KR20230128182A - Method, apparatus and program for diagnosing fat degeneration disease of rotator cuff muscles of shoulder based on ai - Google Patents

Abstract

A shoulder disease interpretation method, apparatus, and program capable of reading fatty degeneration of rotator cuff muscles among shoulder diseases from medical data, comprising obtaining medical data including a shoulder image, pre-processing the obtained medical data The step of reading the fat degeneration of the rotator cuff by inputting the preprocessed medical data into a pretrained neural network model, and generating result information for the medical data based on the read state of fat degeneration of the rotator cuff. It is characterized by including.

Description

Artificial intelligence-based shoulder rotator cuff muscle fat degeneration disease interpretation method, apparatus and program

The present invention relates to a shoulder disease interpretation method, and more particularly, to a shoulder disease interpretation method, apparatus, and program capable of reading fatty degeneration of rotator cuff muscles among shoulder diseases from medical data.

BACKGROUND OF THE INVENTION [0002] In general, medical images greatly assist doctors in diagnosing patients by allowing them to check the inside of a patient's body.

For example, it is possible to check whether or not there are abnormalities in the shoulder, heart, lungs, bronchus, etc. through medical images.

However, in the case of some medical images, the reading difficulty is high, and even in the case of medical staff with many years of experience, there are cases in which it is difficult to quickly make a judgment.

Particularly, in the case of medical images, fatty degeneration due to rotator cuff tear may exist among shoulder diseases, and it is difficult for medical staff to visually accurately read the degree of fatty degeneration through medical images.

Recently, a neural network model capable of performing direct reading from an input image has been used, but this method may cause an overfitting problem due to an insufficient number of data or improperly collected data, It is impossible to check whether the decision was made by looking at it, so its utilization may be reduced.

Therefore, in the future, it is required to develop a shoulder disease interpretation method using a neural network model to assist doctors in diagnosing shoulder diseases.

Republic of Korea Patent No. 10-2291854 (2021. 08. 13)

One object of the present invention to solve the problems described above is to pre-process medical data to divide a target region for reading fatty degeneration of the rotator cuff, and input the target region to a pre-learned neural network model to predict fatty degeneration of the rotator cuff. It is an object of the present invention to provide a shoulder disease reading method, device, and program that can increase the accuracy of reading the fat degeneration of the rotator cuff by reading the condition.

The problems to be solved by 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.

A shoulder disease reading method according to an embodiment of the present invention for solving the above problems includes acquiring medical data including a shoulder image, preprocessing the acquired medical data, and preprocessing the preprocessed medical data. It is characterized in that it includes the step of reading the fat degeneration of the rotator cuff by inputting the input to the learned neural network model, and the step of generating result information for the medical data based on the read state of the fat degeneration of the rotator cuff.

In an embodiment, the step of pre-processing the obtained medical data may include segmenting a target region for reading fatty degeneration of the rotator cuff based on a change in shading of the medical data.

In an embodiment, the preprocessing of the obtained medical data may include calculating a shadow change rate from the medical data, and detecting a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff; It is characterized in that the detected target region is divided.

In an embodiment, the target regions for reading fat degeneration of the rotator cuff include the supraspinatus (SS), infraspinatus (IS), teres minor (TM), and subscapularis (Su) regions. It is characterized in that it includes.

In an embodiment, the step of reading the fat degeneration state of the rotator cuff first reads whether the fat degeneration state of the rotator cuff is normal or abnormal through the pretrained neural network model, and the reading result, If the state of fat degeneration of the rotator cuff is abnormal, it is characterized in that the level of fat degeneration is read a second time.

In an embodiment, the reading of the level of fat degeneration of the rotator cuff is characterized in that a degree of fat degeneration is calculated based on an area of fat existing in the rotator cuff, and the level of fat degeneration is read based on the calculated degree of fat degeneration do.

In an embodiment, the degree of fat degeneration is characterized in that it is calculated based on a formula consisting of (B-A) / B (B is the area of the rotator cuff, and A is the area excluding fat occupying the rotator cuff) .

In an embodiment, the degree of fat degeneration of the supraspinatus muscle is based on a formula consisting of (B-A) / B (B is the total area of the near muscle region, and A is the area excluding fat occupying the near muscle region) characterized in that it is produced.

In an embodiment, the degree of fat degeneration of the infraspinatus is based on a formula consisting of (B-A)/B (B is the total area of the subglossal area, and A is the area excluding fat occupying the subglossal area). It is characterized in that it is calculated as.

In an embodiment, the degree of fat degeneration of the minor muscle is based on a formula consisting of (B-A)/B (B is the total area of the minor muscle region, and A is the area excluding fat occupying the minor muscle region). It is characterized in that it is calculated as.

In an embodiment, the degree of fat degeneration of the subscapularis muscle is based on a formula consisting of (B-A)/B (B is the total area of the subscapularis region, and A is the area excluding fat occupying the subscapular region). It is characterized in that it is calculated as.

