Ultrasonic image detection method, ultrasonic equipment and ultrasonic image network system

Abstract

The invention discloses an ultrasonic image detection method, ultrasonic equipment and an ultrasonic image network system.The method comprises the steps that if an ultrasonic image is not matched with a standard section in a workflow protocol, the ultrasonic image is scanned again, and the section type of the ultrasonic image scanned again is recognized; if the ultrasonic image is matched with a standard section in the workflow protocol, image quality detection is carried out on the ultrasonic image: if the image quality of the ultrasonic image does not meet a preset condition, a first prompt message is output, the ultrasonic image is scanned again based on the first prompt message, and the section type of the ultrasonic image scanned again is identified; and if the section quality of the ultrasonic image meets a preset condition, storing the ultrasonic image as a matched standard section in a workflow protocol. According to the ultrasonic image detection method, the quality and integrity of the section can be ensured, and missed diagnosis and misdiagnosis are reduced.

Description

Technical Field

[0001] This application relates to the field of ultrasound examination, and more particularly to an ultrasound image detection method, ultrasound equipment, and ultrasound imaging network system. Background Technology

[0002] In ultrasound examinations, such as obstetric ultrasound, multiple images corresponding to different sections need to be scanned. Missing sections or poor-quality sections can easily lead to missed diagnoses and misdiagnoses. Therefore, ensuring that complete and standard sections are obtained is crucial for ultrasound diagnosis. Currently, in actual ultrasound operations, the integrity and quality of sections are mainly ensured by the doctor's experience. However, relying solely on manual inspection of the integrity and quality of sections still results in the problem of missed sections or poor-quality sections. Summary of the Invention

[0003] This application provides an acoustic detection method, an ultrasonic device, and an ultrasonic imaging network system, which solves the problem in related technologies that the integrity and quality of the cross-section cannot be guaranteed during ultrasonic examination.

[0004] In a first aspect, this application provides an ultrasound image detection method, the ultrasound image detection method comprising: acquiring an ultrasound image; automatically identifying the section type of the ultrasound image; automatically matching the ultrasound image with a standard section in a workflow protocol based on the identified section type; if the ultrasound image does not match the standard section in the workflow protocol, rescanning the ultrasound image and automatically identifying the section type of the rescanned ultrasound image; if the ultrasound image matches the standard section in the workflow protocol, performing image quality detection on the ultrasound image; if the image quality of the ultrasound image does not meet a preset condition, outputting a first prompt message, rescanning the ultrasound image based on the first prompt message, and automatically identifying the section type of the rescanned ultrasound image; if the section quality of the ultrasound image meets the preset condition, saving the ultrasound image as a matched standard section in the workflow protocol.

[0005] Secondly, this application also provides an ultrasound image detection method, the ultrasound image detection method comprising:

[0006] Acquire an ultrasound image; perform image quality detection on the ultrasound image; if the image quality of the ultrasound image does not meet a preset condition, output a rescan prompt message in the ultrasound device, rescan the ultrasound image based on the rescan prompt message, and perform image quality detection on the ultrasound image; if the image quality of the ultrasound image meets the preset condition, save the ultrasound image as a matched standard section in the workflow protocol, and output a scan pending prompt message in the ultrasound device, the scan pending prompt message being used to instruct the ultrasound device to perform ultrasound scanning on the unmatched standard section in the workflow protocol.

[0007] Thirdly, this application also provides an ultrasound image detection method, the ultrasound image detection method comprising:

[0008] Acquire an ultrasound image; automatically identify the image features of the ultrasound image; based on the identified image features, automatically match the ultrasound image with a standard section in the workflow protocol; if the ultrasound image does not match the standard section in the workflow protocol, rescan the ultrasound image and return to the step of automatically identifying the image features of the ultrasound image; if the ultrasound image matches the standard section in the workflow protocol, perform image quality detection on the ultrasound image; if the image quality of the ultrasound image does not meet the preset conditions, output a first prompt message, rescan the ultrasound image based on the first prompt message, and return to the step of automatically identifying the image features of the ultrasound image; if the section quality of the ultrasound image meets the preset conditions, save the ultrasound image as a matched standard section in the workflow protocol.

[0009] Fourthly, this application also provides an ultrasound image detection method, the ultrasound image detection method comprising:

[0010] Acquire an ultrasound image; automatically identify the section type of the ultrasound image; receive manual input from the user for at least one standard section to be scanned; automatically match the ultrasound image with the at least one standard section based on the identified section type; if the ultrasound image does not match the at least one standard section, rescan the ultrasound image and automatically identify the section type of the rescanned ultrasound image; if the ultrasound image matches the at least one standard section, perform image quality detection on the ultrasound image; if the image quality of the ultrasound image does not meet a preset condition, output a first prompt message, rescan the ultrasound image based on the first prompt message, and automatically identify the section type of the rescanned ultrasound image; if the section quality of the ultrasound image meets the preset condition, save the ultrasound image as a matched standard section among the at least one standard sections.

[0011] Fifthly, this application also provides an ultrasonic device, the ultrasonic device comprising:

[0012] Ultrasonic probe;

[0013] The transmitting and receiving circuit is used to excite the ultrasonic probe to emit ultrasonic waves and control the ultrasonic probe to receive the echo of the ultrasonic waves in order to obtain an ultrasonic echo signal.

[0014] The echo processing module is used to perform beamforming and signal processing on the ultrasonic echo signal to obtain an ultrasonic image.

[0015] A processor for executing the ultrasound image detection method as described above;

[0016] A display for showing ultrasound images and cross-sectional quality control details of the ultrasound images.

[0017] Sixthly, this application also provides an ultrasound imaging network system, the ultrasound imaging network system comprising:

[0018] Ultrasound equipment, used to acquire ultrasound images;

[0019] An image processing device is used to perform the ultrasound image detection method described above.

[0020] The aforementioned ultrasound image detection method automatically identifies the section type of the ultrasound image during the ultrasound scan process. If the identified section type does not match the standard section in the workflow protocol, the ultrasound image is rescanned and the section type of the rescanned ultrasound image is automatically identified. If the identified section type matches the standard section in the workflow protocol, the ultrasound image undergoes section quality detection. When the image quality of the ultrasound image does not meet the preset conditions, a first prompt message is output in the ultrasound device, and the ultrasound image is rescanned based on the first prompt message. When the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved as a matched standard section in the workflow protocol. This method enables real-time detection of whether the image quality of the ultrasound image meets the preset conditions during the ultrasound examination. When the image quality of the ultrasound image is found to be substandard, a timely rescan is performed, thereby ensuring the quality and integrity of the section and reducing missed diagnoses and misdiagnoses. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of an ultrasound imaging network system provided in an embodiment of this application;

[0023] Figure 2 This is a schematic diagram of the structure of an ultrasonic device provided in an embodiment of this application;

[0024] Figure 3 This is a schematic flowchart of an ultrasound image detection method provided in an embodiment of this application;

[0025] Figure 4 This is a schematic flowchart illustrating a sub-step for identifying the type of section provided in an embodiment of this application;

[0026] Figure 5 This is a schematic flowchart illustrating a sub-step of image quality detection provided in an embodiment of this application;

[0027] Figure 6 This is a schematic flowchart illustrating another sub-step of image quality detection provided in an embodiment of this application;

[0028] Figure 7 This is a schematic flowchart of a sub-step of another ultrasound image detection method provided in the embodiments of this application;

[0029] Figure 8 This is a schematic diagram of an image display page provided in an embodiment of this application;

[0030] Figure 9 This is a schematic flowchart of a sub-step of another ultrasound image detection method provided in the embodiments of this application;

[0031] Figure 10 This is a schematic diagram of another image display page provided in an embodiment of this application;

[0032] Figure 11 This is a schematic flowchart of a sub-step of another ultrasound image detection method provided in the embodiments of this application;

[0033] Figure 12 This is a schematic diagram of another image display page provided in an embodiment of this application;

[0034] Figure 13 This is a schematic flowchart of another ultrasound image detection method provided in the embodiments of this application;

[0035] Figure 14 This is a schematic flowchart of another ultrasound image detection method provided in the embodiments of this application;

[0036] Figure 15 This is a schematic flowchart of another ultrasound image detection method provided in the embodiments of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] The flowchart shown in the attached diagram is for illustrative purposes only and does not necessarily include all content and operations / steps, nor does it necessarily have to be performed in the order described. For example, some operations / steps can be broken down, combined, or partially merged, so the actual execution order may change depending on the actual situation.

[0039] It should be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the scope of the application. As used in this specification and the appended claims, the singular terms “a,” “an,” and “the” are intended to include the plural terms unless the context clearly indicates otherwise.

[0040] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0041] Currently, in actual ultrasound procedures, the integrity and quality of the scans are mainly ensured by the doctor's experience. However, relying solely on manual inspection of the scan's integrity and quality still results in missed scans or low-quality scans. Furthermore, the relevant technologies score the scan quality after the ultrasound examination, making real-time quality control during the examination impossible. Even if quality problems are discovered, it is difficult to remedy them.

