Ultrasound image analysis device and ultrasound image analysis program

Abstract

To provide an ultrasound imaging analyzer that facilitates the analysis of muscle movement. [Solution] An ultrasound image analysis device 12 has a transmitting and receiving device 20 connected to an ultrasound probe 11 that converts reflected waves generated by irradiating a subject with ultrasound into signals and outputs them, and a processor 17 that processes the signals acquired from the ultrasound probe 11 to generate a two-dimensional ultrasound image, wherein the processor 17 is configured to perform a first process of generating multiple ultrasound images of the subject's muscles in motion, a second process of setting a region of interest in a single ultrasound image selected from the multiple ultrasound images, a third process of extracting specified points within the region of interest, and a fourth process of determining the amount of muscle movement by automatically tracking the specified points in the ultrasound images other than the selected ultrasound image.

Description

Technical Field

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[0001] The present disclosure relates to an ultrasonic image analysis apparatus and an ultrasonic image analysis program that analyze an ultrasonic image of muscle obtained by irradiating a subject with ultrasonic waves.

Background Art

[0002] An example of an ultrasonic diagnostic apparatus as an ultrasonic image analysis apparatus that analyzes an ultrasonic image of muscle obtained by irradiating a subject with ultrasonic waves is described in Patent Document 1. The ultrasonic diagnostic apparatus described in Patent Document 1 is an ultrasonic diagnostic apparatus configured to be connectable to an ultrasonic probe, and includes a transmission unit that controls the supply of a transmission electrical signal for transmitting ultrasonic waves from the ultrasonic probe to the subject, a reception unit that acquires a reception signal based on the reflected ultrasonic waves received by the ultrasonic probe, an ultrasonic image generation unit that generates tomographic image data of the subject based on the reception signal, a motion reception unit that acquires motion information of the subject, a dynamic schematic diagram data generation unit that generates schematic diagram image data representing the motion of the subject in a schematic diagram based on the motion information, and a display processing unit that generates composite image data obtained by synthesizing the tomographic image data and the schematic diagram image data based on time information.

[0003] Further, Patent Document 1 describes that the reception unit acquires a reception signal including a cross section of the muscle or bone of the subject, and the ultrasonic image generation unit generates tomographic image data including a cross section of the muscle or bone of the subject based on the reception signal. Furthermore, Patent Document 1 describes that the motion of the subject is a motion accompanied by the movement of the muscle or bone of the subject.

Prior Art Documents

Patent Documents

[0006] The objective of this embodiment is to provide an ultrasound image analysis device and an ultrasound image analysis program that facilitate the analysis of muscle movements. [Means for solving the problem]

[0007] This embodiment is an ultrasound image analysis device comprising: a transmitting and receiving device connected to an ultrasound probe that converts reflected waves generated by irradiating a subject with ultrasound into signals and outputs them; and a processor that processes the signals acquired from the ultrasound probe to generate a two-dimensional ultrasound image, wherein the processor is configured to perform: a first process of generating multiple ultrasound images of the subject's muscles in motion; a second process of setting a region of interest in a single ultrasound image selected from the multiple ultrasound images; a third process of extracting a specified point within the region of interest; and a fourth process of determining the amount of muscle movement by automatically tracking the specified point in each of the ultrasound images other than the selected ultrasound image. [Effects of the Invention]

[0008] The ultrasound image analysis device of this embodiment has the advantage of making it easier to analyze muscle movements. [Brief explanation of the drawing]

[0009] [Figure 1] This is a schematic diagram showing the configuration of an ultrasound image analysis system, including an ultrasound image analysis device. [Figure 2] This flowchart shows an example of how to use an ultrasound image analysis system. [Figure 3] This is a schematic diagram showing an example screen of a display device for an ultrasound imaging analysis system. [Figure 4] Figure 4(A) is a schematic diagram of a portion of the display device screen, and Figure 4(B) is a graph showing the amount of movement of a specified point. [Figure 5]This flowchart shows other use cases for the ultrasound image analysis system. [Modes for carrying out the invention]

[0010] (Overview of the ultrasound image analysis system) Several specific examples of ultrasound image analysis devices and ultrasound image analysis programs are described with reference to the drawings. An ultrasound image analysis system including an ultrasound image analysis device is shown in Figure 1. The ultrasound image analysis system 10 is a device that irradiates a subject with ultrasound, processes the reflected ultrasound to generate an image, and processes and analyzes the generated image. The ultrasound image analysis system 10 includes an ultrasound probe 11, an ultrasound image analysis device 12, and an external device 13. The ultrasound image analysis system 10 is operated and used by users. Users include medical professionals and researchers. Medical professionals include physicians, physical therapists, nurses, etc.