In an embodiment, the neural network model is characterized in that it is pre-learned to read the state of fatty degeneration of the rotator cuff when a target region for reading the fat degeneration of the rotator cuff divided from the medical data is input.

In an embodiment, the neural network model reads the fat degeneration state of the rotator cuff when a target region for reading the fat degeneration of the rotator cuff divided from the medical data is input, and based on the result of reading the fat degeneration state of the rotator cuff Characterized in that it is pre-learned to read the level of fat degeneration by calculating the degree of fat degeneration.

In an embodiment, the generating of result information on the medical data may include generating result information including a treatment guide based on the read state of fat degeneration of the rotator cuff.

As a computer program stored on a computer readable storage medium according to an embodiment of the present invention, the computer program, when executed in one or more processors, performs the following operations for reading a shoulder disease, the operations including a shoulder image Obtaining medical data for processing, pre-processing the acquired medical data, inputting the pre-processed medical data to a pre-learned neural network model to read fatty degeneration of the rotator cuff, and reading the read fatty degeneration of the rotator cuff and generating result information on the medical data based on the condition.

A computing device for providing a shoulder disease reading method according to an embodiment of the present invention includes a processor including one or more cores and a memory, wherein the processor acquires medical data including a shoulder image, and obtains the acquired medical data. One medical data is pre-processed, the pre-processed medical data is input into a pre-learned neural network model to read fat degeneration of the rotator cuff, and result information on the medical data is generated based on the read state of fat degeneration of the rotator cuff. It is characterized by doing.

A computer program that provides a shoulder disease reading method according to another embodiment of the present invention for solving the above problems is combined with a computer that is hardware and stored in a medium to perform any one of the above methods.

In addition to this, another method for implementing the present invention, another system, and a computer readable recording medium recording a computer program for executing the method may be further provided.

As described above, according to the present invention, by pre-processing medical data to divide a target region for reading fat degeneration of the rotator cuff, and inputting the target region to a pre-learned neural network model to read the state of fat degeneration of the rotator cuff, It is possible to increase the accuracy of denatured reading.

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

1 is a block diagram of a computing device that performs an operation for providing a method for reading a shoulder disorder according to an embodiment of the present invention.
Figure 2 is a schematic diagram illustrating a network function for reading shoulder disorders, according to an embodiment of the present invention.
3 is a view showing segmented images for reading the level of fat degeneration of the rotator cuff, according to an embodiment of the present invention.
4 is a diagram showing a preprocessed image of a fat degeneration region of the rotator cuff, according to an embodiment of the present invention.
5 and 6 are views showing preprocessed images for explaining a process of calculating the degree of fat degeneration of the rotator cuff, according to an embodiment of the present invention.
7 is a flowchart illustrating a method for reading a shoulder disease according to an embodiment of the present invention.

Advantages and features of the present invention, and methods of achieving them, will become clear with reference to the detailed description of the following embodiments taken in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, only these embodiments are intended to complete the disclosure of the present invention, and are common in the art to which the present invention belongs. It is provided to fully inform the person skilled in the art of the scope of the invention, and the invention is only defined by the scope of the claims.

Terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, singular forms also include plural forms unless specifically stated otherwise in a phrase. As used herein, "comprises" and/or "comprising" does not exclude the presence or addition of one or more other elements other than the recited elements. Like reference numerals throughout the specification refer to like elements, and “and/or” includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various components, these components are not limited by these terms, of course. These terms are only used to distinguish one component from another. Accordingly, it goes without saying that the first element mentioned below may also be the second element within the technical spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used with meanings commonly understood by those skilled in the art to which the present invention belongs. In addition, terms defined in commonly used dictionaries are not interpreted ideally or excessively unless explicitly specifically defined.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Prior to the description, the meaning of the terms used in this specification will be briefly described. However, it should be noted that the description of terms is intended to help the understanding of the present specification, and is not used in the sense of limiting the technical spirit of the present invention unless explicitly described as limiting the present invention.

In this specification, neural networks, artificial neural networks, and network functions may often be used interchangeably.

The term "image" or "image data" as used throughout the description and claims of the present invention refers to multidimensional data composed of discrete image elements (e.g., pixels in the case of a two-dimensional image); in other words, ( A term used to refer to a visible object (e.g., displayed on a video screen) or a digital representation of that object (e.g., a file corresponding to the pixel output of a CT, MRI detector, etc.).

For example, “image” or “image” may refer to computed tomography (CT), magnetic resonance imaging (MRI), hyoid bone imaging, ultrasound, or any other medical imaging known in the art. It may be a medical image of a subject collected by the system. The image does not necessarily have to be provided in a medical context, but may be provided in a non-medical context, such as X-ray imaging for security screening.

Throughout the detailed description and claims of the present invention, the 'DICOM (Digital Imaging and Communications in Medicine)' standard is a term that collectively refers to various standards used for digital image expression and communication in medical devices, The DICOM standard is published by a joint committee formed by the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA).

