/dt (the rate of pressure change using the 4V2 formula over time during isovolumic contraction). The step of determining one or more values may be further included.

Although embodiments of the present invention have been described in more detail with reference to the accompanying drawings, the present invention is not necessarily limited to these embodiments, and may be modified and implemented in various ways without departing from the technical spirit of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but are for illustrative purposes, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood in all respects as illustrative and not restrictive. The scope of protection of the present invention should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be construed as being included in the scope of rights of the present invention.

100: Medical staff device
110: memory interface 120: processor
130: peripheral interface 140: I/O subsystem
141: Touch screen controller 142: Other input controller
143: touch screen
144: Other input control devices
150: Memory 151: Operating system
152: Communication module 153: GUI module
154: sensor processing module 155: phone module
156: Applications
156-1, 156-2: Application
157: Digital assistant client module
158: User data
160: motion sensor 161: light sensor
162: Proximity sensor 163: Other sensors
170: Camera subsystem 171: Optical sensor
180: Communication subsystem
190: Audio subsystem
191: Speaker 192: Microphone
300: Server for providing information
310: communication interface
311: wired communication port 312: wireless circuit
320: memory
321: operating system 322: communication module
323: user interface module 324: application
330: I/O interface 340: Processor

Claims (9)

A method of providing information on vascular ultrasound implemented by a processor, comprising:
Receiving a vascular ultrasound image for an object;
Comprising the step of classifying and determining an ultrasound image using a classification model configured to classify the vascular ultrasound image using the vascular ultrasound image as input, and providing the classification result,
How to provide information about vascular ultrasound. According to claim 1,
The classification result is for the position of each frame for the vascular ultrasound image,
How to provide information about vascular ultrasound. According to claim 1,
The classification model was learned from a training image consisting of 38 target areas under the condition of scanning blood vessels in the short axis and 51 target areas under the condition of scanning blood vessels in the long axis,
How to provide information about vascular ultrasound. According to claim 1,
In the step of classifying and determining the received vascular ultrasound image, if there is a Doppler pattern in the classified vascular ultrasound image, performing Doppler segmentation using an additional Doppler pattern segmentation model,
How to provide information about vascular ultrasound. According to clause 4,
Further comprising the step of segmenting the signal in the image after performing the Doppler segmentation,
How to provide information about vascular ultrasound. According to clause 5,
After the segmentation step, the maximum velocity of blood flow (Vmax), VTI, deceleration time (DT), pressure half time (PHT), acceleration time (AT), end-diastolic velocity (EDV), and Further comprising the step of determining one or more values selected from the group consisting of dP/dt (the rate of pressure change using the 4V2 formula over time during isovolumic contraction),
How to provide information about vascular ultrasound. According to clause 6,
The step of providing the classification result is,
Comprising the step of providing a user interface that aligns images corresponding to each cross-sectional view of the vascular ultrasound image,
How to provide information about vascular ultrasound. According to clause 7,
The user interface is,
A user interface for the probe scanning direction for the corresponding object and the scanned position for the cross-sectional view,
How to provide information about vascular ultrasound. a communication unit configured to transmit and receive data; and
Comprising a control unit configured to connect to the communication unit,
The control unit,
Obtaining a vascular ultrasound image of the object through the communication unit,
Using the vascular ultrasound image as input, a classification model configured to classify the cross-sectional view of the vascular ultrasound image is used to classify and determine the cross-sectional view,
configured to provide the determined classification result,
A device for providing information on vascular ultrasound.

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