[0011] (Description of an ultrasound probe) The ultrasonic probe 11 is composed of elements such as a main body, an acoustic lens, an acoustic matching layer, a transducer (piezoelectric element), and a damper (packing material). The ultrasonic probe 11 is connected to the ultrasonic image analysis device 12 by a communication system 14 so that they can communicate with each other. The communication system 14 is composed of one or more systems, either a wireless communication system or a wired communication system.

[0012] The ultrasound probe 11 is used in contact with the surface of a subject, for example, the skin 15C of a human arm 15. The ultrasound probe 11 is configured to irradiate ultrasound waves towards the muscles 16 inside the arm 15 when it receives a control signal from the ultrasound image analysis device 12. The ultrasound probe 11 is also configured to receive the reflected waves generated by irradiating the muscles 16 with ultrasound waves, convert them into electrical signals, and transmit those electrical signals to the ultrasound image analysis device 12. The specific structure and function of the ultrasound probe 11 are publicly known as described in Japanese Patent Publication No. 4945326, Japanese Patent Publication No. 5192921, Japanese Patent Publication No. 540141, etc., so a detailed explanation of its specific structure and function will be omitted.

[0013] (Explanation of the ultrasound imaging device) The ultrasound image analysis device 12 is a device that generates an ultrasound image by processing the reflected waves obtained by irradiating a subject with ultrasound, and analyzes the generated ultrasound image. The ultrasound image analysis device 12 is a computer equipped with a main unit (casing), a processor 17, main memory 18, auxiliary memory 19, a transmitting / receiving device 20, an operating device 21, a display device 22, a communication device 23, etc. The computer may consist of either a portable computer or a fixed computer.

[0014] Portable computers include smartphones, tablet devices, and notebook computers. Fixed computers include tower computers and desktop computers. The processor 17 is located inside the main unit and consists of a central processing unit (CPU) that integrates an arithmetic unit (arithmetic circuit) and a control unit (control circuit). The processor 17 is connected to the main memory 18, auxiliary memory 19, transceiver 20, operating device 21, display device 22, communication device 23, etc. via the bus 24.

[0015] The processor 17 comprehensively controls other devices and circuits located inside the main unit, as well as devices and circuits located outside the main unit. In addition to the central processing unit, the processor 17 also has processing circuits such as a digital signal processor, an application-specific integrated circuit (ASIC), and a GPU. GPU stands for Graphics Processing Unit, and a GPU is a graphics controller that performs the processing necessary for 3D graphics image processing, etc. Furthermore, the GPU has a configuration that performs machine learning, specifically deep learning, in the process of processing and analyzing ultrasound images.

[0016] When the processor 17 runs a non-temporary program stored in the auxiliary memory 19, the program performs various processes. These processes include the processing itself performed within the processor 17, judgment, analysis, inference, control and instruction of other elements, acquisition of information and signals from other elements, and storage of information in the auxiliary memory 19. The various processing targets handled by the program include signals, information, data, ultrasound images, graphs, maps, charts, etc.

[0017] The main memory 18 is a volatile storage device and functions as a work area and buffer area when the processor 17 performs processing. The auxiliary memory 19 is a non-volatile storage device, that is, a non-temporary storage medium. Non-temporary programs are stored in the auxiliary memory 19. The auxiliary memory 19 also stores various information used by the processor 17 to perform various processes, various information as a result of the processor 17 performing various processes, etc. The various information stored in the auxiliary memory 19 includes the information itself, data, graphs, maps, charts, etc.

[0018] The auxiliary memory 19 has a larger capacity than the main memory 18, and the auxiliary memory 19 operates according to input commands and output commands from the processor 17. The auxiliary memory 19, which is a non-volatile storage medium, is composed of, for example, a magnetic disk, an optical disk, a flash memory, etc. Examples of the magnetic disk include a hard disk drive. Examples of the optical disk include a compact disk, a digital video disk, a Blu-ray disk, etc.

[0019] The flash memory is a type of semiconductor memory, and examples of the flash memory include an SD memory card, a USB flash drive, a solid state drive, etc. One or more elements included in the auxiliary memory 19 can be defined as a storage medium 19A that can be attached to and detached from the main body.