In addition, throughout the detailed description and claims of the present invention, 'Picture Archiving and Communication System (PACS)' is a term that refers to a system that stores, processes, and transmits according to the DICOM standard, and includes X-ray, CT, , Medical imaging images acquired using digital medical imaging equipment such as MRI are stored in DICOM format and can be transmitted to terminals inside and outside the hospital through a network, and reading results and medical records can be added to them.

Also, throughout this specification, a neural network, a neural network, and a network function may be used with the same meaning. A neural network may consist of a set of interconnected computational units, which may be generally referred to as “nodes”. These “nodes” may also be referred to as “neurons”. A neural network includes at least two or more nodes. Nodes (or neurons) constituting neural networks may be interconnected by one or more “links”.

1 is a block diagram of a computing device that performs an operation for providing a method for reading a shoulder disorder according to an embodiment of the present invention.

The configuration of the computing device 100 shown in FIG. 1 is only a simplified example. In one embodiment of the present invention, the computing device 100 may include other components for performing a computing environment of the computing device 100, and only some of the disclosed components may constitute the computing device 100.

The computing device 100 may include a processor 110 , a memory 130 , and a network unit 150 .

In the present invention, the processor 110 acquires medical data including a shoulder image, pre-processes the acquired medical data, inputs the pre-processed medical data to a pre-learned neural network model to read fatty degeneration of the rotator cuff, , result information on medical data can be generated based on the read state of fat degeneration of the rotator cuff.

Here, the medical data may include at least one of image data, audio data, and time series data. That is, any type of data by which a person engaged in the medical industry or a device for diagnosis can determine whether or not a disease exists in the data may be included in the medical data. The image data includes all image data that is output after photographing or measuring a patient's affected part through examination equipment and turning it into an electrical signal. The image data may include image data constituting each frame of a video in a video continuously photographed over time from a medical imaging device. For example, it includes ultrasound examination image data, image data by an MRI device, CT tomography image data, X-ray image data, and the like. Furthermore, when audio data is converted into an electrical signal and output as an image in the form of a graph or time-series data is expressed as visualized data such as a graph, the image or data may be included in the video data. For example, medical data may include CT images. The foregoing example of medical data is only an example and does not limit the present disclosure.

In one embodiment, the medical data may include a shoulder MRI image, which is only an embodiment, but is not limited thereto.

Next, when pre-processing the acquired medical data, the processor 110 may segment a target region for reading fatty degeneration of the rotator cuff from the acquired medical data.

Here, the processor 110, when segmenting the target region for reading fat degeneration of the rotator cuff, divides the rotator cuff region using a pre-learned segmentation model, and the region of interest (ROI: region of interest).

For example, the pretrained segmentation model may be a 2D segmentation model including 2D U-Net or a 3D segmentation model including 3D U-Net.

In addition, the designated region of interest may include the supraspinatus (SS), infraspinatus (IS), teres minor (TM), and subscapularis (Su) regions, which may be an embodiment. but not limited thereto.

In some cases, when preprocessing the obtained medical data, the processor 110 may divide a target region for reading fatty degeneration of the rotator cuff based on a shade change of the medical data.

Here, the processor 110, when pre-processing the obtained medical data, calculates a shadow change rate from the medical data, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff, and detects The target area can be segmented.

For example, the target region for reading fat degeneration of the rotator cuff may include the supraspinatus (SS), infraspinatus (IS), teres minor (TM), and subscapularis (Su) regions. However, this is only an example, but is not limited thereto.

In another case, when the processor 110 pre-processes the acquired medical data, the processor 110 firstly divides a target region for reading fatty degeneration of the rotator cuff from the acquired medical data, and determines the fatty degeneration of the rotator cuff from the primary divided target region. The target region for reading may be divided into two parts.

Here, the processor 110 may divide the target region based on the change in shading of the medical data when first dividing the target region.

For example, the processor 110 calculates a shadow change rate from medical data when the target region is first segmented, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff, and detects One target area can be divided first.

And, when the processor 110 secondly divides the target region, from the first divided target region, the supraspinatus (SS), the infraspinatus (IS), the teres minor (TM), and the shoulder girdle Secondary segmentation can be performed into a plurality of target regions including the Subscapularis (Su) region.

Next, when reading the state of fat degeneration of the rotator cuff, the processor 110 first reads whether the state of fat degeneration of the rotator cuff is normal or abnormal through a pre-learned neural network model, and the first reading result, If the state of fat degeneration of the rotator cuff is abnormal, the level of fat degeneration can be read a second time.

Here, when reading the fat degeneration level of the rotator cuff, the processor 110 may calculate the degree of fat degeneration based on the area of fat present in the rotator cuff and read the level of fat degeneration based on the calculated degree of fat degeneration. there is.

For example, the degree of fat degeneration may be calculated based on a formula consisting of (B-A)/B (B is the area of the rotator cuff, and A is the area excluding fat occupying the rotator cuff).