[0042] Therefore, embodiments of this application provide an acoustic detection method, an ultrasound device, and an ultrasound imaging network system. These systems enable real-time detection of whether the image quality of ultrasound images meets preset conditions during ultrasound examinations. When the image quality is found to be substandard, a rescan is performed promptly, ensuring the quality and integrity of the cross-section. This reduces missed diagnoses and misdiagnoses, preventing medical disputes. Simultaneously, it saves hospitals personnel investment in manual cross-section quality control, reducing costs and improving the level of cross-section quality control management in ultrasound examinations. The ultrasound image detection method will be described in detail below.

[0043] Please refer to Figure 1 , Figure 1 This is a schematic diagram of the structure of an ultrasound imaging network system 1000 provided in an embodiment of this application. Figure 1As shown, the ultrasound imaging network system 1000 includes an ultrasound device 100 and an image processing device 200. The ultrasound device 100 is used to acquire ultrasound images, and the image processing device 200 is used to execute any one of the ultrasound image detection methods in the embodiments of this application. Of course, any one of the ultrasound image detection methods in the embodiments of this application can also be executed by the ultrasound device 100.

[0044] For example, the image processing device 200 can be a server or a terminal. The server can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. The terminal can be an electronic device such as a smartphone, tablet, laptop, or desktop computer. The image processing device 200 and the ultrasound device 100 can communicate via wired or wireless means. It should be noted that, in this embodiment, the image processing device 200 is equivalent to an ultrasound workstation. When medical personnel perform ultrasound examinations, they can directly view the cross-sectional quality inspection results and cross-sectional quality control details of the ultrasound images in the client or web-based report window of the ultrasound workstation.

[0045] In some embodiments, the ultrasound device 100 acquires the currently scanned ultrasound image, automatically identifies the section type of the ultrasound image, and automatically matches the ultrasound image with the standard section in the workflow protocol based on the identified section type. If the ultrasound image does not match the standard section in the workflow protocol, the ultrasound device rescans the ultrasound image and automatically identifies the section type of the rescanned ultrasound image. If the ultrasound image matches the standard section in the workflow protocol, the ultrasound image undergoes image quality detection. If the image quality of the ultrasound image does not meet preset conditions, a first prompt message is output in the ultrasound device 100, the ultrasound image is rescanned based on the first prompt message, and the section type of the rescanned ultrasound image is automatically identified. If the section quality of the ultrasound image meets preset conditions, the ultrasound image is saved as a matched standard section in the workflow protocol.

[0046] In other embodiments, the image processing device 200 can acquire the ultrasound image currently scanned by the ultrasound device 100, automatically identify the section type of the ultrasound image, and automatically match the ultrasound image with the standard section in the workflow protocol based on the identified section type. If the ultrasound image does not match the standard section in the workflow protocol, the ultrasound device 100 is controlled to rescan the ultrasound image, and the image processing device returns to the step of automatically identifying the section type of the rescanned ultrasound image. If the ultrasound image matches the standard section in the workflow protocol, the ultrasound image is subjected to image quality detection. If the image quality of the ultrasound image does not meet the preset conditions, a first prompt message is output in the ultrasound device 100 so that the ultrasound device 100 can rescan the ultrasound image based on the first prompt message, and the image processing device returns to the step of automatically identifying the section type of the rescanned ultrasound image. If the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved as a matched standard section in the workflow protocol.

[0047] Please refer to Figure 2 , Figure 2 This is a structural schematic diagram of an ultrasonic device 100 provided in an embodiment of this application. For example... Figure 2 As shown, the ultrasonic device 100 includes an ultrasonic probe 10, a transmitting and receiving circuit 20, an echo processing module 30, a processor 40, and a display 50. The components are described below.

[0048] An ultrasound probe 10 is used to emit ultrasound waves and receive ultrasound echo signals. In some embodiments, the ultrasound probe 10 includes multiple array elements for mutual conversion between electrical pulse signals and ultrasound waves, thereby emitting ultrasound waves to the biological tissue 60 being tested (biological tissue in a human or animal body) and receiving the ultrasound echoes reflected back from the tissue to obtain ultrasound echo signals. Array elements can emit ultrasound waves according to excitation electrical signals, or convert received ultrasound waves into electrical signals. Therefore, each array element can be used to emit ultrasound waves to biological tissue in a region of interest, or to receive echo signals returned by the tissue. During ultrasound detection, the transmission and reception sequences can be used to control which array elements are used to emit ultrasound waves and which array elements are used to receive ultrasound waves, or to control the array elements to be used for transmitting ultrasound waves or receiving ultrasound echoes in time slots. All array elements involved in ultrasound wave emission can be simultaneously excited by electrical signals to emit ultrasound waves simultaneously; or array elements involved in ultrasound wave emission can be excited by several electrical signals with a certain time interval to continuously emit ultrasound waves with a certain time interval.

[0049] The transmitting and receiving circuit 20 is used to control the ultrasound probe 10 to emit ultrasonic waves and receive the echo signals of the ultrasonic waves. For example, the transmitting and receiving circuit 20 is used on the one hand to control the ultrasound probe 10 to emit ultrasonic waves toward the biological tissue 60, such as the region of interest, and on the other hand to control the ultrasound probe 10 to receive the echo signals of the ultrasonic waves reflected by the tissue. In some specific embodiments, the transmitting and receiving circuit 20 is used to generate a transmission sequence and a reception sequence and output them to the ultrasound probe 10. The transmission sequence is used to control some or all of the multiple array elements in the ultrasound probe 10 to emit ultrasonic waves toward the biological tissue 60. The parameters of the transmission sequence include the number of array elements for transmission and the ultrasonic wave transmission parameters (e.g., amplitude, frequency, number of waves, transmission interval, transmission angle, waveform and / or focusing position, etc.). The reception sequence is used to control some or all of the multiple array elements to receive the echoes of the ultrasonic waves after they have passed through the tissue. The parameters of the reception sequence include the number of array elements for reception and the reception parameters of the echo (e.g., reception angle, depth, etc.). The ultrasonic wave parameters in the transmission sequence and the echo parameters in the reception sequence may vary depending on the purpose of the ultrasonic echo or the image generated by the ultrasonic echo.

[0050] The transmission frame rate, or imaging frame rate, is related to various parameters, such as the number of transmissions, line density, imaging range, and pulse repetition frequency (PRF). The number of transmissions refers to the number of subframes required to form one image frame, and the pulse repetition frequency (PRF) refers to the number of trigger pulses generated per second. To improve the frame rate, one or more of the following can be adopted: reducing the number of transmissions, reducing line density, reducing imaging range, and increasing pulse repetition frequency. For example, some schemes may use plane wave transmission technology or coherence transmission synthesis (CTS) technology to reduce the number of transmissions; some schemes may reduce line density while maintaining image quality; some schemes may narrow the imaging range after lesion localization; and some schemes may select a specific region of interest (ROI) after lesion localization to narrow the imaging range and / or increase the PRF.

[0051] The echo processing module 30 is used to process the ultrasonic echo signal received by the ultrasonic probe 10, such as filtering, amplifying, and beamforming the ultrasonic echo signal. In some specific embodiments, the echo processing module 30 can output the processed signal or data to the processor 40, or it can store the processed signal or data in a memory first, and then the processor 40 reads the ultrasonic echo signal or data from the memory when it needs to perform calculations based on the ultrasonic echo signal. Those skilled in the art should understand that in some embodiments, when it is not necessary to filter, amplify, or beamform the ultrasonic echo signal, the echo processing module 30 can be omitted.

[0052] The processor 40 is used to acquire the echo signal of ultrasound and use relevant algorithms to obtain the required parameters or images. In some embodiments of this application, the processor 40 includes, but is not limited to, devices for interpreting computer instructions and processing data in computer software, such as a central processing unit (CPU), microcontroller unit (MCU), field-programmable gate array (FPGA), and digital signal processor (DSP). In some embodiments, the processor 40 is used to execute various computer applications in the non-transitory computer-readable storage medium, thereby executing the ultrasound image detection method described in the embodiments of this application or sending ultrasound images to an image processing device.

[0053] It should be noted that the terms "data" and "signal" are sometimes used interchangeably in this article and are not strictly distinguished.

[0054] The display 50 can be used to display information, such as parameters and images calculated by the processor 40. Those skilled in the art will understand that in some embodiments, the ultrasound imaging system itself may not integrate a display module, but instead connect to a computer device (e.g., a computer), displaying information through the computer device's display module (e.g., a screen). In some embodiments, the information displayed on the display 50 may also include a graphical interface for user interaction, where one or more controlled objects are set, allowing the user to input operation commands using a human-machine interface to control these controlled objects and execute corresponding control operations. In some embodiments, the ultrasound imaging system may also include other human-machine interface devices besides the display 50, connected to the processor 40. For example, the processor 40 may connect to the human-machine interface device through an external input / output port, which can be a wireless communication module, a wired communication module, or a combination of both. The external input / output port may also be implemented based on USB, bus protocols such as CAN, and / or wired network protocols.

