Information processing device, information processing method, and program

Abstract

Provided are an information processing device, an information processing method, and a program capable of improving the accuracy of discrimination of blood vessels between arteries and veins. This information processing device is designed to process an ultrasound image (U) generated by causing a vibrator array (13) to transmit an ultrasound beam (UB) to the inside of a living body (30), and receiving an ultrasound echo generated within the living body (30). The information processing device is provided with: a blood vessel detection unit (71) that detects a blood vessel region (Ra) including a blood vessel (B) from the ultrasound image (U); a structure detection unit (72) that detects structures other than the blood vessel (B) from the ultrasound image (U); and an artery / vein discrimination unit (73) that discriminates the blood vessel (B) in the blood vessel region (Ra) between an artery and a vein on the basis of a relative positional relationship between the blood vessel region (Ra) and the structures.

Description

[Technical Field]

[0001] The technology disclosed herein relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Ultrasound diagnostic devices have been known for obtaining images of the inside of a subject. Ultrasound diagnostic devices generally have an ultrasound probe equipped with a transducer array in which multiple ultrasound transducers are arranged. This ultrasound probe, when placed in contact with the body surface of the subject, transmits ultrasound beams from the transducer array toward the inside of the subject, and receives ultrasound echoes from the subject with the transducer array. Electrical signals corresponding to the ultrasound echoes are thereby acquired. Furthermore, the ultrasound diagnostic device processes the acquired electrical signals to generate an ultrasound image of the corresponding part of the subject.

[0003] Incidentally, a procedure (so-called echo-guided puncture) is known in which a puncture needle is inserted into a blood vessel of a subject while observing the inside of the subject using an ultrasound diagnostic device. In the echo-guided puncture, the surgeon usually needs to check the ultrasound image to grasp the position, shape, etc. of the blood vessels contained in the ultrasound image, but accurately grasping these requires a certain level of skill. Therefore, it has been proposed to automatically detect blood vessels contained in the ultrasound image and present the detected blood vessels to the surgeon (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Special Publication No. 2017-524455 Summary of the Invention [Problem to be solved by the invention]

[0005] When performing puncture, the surgeon must accurately determine whether the blood vessel is an artery or a vein based on the ultrasound image. This determination will be referred to as arteriovenous determination hereinafter.

[0006] It is possible to determine whether a blood vessel is an artery or vein by performing information processing such as image analysis based on ultrasound images. However, if blood vessels are determined to be arteries or veins individually, it is easy to make an error in the determination if the shapes of arteries and veins are similar.

[0007] The technology of the present disclosure aims to provide an information processing device, an information processing method, and a program that enable improvement in the accuracy of determining whether a blood vessel is an artery or a vein. [Means for solving the problem]

[0008] The information processing device disclosed herein is an information processing device that processes ultrasound images generated by transmitting ultrasound beams from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, and is equipped with a blood vessel detection unit that detects vascular regions including blood vessels from within the ultrasound image, a structure detection unit that detects structures other than blood vessels from within the ultrasound image, and an arteriovenous determination unit that determines whether the blood vessels included in the vascular region are arteries or veins based on the relative positional relationship between the vascular region and the structures.

[0009] It is preferable that the ultrasound image displayed on the display device further includes a highlighting section that displays the vascular region in a manner that makes it possible to distinguish whether the blood vessels included in the vascular region are arteries or veins.

[0010] In addition to detecting a blood vessel region, the blood vessel detection unit preferably determines whether the blood vessels included in the blood vessel region are arteries or veins.

[0011] It is preferable to further include a correction unit that corrects the result of the artery / vein determination made by the blood vessel detection unit based on the result of the artery / vein determination made by the artery / vein determination unit.

[0012] The correction unit preferably compares the certainty of the artery / vein determination made by the blood vessel detection unit with the certainty of the artery / vein determination made by the artery / vein determination unit, and selects the determination result with the higher certainty.

[0013] The highlighting unit preferably displays the degree of certainty of the determination result selected by the correcting unit on the display device.

[0014] It is preferable that the highlighting unit displays a message on the display device to call attention when the confidence level for the determination result selected by the correction unit is lower than a certain value.

[0015] The information processing method disclosed herein is an information processing method that processes an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, and detects a vascular region including blood vessels from the ultrasound image, detects structures other than blood vessels from the ultrasound image, and determines whether the blood vessels included in the vascular region are arteries or veins based on the relative positional relationship between the vascular region and the structures.

[0016] The program disclosed herein is a program that causes a computer to perform processing on an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, and causes the computer to detect a vascular region including blood vessels from the ultrasound image, detect structures other than blood vessels from the ultrasound image, and determine whether the blood vessels included in the vascular region are arteries or veins based on the relative positional relationship between the vascular region and the structures. [Effects of the Invention]

[0017] According to the technology of the present disclosure, it is possible to provide an information processing device, an information processing method, and a program that enable improvement in the accuracy of determining whether a blood vessel is an artery or a vein. [Brief explanation of the drawings]

[0018] [Figure 1]1 is an external view showing an example of the configuration of an ultrasound diagnostic apparatus according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating an example of an ultrasound-guided puncture method. [Figure 3] FIG. 1 is a block diagram showing an example of the configuration of an ultrasound diagnostic apparatus. [Figure 4] FIG. 2 is a block diagram showing an example of the configuration of a receiving circuit. [Figure 5] FIG. 2 is a block diagram showing an example of the configuration of an image generating unit. [Figure 6] FIG. 2 is a block diagram showing an example of the configuration of an image analysis unit. [Figure 7] FIG. 10 is a diagram illustrating an example of blood vessel detection processing. [Figure 8] FIG. 10 is a diagram illustrating an example of a learning phase for learning a blood vessel detection model. [Figure 9] FIG. 10 is a diagram illustrating an example of a structure detection process. [Figure 10] FIG. 10 is a diagram illustrating an example of a learning phase for learning a structure detection model. [Figure 11] 10A and 10B are diagrams illustrating an example of an artery / vein determination process. [Figure 12] FIG. 10 is a diagram illustrating an example of a correction process. [Figure 13] FIG. 10 is a diagram illustrating an example of highlighting processing. [Figure 14] 10 is a flowchart illustrating an example of the operation of the ultrasound diagnostic apparatus. [Figure 15] FIG. 10 is a diagram showing an example of blood vessel detection processing for another ultrasound image. [Figure 16] FIG. 10 is a diagram showing an example of structure detection processing for another ultrasound image. [Figure 17] 10A and 10B are diagrams illustrating an example of artery and vein determination processing for another ultrasound image. [Figure 18] FIG. 10 is a diagram showing an example of correction processing for another ultrasound image. [Figure 19] FIG. 10 is a diagram showing an example of highlighting processing for another ultrasound image. [Figure 20] FIG. 10 is a diagram illustrating a modified example of the artery / vein determination process. [Figure 21] FIG. 10 is a diagram illustrating a modified example of the artery / vein determination process. [Figure 22] FIG. 10 is a diagram showing an example of displaying a score. [Figure 23] FIG. 10 is a diagram showing an example of displaying a message to alert the surgeon. [Figure 24] FIG. 10 is a diagram showing a first modified example of an ultrasonic diagnostic apparatus. [Figure 25] FIG. 10 is a diagram showing a second modified example of the ultrasonic diagnostic device. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, embodiments of the technology of the present disclosure will be described with reference to the accompanying drawings. The following description of the components will be based on a representative embodiment of the present invention, but the technology of the present disclosure is not limited to such an embodiment.