As another example, the degree of fat degeneration of the supraspinatus muscle can be calculated based on a formula consisting of (B-A)/B (B is the total area of the near muscle region, and A is the area excluding fat occupying the near muscle region). may be

As another example, the degree of fat degeneration of the infraspinatus muscle is based on a formula consisting of (B-A) / B (B is the total area of the subglossal area, and A is the area excluding fat occupying the subglossal area) may be derived.

As another example, the degree of fat degeneration of the minor muscle is based on a formula consisting of (B-A) / B (B is the total area of the minor muscle region, and A is the area excluding fat occupying the minor muscle region) may be derived.

As another example, the degree of fat degeneration of the subscapularis is based on a formula consisting of (B-A) / B (B is the total area of the subscapularis region, and A is the area excluding the fat occupying the subscapular region) may be derived.

Subsequently, the neural network model of the present invention may be pre-learned to read the fat degeneration state of the rotator cuff when a target region for reading the fat degeneration of the rotator cuff divided from the medical data is input.

In some cases, the neural network model of the present invention reads the fat degeneration state of the rotator cuff when a target region for reading the fat degeneration of the rotator cuff segmented from medical data is input, and based on the result of reading the fat degeneration state of the rotator cuff It may also be pre-learned to read the level of fat degeneration by calculating degrees.

Also, when generating result information on the medical data, the processor 110 may generate result information including a treatment guide based on the read state of fat degeneration of the rotator cuff.

In another case, when generating result information for medical data, the processor 110 classifies a class for fatty degeneration based on the read status of fatty degeneration of the rotator cuff, and includes a treatment guide corresponding to the classified class. It is also possible to generate result information that

Also, the aforementioned neural network model may be a deep neural network. Throughout this specification, neural network, network function, and neural network may be used interchangeably. A deep neural network (DNN) may refer to a neural network including a plurality of hidden layers in addition to an input layer and an output layer. Deep neural networks can reveal latent structures in data. In other words, it can identify the latent structure of a photo, text, video, sound, or music (e.g., what objects are in the photo, what the content and emotion of the text are, what the content and emotion of the audio are, etc.). . Deep neural networks include a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), and a deep belief network (DBN). , Q network, U network, Siamese network, and the like.

A convolutional neural network is a type of deep neural network and includes a neural network including a convolutional layer. A convolutional neural network is a type of multilayer perceptron designed to use minimal preprocessing. A CNN may be composed of one or several convolutional layers and artificial neural network layers combined therewith. CNNs can additionally utilize weights and pooling layers. Thanks to this structure, CNNs can fully utilize the two-dimensional structure of the input data. Convolutional neural networks can be used to recognize objects in images. A convolutional neural network can represent and process image data as a matrix having dimensions. For example, in the case of image data encoded in RGB (red-green-blue), each R, G, and B color may be represented as a two-dimensional (eg, two-dimensional image) matrix. That is, the color value of each pixel of the image data can be a component of a matrix, and the size of the matrix can be the same as the size of the image. Thus, image data can be represented by three two-dimensional matrices (a three-dimensional data array).

In a convolutional neural network, a convolutional process (input/output of a convolutional layer) can be performed by moving the convolutional filter and multiplying the convolutional filter with matrix components at each position of the image. A convolutional filter may be composed of an n*n matrix. A convolutional filter can consist of a fixed shape filter, which is usually smaller than the total number of pixels in an image. That is, when an m*m image is input to a convolutional layer (for example, a convolutional layer whose size of convolutional filter is n*n), a matrix representing n*n pixels including each pixel of the image It can be component multiplied with this convolutional filter (ie, multiplication of each component of the matrix). A component matching the convolutional filter can be extracted from the image by multiplication with the convolutional filter. For example, a 3*3 convolutional filter to extract top and bottom linear components from an image would be constructed as [[0,1,0], [0,1,0], [0,1,0]] can When a 3*3 convolutional filter for extracting up and down linear components from an image is applied to an input image, up and down linear components matching the convolutional filter may be extracted from the image and output. The convolutional layer may apply a convolutional filter to each matrix for each channel representing an image (ie, R, G, B colors in the case of an R, G, B coded image). The convolutional layer may extract features matching the convolutional filter from the input image by applying the convolutional filter to the input image. The filter value of the convolutional filter (that is, the value of each element of the matrix) may be updated by backpropagation during the learning process of the convolutional neural network.

A subsampling layer is connected to the output of the convolutional layer to simplify the output of the convolutional layer, thereby reducing memory usage and computational complexity. For example, if the output of the convolutional layer is input to a pooling layer with a 2*2 max pooling filter, the image is compressed by outputting the maximum value included in each patch for each 2*2 patch in each pixel of the image. can The aforementioned pooling may be a method of outputting a minimum value of a patch or an average value of a patch, and any pooling method may be included in the present invention.

A convolutional neural network may include one or more convolutional layers and subsampling layers. A convolutional neural network may extract features from an image by repeatedly performing a convolutional process and a subsampling process (eg, max pooling described above). Through an iterative convolutional process and subsampling process, a neural network can extract global features of an image.