[0055] The human-computer interaction device may include an input device for detecting user input information. This input information may be, for example, control commands for the timing of ultrasound transmission / reception, operational input commands for drawing points, lines, or boxes on an ultrasound image, or other types of commands. The input device may include one or a combination of several of the following: a keyboard, mouse, scroll wheel, trackball, mobile input device (e.g., a mobile device with a touchscreen, a mobile phone, etc.), a multi-function knob, etc. The human-computer interaction device may also include an output device such as a printer.

[0056] It should be noted that the ultrasound image detection method provided in this application embodiment can be executed by the ultrasound device 100 or by the image processing device 200. When the image processing device 200 acts as the executing entity, during the ultrasound image scanning process by the ultrasound device 100, the image processing device 200 can receive ultrasound images sent by the ultrasound device 100 in real time via the DICOM (Digital Imaging and Communications in Medicine) protocol or acquire ultrasound images generated by the ultrasound device 100 via a data acquisition card. It can then perform section recognition and / or image quality detection on the ultrasound images. If the section type of the ultrasound image is not recognized or the image quality is poor, it can promptly instruct the ultrasound device 100 to rescan the ultrasound image, thereby ensuring the quality and integrity of the ultrasound image and reducing missed diagnoses and misdiagnoses. For ease of explanation, the following description uses the ultrasound device 100 as the executing entity.

[0057] The following detailed description, in conjunction with the accompanying drawings, outlines some embodiments of this application. Unless otherwise specified, the following embodiments and features described herein can be combined with each other. Please refer to... Figure 3 , Figure 3 This is a schematic flowchart illustrating an ultrasound image detection method provided in an embodiment of this application. Figure 3 As shown, the ultrasound image detection method includes steps S101 to S105.

[0058] Step S101: Acquire ultrasound images.

[0059] For example, during an ultrasound scan of the human body, the ultrasound images being scanned by the ultrasound equipment can be acquired in real time.

[0060] It should be noted that, in this embodiment of the application, by acquiring the ultrasound image currently being scanned by the ultrasound device in real time during the ultrasound scan of the human body, it is possible to perform section recognition and section quality detection on the ultrasound image during the ultrasound scan. This avoids the problem of missing sections or low section quality when relying on manual spot checks of the ultrasound image quality after the ultrasound scan. Furthermore, relying on manual spot checks is not only labor-intensive, but also difficult to remedy even if section quality problems are found.

[0061] Step S102: Automatically identify the section type of the ultrasound image.

[0062] For example, each frame of ultrasound image generated by the ultrasound device can be subjected to section recognition. Section recognition refers to identifying the section type of the ultrasound image and matching the ultrasound image with a standard section in the workflow protocol based on the section type.

[0063] It's important to note that a workflow protocol is a working mode for automating ultrasound scans. With the release of various ultrasound examination guidelines and standards, physicians are required to perform a series of standard scans as a diagnostic basis for all types of ultrasound examinations. Since different types of examinations require different scan sections—for example, level two screening only examines the basic anatomical structures of the fetus, with fewer sections and structures examined, while level three screening requires a comprehensive and systematic examination of the fetus's anatomy, with many sections and structures examined—the standard scan sections in the workflow protocol can be set according to the examination type. A workflow protocol can include multiple standard scan sections, such as the thalamic level transverse section, the lateral ventricle level transverse section, the transcerebellar transverse section, the nasolabial coronal section, and the abdominal transverse section. Standard scan sections are generally selected by the industry as important sections that can detect a relatively large number of abnormalities. Ultrasound examinations typically require sliding scans of multiple standard scan sections to avoid missing abnormal tissues or structures. Automated ultrasound scanning workflows can organize standard sections to be scanned in a specific order or format by providing template protocols. Part of the work is automated by the ultrasound equipment, improving doctors' efficiency and standardizing their procedures. However, with the development of ultrasound technology, the set of standard sections may change in various examinations.

[0064] For example, the type of section in an ultrasound image can be determined by identifying anatomical structures within the image.

[0065] In the above embodiments, by performing section recognition on ultrasound images, the section type corresponding to the ultrasound image can be identified. Then, the ultrasound image can be matched with the standard section in the workflow protocol according to the identified section type, and it can be determined whether the section needs to be re-scanned based on the matching result.

[0066] Step S103: Based on the identified section type of the ultrasound image, automatically match the ultrasound image with the standard section in the workflow protocol.

[0067] For example, after performing section recognition on an ultrasound image to obtain its section type, the ultrasound image can be matched with multiple standard sections in the workflow protocol based on its section type. If the section type of the ultrasound image is the same as the section type of one of the standard sections in the workflow protocol, then the ultrasound image is determined to match the standard section in the workflow protocol; if the section type of the ultrasound image is different from the section types of all the standard sections in the workflow protocol, then the ultrasound image is determined to not match the standard sections in the workflow protocol.

[0068] Step S104: If the ultrasound image does not match the standard section in the workflow protocol, then rescan the ultrasound image for the section type.

[0069] In some embodiments, if the ultrasound image does not match the standard section in the workflow protocol, the ultrasound device rescans the ultrasound image and returns to the step of automatically identifying the section type of the ultrasound image until the scanned ultrasound image matches the standard section in the workflow protocol.

[0070] By rescanning the ultrasound image when it is determined that the ultrasound image does not match the standard section in the workflow protocol based on the section type of the ultrasound image, the ultrasound equipment can be promptly prompted to rescan when the section type of the ultrasound image does not conform to the standard section in the workflow protocol, thus avoiding scanning unwanted ultrasound images. At the same time, it can also avoid the situation where the section type of the ultrasound image does not meet the requirements is discovered only after the ultrasound scan has been completed, making it difficult to remedy the situation.

[0071] In other embodiments, if the ultrasound image does not match the standard section in the workflow protocol, the mismatched ultrasound image can be cached and marked as an unrecognized image; if a section matching operation for an unrecognized image is detected, the section type corresponding to the unrecognized image is determined based on the section matching operation.

[0072] It should be noted that, in this embodiment, for ultrasound images that do not match the standard sections in the workflow protocol, medical personnel can manually perform section matching and image quality checks on the unidentified images. For example, medical personnel can perform section matching on unidentified images based on experience or according to other standard sections outside the workflow protocol. By marking mismatched ultrasound images as unidentified images, and if a section matching operation on an unidentified image is detected, the section type corresponding to the unidentified image is determined based on the section matching operation. This allows for manual section matching of unidentified images by medical personnel, solving the problem of not being able to perform section matching on unidentified images, enriching the application scenarios of ultrasound detection, and effectively improving the user experience.

[0073] Step S105: If the ultrasound image matches the standard section in the workflow protocol, then perform image quality detection on the ultrasound image: If the image quality of the ultrasound image does not meet the preset conditions, then output a first prompt message, rescan the ultrasound image based on the first prompt message, and automatically identify the section type of the rescanned ultrasound image; If the section quality of the ultrasound image meets the preset conditions, then save the ultrasound image as a matched standard section in the workflow protocol.

[0074] For example, when automatically matching an ultrasound image with a standard section in a workflow protocol based on the identified section type of the ultrasound image, if the ultrasound image matches the standard section in the workflow protocol, then the ultrasound image is subjected to image quality detection.

[0075] It should be noted that image quality inspection refers to checking whether the image quality of an ultrasound image meets the requirements. This may include checking the standardization of anatomical structures in the ultrasound image, and / or checking the clarity, contrast, etc. of the ultrasound image.

[0076] In one embodiment, if the image quality of the ultrasound image does not meet the preset conditions, a first prompt message is output in the ultrasound device so that the ultrasound device can rescan the ultrasound image based on the first prompt message and return to the step of automatically identifying the cross-section type of the rescanned ultrasound image; if the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved with the standard cross-section that has been matched in the workflow protocol.

[0077] Image quality can be measured using the section score of ultrasound images. For example, when the section score of an ultrasound image is greater than or equal to a certain value, the image quality of the ultrasound image is determined to meet a preset condition; when the section score of an ultrasound image is less than a certain value, the image quality of the ultrasound image is determined to not meet the preset condition.

[0078] The above embodiments, by outputting a first prompt message in the ultrasound device when the image quality of the ultrasound image does not meet preset conditions, allow the ultrasound device to rescan the ultrasound image based on the first prompt message. This enables timely prompting of the ultrasound device to rescan the ultrasound image when the image quality is poor, avoiding misdiagnosis due to image quality issues, thereby ensuring the quality and integrity of the slice. By saving the ultrasound image as a matched standard slice in the workflow protocol when the image quality meets preset conditions, medical staff can avoid scanning slices that are already associated with the ultrasound image, avoiding repeated scanning of slices with acceptable image quality, thus effectively improving the efficiency of ultrasound scanning.

[0079] In some embodiments, after determining that the image quality of the ultrasound image meets preset conditions, the method may further include: outputting a second prompt message in the ultrasound device, the second prompt message being used to instruct the ultrasound device to perform an ultrasound scan on a standard section that does not match in the workflow protocol.