[0020] The transceiver 20 is configured to be connected to the ultrasonic probe 11 via the communication system 14. The transceiver 20 is composed of a communication cable, a communication circuit, a communication antenna, etc. The transceiver 20 may be composed of either a wireless communication system or a wired communication system.

[0021] The operating device 21 is operated by the user. The operating device 21 is composed of elements such as, for example, an operation button, an operation lever, an operation knob, a keyboard, a mouse, a liquid crystal display, an organic electroluminescence display, etc. The operating device 21 may have either a structure directly provided on the main body or a structure connected to the main body via a communication interface. The communication interface includes a wireless communication system or a wired communication system. The structure and function of the operating device 21 are selected according to whether the computer constituting the ultrasonic image analysis device 12 is a portable computer or a fixed computer.

[0022] The operating device 21 is operated when inputting various information to the ultrasound image analysis device 12, when executing various processes on the processor 17, when using the ultrasound probe 11, when displaying information on the display device 22, when sending information to an external device 13, etc. By operating the operating device 21, the user can adjust the brightness of the ultrasound image, the overall brightness of the display device 22 screen, the depth at which the ultrasound image is generated, etc. The depth at which the ultrasound image is generated is, for example, the depth from the surface of the arm 15.

[0023] Furthermore, the user can input auxiliary information by operating the control device 21. The auxiliary information includes the type of subject, the subject's identification number, the part of the subject from which the ultrasound image is acquired, the cross-sectional direction of the muscle from which the ultrasound image is acquired, identification of the superficial and deep layers of the muscle, whether or not the muscle is subjected to external force, and if so, the direction and value of the external force. The type of subject is, for example, a human, an animal, etc., and animals include dogs, cats, pigs, horses, etc. The subject's identification number is a number that identifies multiple subjects from each other and may include symbols.

[0024] The body parts of the subject from which ultrasound images are acquired include the right arm, left arm, right leg, left leg, torso, tail, etc. These body parts can also be further subdivided and entered. The cross-sectional directions of the muscles from which ultrasound images are acquired include, for example, the direction along the skin, the direction perpendicular to the skin, the direction of muscle depth, the direction along the bone inside the muscle, the direction perpendicular to the bone inside the muscle, etc. Auxiliary information entered by the user operating the control device 21 is stored in the auxiliary memory 19 in association with the subject's identification number.

[0025] The display device 22 is either directly attached to the main unit or connected to the main unit via a cable. The display device 22 is a display that is viewed by the user, and the display includes structures such as liquid crystal displays and organic electroluminescent displays. The connection structure of these displays to the main unit is appropriately selected depending on whether the computer constituting the ultrasound image analysis device 12 is a portable computer or a fixed computer. Note that the display may also be defined as a monitor. Various information is displayed on the screen of the display device 22. The display device 22 may also be configured to display operation buttons, operation tabs, etc. on its screen. In other words, the display device 22 can also perform the functions of the operation device 21.

[0026] The communication device 23 includes devices, equipment, and standards for connecting the ultrasound image analysis device 12 to the external device 13 via the network 25. The communication device 23 includes communication circuits, cables, antennas, communication ports, communication connectors, communication hubs, etc. The network 25 consists of at least one of either a wireless communication system or a wired communication system.

[0027] The configuration of the processor 17 will be described in detail. The processor 17 is configured to function as a signal processing unit 26, an ultrasound image processing unit 27, a designated point extraction unit 28, a movement amount determination unit 29, an information processing unit 30, and an artificial intelligence unit 33 by running a non-temporary program stored in the auxiliary memory 19.

[0028] The signal processing unit 26 is configured to process control signals sent from the ultrasound image analysis device 12 to the ultrasound probe 11, and to process electrical signals received from the ultrasound probe 11. The ultrasound image processing unit 27 is configured to generate ultrasound images by processing the electrical signals received from the ultrasound probe 11. The ultrasound image processing unit 27 is configured to generate multiple two-dimensional ultrasound images in a continuous time series. The two-dimensional ultrasound images are cross-sectional images of the arm 15, including the muscles 16 and membranes. The ultrasound image processing unit 27 is also configured to perform processing, analysis, judgment, and manipulation of the ultrasound images.

[0029] The designated point extraction unit 28 is configured to automatically extract multiple designated points within regions of interest A1 and B1 from a single ultrasound image 32, such as the one shown in Figure 3. The ultrasound image 32 shown in Figure 3 was selected by the user using the operating device 21. Examples of designated points A2 and B2 include contrast (grayish / dark) boundaries, muscle fiber edges (bending points), etc., that exist within regions of interest A1 and B1, respectively. The technical meaning of regions of interest A1 and B1 will be explained later.