An output of the convolutional layer or the subsampling layer may be input to a fully connected layer. A fully connected layer is a layer in which all neurons in one layer are connected to all neurons in neighboring layers. A fully connected layer may refer to a structure in which all nodes of each layer are connected to all nodes of other layers in a neural network.

According to an embodiment of the present invention, the processor 110 may be composed of one or more cores, a central processing unit (CPU) of a computing device, a general purpose graphics processing unit (GPGPU) ), a processor for data analysis and deep learning, such as a tensor processing unit (TPU). The processor 110 may read a computer program stored in the memory 130 and perform data processing for machine learning according to an embodiment of the present invention. According to an embodiment of the present invention, the processor 110 may perform an operation for learning a neural network. The processor 110 performs neural network learning, such as processing input data for learning in deep learning (DL), extracting features from input data, calculating errors, and updating neural network weights using backpropagation. calculations can be performed for At least one of the CPU, GPGPU, and TPU of the processor 110 may process learning of the network function. For example, the CPU and GPGPU can process learning of network functions and data classification using network functions. In addition, in one embodiment of the present invention, the learning of a network function and data classification using a network function may be processed by using processors of a plurality of computing devices together. In addition, a computer program executed in a computing device according to an embodiment of the present invention may be a CPU, GPGPU or TPU executable program.

According to an embodiment of the present invention, the memory 130 may store a computer program for performing shoulder disease reading and providing a shoulder disease reading result, and the stored computer program may be read and driven by the processor 120. there is. The memory 130 may store any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

According to an embodiment of the present invention, the memory 130 is a flash memory type, a hard disk type, a multimedia card micro type, or a card type memory (eg SD or XD memory, etc.), RAM (Random Access Memory, RAM), SRAM (Static Random Access Memory), ROM (Read-Only Memory, ROM), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Memory) Read-Only Memory), a magnetic memory, a magnetic disk, and an optical disk may include at least one type of storage medium. The computing device 100 may operate in relation to a web storage that performs a storage function of the memory 130 on the Internet. The description of the above memory is only an example, and is not limited thereto.

The network unit 150 according to an embodiment of the present invention may transmit and receive shoulder disease reading result information and the like to other computing devices, servers, and the like. In addition, the network unit 150 may enable communication between a plurality of computing devices so that operations for reading a shoulder disease or learning a model may be performed in a distributed manner in each of the plurality of computing devices. The network unit 150 enables communication between a plurality of computing devices to perform distributed processing of calculations for shoulder disease reading or model learning using a network function.

The network unit 150 according to an embodiment of the present invention may operate based on any type of wired or wireless communication technology currently used and implemented, such as short-distance (short-distance), long-distance, wired, and wireless, and other networks. can also be used in

The computing device 100 of the present invention may further include an output unit and an input unit.

The output unit according to an embodiment of the present invention may display a user interface (UI) for providing a shoulder disease reading result. The output unit may output any type of information generated or determined by the processor 110 and any type of information received by the network unit 150 .

In one embodiment of the present invention, the output unit is a liquid crystal display (liquid crystal display, LCD), thin film transistor liquid crystal display (thin film transistor-liquid crystal display, TFT LCD), organic light-emitting diode (organic light-emitting diode, OLED) , a flexible display, and a 3D display. Some of these display modules may be of a transparent type or a light transmissive type so that the outside can be seen through them. This may be referred to as a transparent display module, and a representative example of the transparent display module is TOLED (Transparent OLED) and the like.

The input unit according to an embodiment of the present invention may receive a user input. The input unit may include keys and/or buttons on a user interface for receiving user input, or physical keys and/or buttons. A computer program for controlling a display according to embodiments of the present invention may be executed according to a user input through an input unit.

The input unit according to embodiments of the present invention may detect a user's button operation or touch input and receive a signal, or may receive a user's voice or motion through a camera or microphone and convert it into an input signal. For this purpose, speech recognition technology or motion recognition technology may be used.

The input unit according to embodiments of the present invention may be implemented as an external input device connected to the computing device 100 . For example, the input device may be at least one of a touch pad, a touch pen, a keyboard, or a mouse for receiving a user input, but this is only an example and is not limited thereto.

The input unit according to an embodiment of the present invention may recognize a user touch input. The input unit according to an embodiment of the present invention may have the same configuration as the output unit. The input unit may include a touch screen implemented to receive a user's selection input. The touch screen may use any one of a contact capacitive method, an infrared light sensing method, a surface ultrasonic (SAW) method, a piezoelectric method, and a resistive film method. Detailed description of the touch screen described above is only an example according to an embodiment of the present invention, and various touch screen panels may be employed in the computing device 100 . The input unit configured as a touch screen may include a touch sensor. The touch sensor may be configured to convert a change in pressure applied to a specific portion of the input unit or capacitance generated at a specific portion of the input unit into an electrical input signal. The touch sensor may be configured to detect not only the touched position and area, but also the pressure upon touch. When there is a touch input to the touch sensor, the corresponding signal(s) is sent to the touch controller. The touch controller may process the signal(s) and then transmit corresponding data to processor 110 . Accordingly, the processor 110 can recognize which area of the input unit has been touched.