[0080] For example, when the unmatched standard sections in the workflow protocol are the transcerebellar transverse section and the nasolabial coronal section, the corresponding section markers of the transcerebellar transverse section and the nasolabial coronal section can be highlighted. Furthermore, the number of unmatched standard sections can be displayed on the image display page of the ultrasound device or image processing device.

[0081] In the above embodiments, by outputting a prompt message in the ultrasound device instructing the ultrasound device to perform ultrasound scanning on the standard sections that do not match in the workflow protocol, medical staff can be reminded to perform ultrasound scanning on the unmatched standard sections, avoiding omissions in scanning some sections, thereby ensuring the integrity of the sections.

[0082] Please see Figure 4 , Figure 4 This is a schematic flowchart illustrating a sub-step for identifying the section type of an ultrasound image, as provided in an embodiment of this application. Figure 4 As shown, the automatic identification of the section type of the ultrasound image in step S102 may include steps S201 and S202.

[0083] Step S201: Identify anatomical structures in the ultrasound image to obtain at least one anatomical structure in the ultrasound image.

[0084] It should be noted that, in the embodiments of this application, methods such as sliding window-based methods, deep learning-based bounding-box detection methods, or deep learning-based end-to-end semantic segmentation network methods can be used to locate and identify anatomical structures in ultrasound images.

[0085] In one embodiment, a sliding window-based method can be used to identify anatomical structures in ultrasound images, obtaining at least one anatomical structure from the ultrasound image. Specifically, firstly, the ultrasound image is processed using a sliding window with a preset window size and step size to obtain multiple adjacent sliding windows. Then, features are extracted from the regions within the sliding windows. The feature extraction methods can be traditional PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), Haar features, texture features, etc., or deep neural networks can be used for feature extraction. Finally, the extracted features are matched with a database. Discriminators such as KNN (k-Nearest Neighbor), SVM (Support Vector Machine), random forest, and neural networks can be used for classification to determine whether the current sliding window is a region of interest and to obtain the category of the anatomical structure corresponding to the region of interest. The database can include multiple ultrasound images and the location and category of the anatomical structure labeled for each ultrasound image.

[0086] In another embodiment, a deep learning-based bounding-box detection method can be used to identify anatomical structures in ultrasound images, thereby obtaining at least one anatomical structure in the ultrasound image. Specifically, the bounding-box of the region of interest in the ultrasound image can be directly regressed using an object detection model, while simultaneously obtaining the category of the tissue structure within the region of interest, i.e., the type corresponding to the anatomical structure. The object detection model can include, but is not limited to, R-CNN (Region-Convolutional Neural Networks), Fast R-CNN, Faster R-CNN, SSD (Single Shot MultiBox Detector), YOLO (You Only Look Once), etc.

[0087] In another embodiment, an end-to-end semantic segmentation network method based on deep learning can be used to identify anatomical structures in ultrasound images, thereby obtaining at least one anatomical structure in the ultrasound image. Specifically, anatomical structure identification in ultrasound images can be performed using a semantic segmentation network. The difference between the semantic segmentation network and the aforementioned object detection model is that the fully connected layers are removed, and upsampling or deconvolutional layers are added to make the input and output sizes the same, thereby directly obtaining the region of interest and its corresponding category in the ultrasound image, i.e., the type corresponding to the anatomical structure. For example, the semantic segmentation network may include, but is not limited to, FCN (Fully Convolutional Networks), U-Net, Mask R-CNN (Faster R-CNN with added mask layers), etc.

[0088] In this embodiment, the anatomical structures in the ultrasound image can be located using one of the following methods: a sliding window method, a deep learning-based bounding-box detection method, or a deep learning-based end-to-end semantic segmentation network method. This allows for the determination of the target location of the anatomical structure, followed by classification and detection of the target location. The specific classification and detection process may include: first, feature extraction of the target location. Feature extraction methods can be traditional PCA, LDA, Haar features, texture features, etc., or deep neural networks can be used for feature extraction. Then, the extracted features are matched with a database. Discriminators such as KNN, SVM, random forest, and CNN can be used for classification to obtain the type of the anatomical structure at the target location.

[0089] Step S202: Determine the section type corresponding to the ultrasound image based on at least one anatomical structure.

[0090] It should be noted that different cross-sections correspond to different anatomical structures. For example, for the upper abdominal cross-section in prenatal screening examinations, clinical guidelines require that four anatomical structures be visible on the cross-section: the gastric bubble, umbilical vein and portal sinus, inferior vena cava, and spine.

[0091] For example, the section type of an ultrasound image can be determined based on the type of anatomical structure in the image. For instance, when an ultrasound image includes four anatomical structures—the stomach bubble, the umbilical vein and portal sinus, the inferior vena cava, and the spine—the section type can be determined to be an upper abdominal section.

[0092] In the above embodiments, by identifying anatomical structures in ultrasound images, it is possible to determine the corresponding section type of the ultrasound image based on at least one anatomical structure in the ultrasound image.

[0093] In some embodiments, image quality detection of ultrasound images may include: quality detection of anatomical structures in ultrasound images; and / or quality detection of the clarity and contrast of ultrasound images.

[0094] It should be noted that, in the embodiments of this application, the quality of anatomical structures in the ultrasound image can be detected separately, or the clarity and contrast of the ultrasound image can be detected separately. Alternatively, the quality of anatomical structures in the ultrasound image can be detected simultaneously to obtain a first quality detection result, and the clarity and contrast of the ultrasound image can be detected simultaneously to obtain a second quality detection result. The first and second quality detection results are then used as the final quality detection result. The specific process for detecting the clarity and contrast of the ultrasound image can be found in related technologies, and will not be elaborated upon here.

[0095] Please see Figure 5 , Figure 5 This is a schematic flowchart illustrating a sub-step of image quality detection provided in an embodiment of this application. For example... Figure 5 As shown, step S104, which involves image quality detection of the ultrasound image, may include steps S301 to S303.

[0096] Step S301: Based on the section recognition results obtained from identifying the section type of the ultrasound image, perform section scoring on the ultrasound image to obtain the total section score corresponding to the ultrasound image.

[0097] For example, the section recognition result may include section type and at least one anatomical structure. In the embodiments of this application, ultrasound images can be scored based on the section type and the type of anatomical structure. The section scoring will be described in detail below.

[0098] In some embodiments, the ultrasound image is scored based on the section recognition result obtained by identifying the section type of the ultrasound image to obtain the total section score corresponding to the ultrasound image. This may include: determining the structural score corresponding to each anatomical structure; determining the weight value corresponding to each anatomical structure according to the section type; performing a weighted summation of the weight value and structural score corresponding to each anatomical structure in the ultrasound image to obtain a first section score; inputting the ultrasound image into a preset section scoring model for scoring to obtain a second section score; and determining the total section score based on the first section score and the second section score.

[0099] For example, AI (Artificial Intelligence) image detection methods can be used to detect the standardization of each anatomical structure in an ultrasound image, obtaining a structural score for each anatomical structure. The specific detection process can be found in relevant technologies and is not limited here.

[0100] For example, the weight value corresponding to each anatomical structure can be determined according to the section type. Then, the weight value and structure score corresponding to each anatomical structure in the ultrasound image are weighted and summed to obtain the first section score. It should be noted that the importance of different anatomical structures varies in different sections, and the embodiments of this application can pre-set different weight values ​​for the anatomical structures corresponding to different sections.

[0101] For example, the pre-set section scoring model may include, but is not limited to, models such as R-CNN, SVM, random forest, and FCN. In this embodiment, the section scoring model can be pre-trained to learn the mapping relationship between ultrasound images and standard score, and the trained section scoring model can be used to predict the section score of ultrasound images.

[0102] For example, an ultrasound image can be input into a trained section scoring model to obtain a second section score. Then, a weighted sum of the first and second section scores is performed to obtain the total section score corresponding to the ultrasound image. The weight values ​​corresponding to the first and second section scores can be set according to the actual situation, and the specific values ​​are not limited here.

[0103] The above embodiments, by combining anatomical structure-based section scoring and section scoring model-based section scoring to jointly determine the total section score corresponding to the ultrasound image, can further improve the accuracy of section scoring.

[0104] In other embodiments, based on the section recognition results obtained by section recognition of the ultrasound image, section scoring is performed on the ultrasound image to obtain a total section score corresponding to the ultrasound image. This may include: determining the structural score corresponding to each anatomical structure; determining the weight value corresponding to each anatomical structure according to the section type; and performing a weighted summation of the weight value and structural score corresponding to each anatomical structure in the ultrasound image to obtain the total section score. The specific process of section scoring based on anatomical structures can be found in the detailed description of the above embodiments, and will not be repeated here.

[0105] It should be noted that, in the embodiments of this application, when calculating the total score of the ultrasound image corresponding to the section, in addition to the anatomical structure, the total score of the ultrasound image can also be calculated comprehensively based on information such as the clarity, signal-to-noise ratio, and contrast of the ultrasound image. For example, the total score of the section can be obtained by weighted summation based on the structural score of the anatomical structure and the clarity, signal-to-noise ratio, and contrast of the ultrasound image; the specific process will not be elaborated here.