[0030] The movement amount determination unit 29 determines the amount of movement of the muscle 16, or in other words, the amount of motion, by continuously and automatically tracking the positions of each designated point A2 and B2 in a plurality of ultrasound images 32 that are generated sequentially over time. The process of continuously and automatically tracking the positions of designated points in the ultrasound images across a plurality of ultrasound images can be achieved, for example, by the Lucas-Canade method. The Lucas-Canade method is publicly known, as shown in, for example, the following Information Example 1 and Information Example 2, so a detailed explanation is omitted.

[0031] Example Information 1: "About the Lucas-Kanade method https: / / www.slideshare.net / slideshow / lucas-kanade / 75287273 [Accessed September 18, 2024]" Example of information 2: "Lucas Kanade method - Overview - http: / / www.thothchildren.com / chapter / 5bcc763e51d9305189030e26 [Accessed September 18, 2024]" Furthermore, the process of automatically tracking the position of a specified point in an ultrasound image across multiple ultrasound images is disclosed in Japanese Patent Publication No. 3688562, Japanese Patent Publication No. 4598260, and others.

[0032] The information processing unit 30 is configured to process information input by the operation of the control device 21, process information sent from the ultrasound image analysis device 12 to the external device 13, process information received from the external device 13, and so on. The information processing unit 30 also stores the information input by the operation of the control device 21 and the information received from the external device 13 in the auxiliary memory 19.

[0033] The artificial intelligence unit 33 is configured to create history data and update data for the various information stored in the auxiliary memory 19 based on the various information acquired by the processor 17, the various information stored in the auxiliary memory 19, and the learning data, and to store the created data in the auxiliary memory 19. The artificial intelligence unit 33 is configured to process the ultrasound image 32 in cooperation with the signal processing unit 26, the ultrasound image processing unit 27, the specified point extraction unit 28, the movement amount determination unit 29, and the information processing unit 30.

[0034] The artificial intelligence unit 33 is an artificial intelligence (AI) equipped with transformers including GPT (Generative Pre-trained Transformer), BERT (Bidirectional Encoder Representations from Transformers), etc., and language models such as recurrent neural networks, and is capable of performing generative artificial intelligence processing.

[0035] The language model is an example of a learning model using a machine learning algorithm. Specific machine learning algorithms include nearest neighbors, naive Bayes, decision trees, support vector machines, and deep learning using neural networks. The artificial intelligence unit 33 can apply the above algorithms as appropriate.

[0036] The machine learning performed by the Artificial Intelligence Department 33 includes three types: supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, machine learning is performed using training data that includes training data. Training data is a method of training a machine using input data for training and output data that includes correct answer data, forming a predictive model based on the dataset (sample data). Unsupervised learning is a method of training using data that does not have correct answer information attached, and the machine forms a predictive model based on the regularity and similarity of the dataset. Reinforcement learning is a method of training a machine through trial and error in order to maximize a set "score".

[0037] The artificial intelligence unit 33 analyzes and processes various information and data stored in the auxiliary memory 19, information input via the operating device 21, and information and data processed or judged by the signal processing unit 26, ultrasound image processing unit 27, designated point extraction unit 28, and displacement amount determination unit 29 to perform machine learning. In machine learning, for example, deep learning using a neural network is performed to generate a trained model. The generated trained model can output the correct answer even for unknown data. The generated trained model is stored in the auxiliary memory 19. The trained model generated by the artificial intelligence unit 33 includes the part of the subject, the cross-sectional direction of the muscle, the direction and value of the external force applied to the muscle, the identification of the superficial layer 16A and deep layer 16B of the muscle 16, the amount of displacement of the superficial layer 16A of the muscle 16 in response to the external force, the amount of displacement of the deep layer 16B of the muscle 16 in response to the external force, the positions of designated points A2 and B2, the positions of representative designated points A3 and B3, etc. The trained model generated by the artificial intelligence unit 33 is stored in the auxiliary memory 19 in association with the type of subject and identification number.

[0038] (Description of external devices) The external device 13 includes a printer, various types of computers, etc. The printer can print images, graphs, data, etc., showing the amount of muscle movement 16 onto paper media. The various types of computers include workstations, supercomputers, mainframes, and other types of computers.