In one embodiment of the present invention, the server may include other configurations for performing the server environment of the server. The server may include any type of device. The server may be a digital device, such as a laptop computer, a notebook computer, a desktop computer, a web pad, or a mobile phone, equipped with a processor and having an arithmetic capability with a memory.

A server (not shown) performing an operation for providing a user interface displaying a result of shoulder disease reading according to an embodiment of the present invention to a user terminal may include a network unit, a processor, and a memory.

The server may generate a user interface according to embodiments of the present invention. The server may be a computing system that provides information to clients (eg, user terminals) over a network. The server may transmit the generated user interface to the user terminal. In this case, the user terminal may be any type of computing device 100 capable of accessing the server. The processor of the server may transmit the user interface to the user terminal through the network unit. A server according to embodiments of the present invention may be, for example, a cloud server. The server may be a web server that processes services. The types of servers described above are examples only and are not limited thereto.

As such, the present invention preprocesses medical data to divide a target region for reading fatty degeneration of the rotator cuff, inputs the target region to a pre-learned neural network model, and reads the state of fatty degeneration of the rotator cuff. Reading accuracy can be improved.

Figure 2 is a schematic diagram illustrating a network function for reading shoulder disorders, according to an embodiment of the present invention.

As shown in FIG. 2, the present invention acquires medical data including shoulder images, pre-processes the obtained medical data, and inputs the pre-processed medical data to a pre-learned neural network model to detect fatty degeneration of the rotator cuff. It is possible to read and generate result information about medical data based on the read state of fat degeneration of the rotator cuff.

Here, the present invention divides the rotator cuff region using a 3D segmentation model including a pre-learned 3D U-Net, and uses a region of interest (ROI: Region Of interest) can be specified.

For example, the designated region of interest may include the supraspinatus (SS), the infraspinatus (IS), the teres minor (TM), and the subscapularis (Su) region. It is an example only, but is not limited thereto.

Subsequently, the present invention first reads whether the fat degeneration state of the rotator cuff is normal or abnormal through the pre-learned neural network model, and if the first reading result indicates that the fat degeneration state of the rotator cuff is abnormal, the level of fat degeneration can be read a second time.

Here, in the present invention, when reading the level of fat degeneration of the rotator cuff, the degree of fat degeneration can be calculated based on the area of fat present in the rotator cuff, and the level of fat degeneration can be read based on the calculated degree of fat degeneration.

In this way, the neural network model of the present invention may be pre-learned to read the fat degeneration state of the rotator cuff when a target region for reading fat degeneration of the rotator cuff segmented from medical data is input.

In some cases, the neural network model of the present invention reads the fat degeneration state of the rotator cuff when a target region for reading the fat degeneration of the rotator cuff segmented from medical data is input, and based on the result of reading the fat degeneration state of the rotator cuff It may also be pre-learned to read the level of fat degeneration by calculating degrees.

In addition, the present invention may generate result information including a treatment guide based on the read state of fat degeneration of the rotator cuff.

3 is a view showing segmented images for reading the level of fat degeneration of the rotator cuff, according to an embodiment of the present invention.

As shown in FIG. 3, the present invention first reads whether the state of fat degeneration of the rotator cuff is normal or abnormal through a pre-learned neural network model, and as a result of the first reading, the state of fat degeneration of the rotator cuff If it is abnormal, the fat degeneration level can be read a second time.

In the present invention, as shown in FIG. 3 , when the preprocessed segmented images of medical data are input to the pretrained neural network model, the state of fat degeneration of the rotator cuff can be read as a normal state and read as Grade 0, which is a level of fat degeneration.

In addition, as shown in FIG. 3, the present invention reads the state of fat degeneration of the rotator cuff as an abnormal state when the segmented image of the preprocessed medical data is input to the pre-learned neural network model, and the grade 1 level of fat degeneration is determined according to the degree of fat degeneration. to Grade 4.

Here, in the present invention, when reading the level of fat degeneration of the rotator cuff, the degree of fat degeneration can be calculated based on the area of fat present in the rotator cuff, and the level of fat degeneration can be read based on the calculated degree of fat degeneration.

For example, the degree of fat degeneration may be calculated based on a formula consisting of (B-A)/B (B is the area of the rotator cuff, and A is the area excluding fat occupying the rotator cuff).

For example, as shown in FIG. 3, Grade 1, which is a level of fat degeneration, is a case where a slight layer of fat is present in the rotator cuff area, and Grade 2, which is a level of fat degeneration, is a case where there is less fat than muscle in the rotator cuff area. Grade 3, the level of fat degeneration, may be the case where there is muscle-level fat in the rotator cuff area, and Grade 4, the level of fat degeneration, may be the case where there is more fat than muscle in the rotator cuff area.

4 is a diagram showing a preprocessed image of a fat degeneration region of the rotator cuff, according to an embodiment of the present invention.

As shown in FIG. 5 , when the acquired medical data is pre-processed, the target region for reading the fat degeneration of the rotator cuff may be divided based on the shade change of the medical data.