[0106] The above embodiments determine the structural score corresponding to each anatomical structure, determine the weight value corresponding to each anatomical structure according to the section type, and perform a weighted summation of the weight value and structural score corresponding to each anatomical structure in the ultrasound image. The calculation process is simple and can quickly and accurately obtain the total score of the section corresponding to the ultrasound image.

[0107] Step S302: When the total score of the section is greater than or equal to the preset first score threshold, the image quality of the ultrasound image is determined to meet the preset conditions.

[0108] For example, after calculating the total score of the ultrasound image corresponding to the slice, the total score can be compared with a preset first score threshold. When the total score is greater than or equal to the first score threshold, the image quality of the ultrasound image is determined to meet the preset condition. The first score threshold can be set according to the actual situation, and the specific value is not limited here.

[0109] It should be noted that when the total score of the section is greater than or equal to the first score threshold, it means that the quality of the ultrasound image meets the requirements. At this time, the ultrasound image can be saved as a standard section that has been matched in the workflow protocol.

[0110] Step S303: When the total score of the section is less than the first score threshold, it is determined that the image quality of the ultrasound image does not meet the preset conditions.

[0111] For example, when the total score of the section is less than the first score threshold, it is determined that the image quality of the ultrasound image does not meet the preset conditions. At this time, a first prompt message can be output in the ultrasound device, the ultrasound image can be re-scanned based on the first prompt message, and the step of automatically identifying the section type of the re-scanned ultrasound image can be returned.

[0112] The above embodiments, by comprehensively scoring ultrasound images based on the section type and anatomical structure, can achieve multi-dimensional calculation of the total section score corresponding to the ultrasound image, thereby effectively improving the accuracy of section scoring.

[0113] Please see Figure 6 , Figure 6 This is a schematic flowchart illustrating another sub-step of image quality detection provided in an embodiment of this application. For example... Figure 6 As shown, step S104, which involves image quality detection of the ultrasound image, may include steps S401 to S403.

[0114] Step S401: Input the ultrasound image into the preset section scoring model to perform section scoring and obtain the total section score corresponding to the ultrasound image.

[0115] For example, the preset section scoring model can be a trained model such as R-CNN, SVM, Random Forest, or FCN. For instance, the section scoring model can be an R-CNN model, where ultrasound images can be input into the R-CNN model for section scoring to obtain the total section score corresponding to the ultrasound image. The specific process of section scoring can be found in relevant technologies and will not be elaborated upon here.

[0116] Step S402: When the total score of the section is greater than or equal to the preset second score threshold, it is determined that the image quality of the ultrasound image meets the preset conditions.

[0117] For example, after calculating the total score of the section corresponding to the ultrasound image, the total score can be compared with a preset second score threshold. When the total score is greater than or equal to the second score threshold, the section quality of the ultrasound image is determined to meet the preset conditions. The second score threshold can be set according to actual conditions, and its specific value is not limited here. When the total score is greater than or equal to the second score threshold, it indicates that the quality of the ultrasound image meets the requirements. At this time, the ultrasound image can be associated with and saved as the target section matched in the workflow protocol, serving as the ultrasound section image corresponding to the target section.

[0118] Step S403: When the total score of the section is less than the second score threshold, it is determined that the image quality of the ultrasound image does not meet the preset conditions.

[0119] For example, when the total score of the section is less than the second score threshold, it is determined that the image quality of the ultrasound image does not meet the preset conditions. At this time, a first prompt message can be output in the ultrasound device, the ultrasound image can be re-scanned based on the first prompt message, and the step of automatically identifying the section type of the re-scanned ultrasound image can be returned.

[0120] In the above embodiments, ultrasound images are input into a trained section scoring model for section scoring. Since the trained section scoring model has high prediction speed and accuracy, it can effectively improve the accuracy and speed of section scoring.

[0121] Please see Figure 7 , Figure 7 This is a schematic flowchart illustrating a sub-step of another ultrasound image detection method provided in this application embodiment. For example... Figure 7 As shown, it may include steps S501 to S503.

[0122] Step S501: Obtain the total score of each scanned section. The scanned section refers to the section that has been scanned by the ultrasound equipment.

[0123] For example, after performing image quality inspection on the ultrasound images, the total score of each scanned section can be obtained. For instance, if each scanned section corresponds to at least one saved ultrasound image, the highest total score of the section corresponding to at least one ultrasound image can be determined as the total score of the scanned section.

[0124] Step S502: Based on the total score of each scanned section, divide each scanned section into multiple preset section level groups.

[0125] For example, based on a preset grouping strategy, each scanned section can be divided into multiple preset section level groups according to its total section score. These multiple section level groups may include non-standard section level groups, basic standard section level groups, standard section level groups, and so on. For instance, the preset grouping strategy may include: classifying sections with a total score less than 60 into the non-standard section level group, classifying sections with a total score greater than or equal to 60 and less than 80 into the basic standard section level group, and classifying sections with a total score greater than or equal to 80 into the standard section level group.

[0126] Step S503: Display the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group on the image display page.

[0127] For example, after each scanned section is divided into multiple preset section level groups, the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group, can be displayed on the image display page.

[0128] Please see Figure 8 , Figure 8 This is a schematic diagram of an image display page provided in an embodiment of this application. For example... Figure 8 As shown, the image display page can be used to display the results of cross-sectional quality inspection. The image display page can include non-standard cross-sectional grade groups, basic standard cross-sectional grade groups, and standard cross-sectional grade groups. The non-standard cross-sectional grade group includes 3 types of cross-sections, the basic standard cross-sectional grade group includes 4 types of cross-sections, and the standard cross-sectional grade group includes 9 types of cross-sections. For example, the cross-sectional name labels corresponding to the scanned cross-sections in the non-standard cross-sectional grade group are: bilateral renal level transverse section, bladder level transverse section, bilateral umbilical artery blood flow map, and transthalamic transverse section, with corresponding image counts of 2, 2, and 2, respectively. As another example, the cross-sectional name labels corresponding to the scanned cross-sections in the basic standard cross-sectional grade group are: translateral ventricle transverse section, transcerebellar transverse section, sagittal section of the spine, and four-chamber heart section, with corresponding image counts of 2, 1, 2, and 3, respectively.

[0129] In the above embodiments, by dividing each scanned section into multiple preset section level groups according to the total score of each scanned section, and displaying the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group on the image display page, medical staff can intuitively and clearly see the section quality corresponding to each scanned section, and make it easier for medical staff to determine whether to re-scan based on the section quality, thereby ensuring the quality of the sections and minimizing misdiagnosis.

[0130] In some embodiments, the image display page may further include the cross-section to be scanned and the total number of cross-sections corresponding to the cross-section to be scanned. For example... Figure 8 As shown, the scan plane can be set above multiple scan plane level groups. The total number of scan planes corresponding to the scan plane is five, which can include transverse sections of the orbits, coronal sections of the upper lip, midsagittal sections of the face, left ventricular outflow tract sections, and right ventricular outflow tract sections. It should be noted that the scan plane can be determined based on already scanned planes and planes in the workflow protocol.

[0131] The above embodiments, by displaying the cut surface to be scanned and the total number of cut surfaces corresponding to the cut surface to be scanned on the image display page, make it easier for medical staff to intuitively see the total number of cut surfaces that have not yet been scanned and the specific cut surfaces, thus avoiding missed cut surfaces.

[0132] Please see Figure 9 , Figure 9 This is a schematic flowchart illustrating a sub-step of another ultrasound image detection method provided in this application embodiment. For example... Figure 9 As shown, it may include steps S601 to S605.

[0133] Step S601: Obtain the total score of each scanned section. The scanned section refers to the section that has been scanned by the ultrasound equipment.

[0134] Step S602: Based on the total score of each scanned section, divide each scanned section into multiple preset section level groups.

[0135] Step S603: Display the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group on the image display page.

[0136] It is understood that steps S601 to S603 are the same as steps S501 to S503 above, and will not be repeated here.

[0137] Step S604: If a click operation on one of the slice name labels in the image display page is detected, the target ultrasound image corresponding to the currently clicked slice name label is determined, and the slice quality control details information corresponding to the target ultrasound image is obtained.

[0138] For example, such as Figure 8 As shown, users can click on the section name label corresponding to the scanned section to view the section quality control details of the ultrasound images associated with the scanned section. For example, when a user clicks on the section name label "Transventricular Transverse Section," they can obtain the section quality control details of the target ultrasound image associated with the transventricular transverse section.

[0139] Step S605: Display the target ultrasound image and the corresponding section quality control details on the image display page. The section quality control details include the section name, total section score, section grade, and the structural score and weight value corresponding to the anatomical structure in the target ultrasound image.

[0140] For example, after obtaining the section quality control details information corresponding to the target ultrasound image, the target ultrasound image and the section quality control details information corresponding to the target ultrasound image can be displayed on the image display page.