[0020] [First embodiment] FIG. 1 shows an example of the configuration of an ultrasound diagnostic device 2 according to the technique of the present disclosure. The ultrasound diagnostic device 2 according to this embodiment is composed of an ultrasound probe 10 and a device main body 20. The ultrasound probe 10 is held by an operator and is brought into contact with the surface of a living body that is the measurement target. The ultrasound probe 10 transmits and receives an ultrasound beam UB to and from the interior of the living body.

[0021] The device main body 20 is, for example, a smartphone or a tablet terminal. When a program such as application software is installed on the device main body 20, the device main body 20 performs imaging of the signal output from the ultrasound probe 10. The ultrasound probe 10 and the device main body 20 communicate with each other wirelessly (i.e., wirelessly) via, for example, WiFi or Bluetooth (registered trademark). The device main body 20 is not limited to a mobile terminal such as a smartphone or a tablet terminal, but may also be a PC (Personal Computer) or the like. The device main body 20 is an example of an "information processing device" according to the technology of the present disclosure.

[0022] The ultrasonic probe 10 has a housing 11. The housing 11 is composed of an array housing portion 11A and a grip portion 11B. The array housing portion 11A houses the transducer array 13 (see FIG. 3). The grip portion 11B is connected to the array housing portion 11A and is held by the surgeon. For the sake of explanation, the direction from the grip portion 11B toward the array housing portion 11A is defined as the +Y direction, the width direction of the ultrasonic probe 10 perpendicular to the Y direction is defined as the X direction, and the direction perpendicular to the X and Y directions (i.e., the thickness direction of the ultrasonic probe 10) is defined as the Z direction.

[0023] An acoustic lens is disposed at the +Y direction end of the array housing portion 11A. A so-called acoustic matching layer (not shown) is disposed on the transducer array 13, and an acoustic lens is disposed on the acoustic matching layer. The multiple transducers included in the transducer array 13 are arranged linearly along the X direction. That is, the ultrasonic probe 10 of this embodiment is a linear type, and transmits an ultrasonic beam UB linearly. Note that the ultrasonic probe 10 may be a convex type in which the transducer array 13 is arranged on a convex curved surface. In this case, the ultrasonic probe 10 transmits the ultrasonic beam UB radially. The ultrasonic probe 10 may also be a sector type.

[0024] Furthermore, linear guide markers M extending along the Y direction are attached to the outer periphery of the array housing portion 11A. The guide markers M are used as a guide when the operator brings the ultrasonic probe 10 into contact with the living body.

[0025] The device main body 20 has a display device 21 for displaying an ultrasound image based on a signal transmitted from the ultrasound probe 10. The display device 21 is, for example, a display device such as an organic electroluminescence (EL) display or a liquid crystal display. A touch panel is incorporated in the display device 21. The surgeon can perform various operations on the device main body 20 using the touch panel.

[0026] FIG. 2 is a diagram illustrating an example of an ultrasound-guided puncture method. As shown in FIG. 2, an ultrasound probe 10 is used when an operator inserts a puncture needle 31 into a blood vessel B in a living body 30 while checking an ultrasound image displayed on the device main body 20. The living body 30 is, for example, a human arm. The ultrasound probe 10 is placed against the surface of the living body 30 so that the width direction (i.e., the X direction) of the ultrasound probe 10 crosses the running direction of the blood vessel B. This procedure is called the short-axis method (or cross-sectional method). A cross section of the blood vessel B is displayed on the ultrasound image. The operator punctures, for example, a vein from one or more blood vessels B displayed on the ultrasound image.

[0027] In addition to blood vessels, there are anatomical structures (hereinafter simply referred to as structures) within the living body 30. The structures are biological tissues such as tendons, bones, nerves, and muscles. The types and characteristics of the structures contained in the living body 30 differ depending on the part of the living body 30 (arms, legs, abdomen, etc.).

[0028] After detecting blood vessels from the ultrasound image, the device main body 20 determines whether the blood vessels are arteries or veins, and displays the results of the artery and vein determination within the ultrasound image displayed on the display device 21, thereby assisting the surgeon in performing puncture.

[0029] 3 shows an example of the configuration of the ultrasound diagnostic device 2. The ultrasound probe 10 has a transducer array 13, a transmission / reception circuit 14, and a communication unit 15. The transmission / reception circuit 14 includes a transmission circuit 16 and a reception circuit 17. The transmission circuit 16 and the reception circuit 17 are each connected to the transducer array 13. In addition, the transmission / reception circuit 14 inputs and outputs signals to and from a processor 25 of the device main body 20 via the communication unit 15.

[0030] The transducer array 13 has a plurality of transducers (not shown) arranged one-dimensionally or two-dimensionally. Each of these transducers transmits an ultrasonic beam UB in accordance with a drive signal supplied from the transmission circuit 16, and receives ultrasonic echoes from the living body 30. The transducer outputs a signal based on the received ultrasonic echoes. The transducer is configured, for example, by forming electrodes on both ends of a piezoelectric body. The piezoelectric body is made of piezoelectric ceramics such as PZT (Lead Zirconate Titanate), polymer piezoelectric elements such as PVDF (Poly Vinylidene Di Fluoride), and piezoelectric single crystals such as PMN-PT (Lead Magnesium Niobate-Lead Titanate).