Here, the present invention, when preprocessing the acquired medical data, calculates a shadow change rate from the medical data, detects a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff, and detects the detected target region can be split.

In another case, the present invention, when preprocessing the acquired medical data, first divides a target region for reading local degeneration of the rotator cuff from the acquired medical data, and reads fatty degeneration of the rotator cuff from the first divided target region. The target region for the target area may be divided secondly.

Here, the processor 110 may divide the target region based on the shade change of the medical data when the target region is firstly divided, and when the target region is secondly divided, the supraspinatus (Supraspinatus: SS), Infraspinatus (IS), Teres Minor (TM), and Subscapularis (Su) regions.

Subsequently, when the present invention reads the fat degeneration state of the rotator cuff, through a pre-learned neural network model, firstly reads whether the fat degeneration state of the rotator cuff is normal or abnormal, and the first reading result, the fat degeneration of the rotator cuff If the state of fat degeneration is abnormal, the level of fat degeneration can be read a second time.

Here, when reading the fat degeneration level of the rotator cuff, the processor 110 may calculate the degree of fat degeneration based on the area of fat present in the rotator cuff and read the level of fat degeneration based on the calculated degree of fat degeneration. there is.

5 and 6 are views showing preprocessed images for explaining a process of calculating the degree of fat degeneration of the rotator cuff, according to an embodiment of the present invention.

As shown in FIGS. 5 and 6 , the present invention can read whether the fat degeneration state of the rotator cuff is a normal state or an abnormal state through a pre-learned neural network model.

Here, as shown in FIG. 5, according to the present invention, if the reading result indicates that the fat degeneration of the rotator cuff is in a normal state, it can be read as Grade 0, which is the level of fat degeneration.

However, as shown in FIG. 6, in the present invention, as a result of the first reading, if the state of fat degeneration of the rotator cuff is abnormal, the degree of fat degeneration (%) is calculated based on the area of fat present in the rotator cuff, and the degree of fat degeneration is calculated. It is possible to read the level of fat degeneration based on.

For example, the degree of fat degeneration may be calculated based on a formula consisting of (B-A)/B (B is the area of the rotator cuff, and A is the area excluding fat occupying the rotator cuff).

As another example, the degree of fat degeneration of the supraspinatus muscle (SS) is based on a formula consisting of (B-A)/B (B is the total area of the near muscle region, and A is the area excluding fat occupying the near muscle region) may be calculated as

As another example, the degree of fat degeneration of the infraspinatus muscle (IS) is a formula consisting of (B-A)/B (B is the total area of the subglossal muscle area, and A is the area excluding fat occupying the subglossal muscle area) may be calculated based on

As another example, the degree of fat degeneration of the minor muscle (TM) is a formula consisting of (B-A)/B (B is the total area of the minor muscle region, and A is the area excluding fat occupying the minor muscle region) may be calculated based on

As another example, the degree of fat degeneration of the subscapularis muscle (Su) is a formula consisting of (B-A)/B (B is the total area of the subscapularis region, and A is the area excluding the fat occupying the subscapular region) may be calculated based on

As such, the neural network model of the present invention reads the fat degeneration state of the rotator cuff when a target region for reading fat degeneration of the rotator cuff segmented from medical data is input, and calculates the degree of fat degeneration based on the result of reading the fat degeneration state of the rotator cuff. It can be pretrained to calculate and read the level of fat degeneration.

7 is a flowchart illustrating a method for reading a shoulder disease according to an embodiment of the present invention.

As shown in FIG. 7 , in the present invention, medical data including a shoulder image may be acquired (S10).

In addition, the present invention may pre-process the acquired medical data (S20).

Here, the present invention may segment the fatty degeneration region of the rotator cuff using a pre-learned segmentation model, and designate a region of interest (ROI) for a region that affects classification.

In some cases, the present invention may divide a target region for reading fatty degeneration of the rotator cuff based on a change in shading of medical data.

Here, the present invention may calculate a shadow change rate from medical data, detect a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff, and divide the detected target region.

In another case, the present invention may firstly divide a target region for reading fat degeneration of the rotator cuff from the obtained medical data, and secondly divide the target region for reading fatty degeneration of the rotator cuff from the first divided target region. there is.

Here, in the present invention, when the target region is firstly divided, the division can be performed based on the shade change of the medical data, and when the target region is divided secondly, the supraspinatus (SS) is formed from the firstly divided target region. , Infraspinatus (IS), Teres Minor (TM), and Subscapularis (Su) areas can be divided into multiple target regions.

Next, the present invention may read the fatty degeneration of the rotator cuff by inputting the preprocessed medical data to the pre-learned neural network model (S30).

Here, the present invention first reads whether the fat degeneration state of the rotator cuff is normal or abnormal through a pre-learned neural network model, and if the first reading result indicates that the fat degeneration state of the rotator cuff is abnormal, the level of fat degeneration can be read a second time.