[0141] In this embodiment, when a user clicks on the section name tag corresponding to the scanned section to view the section quality control details of the ultrasound image associated with the scanned section, the user can jump from the page displaying the section quality inspection results to the page displaying the section quality control details.

[0142] Please see Figure 10 , Figure 10 This is a schematic diagram of another image display page provided in an embodiment of this application. For example... Figure 10As shown, the image display page can also display detailed section quality control information. The target ultrasound image is displayed on the left side of the page, and the corresponding section quality control details are displayed on the right side. This detailed information includes the section name, overall section score (AI section score), section grade (e.g., standard), and structural evaluation information such as the structural scores (AI scores in the figure) and weight values ​​of the anatomical structures in the target ultrasound image. The section grade can be categorized as standard, basic standard, or non-standard.

[0143] The above embodiments, by displaying detailed information on the quality control of the target ultrasound image, such as the section name, total section score, section grade, and structural scores and weight values ​​of the anatomical structures in the target ultrasound image, on the image display page, enable medical staff to clearly understand the quality of each ultrasound image and any existing quality problems. This allows medical staff to decide whether to scan the section based on the quality of the ultrasound image and the physical condition of the patient.

[0144] like Figure 10 As shown, the image display page may also include a switch option to display image bounding boxes. When this option is enabled, different bounding box colors can be used in the target ultrasound image to indicate the location or area of ​​different anatomical structures. By using different bounding box colors in the target ultrasound image to indicate the location or area of ​​different anatomical structures, medical staff can more intuitively and specifically view the main anatomical structures in the target ultrasound image.

[0145] like Figure 10 As shown, in the image display interface, there are arrows on both sides of the target ultrasound image and a thumbnail at the bottom of the target ultrasound image. Medical staff can use the arrows or thumbnails to select and view ultrasound images corresponding to other sections and section quality control details.

[0146] Please see Figure 11 , Figure 11 This is a schematic flowchart illustrating a sub-step of another ultrasound image detection method provided in this application embodiment. For example... Figure 11 As shown, it may include steps S701 to S707.

[0147] Step S701: Obtain the total score of each scanned section. The scanned section refers to the section that has been scanned by the ultrasound equipment.

[0148] Step S702: Based on the total score of each scanned section, divide each scanned section into multiple preset section level groups.

[0149] Step S703: Display the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group on the image display page.

[0150] It is understood that steps S701 to S703 are the same as steps S501 to S503 above, and will not be repeated here.

[0151] Step S704: If a click operation on an unrecognized image label is detected, an abnormal ultrasound image of an unrecognized section type is displayed on the image display page.

[0152] It should be noted that, in the embodiments of this application, if there are ultrasound images in which the type of the section cannot be identified during the scanning process, medical staff can manually select the section type corresponding to the ultrasound image for image quality detection.

[0153] For example, such as Figure 8 As shown, the image display page may also include unrecognized image labels, where the number of unrecognized images is 4.

[0154] In one embodiment, when a medical staff member clicks on an unrecognized image tag, an abnormal ultrasound image with an unrecognizable section type is acquired and displayed on the image display page along with the abnormal ultrasound image and the corresponding section quality control details.

[0155] Please see Figure 12 , Figure 12 This is a schematic diagram of another image display page provided in an embodiment of this application. For example... Figure 12 As shown, since the section type of the abnormal ultrasound image cannot be identified, the section name and anatomical structure in the section quality control details information corresponding to the abnormal ultrasound image are empty values.

[0156] Step S705: When an editing operation on the section name of an abnormal ultrasound image is detected, determine the section type and anatomical structure corresponding to the abnormal ultrasound image based on the section name editing operation.

[0157] For example, when an editing operation on the section name of an abnormal ultrasound image is detected, the section type and anatomical structure corresponding to the abnormal ultrasound image are determined based on the section name editing operation. For example, medical staff can manually edit the section type of the abnormal ultrasound image to a horizontal cross section of both kidneys and the anatomical structure to both kidneys, both renal pelvises, and vertebral bodies of the spine.

[0158] Step S706: Perform section quality detection on the abnormal ultrasound image according to the section type and anatomical structure corresponding to the abnormal ultrasound image to obtain the section quality control details information corresponding to the abnormal ultrasound image.

[0159] For example, the specific process of performing section quality detection on abnormal ultrasound images based on the section type and anatomical structure corresponding to the abnormal ultrasound image can be found in the detailed description of section scoring of ultrasound images in the above embodiments, and the specific process will not be repeated here. The section quality control details may include the section name corresponding to the abnormal ultrasound image, the total section score, the section grade, and the structural score and weight value corresponding to the anatomical structure in the abnormal ultrasound image, etc.

[0160] Step S707: Display the section quality control details information corresponding to the abnormal ultrasound image on the image display page.

[0161] For example, after obtaining the section quality control details information corresponding to the abnormal ultrasound image, the section quality control details information corresponding to the abnormal ultrasound image can be displayed on the image display page.

[0162] The above embodiments, by displaying abnormal ultrasound images with unidentifiable section types on the image display page, determine the corresponding section type and anatomical structure of the abnormal ultrasound image based on the section name editing operation, and perform image quality detection on the abnormal ultrasound image based on the corresponding section type and anatomical structure. This can realize image quality detection based on the section type and anatomical structure manually edited by medical staff on unidentified images, which can solve the problem of not being able to perform image quality detection on unidentified images, making the application scenarios of ultrasound detection more diverse, thereby effectively improving the user experience.

[0163] In some embodiments, after displaying the target ultrasound image and the corresponding section quality control details on the image display page, the method may further include: if a section name switching operation on the target ultrasound image is detected, performing image quality detection on the target ultrasound image according to the section type after the target ultrasound image is switched, obtaining the updated section quality control details of the target ultrasound image; and displaying the updated section quality control details of the target ultrasound image on the image display page.

[0164] It should be noted that, in the embodiments of this application, when medical staff find that the total score of a certain ultrasound image is inaccurate, they can manually modify the section type or the total score of the ultrasound image.

[0165] For example, if the current section name or section type of the target ultrasound image is a bilateral kidney horizontal transverse section, and a section name switching operation by medical staff is detected, the new section type of the target ultrasound image is determined based on the section name and operation. For example, the new section type is a bladder horizontal transverse section. Then, the section quality of the target ultrasound image is checked based on the new section type "bladder horizontal transverse section" to obtain the updated section quality control details of the target ultrasound image. The specific process of checking the image quality of the target ultrasound image based on the new section type can be found in the description of section scoring of ultrasound images in the above embodiments, and the specific process will not be repeated here.

[0166] The above embodiments, by detecting when medical staff switch the section name of the target ultrasound image, perform image quality detection on the target ultrasound image according to the section type after the switch, which makes it easier for medical staff to manually modify the section name of the ultrasound image according to the actual situation, and makes the image quality detection more accurate.

[0167] In some embodiments, after displaying the target ultrasound image and the corresponding section quality control details on the image display page, the method may further include: if a section score editing operation on the target ultrasound image is detected, obtaining the modified structural score of each anatomical structure in the target ultrasound image; weighting and summing the weight value of each anatomical structure in the target ultrasound image with the modified structural score to obtain the modified total section score; and displaying the modified total section score of the target ultrasound image on the image display page.

[0168] For example, the section score editing operation may include clicking the edit button for the section score and entering the structural score for each anatomical structure. Figure 8 As shown, when medical staff click the edit button corresponding to the AI ​​section score, the AI ​​score (i.e., structural score) for each anatomical structure becomes editable, allowing them to manually input the AI ​​score for each structure. After obtaining the modified structural score for each anatomical structure in the target ultrasound image, the weighted sum of the weighted value of each anatomical structure and the modified structural score is calculated to obtain the modified total section score; this modified total section score is then displayed on the image display interface.

[0169] The above embodiments, by detecting when medical staff edit the section score of the target ultrasound image, obtain the modified structural score of each anatomical structure in the target ultrasound image, and perform a weighted summation of the weight value of each anatomical structure in the target ultrasound image with the modified structural score, can facilitate medical staff to manually modify the total section score of the ultrasound image according to the actual situation, thereby making the image quality detection more accurate.

[0170] Please see Figure 13 , Figure 13 This is a schematic flowchart illustrating another ultrasound image detection method provided in an embodiment of this application. Figure 13 As shown, it may include steps S801 to S804.

[0171] Step S801: Acquire ultrasound images.

[0172] For example, during an ultrasound scan of the human body, each frame of ultrasound image scanned by the ultrasound equipment can be acquired in real time, and cross-sectional detection can be performed on each frame of ultrasound image.

[0173] Step S802: Perform image quality detection on the ultrasound image.

[0174] It should be noted that image quality inspection can include detecting the section type of the ultrasound image and determining the section quality of the ultrasound image based on the confidence level corresponding to the section type. For example, the confidence level can be used as the total section score of the ultrasound image.