[0031] The transmission circuit 16 includes, for example, multiple pulse generators. The transmission circuit 16 adjusts the delay amount of the drive signal based on a transmission delay pattern selected in response to a control signal transmitted from the processor 25 of the device main body 20, and supplies the drive signal to the multiple transducers included in the transducer array 13. The transmission circuit 16 adjusts the delay amount of the drive signal so that ultrasonic waves transmitted from the multiple transducers form an ultrasonic beam UB. The drive signal is a pulsed or continuous wave voltage signal. When the drive signal is applied, the transducer transmits pulsed or continuous wave ultrasonic waves by expanding and contracting. The ultrasonic waves transmitted from the multiple transducers are combined to form an ultrasonic beam UB as a composite wave.

[0032] The ultrasonic beam UB transmitted into the living body 30 is reflected by a part such as a blood vessel B in the living body 30, and becomes an ultrasonic echo, which propagates toward the transducer array 13. The ultrasonic echo propagating toward the transducer array 13 in this manner is received by the multiple transducers that make up the transducer array 13. The transducers expand and contract upon receiving the ultrasonic echo, generating an electrical signal. The electrical signal generated by the transducer is output to the receiving circuit 17.

[0033] The receiving circuit 17 generates sound ray signals by processing the electrical signals output from the transducer array 13 in accordance with a control signal transmitted from the processor 25 of the device main body 20. As an example, as shown in Fig. 4, the receiving circuit 17 is configured by connecting an amplifier 41, an A / D (Analog to Digital) converter 42, and a beam former 43 in series.

[0034] The amplifier 41 amplifies signals input from the multiple transducers that make up the transducer array 13 and transmits the amplified signals to the A / D converter 42. The A / D converter 42 converts the signals transmitted from the amplifier 41 into digital reception data and transmits the converted reception data to the beamformer 43. The beamformer 43 adds each reception data converted by the A / D converter 42 with a delay in accordance with the speed of sound or the distribution of sound speeds that is set based on the reception delay pattern selected in response to a control signal transmitted from the processor 25 of the device main body 20. This addition process is called reception focusing process. By this reception focusing process, each reception data converted by the A / D converter 42 is phased and added, and a sound ray signal with a narrowed focus of the ultrasonic echo is acquired.

[0035] The device main body 20 has a display device 21, an input device 22, a communication unit 23, a storage device 24, and a processor 25. The input device 22 is, for example, a touch panel incorporated in the display device 21. When the device main body 20 is a PC or the like, the input device 22 may be a keyboard, a mouse, a trackball, a touchpad, or the like. The communication unit 23 performs wireless communication with the communication unit 15 of the ultrasound probe 10.

[0036] The processor 25 is connected to an input device 22 and a storage device 24. The processor 25 and the storage device 24 are connected so as to enable bidirectional information exchange between them.

[0037] The storage device 24 is a device that stores the program 26 that operates the ultrasound diagnostic device 2, and is, for example, a flash memory, an HDD (Hard Disc Drive), or an SSD (Solid State Drive). When the device main body 20 is a PC or the like, the storage device 24 can be a recording medium such as an FD (Flexible Disc), an MO (Magneto-Optical) disk, a magnetic tape, a CD (Compact Disc), a DVD (Digital Versatile Disc), an SD (Secure Digital) card, or a USB (Universal Serial Bus) memory, or a server.

[0038] The processor 25 is, for example, a CPU (Central Processing Unit). The processor 25 performs processing based on a program 26 in cooperation with a RAM (Random Access Memory) (not shown) and the like, thereby functioning as a main control unit 50, an image generation unit 51, a display control unit 52, an image analysis unit 53, and a highlighting unit 54.

[0039] The processor 25 is not limited to a CPU, but may be configured using an FPGA (Field Programmable Gate Array), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a GPU (Graphics Processing Unit), or other ICs (Integrated Circuits), or may be configured using a combination of these.

[0040] The main controller 50 controls each part of the ultrasound diagnostic apparatus 2 based on input operations by the operator via the input device 22. The main controller 50 transmits the above-mentioned control signal to the ultrasound probe 10 via the communication unit 23. The processor 25 receives, via the communication unit 23, an acoustic ray signal generated by the receiving circuit 17 from the ultrasound probe 10.

[0041] The image generation unit 51, under the control of the main control unit 50, acquires sound ray signals input from the ultrasonic probe 10 and generates an ultrasonic image U based on the acquired sound ray signals. As an example, as shown in Fig. 5, the image generation unit 51 is configured by connecting a signal processing unit 61, a DSC (Digital Scan Converter) 62, and an image processing unit 63 in series.

[0042] The signal processing unit 61 performs correction for attenuation due to distance on the sound ray signals generated by the receiving circuit 17 in accordance with the depth of the ultrasonic wave reflection position, and then performs envelope detection processing to generate a B-mode image signal, which is tomographic image information regarding the tissue within the subject.

[0043] The DSC 62 converts the B-mode image signal generated by the signal processing unit 61 into an image signal that conforms to the scanning method of a normal television signal (so-called raster conversion). The image processing unit 63 performs various image processing such as gradation processing on the B-mode image signal input from the DSC 62, and then outputs the B-mode image signal to the display control unit 52 and the image analysis unit 53. Hereinafter, the B-mode image signal that has been subjected to image processing by the image processing unit 63 will be simply referred to as an ultrasound image U.

[0044] The transmission / reception circuit 14 and the image generation unit 51 of the ultrasonic probe 10 are controlled by the main control unit 50 so that the ultrasonic images U are periodically generated at a constant frame rate. The transmission / reception circuit 14 and the image generation unit 51 function as an image acquisition unit that acquires the ultrasonic images U.

[0045] The display control unit 52, under the control of the main control unit 50, performs predetermined processing on the ultrasound image U generated by the image generation unit 51, and causes the display device 21 to display the processed ultrasound image U.

[0046] Under the control of the main controller 50, the image analyzer 53 generates a blood vessel information DB by performing image analysis on the ultrasound image U input from the image generator 51, and outputs the generated blood vessel information DB to the highlighting unit 54. The blood vessel information DB includes, for example, the detection results of the blood vessel region included in the ultrasound image U and the arterial / venous determination results of the detected blood vessels.

[0047] Under the control of the main controller 50, the highlighting unit 54 controls the display controller 52 based on the blood vessel information DB input from the image analyzer 53, thereby highlighting the blood vessel region in the ultrasound image U displayed on the display device 21. Furthermore, based on the artery / vein determination result, the highlighting unit 54 displays the blood vessel region in a manner that enables identification of whether the blood vessels included in the blood vessel region are arteries or veins.