[0039] (Examples of using an ultrasound image analysis system) An example of using the ultrasound image analysis system 10 is shown in the flowchart of Figure 2. In S10, the process of acquiring an ultrasound image (image frame) 32 is performed using the ultrasound probe 11. Specifically, the user brings the tip of the ultrasound probe 11 into contact with the surface of the subject, for example, the skin 15C of a human arm 15. Next, the transducer of the ultrasound probe 11 emits, or irradiates, pulsed ultrasound waves towards the muscle 16 inside the arm 15.

[0040] Furthermore, the transducer of the ultrasound probe 11 receives the reflected waves generated by irradiating the muscle with ultrasound, and the received signal is sent from the ultrasound probe 11 to the ultrasound image analysis device 12. In step S10, the processor 17 of the ultrasound image analysis device 12 operates a program stored in the auxiliary memory 19 and processes the signal received from the ultrasound probe 11 to generate a two-dimensional ultrasound image (cross-sectional image) 32. The processor 17 can continuously generate, for example, several dozen ultrasound images 32 per second and display them on the display device 22 as a video or still image.

[0041] Figure 3 is a cross-sectional view of muscle 16 along line III-III in Figure 1. The cross-sectional view in Figure 3 is a cross-sectional view along the length of bones 15A and 15B of arm 15 shown in Figure 1. Bone 15A is the radius, and bone 15B is the ulna. Figure 3 is an example of an ultrasound image 32 displayed on the screen 31 of the display device 22. The ultrasound image 32 is displayed as a rectangle, for example, a square, on the screen 31. The ultrasound image 32 shows the superficial layer 16A and the deep layer 16B of muscle 16. The superficial layer 16A is located between the skin 15C and the deep layer 16B of arm 15. The fascia 16C is also shown between the superficial layer 16A and the deep layer 16B. On the screen 31, a scale 31A is displayed outside the ultrasound image 32. The scale 31A is displayed along one side of the ultrasound image 32. The scale 31A is marked at intervals of several millimeters [mm], corresponding to the actual size of muscle 16, for example.

[0042] The ultrasound image 32 is displayed with a gradient of grayscale corresponding to the reflection intensity of each point in the muscle 16. Furthermore, during the process of generating the ultrasound image 32, the arm 15 can be moved or external force can be applied to the arm 15. Additionally, during the process of generating the ultrasound image 32, the ultrasound probe 11 remains in contact with the skin 15C at the same position and does not move. In this way, multiple ultrasound images 32 are acquired in a time-series sequence.

[0043] In step S20, the user operates the operating device 21 to select one ultrasound image 32 from among the multiple ultrasound images 32 acquired in step S10, which is taken before the arm 15 is moved or before an external force is applied to the arm 15. The user also operates the operating device 21 to set regions of interest (ROIs) A1 and A2 at predetermined locations on the selected ultrasound image 32. The outer shape of region of interest A1 shown in Figure 3 is, for example, approximately rectangular. In Figure 3, region of interest A1 is set in the surface layer 16A, and region of interest B1 is set in the deep layer 16B.

[0044] In step S30, the processor 17 automatically extracts a number of designated points A2 within region of interest A1, and a number of designated points B2 within region of interest B1. Examples of designated points include contrast boundaries (shades of gray) and muscle fiber edges (flexion points) within regions of interest A1 and B1. The extracted number of designated points A2 and B2 are displayed as colored marks in the ultrasound image 32. The color of designated point A2 is different from the color of designated point B2. The user can visually confirm the position and movement of designated points A2 and B2 displayed on screen 31.

[0045] In step S40, the processor 17 processes multiple consecutive ultrasound images 32 to determine the amount of movement (motion) of the muscle 16. The details of the processing performed by the processor 17 in step S40 are as follows.

[0046] In step S41, the processor 17 performs automatic tracking on multiple consecutive ultrasound images 32 to determine the amount of movement of each designated point A2 and B2. In step S42, the processor 17 compares two ultrasound images 32 that are located one before and one after each other in the time series, and deletes designated points whose movement amount in the later ultrasound image 32 is relatively larger than that of the earlier ultrasound image 32. For example, it deletes the top 10% of designated points with relatively large movement amounts, i.e., excludes them from tracking.

[0047] In step S43, the processor 17 averages the movement amounts of the top 10% of the remaining designated points with relatively large movement amounts and calculates the average value. The processor 17 can treat the calculated average value as the total movement amount of the muscle 16. Furthermore, the processor 17 can determine representative designated points A3 and B3 from among the many designated points A2 and B2, as shown in Figure 4(A), and display them on the display device 22. Representative designated point A3 is designated point A2 located in the center of the region of interest A1. Representative designated point B3 is designated point B2 located in the center of the region of interest B1.