[0175] In some embodiments, image quality detection of ultrasound images may include: inputting the ultrasound image into a preset section prediction model to predict the section type, obtaining the section prediction type corresponding to the ultrasound image and the confidence level corresponding to the section prediction type; determining the total section score corresponding to the ultrasound image based on the confidence level; determining that the image quality of the ultrasound image meets the preset conditions when the total section score is greater than or equal to a preset third score threshold; and determining that the image quality of the ultrasound image does not meet the preset conditions when the total section score is less than the third score threshold.

[0176] For example, the section prediction model can include, but is not limited to, SSD models, CNN models, RBM (Restricted Boltzmann Machine) models, or RNN (Recurrent Neural Network) models, etc. For instance, an ultrasound image can be input into a trained RNN model to predict the section type, obtaining the predicted section type and its corresponding confidence level. The specific process of section type prediction can be found in related technologies, and will not be elaborated here.

[0177] For example, the confidence level can be determined as the total score of the section corresponding to the ultrasound image. Then, the total score of the section is compared with a preset third score threshold. When the total score of the section is greater than or equal to the third score threshold, it is determined that the image quality of the ultrasound image meets the preset condition; when the total score of the section is less than the third score threshold, it is determined that the image quality of the ultrasound image does not meet the preset condition. The third score threshold can be set according to the actual situation, and the specific value is not limited here.

[0178] Step S803: If the image quality of the ultrasound image does not meet the preset conditions, a rescan prompt message is output in the ultrasound device. Based on the rescan prompt message, the ultrasound image is rescanned, and the image quality of the ultrasound image is detected.

[0179] In one embodiment, if the cross-sectional quality of the ultrasound image does not meet the preset conditions, a rescan prompt message is output in the ultrasound device so that the ultrasound device can rescan the ultrasound image based on the rescan prompt message and return to the step of performing image quality detection on the ultrasound image until the image quality of the scanned ultrasound image meets the preset conditions.

[0180] Step S804: If the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved as a standard section that has been matched in the workflow protocol, and a scan prompt message is output in the ultrasound device. The scan prompt message is used to instruct the ultrasound device to perform ultrasound scans on the standard sections that have not been matched in the workflow protocol.

[0181] For example, if the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved as a standard section that has been matched in the workflow protocol, and a scan prompt message is output in the ultrasound device. The scan prompt message is used to instruct the ultrasound device to perform ultrasound scans on standard sections that have not been matched in the workflow protocol.

[0182] The above embodiments, by outputting a rescan prompt message to the ultrasound device when the image quality of the ultrasound image does not meet preset conditions, allow the ultrasound device to rescan the ultrasound image based on the rescan prompt message. This ensures that when the image quality of the ultrasound image is poor, the ultrasound device is promptly prompted to rescan the ultrasound image, avoiding misdiagnosis due to image quality issues, thus ensuring the quality and integrity of the slice. By saving the ultrasound image as a matched standard slice in the workflow protocol when the image quality meets preset conditions, medical staff do not need to scan the matched standard slice, avoiding repeated scanning of slices that meet the quality requirements, thereby effectively improving the efficiency of slice scanning. By outputting a scan prompt message to the ultrasound device, medical staff are reminded to scan unmatched standard slices, avoiding missed scans, thus ensuring the integrity of the slice.

[0183] Please see Figure 14 , Figure 14 This is a schematic flowchart illustrating another ultrasound image detection method provided in an embodiment of this application. Figure 14 As shown, it may include steps S901 to S905.

[0184] Step S901: Acquire ultrasound images.

[0185] Step S902: Automatically identify the image features of the ultrasound image.

[0186] For example, image features may include the cross-sectional type of the ultrasound image, and may also include other features of the ultrasound image, such as color features, texture features, shape features, or spatial relationship features, etc.

[0187] Step S903: Based on the image features of the identified ultrasound image, automatically match the ultrasound image with the standard cross-section in the workflow protocol.

[0188] For example, when the image feature is a section type, if the section type of the ultrasound image is the same as the section type of one of the standard sections in the workflow protocol, then the ultrasound image is determined to match the standard section in the workflow protocol; if the section type of the ultrasound image is different from the section types of all the standard sections in the workflow protocol, then the ultrasound image is determined to not match the standard section in the workflow protocol.

[0189] Step S904: If the ultrasound image does not match the standard section in the workflow protocol, rescan the ultrasound image and return to the step of automatically identifying the image features of the ultrasound image.

[0190] Step S905: If the ultrasound image matches the standard section in the workflow protocol, then perform image quality detection on the ultrasound image. If the image quality of the ultrasound image does not meet the preset conditions, then output a first prompt message, rescan the ultrasound image based on the first prompt message, and return to the step of automatically identifying the image features of the ultrasound image. If the section quality of the ultrasound image meets the preset conditions, then save the ultrasound image as a matched standard section in the workflow protocol.

[0191] It is understood that steps S904 to S905 are the same as steps S104 to S105 above, and will not be repeated here.

[0192] Please see Figure 15 , Figure 15 This is a schematic flowchart illustrating another ultrasound image detection method provided in an embodiment of this application. Figure 15 As shown, it may include steps S1001 to S1006.

[0193] Step S1001: Acquire ultrasound images.

[0194] Step S1002: Automatically identify the section type of the ultrasound image.

[0195] In some embodiments, automatically identifying the section type of an ultrasound image may include: identifying anatomical structures in the ultrasound image to obtain at least one anatomical structure in the ultrasound image; and determining the section type corresponding to the ultrasound image based on the at least one anatomical structure.

[0196] Step S1003: Receive at least one standard cross section to be scanned, manually input by the user.

[0197] It should be noted that, in the embodiments of this application, the user can manually input at least one standard section on the image display page of the ultrasound device or image processing device according to the type of section to be scanned for the current examination. For example, the user, i.e., the medical staff, can input sections such as the thalamic horizontal section, the lateral ventricle horizontal section, the transcerebellar horizontal section, the nasolabial coronal section, and the abdominal circumference horizontal section according to the examination type.

[0198] By receiving at least one standard section to be scanned manually input by the user, the ultrasound equipment can be instructed to perform section scanning based on the standard section input by the user, making the operation more flexible and meeting the actual needs of medical staff.

[0199] Step S1004: Based on the identified section type of the ultrasound image, automatically match the ultrasound image with at least one standard section.

[0200] Step S1005: If the ultrasound image does not match at least one standard section, the ultrasound image is re-scanned, and the section type of the re-scanned ultrasound image is automatically identified.

[0201] For example, when an ultrasound image does not match the standard section of each frame, the ultrasound image is re-scanned using an ultrasound device, and the section type of the re-scanned ultrasound image is automatically identified.

[0202] Step S1006: If the ultrasound image matches at least one frame of standard sections, then perform image quality detection on the ultrasound image: If the image quality of the ultrasound image does not meet the preset conditions, then output a first prompt message, rescan the ultrasound image based on the first prompt message, and automatically identify the section type of the rescanned ultrasound image; If the section quality of the ultrasound image meets the preset conditions, then save the ultrasound image as a matched standard section in at least one frame of standard sections.

[0203] For example, the ultrasound image is quality checked when it is matched with a standard cross-section of one of the frames.

[0204] In some embodiments, image quality detection of an ultrasound image may include: scoring the ultrasound image based on the section recognition result obtained by identifying the section type of the ultrasound image, and obtaining a total section score corresponding to the ultrasound image; when the total section score is greater than or equal to a preset first score threshold, determining that the image quality of the ultrasound image meets the preset conditions; when the total section score is less than the first score threshold, determining that the image quality of the ultrasound image does not meet the preset conditions.

[0205] In some embodiments, after performing image quality detection on the ultrasound images, the method further includes: obtaining the total score of each scanned section, where a scanned section refers to a section that has been scanned by the ultrasound device; classifying each scanned section into multiple preset section level groups based on the total score; and displaying the total number of sections in each section level group, as well as the section name label and image count for each scanned section in each section level group, on the image display page. By classifying each scanned section into multiple preset section level groups based on the total score and displaying the total number of sections in each section level group, as well as the section name label and image count for each scanned section in each section level group, on the image display page, medical staff can intuitively and clearly see the section quality corresponding to each scanned section. This facilitates medical staff in determining whether to rescan based on the section quality, thereby ensuring section quality and minimizing misdiagnosis.

[0206] The above embodiments, by outputting a first prompt message in the ultrasound device when the image quality of the ultrasound image does not meet preset conditions, allow the ultrasound device to rescan the ultrasound image based on the first prompt message. This enables timely prompting of the ultrasound device to rescan the ultrasound image when the image quality is poor, avoiding misdiagnosis due to image quality issues, thereby ensuring the quality and integrity of the slice. By saving the ultrasound image as a matched standard slice in the workflow protocol when the image quality meets preset conditions, medical staff can avoid scanning slices that are already associated with the ultrasound image, avoiding repeated scanning of slices with acceptable image quality, thus effectively improving the efficiency of ultrasound scanning.