[0048] 6, the image analysis unit 53 is composed of a blood vessel detection unit 71, a structure detection unit 72, an artery / vein determination unit 73, and a correction unit 74. The blood vessel detection unit 71 and the structure detection unit 72 receive an ultrasound image U generated by the image generation unit 51 as input.

[0049] The blood vessel detection unit 71 identifies a blood vessel region by individually detecting each blood vessel included in the ultrasound image U, and performs artery / vein determination of the blood vessels included in the blood vessel region. The blood vessel detection unit 71 outputs information including the detection results of the blood vessel region and the artery / vein determination results for each blood vessel region as blood vessel detection information D1 to the correction unit 74 and the artery / vein determination unit 73. It is sufficient that at least information related to the blood vessel region detected by the blood vessel detection unit 71 is input to the artery / vein determination unit 73.

[0050] The structure detection unit 72 detects a structure area including structures such as tendons, bones, nerves, or muscles based on the ultrasound image U, and outputs information representing the detected structure area to the artery / vein determination unit 73 as structure detection information D2.

[0051] The artery / vein determination unit 73 performs artery / vein determination of blood vessels included in the vascular region based on the anatomical relative positional relationship between the vascular region included in the blood vessel detection information D1 and the structure region included in the structure detection information D2. In other words, the artery / vein determination unit 73 uses the structure region as a landmark and performs artery / vein determination based on the relative positional relationship of the blood vessel with reference to the landmark. The artery / vein determination unit 73 outputs information representing the result of the artery / vein determination to the correction unit 74 as artery / vein determination information D3.

[0052] The correction unit 74 corrects the artery / vein determination result included in the blood vessel detection information D1 based on the artery / vein determination information D3. The correction unit 74 outputs the corrected blood vessel detection information D1 to the highlighting unit 54 as the above-mentioned blood vessel information DB.

[0053] FIG. 7 shows an example of blood vessel detection processing by the blood vessel detection unit 71. The blood vessel detection unit 71 uses a known algorithm to detect a blood vessel region Ra including a blood vessel B from within the ultrasound image U, and determines whether the blood vessel B included in the blood vessel region Ra is an artery or a vein. If the blood vessel region Ra includes a blood vessel B determined to be an artery, it is represented by a dashed line. Furthermore, if the blood vessel region Ra includes a blood vessel B determined to be a vein, it is represented by a solid line. The blood vessel region Ra shown in FIG. 7 includes a blood vessel B determined to be an artery. Note that if the ultrasound image U includes multiple blood vessels B, the blood vessel detection unit 71 detects a blood vessel region Ra for each of the blood vessels B.

[0054] The vascular region Ra is assigned a "label" that indicates the artery / vein determination result and a "score" that indicates the certainty (i.e., likelihood) of the artery / vein determination result. The label indicates whether the blood vessel B included in the vascular region Ra is an "artery" or a "vein." The score is a value within the range of 0 to 1, with the closer to 1 the higher the certainty. The vascular region Ra to which the label and score are assigned corresponds to the above-mentioned vascular detection information D1.

[0055] In addition to blood vessel B, ultrasound image U shown in Figure 7 also shows the extensor carpi radialis longus tendon (hereinafter referred to as ECRL (Extensor Carpi Radialis Longus)) and the extensor carpi radialis brevis tendon (hereinafter referred to as ECRB (Extensor Carpi Radialis Brevis)) present in the human wrist. The ECRL and ECRB are examples of structures.

[0056] In this embodiment, the blood vessel detection unit 71 performs blood vessel detection processing using a blood vessel detection model 71A (see FIG. 8), which is a trained model generated by machine learning. The blood vessel detection model 71A is, for example, an object detection algorithm using deep learning. For example, an object detection model configured by R-CNN (Regional CNN), which is a type of convolutional neural network (CNN), can be used as the blood vessel detection model 71A.

[0057] The blood vessel detection model 71A detects an area containing a single blood vessel as an object from within the ultrasound image U, and determines a label for the detected area. The blood vessel detection model 71A then outputs information representing the detected blood vessel area Ra together with the label and score.

[0058] FIG. 8 is a diagram illustrating an example of a learning phase in which the blood vessel detection model 71A is trained by machine learning. The blood vessel detection model 71A learns using training data TD1. The training data TD1 includes a plurality of training images P to which a correct answer label L is attached. The training images P included in the training data TD1 are sample images of individual blood vessels (arteries and veins). The training data TD1 includes various training images P of blood vessels with different shapes, sizes, etc.

[0059] In the learning phase, a teacher image P is input to the blood vessel detection model 71A. The blood vessel detection model 71A outputs a judgment result A for the teacher image P. A loss calculation is performed using a loss function based on this judgment result A and the correct label L. Then, update settings of various coefficients of the blood vessel detection model 71A are performed according to the result of the loss calculation, and the blood vessel detection model 71A is updated according to the update settings.

[0060] In the learning phase, a series of processes is repeatedly performed, including input of the teacher image P to the blood vessel detection model 71A, output of the determination result A from the blood vessel detection model 71A, loss calculation, update setting, and updating of the blood vessel detection model 71A. This series of processes is terminated when the detection accuracy reaches a predetermined set level. The blood vessel detection model 71A whose detection accuracy has thus reached the set level is stored in the storage device 24 and is then used by the blood vessel detection unit 71 in the blood vessel detection process, which is the operation phase.

[0061] 9 shows an example of structure detection processing by the structure detection unit 72. The structure detection unit 72 performs processing to detect a structure region Rb containing a structure from within the ultrasound image U using a known algorithm.

[0062] In this embodiment, the structure detection unit 72 performs the structure detection process using a structure detection model 72A (see FIG. 10), which is a trained model generated by machine learning. The structure detection model 72A is, for example, an object detection algorithm using deep learning. For example, an object detection model configured by R-CNN, which is a type of CNN, can be used as the structure detection model 72A.

[0063] The structure detection unit 72 detects a structure region Rb that includes a structure as an object from within the ultrasound image U. Information representing the structure region Rb corresponds to the above-mentioned structure detection information D2. In the example shown in FIG. 9, the structure detection unit 72 detects a region that includes ECRL and ECRB as the structure region Rb. Note that the structure detection unit 72 may detect a region that includes ECRL and a region that includes ECRB separately as the structure region Rb.