[0048] Furthermore, the processor 17 can calculate the amount of movement of the representative designated point relative to the reference position of the representative designated point before an external force is applied to the arm 15, or when the arm 15 is stopped, and display it on the display device 22. Figure 4(B) shows the amount of movement of the representative designated point in graph 40. In graph 40, time is shown on the horizontal axis and the amount of movement is shown on the vertical axis.

[0049] Furthermore, the processor 17 can automatically perform the process in step S45 to return all designated points A2 and B2 to their original positions once the muscle 16 has returned to its original shape after the muscle 16 has repeatedly contracted. The processor 17 determines that the muscle 16 has returned to its original shape if all designated points A2 and B2 remain stationary for a predetermined period of time.

[0050] Then, in step S50 following step S40, the processor 17 stores the processing results and analysis results from steps S20, S30, and S40 in the auxiliary memory 19, displays them on the display device 22, and outputs them to the external device 13. The processor 17 also stores a large amount of data, including the processing results and analysis results from step S40, in the auxiliary memory 19, and stores the trained model generated by machine learning in the auxiliary memory 19.

[0051] (Effects of use examples) According to the ultrasound image analysis system 10, the user can visually confirm the amount of muscle movement 16 by looking at the screen 31 of the display device 22. Furthermore, when a scale 31A is displayed on the screen 31, the user can visually confirm the amount of muscle movement 16 more reliably. In addition, both the ultrasound image 32 and the graph 40 may be displayed side by side on the screen 31.

[0052] Furthermore, by using the ultrasound image analysis system 10, it is possible to determine in which direction and to what extent (how much) the muscle 16 moved within the two-dimensional ultrasound image 32. Therefore, it is possible to obtain the effect of making it easier to analyze the movement of the muscle 16. It can also contribute to estimating the stiffness and flexibility of the muscle 16. In other words, it can contribute to estimating the stiffness (rigidity) of the muscle 16, which is how much it moves (stretches, compresses) when a certain load is applied to it.

[0053] (Other usage examples) Figure 5 shows another usage example that modifies part of the usage example in Figure 2. As shown in Figure 5, the user can input some of the auxiliary information by operating the operating device 21 in step S60, before executing steps S10, S20, S30, and S40. The user can also input some of the auxiliary information in step S20, before setting the region of interest. For example, the user can input the position of the superficial layer 16A and the position of the deep layer 16B, the direction in which an external force is applied to the muscle 16, and the value of the external force for the acquired ultrasound image 32. The auxiliary information input in steps S20 and S60 is stored in the auxiliary memory 19. In step S42, following step S41, the processor 17 can delete specified points where the amount of movement of a specified point displayed in a later ultrasound image 32 in the time series is greater than or equal to a threshold value relative to a specified point displayed in a earlier ultrasound image 32 in the time series.

[0054] To explain in more detail, in step S46, the processor 17 determines whether or not there is an external force applied to the outer surface of the arm 15. If the processor 17 determines Yes in step S46, it proceeds to step S47 and selects a value that exceeds the second threshold used when processing the amount of movement of designated point B2 in region B1 as the first threshold used when processing the amount of movement of designated point A2 in region A1.

[0055] On the other hand, if the processor 17 determines No in step S46, it proceeds to step S48 and sets the first threshold used when processing the amount of movement of designated point A2 in region of interest A1 and the second threshold used when processing the amount of movement of designated point B2 in region of interest B1 to the same value. The first threshold and the second threshold used by the processor 17 for processing the amount of movement of designated points are stored in the auxiliary memory 19 in advance. Alternatively, a reference threshold may be stored in the auxiliary memory 19, and the processor 17 may correct the reference threshold to determine the first threshold and the second threshold.

[0056] Furthermore, the processor 17 may set the first threshold used in step S47 to be greater than the first threshold used in step S48, and the second threshold used in step S47 to be greater than the second threshold used in step S48. In addition, when the processor 17 determines the amount of muscle movement of muscle 16 when there is no distinction between the superficial layer 16A and the deep layer 16B of muscle 16, it may set the threshold used in step S48 to be less than the threshold used in step S47.