Here, in the present invention, when reading the level of fat degeneration of the rotator cuff, the degree of fat degeneration can be calculated based on the area of fat present in the rotator cuff, and the level of fat degeneration can be read based on the calculated degree of fat degeneration.

Subsequently, the present invention may generate result information on medical data based on the read state of fat degeneration of the rotator cuff (S40).

Here, the present invention may generate result information including a treatment guide based on the read state of fat degeneration of the rotator cuff.

In some cases, the present invention may classify a class for fatty degeneration based on the read state of fatty degeneration of the rotator cuff, and generate result information including a treatment guide corresponding to the classified class.

As such, the present invention preprocesses medical data to divide a target region for reading fatty degeneration of the rotator cuff, inputs the target region to a pre-learned neural network model, and reads the state of fatty degeneration of the rotator cuff. Reading accuracy can be improved.

The method according to an embodiment of the present invention described above may be implemented as a program (or application) to be executed in combination with a server, which is hardware, and stored in a medium.

The aforementioned program is C, C++, JAVA, machine language, etc. It may include a code coded in a computer language of. These codes may include functional codes related to functions defining necessary functions for executing the methods, and include control codes related to execution procedures necessary for the processor of the computer to execute the functions according to a predetermined procedure. can do. In addition, these codes may further include memory reference related codes for which location (address address) of the computer's internal or external memory should be referenced for additional information or media required for the computer's processor to execute the functions. there is. In addition, when the processor of the computer needs to communicate with any other remote computer or server in order to execute the functions, the code uses the computer's communication module to determine how to communicate with any other remote computer or server. It may further include communication-related codes for whether to communicate, what kind of information or media to transmit/receive during communication, and the like.

The storage medium is not a medium that stores data for a short moment, such as a register, cache, or memory, but a medium that stores data semi-permanently and is readable by a device. Specifically, examples of the storage medium include ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc., but are not limited thereto. That is, the program may be stored in various recording media on various servers accessible by the computer or various recording media on the user's computer. In addition, the medium may be distributed to computer systems connected through a network, and computer readable codes may be stored in a distributed manner.

Steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented in a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any form of computer readable recording medium well known in the art to which the present invention pertains.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (10)

In a method performed by a device for reading shoulder disease,
obtaining medical data including a shoulder image;
pre-processing the acquired medical data;
reading the fatty degeneration of the rotator cuff by inputting the preprocessed medical data into a pretrained neural network model; and
and generating result information about the medical data based on the read state of fat degeneration of the rotator cuff. According to claim 1,
The step of pre-processing the acquired medical data,
Shoulder disease reading method, characterized in that for dividing the target region for reading the fat degeneration of the rotator cuff based on the shade change of the medical data. According to claim 2,
The step of pre-processing the acquired medical data,
A shoulder disease reading method characterized by calculating a shadow change rate from the medical data, detecting a region in which the calculated shadow change rate is equal to or greater than a reference value as a target region for reading fatty degeneration of the rotator cuff, and dividing the detected target region. . According to claim 3,
The target region for reading the fat degeneration of the rotator cuff,
A shoulder disease interpretation method characterized by including supraspinatus (SS), infraspinatus (IS), teres minor (TM), and subscapularis (Su) regions. According to claim 1,
The step of reading the fat degeneration state of the rotator cuff,
Through the pre-trained neural network model, whether the fat degeneration state of the rotator cuff is normal or abnormal is first read, and if the read result indicates that the fat degeneration state of the rotator cuff is abnormal, the level of fat degeneration is secondarily read. Shoulder disease reading method, characterized in that. According to claim 5,
The level of fat degeneration of the rotator cuff is read,
Shoulder disease reading method, characterized in that for calculating the degree of fat degeneration based on the area of fat present in the rotator cuff, and reading the level of fat degeneration based on the calculated degree of fat degeneration. According to claim 6,
The degree of fat degeneration,
(BA) / B (B is the area of the rotator cuff, A is the area excluding fat occupying the rotator cuff) Shoulder disease reading method, characterized in that calculated based on the formula. According to claim 1,
The step of generating result information for the medical data,
Shoulder disease reading method, characterized in that for generating result information including a treatment guide based on the read state of fat degeneration of the rotator cuff. A computer program stored on a computer-readable storage medium, which, when executed in one or more processors, causes the following operations for reading shoulder disease, the operations to:
obtaining medical data including a shoulder image;
pre-processing the obtained medical data;
reading the fatty degeneration of the rotator cuff by inputting the preprocessed medical data to a pretrained neural network model; and
A computer program stored in a computer readable storage medium comprising an operation of generating result information about the medical data based on the read state of fatty degeneration of the rotator cuff. As a computing device for providing a shoulder disease reading method,
a processor comprising one or more cores; and
Memory;
including,
the processor,
Obtaining medical data including a shoulder image,
Preprocessing the acquired medical data,
The preprocessed medical data is input to a pre-learned neural network model to read the fatty degeneration of the rotator cuff;
Computing device characterized in that for generating result information on the medical data based on the read state of fat degeneration of the rotator cuff.

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