[0064] 10 is a diagram illustrating an example of a learning phase in which the structure detection model 72A is trained by machine learning. The structure detection model 72A is trained using training data TD2. The training data TD2 includes a plurality of training images P to which a correct answer label L is attached. The training images P included in the training data TD2 are sample images of structures. The training data TD2 includes training images P of various structures that differ in the type of biological tissue (tendon, bone, nerve, muscle, etc.), number, shape, size, texture, etc.

[0065] In the learning phase, a teacher image P is input to the structure detection model 72A. The structure detection model 72A outputs a judgment result A for the teacher image P. A loss calculation is performed using a loss function based on this judgment result A and the correct label L. Then, update settings of various coefficients of the structure detection model 72A are performed according to the result of the loss calculation, and the structure detection model 72A is updated according to the update settings.

[0066] In the learning phase, a series of processes is repeatedly performed, including input of the teacher image P to the structure detection model 72A, output of the determination result A from the structure detection model 72A, loss calculation, update setting, and updating of the structure detection model 72A. This series of processes is repeated until the detection accuracy reaches a predetermined set level. The structure detection model 72A whose detection accuracy has thus reached the set level is stored in the storage device 24, and then used by the structure detection unit 72 in the structure detection process, which is the operation phase.

[0067] 11 shows an example of artery / vein determination processing by the artery / vein determination unit 73. The artery / vein determination unit 73 performs artery / vein determination of a blood vessel B included in the blood vessel region Ra based on the anatomical relative positional relationship between the blood vessel region Ra included in the blood vessel detection information D1 and the structure region Rb included in the structure detection information D2. The artery / vein determination unit 73 obtains a score for each of "artery" and "vein" as labels for the blood vessel B, and selects the label with the larger score. The artery / vein determination unit 73 generates artery / vein determination information D3 by obtaining the label and score for the blood vessel B included in the blood vessel region Ra.

[0068] The artery / vein determination unit 73 performs the artery / vein determination process using, for example, a trained model that is trained using training data that represents the anatomical relative positional relationship between the vascular region Ra and the structure region Rb. Note that the artery / vein determination unit 73 may perform the artery / vein determination using known data that represents the anatomical relative positional relationship.

[0069] 11 is located on the radial side (i.e., the radial side) of the structure region Rb including the ECRL and ECRB, the blood vessel B included in the blood vessel region Ra is determined to be a vein (specifically, a cephalic vein). In this way, by determining whether the blood vessel is an artery or a vein using the anatomical relative positional relationship with the structure, the accuracy of the artery / vein determination is improved.

[0070] 12 shows an example of the correction process by the correction unit 74. The correction unit 74 compares the score included in the artery / vein determination information D3 with the score included in the blood vessel detection information D1 for the corresponding blood vessel B, and selects the label with the higher score. For example, in the example shown in FIG. 12, the score (0.90) for blood vessel B included in the artery / vein determination information D3 is higher than the score (0.75) included in the blood vessel detection information D1. Therefore, the correction unit 74 selects the label (vein) included in the artery / vein determination information D3 as the label for blood vessel B, instead of the label (artery) included in the blood vessel detection information D1.

[0071] In this way, if the score included in the artery / vein determination information D3 is higher than the score included in the blood vessel detection information D1, the label included in the blood vessel detection information D1 is corrected.

[0072] The correction unit 74 outputs the blood vessel detection information D1 after correcting the label as the above-mentioned blood vessel information DB to the highlighting unit 54. The blood vessel information DB includes position information of the blood vessel region Ra in the ultrasound image U, and the label and score for the blood vessel region Ra.

[0073] FIG. 13 shows an example of highlighting processing by the highlighting unit 54. The highlighting unit 54 displays the vascular region Ra using a rectangular frame based on the vascular information DB within the ultrasound image U displayed on the display device 21 of the device main body 20. Furthermore, the highlighting unit 54 displays the vascular region Ra in a manner that enables distinguishing whether the blood vessels included in the vascular region Ra are arteries or veins based on the artery / vein determination result. In the example shown in FIG. 13, the vascular region Ra that includes veins is indicated by a solid line, and the vascular region Ra that includes arteries is indicated by a dashed line. Note that the highlighting unit 54 may display the vascular region Ra in a manner that enables distinguishing, not limited to the type of line, but also by line thickness, line color, line brightness, etc.

[0074] Next, an example of the operation of the ultrasound diagnostic apparatus 2 will be described using the flowchart shown in Fig. 14. First, the main controller 50 determines whether or not a start operation has been performed by the surgeon using the input device 22 or the like (step S10). If the main controller 50 determines that a start operation has been performed (step S10: YES), it operates the transmission / reception circuit 14 and the image generator 51 of the ultrasound probe 10 to generate an ultrasound image U (step S11). The generated ultrasound image U is displayed on the display device 21 by the display controller 52.

[0075] At this time, the surgeon brings the ultrasonic probe 10 into contact with the surface of the living body 30, as shown in Fig. 2. An ultrasonic beam UB is transmitted from the transducer array 13 into the living body 30 in accordance with a drive signal input from the transmission circuit 16. Ultrasonic echoes from within the living body 30 are received by the transducer array 13, and a received signal is output to the reception circuit 17. The received signal received by the reception circuit 17 becomes a sound ray signal via an amplifier 41, an A / D converter 42, and a beam former 43. This sound ray signal is output to the device main body 20 via the communication unit 15.

[0076] The device main body 20 receives the sound ray signals output from the ultrasonic probe 10 via the communication unit 23. The sound ray signals received by the device main body 20 are input to the image generation unit 51. In the image generation unit 51, the sound ray signals are subjected to envelope detection processing in the signal processing unit 61 to become B-mode image signals, which are then output to the display control unit 52 as an ultrasound image U after passing through the DSC 62 and image processing unit 63. The ultrasound image U is also output to the image analysis unit 53.

[0077] In the image analysis unit 53, the blood vessel detection unit 71 performs the above-mentioned blood vessel detection process (see FIG. 7) (step S12). The blood vessel detection information D1 generated by this blood vessel detection process is output to the correction unit 74 and the artery / vein determination unit 73.