[0057] (Effects of other usage examples) When the processor 17 performs the above processing, it can determine the amount of movement of the superficial layer 16A, which is susceptible to external force, and the amount of movement of the deep layer 16B, which is less susceptible to external force, according to the applied external force. Furthermore, under the conditions that the type and identification number of the subject are the same, the location is the same, the cross-sectional direction of the muscle is the same, the presence or absence of external force on the muscle is the same, and the direction and value of the external force when external force is present is the same, the processor 17 can use the ultrasound image analysis system 10 to generate ultrasound images 32 at predetermined intervals, for example every month, and determine the amount of movement of the muscle 16. Therefore, it is possible to estimate the change in the state of the muscle 16 over time in response to the applied external force. The predetermined period is not limited to one month, but can be arbitrarily set by the user, such as one week, one year, etc.

[0058] Furthermore, using the same identification number and the same subject under the same conditions, the user can repeat the usage example in Figure 5 multiple times with a predetermined period of time in between. When the processor 17 executes the process shown in Figure 5 for the second time or later, the processor 17 can perform the processing, analysis, and judgment for the second time and beyond based on the learned data generated in the previous processing in Figure 5. For example, the processor 17 may automatically set the regions of interest A1 and B1 on the screen 31 without the user operating the operating device 21 in step S20. If the processor 17 performs this processing, the user's operation becomes simpler.

[0059] Furthermore, when the processor 17 extracts the specified points A2 and B2 in step S30, it can choose not to extract specified points that were previously deleted by executing the usage example in Figure 5. If the processor 17 performs this operation, it can simplify the process of excluding any of the specified points A2 and B2 in step S42.

[0060] (supplementary explanation) An example of the technical meaning of the matters disclosed in this embodiment is as follows: The ultrasound image analysis device 12 is an example of an ultrasound image analysis device and a computer. The ultrasound probe 11 is an example of an ultrasound probe. The transmitting / receiving device 20 is an example of a transmitting / receiving device. The processor 17 is an example of a processor. Step S10 is an example of a first process. Step S20 is an example of a second process. Step S30 is an example of a third process. Step S40 is an example of a fourth process. Steps S20 and S60 are examples of a fifth process. Step 60 is an example of a sixth process.

[0061] Arm 15 is an example of a subject. Muscle 16 is an example of a muscle. Superficial layer 16A is an example of the superficial layer of a muscle. Deep layer 16B is an example of the deep layer of a muscle. Region of interest A1 is an example of the first region of interest. Region of interest B1 is an example of the second region of interest. Designation point A2 is an example of the first designation point. Designation point B2 is an example of the second designation point. Representative designation points A3 and B3 are examples of representative designation points. The outer shape of the region of interest is not limited to a rectangle, but may be an ellipse, circle, triangle, pentagon, hexagon, etc. The subject is not limited to a human arm, but may be a human leg, torso, etc. The subject may also be an animal other than a human.

[0062] This embodiment describes the following features. For example, an ultrasound image analysis method is described which is performed by a computer connected to an ultrasound probe that converts reflected waves generated by irradiating a subject with ultrasound into a signal and outputs it, and processes the signal acquired from the ultrasound probe to generate a two-dimensional ultrasound image, wherein the computer performs a first process of generating multiple ultrasound images of the subject's muscles in motion, a second process of setting a region of interest in a selected single ultrasound image from the multiple ultrasound images, a third process of extracting a specified point within the region of interest, and a fourth process of determining the amount of muscle movement by automatically tracking the specified point in each of the ultrasound images other than the selected ultrasound image. The ultrasound image analysis method is disclosed based on the flowcharts in Figure 2 and Figure 5.

[0063] Furthermore, the storage medium contains an ultrasound image analysis program that is connected to an ultrasound probe that converts reflected waves generated by irradiating a subject with ultrasound into a signal and outputs it, and that causes a computer to perform processing to generate a two-dimensional ultrasound image by processing the signal acquired from the ultrasound probe, wherein the program causes the computer to perform a first process of generating multiple ultrasound images of the subject's muscles in motion; a second process of setting a region of interest in a single ultrasound image selected from the multiple ultrasound images; a third process of extracting a specified point within the region of interest; and a fourth process of determining the amount of muscle movement by automatically tracking the specified point in each of the ultrasound images other than the selected ultrasound image. The storage medium can also be understood as a program product in which the program is stored. [Industrial applicability]

[0064] This embodiment can be used as an ultrasound image analysis device and ultrasound image analysis program for analyzing ultrasound images of muscles obtained by irradiating a subject with ultrasound waves. [Explanation of symbols]

[0065] 11...Ultrasound probe, 12...Ultrasound image analyzer, 15...Arm, 16...Muscle, 16A...Surface layer, 16B...Deep layer, 17...Processor, 20...Transmitting / receiving device, A1, B1...Region of interest, A2, B2...Designated points

Claims

1. A transmitting and receiving device connected to an ultrasound probe that converts the reflected waves generated by irradiating a subject with ultrasound into a signal and outputs it, A processor that processes signals acquired from the ultrasound probe to generate a two-dimensional ultrasound image, An ultrasonic image analysis device having, The aforementioned processor, A first process that generates multiple ultrasound images showing the muscles of the subject in motion, A second process involves setting a region of interest in a single ultrasound image selected from the aforementioned multiple ultrasound images, A third process for extracting specified points within the aforementioned region of interest, A fourth process is performed to determine the amount of muscle movement by automatically tracking the specified points in each of the ultrasound images other than the selected ultrasound image, An ultrasonic image analysis device configured to perform the following actions.

2. An ultrasonic image analysis apparatus according to claim 1, The fourth process performed by the aforementioned processor is: An ultrasound image analysis device configured to exclude the specified points with relatively large displacements from the tracking target, and to determine the amount of muscle displacement based on the displacement of the remaining specified points.

3. An ultrasonic image analysis apparatus according to claim 1, The fourth process performed by the aforementioned processor is: An ultrasound image analysis device configured to determine the amount of muscle movement based on the average value of the movement amounts of the above-mentioned multiple specified points.

4. An ultrasonic image analysis apparatus according to claim 1, The fourth process performed by the aforementioned processor is: An ultrasound image analysis device configured to determine the amount of muscle movement by automatically tracking a representative designated point located in the center of the region of interest.

5. An ultrasonic image analysis apparatus according to claim 1, The system is further provided with an operating device that allows the user to input the superficial and deep positions of the muscle in the ultrasound image, and to input whether or not the muscle is subjected to external force. The aforementioned processor, Before performing the second process described above, a fifth process is performed to determine the superficial and deep positions of the muscle in the ultrasound image, Before performing the second process described above, a sixth process is performed to determine whether or not the muscle is subjected to an external force, Further execution, The second process performed by the processor includes setting a first region of interest in the superficial layer of the muscle in the ultrasound image and setting a second region of interest in the deep layer of the muscle. The third process performed by the processor includes a process for extracting a first designated point in the first region of interest, and a process for extracting a second designated point in the second region of interest. The fourth process performed by the aforementioned processor is: A process to determine the amount of muscle movement within the surface layer by excluding the amount of movement of a first designated point in the first region of interest if that amount of movement is greater than or equal to a first threshold, A process to determine the amount of movement of the muscle in the deep layer by excluding the amount of movement of the second designated point in the second region of interest if that amount of movement is greater than or equal to the second threshold, Includes, If the processor determines in the sixth process that the muscle is subjected to an external force, it executes the fourth process using the first threshold that exceeds the second threshold, If the processor determines in the fifth process that the muscle is not subjected to an external force, it executes the fourth process using the first and second thresholds of the same magnitude. Ultrasonic imaging analyzer.

6. An ultrasonic image analysis apparatus according to claim 1, An operating device is further provided that allows the user to input whether or not the muscle is subjected to an external force. Before executing the second process, the processor determines whether or not the muscle is subjected to an external force. The fourth process performed by the processor is a process that determines the amount of muscle movement by excluding the amount of movement of the specified point if that amount of movement is greater than or equal to a threshold, When the processor determines that the muscle is subjected to an external force, it executes the fourth process using the first threshold, If the processor determines that the muscle is not subjected to an external force, it executes the fourth process using a second threshold less than the first threshold. Ultrasonic imaging analyzer.

7. An ultrasound image analysis program is connected to an ultrasound probe that converts reflected waves generated by irradiating a subject with ultrasound into a signal and outputs it, and causes a computer to perform processing to process the signal acquired from the ultrasound probe and generate a two-dimensional ultrasound image, To the aforementioned computer, A first process that generates multiple ultrasound images showing the muscles of the subject in motion, A second process involves setting a region of interest in a single ultrasound image selected from the aforementioned multiple ultrasound images, A third process for extracting specified points within the aforementioned region of interest, A fourth process is performed to determine the amount of muscle movement by automatically tracking the specified points in each of the ultrasound images other than the selected ultrasound image, An ultrasound image analysis program that performs the following actions.

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