[0078] Furthermore, step S13 is performed in parallel with step S12. In step S13, the structure detection unit 72 performs the structure detection process described above (see FIG. 9). Structure detection information D2 generated by this structure detection process is output to the artery / vein determination unit 73. In step S14, the artery / vein determination unit 73 performs the artery / vein determination process described above (see FIG. 11). Artery / vein determination information D3 generated by this artery / vein determination process is output to the correction unit 74.

[0079] Next, the correction unit 74 performs the above-mentioned correction process (see FIG. 12) (step S15). In this correction process, the label for the vascular region Ra included in the blood vessel detection information D1 is corrected based on the artery / vein determination information D3. As a result of this correction process, the blood vessel information DB is output to the highlighting unit 54.

[0080] Then, the highlighting unit 54 performs the highlighting process described above (see FIG. 13) (step S16). This highlighting process highlights the vascular region Ra in the ultrasound image U displayed on the display device 21. The vascular region Ra is also displayed so that it is possible to distinguish whether the blood vessels contained therein are arteries or veins. By performing the highlighting in this manner, the surgeon can accurately grasp the positions of the blood vessels present in the ultrasound image U, and can also accurately grasp whether the blood vessels are arteries or veins.

[0081] Next, the main controller 50 determines whether or not a termination operation has been performed by the surgeon using the input device 22 or the like (step S17). If the main controller 50 determines that a termination operation has not been performed (step S17: NO), the main controller 50 returns the process to step S11. As a result, a new ultrasound image U is generated. On the other hand, if the main controller 50 determines that a termination operation has been performed (step S17: YES), the main controller 50 terminates the operation of the ultrasound diagnostic apparatus 2.

[0082] Conventionally, blood vessels are detected by a blood vessel detection process, and then artery / vein determination is performed for each detected blood vessel. This method often results in errors in determining whether a blood vessel is an artery or a vein, and the determination can change from frame to frame. When a surgeon attempts to perform puncture based on such an artery / vein determination result, they may end up selecting the wrong blood vessel.

[0083] In contrast, the technology disclosed herein uses the anatomical relative positional relationship between blood vessels and structures detected from ultrasound images to determine whether a blood vessel is an artery or vein, improving the accuracy of the determination, allowing the surgeon to accurately identify the blood vessel (e.g., vein) to be punctured.

[0084] Next, examples of blood vessel detection processing, structure detection processing, artery / vein determination processing, correction processing, and highlighting processing for an ultrasound image U different from the ultrasound image U shown in FIG. 7 will be described with reference to FIGS.

[0085] As shown in Figure 15, two blood vessels B1 and B2 are visible in the ultrasound image U of this example. The ultrasound image U also shows the abductor pollicis longus tendon (hereinafter referred to as APL (Abductor Pollicis Longus)) and the radial styloid process (hereinafter simply referred to as bony process). The APL and bony process are examples of structures.

[0086] In the blood vessel detection process, the blood vessel detection unit 71 detects an area including a blood vessel B1 and an area including a blood vessel B2 in the ultrasound image U as blood vessel areas Ra, and assigns a label and a score to each of the blood vessel areas Ra. The two blood vessel areas Ra to which labels and scores are assigned correspond to the above-mentioned blood vessel detection information D1. In this example, the labels of the blood vessels B1 and B2 are both determined to be "vein."

[0087] 16, in the structure detection process, the structure detection unit 72 detects, as structure regions Rb, a region including an APL and a region including a bony protrusion in the ultrasound image U. Information representing the two structure regions Rb corresponds to the above-mentioned structure detection information D2.

[0088] 17, in the artery / vein determination process, the artery / vein determination unit 73 performs artery / vein determination for each of the two vascular regions Ra based on the anatomical relative positional relationship between the two structure regions Rb. The artery / vein determination unit 73 generates artery / vein determination information D3 by obtaining a label and a score for each of the blood vessels B1 and B2.

[0089] 18, in the correction process, the correction unit 74 compares the scores included in the artery / vein determination information D3 with the scores included in the blood vessel detection information D1 for each of the blood vessels B1 and B2, and selects the label with the higher score. In this example, the scores included in the artery / vein determination information D3 are higher than the scores included in the blood vessel detection information D1 for both the blood vessels B1 and B2, so the label included in the artery / vein determination information D3 is selected. In this example, of the labels of the blood vessels B1 and B2 included in the blood vessel detection information D1, the label of the blood vessel B1 is corrected from "vein" to "artery."

[0090] 19, in the highlighting process, the vascular region Ra is highlighted based on the corrected label in the ultrasound image U. In this example, the vascular region Ra including the blood vessel B1 determined to be an "artery" is shown by a dashed line, and the vascular region Ra including the blood vessel B2 determined to be a "vein" is shown by a solid line.

[0091] [Variations] Various modifications of the ultrasonic diagnostic apparatus 2 according to the first embodiment will be described below.

[0092] In the first embodiment, in the artery / vein determination process (see FIG. 11), the artery / vein determination unit 73 calculates a score for each of "artery" and "vein" as labels for blood vessel B, and selects the label with the larger score. Alternatively, as shown in FIG. 20, for example, a threshold value for the score may be set, and a label with a score equal to or greater than the threshold may be selected.

[0093] Also, for example, FIG. 21 shows a case where scores are calculated for each of "artery" and "vein" as labels for blood vessel B, and both scores are below the threshold. This corresponds to low accuracy in determining arteries and veins based on the relative positional relationship with the structure. In this case, the artery / vein determination unit 73 may determine that it is difficult to determine the label (i.e., to determine arteries and veins), and may stop the artery / vein determination. When it is difficult to determine arteries and veins in this way, the highlighting unit 54 may display the blood vessel region Ra in the ultrasound image U without distinguishing between an "artery" and a "vein." In this case, the highlighting unit 54 may simply display the blood vessel region Ra as a "blood vessel."

[0094] In addition, if no structure is found in the ultrasound image U by the structure detection process, the highlighting unit 54 may display the vascular region Ra in the ultrasound image U without distinguishing between an "artery" and a "vein."

[0095] In addition, the highlighting unit 54 may perform highlighting based on the label included in the blood vessel detection information D1 when a structure cannot be found in the ultrasound image U through the structure detection process, or when the accuracy of artery / vein determination based on the relative positional relationship with the structure is low.

[0096] 22, the highlighting unit 54 may display the score for the label selected by the correction unit 74 (i.e., the confidence level for the determination result selected by the correction unit 74) in association with the vascular region Ra. This allows the surgeon to understand the confidence level of the artery / vein determination for each blood vessel.

[0097] 23, the highlighting unit 54 may display a message urging the operator to be careful when the score for the label selected by the correction unit 74 (i.e., the confidence level for the determination result selected by the correction unit 74) is lower than a certain value. This allows the operator to reliably understand that the confidence level for the arteriovenous determination is low and that caution is required when performing puncture.

[0098] Furthermore, the artery / vein determination unit 73 may change the criteria for determining arteries and veins depending on the type of structure detected by the structure detection unit 72. This is because, for example, if a structure has anatomically typical characteristics in relation to a blood vessel, the determination result is likely to be correct even if the artery / vein determination score is low. The artery / vein determination unit 73 changes the threshold value for the score (see FIG. 21 ) depending on, for example, the type of structure. Specifically, if a structure has anatomically typical characteristics in relation to a blood vessel, the threshold value is set lower than when the structure does not have anatomically typical characteristics. Note that the criteria for determining arteries and veins may be changed not only by the threshold value for the score, but also by changing the algorithm for determining arteries and veins.

[0099] Furthermore, in the first embodiment, blood vessels are labeled with two types, "artery" and "vein," but the labels may be further subdivided. For example, "vein" may be subdivided into "cephalic vein," "basilar vein," etc. This allows the artery / vein determination unit 73 to identify the type of blood vessel in addition to determining the artery / vein. In this case, the highlighting unit 54 may display the type of blood vessel in association with the blood vessel region Ra.

[0100] In the first embodiment, the blood vessel detection unit 71 performs the determination of the arteries and veins of the blood vessels, but the blood vessel detection unit 71 may not perform the determination of the arteries and veins and may only detect the blood vessel region Ra. In this case, the correction unit 74 that corrects the result of the determination of the arteries and veins by the blood vessel detection unit 71 is not necessary.

[0101] Furthermore, in the first embodiment, the blood vessel detection unit 71 and the structure detection unit 72 are configured using separate object detection models, but it is also possible to configure the blood vessel detection unit 71 and the structure detection unit 72 using a single object detection model. In this case, the object detection model can be trained using training data including training images of blood vessels alone and training images of structures. It is also possible to configure the blood vessel detection unit 71, the structure detection unit 72, and the artery / vein determination unit 73 using a single object detection model. Furthermore, it is also possible to configure the blood vessel detection unit 71, the structure detection unit 72, the artery / vein determination unit 73, and the correction unit 74 using a single object detection model.

[0102] In addition, in the first embodiment, the blood vessel detection unit 71 and the structure detection unit 72 are configured using an object detection model consisting of CNN, but the object detection model is not limited to CNN and may be a segmentation or other general detection model.

[0103] Furthermore, the object detection model that constitutes the blood vessel detection unit 71 and the structure detection unit 72 may be configured with a classifier that classifies objects based on image features such as AdaBoost or SVM (Support Vector Machine). In this case, the teacher image may be converted into a feature vector, and then the classifier may be trained based on the feature vector.

[0104] Furthermore, the blood vessel detection unit 71 and the structure detection unit 72 are not limited to object detection models based on machine learning, and may perform object detection using template matching. In this case, the blood vessel detection unit 71 stores typical pattern data of a single blood vessel as a template in advance, and calculates the similarity to the pattern data while searching the ultrasound image U with the template. The blood vessel detection unit 71 then identifies, as the blood vessel region Ra, a location where the similarity is greater than or equal to a certain level and is at its maximum. The structure detection unit 72 stores, in advance, typical pattern data of a structure as a template, and calculates the similarity to the pattern data while searching the ultrasound image U with the template. The structure detection unit 72 then identifies, as the blood vessel region Ra, a location where the similarity is greater than or equal to a certain level and is at its maximum. The template may be a portion of an actual ultrasound image, or may be an image that schematically depicts a blood vessel or a structure.

[0105] In addition to simple template matching, similarity calculations can also use, for example, the machine learning method described in Csurka et al.: Visual Categorization with Bags of Keypoints, Proc. of ECCV Workshop on Statistical Learning in Computer Vision, pp. 59-74 (2004), or the general image recognition method using deep learning described in Krizhevsk et al.: ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25, pp. 1106-1114 (2012).

[0106] In the first embodiment, the ultrasonic probe 10 and the device main body 20 are connected by wireless communication, but instead, the ultrasonic probe 10 and the device main body 20 may be connected by wire.

[0107] Furthermore, in the first embodiment, the image generation unit 51 that generates the ultrasound image U based on the sound ray signal is provided in the device main body 20, but instead, the image generation unit 51 may be provided in the ultrasound probe 10. In this case, the ultrasound probe 10 generates the ultrasound image U and outputs it to the device main body 20. The processor 25 of the device main body 20 performs image analysis and the like based on the ultrasound image U input from the ultrasound probe 10.

[0108] In addition, in the first embodiment, the display device 21, the input device 22, and the ultrasonic probe 10 are directly connected to the processor 25, but the display device 21, the input device 22, the ultrasonic probe 10, and the processor 25 may be indirectly connected via a network.

[0109] 24 shows an example of an ultrasound diagnostic device 2A in which a display device 21, an input device 22, and an ultrasound probe 10A are connected to an ultrasound diagnostic device main body 20A via a network NW. The ultrasound diagnostic device 2A is the ultrasound diagnostic device 20 according to the first embodiment except for the display device 21 and the input device 22, and is configured with the transmission / reception circuit 14, a storage device 24, and a processor 25. The ultrasound probe 10A is the ultrasound probe 10 according to the first embodiment except for the transmission / reception circuit 14.

[0110] In this way, in the ultrasound diagnostic device 2A, the display device 21, the input device 22, and the ultrasound probe 10A are connected to the device main body 20A via the network NW, so that the device main body 20A can be used as a so-called remote server. This allows, for example, the surgeon to have the display device 21, the input device 22, and the ultrasound probe 10A at hand, improving convenience. Furthermore, by configuring the display device 21 and the input device 22 as a mobile terminal such as a smartphone or a tablet terminal, convenience is further improved.

[0111] As another example, in an ultrasound diagnostic device 2B shown in Fig. 25, a display device 21 and an input device 22 are mounted on a device main body 20B, and an ultrasound probe 10A is connected to the device main body 20B via a network NW. In this case, the device main body 20B may be configured as a remote server. Alternatively, the device main body 20B may be configured as a mobile terminal such as a smartphone or a tablet terminal.

[0112] In the first embodiment, the following various processors can be used as the hardware structure of processing units that perform various processes, such as the main control unit 50, image generation unit 51, display control unit 52, image analysis unit 53, and highlighting unit 54. As described above, the various processors include a CPU, which is a general-purpose processor that executes software (program 26) and functions as various processing units, as well as a programmable logic device (PLD), which is a processor whose circuit configuration can be changed after manufacture, such as an FPGA, and a dedicated electrical circuit, such as an ASIC, which is a processor with a circuit configuration designed specifically for performing specific processes.

[0113] A single processing unit may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs and / or a combination of a CPU and an FPGA). Also, multiple processing units may be configured with a single processor.

[0114] Examples of configuring multiple processing units with a single processor include, first, a form in which one processor is configured with a combination of one or more CPUs and software, and this processor functions as multiple processing units, as typified by client and server computers. Second, a form in which a processor is used to realize the functions of an entire system including multiple processing units on a single IC chip, as typified by a system on chip (SoC). In this way, various processing units are configured using one or more of the above-mentioned various processors as a hardware structure.

[0115] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements.

[0116] From the above description, the techniques described in Supplementary Items 1 to 9 below can be understood.

[0117] [Additional note 1] An information processing device that processes an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, a processor; The processor: detecting a blood vessel region including a blood vessel from the ultrasound image; Detecting structures other than blood vessels from within the ultrasound image; determining whether the blood vessel included in the blood vessel region is an artery or a vein based on the relative positional relationship between the blood vessel region and the structure; Information processing device. [Additional note 2] The processor: The vascular region is displayed in the ultrasound image displayed on a display device so that it is possible to distinguish whether the blood vessels included in the vascular region are arteries or veins. Item 1. An information processing device according to item 1. [Additional note 3] The processor: Based on the blood vessel region, it is determined whether the blood vessel included in the blood vessel region is an artery or a vein. Item 2. An information processing device according to claim 2. [Additional note 4] The processor: The result of the artery-vein determination based on the blood vessel region is corrected based on the result of the artery-vein determination based on the relative positional relationship between the blood vessel region and the structure. Item 3. An information processing device according to item 3. [Additional note 5] The processor: The certainty of the artery / vein determination based on the blood vessel region is compared with the certainty of the artery / vein determination based on the relative positional relationship between the blood vessel region and the structure, and the determination result with the higher certainty is selected. Item 4. An information processing device according to claim 4. [Additional note 6] The processor: The confidence level for the selected determination result is displayed on the display device. Item 5. An information processing device according to item 5. [Additional note 7] The processor: If the confidence level for the selected determination result is lower than a certain value, a message calling for attention is displayed on the display device. Item 6. An information processing device according to claim 6.

[0118] The technology of the present disclosure can be appropriately combined with the various embodiments and / or various modified examples described above. Furthermore, it is needless to say that it is not limited to the above-described embodiments, and various configurations can be adopted as long as they do not deviate from the gist of the present disclosure.

[0119] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0120] In this specification, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B.

[0121] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0122] 2, 2A, 2B Ultrasound diagnostic equipment 10,10A Ultrasonic Probe 11. Housing 11A Array housing 11B Grip section 13 Transducer array 14 Transmitting and receiving circuit 15 Communications Department 16 Transmitting circuit 17 Receiving circuit 20 Device body 20A, 20B Device body 21 Display device 22 Input Devices 23 Communications Department 24 Storage device 25 processors 26 Programs 30 Living organisms 31 Puncture needle 41 Amplification section 42 A / D conversion section 43 Beamformer 50 Main control unit 51 Image generation unit 52 Display control unit 53 Image analysis unit 54 Highlight section 61 Signal processing section 62 DSC 63 Image processing section 71 Blood vessel detection unit 71A Blood Vessel Detection Model 72 Structure detection unit 72A Structure Detection Model 73 Arteriovenous determination section 74 Correction section A Judgment result B,B1,B2 Blood vessels D1 Blood vessel detection information D2 Structure detection information D3 Arteriovenous determination information DB blood vessel information L Correct label M Guide Marker NW Network P Teacher image Ra vascular area Rb structure area TD1,TD2 training data U Ultrasound image UB ultrasonic beam

Claims

1. An information processing device that processes an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, a blood vessel detection unit that detects a blood vessel region including a blood vessel from within the ultrasound image; a structure detection unit that detects structures other than blood vessels from within the ultrasound image; an artery-vein determining unit that uses the structure as a landmark and determines whether a blood vessel included in the vascular region is an artery or a vein based on a relative positional relationship between the landmark and the vascular region; An information processing device comprising:

2. a highlighting section that displays the vascular region in the ultrasound image displayed on the display device so that the blood vessels included in the vascular region can be distinguished as either arteries or veins; The information processing device according to claim 1 , further comprising:

3. The blood vessel detection unit detects the blood vessel region and determines whether the blood vessel included in the blood vessel region is an artery or a vein. The information processing device according to claim 2 .

4. a correction unit that corrects the result of the artery-vein determination made by the blood vessel detection unit based on the result of the artery-vein determination made by the artery-vein determination unit; The information processing device according to claim 3 , further comprising:

5. The correction unit compares the certainty of the artery-vein determination by the blood vessel detection unit with the certainty of the artery-vein determination by the artery-vein determination unit, and selects the determination result with the higher certainty. The information processing device according to claim 4 .

6. The highlighting unit displays, on the display device, a degree of certainty for the determination result selected by the correction unit. The information processing device according to claim 5 .

7. The highlighting unit displays a message on the display device to call attention when the confidence level of the determination result selected by the correction unit is lower than a certain value. The information processing device according to claim 6 .

8. An information processing method for processing an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, the method comprising: detecting a blood vessel region including a blood vessel from the ultrasound image; Detecting structures other than blood vessels from within the ultrasound image; The structure is used as a landmark, and based on the relative positional relationship between the landmark and the vascular region, it is determined whether the blood vessel included in the vascular region is an artery or a vein. Information processing methods.

9. A program that causes a computer to execute processing on an ultrasound image generated by transmitting an ultrasound beam from a transducer array toward a living body and receiving ultrasound echoes generated within the living body, detecting a blood vessel region including a blood vessel from the ultrasound image; Detecting structures other than blood vessels from within the ultrasound image; using the structure as a landmark, and determining whether the blood vessel included in the vascular region is an artery or a vein based on a relative positional relationship between the landmark and the vascular region; A program that makes a computer do something.

Images