[0207] This document describes various exemplary embodiments with reference to them. However, those skilled in the art will recognize that changes and modifications can be made to the exemplary embodiments without departing from the scope of this document. For example, various operational steps and components for performing operational steps can be implemented in different ways depending on the specific application or considering any number of cost functions associated with the operation of the system (e.g., one or more steps can be deleted, modified, or combined with other steps).

[0208] In the above embodiments, implementation can be achieved, in whole or in part, by software, hardware, firmware, or any combination thereof. Furthermore, as those skilled in the art will understand, the principles herein can be reflected in a computer program product on a computer-readable storage medium pre-loaded with computer-readable program code. Any tangible, non-transitory computer-readable storage medium may be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu-ray discs, etc.), flash memory, and / or the like. These computer program instructions can be loaded onto a general-purpose computer, special-purpose computer, or other programmable data processing apparatus to form a machine, such that instructions executing on the computer or other programmable data processing apparatus can generate means for implementing a specified function. These computer program instructions can also be stored in a computer-readable storage medium that can instruct the computer or other programmable data processing apparatus to operate in a particular manner, such that instructions stored in the computer-readable storage medium can form an article of manufacture including means for implementing the specified function. The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to perform a series of operational steps on the computer or other programmable apparatus to produce a computer-implemented process, such that instructions executing on the computer or other programmable apparatus can provide steps for implementing the specified function.

Claims

1. A method for detecting ultrasound images, characterized in that, The method includes: Acquire ultrasound images; Automatically identify the section type of the ultrasound image; Based on the identified section type of the ultrasound image, the ultrasound image is automatically matched with the standard section in the workflow protocol: If the ultrasound image does not match the standard section in the workflow protocol, the ultrasound image is re-scanned, and the section type of the re-scanned ultrasound image is automatically identified. If the ultrasound image matches the standard section in the workflow protocol, then the ultrasound image is subjected to image quality detection: if the image quality of the ultrasound image does not meet the preset conditions, then a first prompt message is output, the ultrasound image is re-scanned based on the first prompt message, and the section type of the re-scanned ultrasound image is automatically identified; if the section quality of the ultrasound image meets the preset conditions, then the ultrasound image is saved as a matched standard section in the workflow protocol.

2. The ultrasound image detection method according to claim 1, characterized in that, The automatic identification of the section type of the ultrasound image includes: Anatomical structure identification is performed on the ultrasound image to obtain at least one anatomical structure in the ultrasound image; Based on the at least one anatomical structure, the section type corresponding to the ultrasound image is determined.

3. The ultrasound image detection method according to claim 1, characterized in that, After automatically matching the ultrasound image with the standard cross-section in the workflow protocol, the process further includes: If the ultrasound image does not match the standard cross-section in the workflow protocol, the ultrasound image is cached and marked as an unrecognized image; If a cross-section matching operation is detected on the unrecognized image, the cross-section type corresponding to the unrecognized image is determined based on the cross-section matching operation.

4. The ultrasound image detection method according to claim 1, characterized in that, The image quality detection of the ultrasound image includes: Quality inspection of the anatomical structures in the ultrasound images; and / or The clarity and contrast of the ultrasound images are tested for quality.

5. The ultrasound image detection method according to claim 2, characterized in that, The image quality detection of the ultrasound image includes: Based on the section recognition results obtained by identifying the section type of the ultrasound image, the ultrasound image is scored to obtain the total section score corresponding to the ultrasound image. When the total score of the section is greater than or equal to a preset first score threshold, it is determined that the image quality of the ultrasound image meets the preset condition; When the total score of the cross section is less than the first score threshold, it is determined that the image quality of the ultrasound image does not meet the preset condition.

6. The ultrasound image detection method according to claim 5, characterized in that, The section recognition result includes section type and at least one anatomical structure; the section recognition result obtained based on the section type of the ultrasound image is used to score the ultrasound image to obtain a total section score corresponding to the ultrasound image, including: Determine the structural score corresponding to each of the anatomical structures; Based on the section type, determine the weight value corresponding to each anatomical structure; The weighted sum of the weight value and structural score corresponding to each anatomical structure in the ultrasound image is used to obtain the first section score. The ultrasound image is input into a preset section scoring model for scoring, and a second section score is obtained; The total score of the cross section is determined based on the first cross section score and the second cross section score.

7. The ultrasound image detection method according to claim 1, characterized in that, The image quality detection of the ultrasound image includes: The ultrasound image is input into a preset section scoring model for section scoring to obtain the total section score corresponding to the ultrasound image. When the total score of the section is greater than or equal to the preset second score threshold, it is determined that the image quality of the ultrasound image meets the preset condition; When the total score of the cross section is less than the second score threshold, it is determined that the image quality of the ultrasound image does not meet the preset condition.

8. The ultrasound image detection method according to claim 1, characterized in that, After performing image quality detection on the ultrasound image, the method further includes: Obtain the total score for each scanned section, where the scanned section refers to the section that has been scanned by the ultrasound equipment; Based on the total score of each scanned section, each scanned section is divided into multiple preset section level groups; The image display page shows the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group.

9. The ultrasound image detection method according to claim 8, characterized in that, After displaying the total number of sections in each section level group, the section name label, and the number of images corresponding to each scanned section in each section level group on the image display page, the method further includes: If a click operation on one of the section name labels in the image display page is detected, the target ultrasound image corresponding to the currently clicked section name label is determined, and the section quality control details information corresponding to the target ultrasound image is obtained; The image display page displays the target ultrasound image and the section quality control details corresponding to the target ultrasound image. The section quality control details include the section name, total section score, section grade, and structural score and weight value corresponding to the anatomical structure in the target ultrasound image.

10. The ultrasonic image detection method according to claim 8, characterized in that, The image display page also includes unrecognized image tags; after displaying the total number of sections in each section level group and the section name tag and image number corresponding to each scanned section in each section level group on the image display page, it further includes: If a click operation on the unidentified image label is detected, an abnormal ultrasound image with an unidentifiable section type will be displayed on the image display page; When an editing operation on the section name of the abnormal ultrasound image is detected, the section type and anatomical structure corresponding to the abnormal ultrasound image are determined based on the section name editing operation; Based on the section type and anatomical structure corresponding to the abnormal ultrasound image, the section quality of the abnormal ultrasound image is detected to obtain detailed section quality control information corresponding to the abnormal ultrasound image. The image display page displays the cross-sectional quality control details corresponding to the abnormal ultrasound image.

11. The ultrasound image detection method according to claim 9, characterized in that, After displaying the target ultrasound image and the corresponding section quality control details on the image display page, the method further includes: If a section name switching operation is detected for the target ultrasound image, then the image quality of the target ultrasound image is detected according to the section type after the switch, and the updated section quality control details of the target ultrasound image are obtained. The image display page displays updated section quality control details of the target ultrasound image.

12. The ultrasound image detection method according to claim 9, characterized in that, After displaying the target ultrasound image and the corresponding section quality control details on the image display page, the method further includes: If a section scoring editing operation on the target ultrasound image is detected, the modified structural score of each anatomical structure in the target ultrasound image is obtained; The weighted sum of the weight value of each anatomical structure in the target ultrasound image and the modified structure score is obtained to obtain the modified total score of the section. The modified cross-sectional total score of the target ultrasound image is displayed on the image display page.

13. A method for detecting ultrasound images, characterized in that, The method includes: Acquire ultrasound images; The ultrasound images are subjected to image quality detection; If the image quality of the ultrasound image does not meet the preset conditions, a rescan prompt message is output in the ultrasound device, and the ultrasound image is rescanned based on the rescan prompt message, and the image quality of the ultrasound image is detected. If the image quality of the ultrasound image meets the preset conditions, the ultrasound image is saved as a matched standard section in the workflow protocol, and a scan prompt message is output in the ultrasound device. The scan prompt message is used to instruct the ultrasound device to perform an ultrasound scan on the unmatched standard section in the workflow protocol.

14. The ultrasound image detection method according to claim 13, characterized in that, The image quality detection of the ultrasound image includes: The ultrasound image is input into a preset section prediction model to predict the section type, thereby obtaining the section prediction type corresponding to the ultrasound image and the confidence level corresponding to the section prediction type. Based on the confidence level, determine the total score of the section corresponding to the ultrasound image; When the total score of the section is greater than or equal to the preset third score threshold, the image quality of the ultrasound image is determined to meet the preset condition. When the total score of the section is less than the third score threshold, it is determined that the image quality of the ultrasound image does not meet the preset condition.

15. A method for detecting ultrasound images, characterized in that, The method includes: Acquire ultrasound images; Automatically identify the image features of the ultrasound image; Based on the identified image features of the ultrasound image, the ultrasound image is automatically matched with the standard cross-section in the workflow protocol: If the ultrasound image does not match the standard section in the workflow protocol, the ultrasound image is re-scanned, and the process returns to the step of automatically identifying the image features of the ultrasound image. If the ultrasound image matches the standard section in the workflow protocol, then the ultrasound image is subjected to image quality detection: if the image quality of the ultrasound image does not meet the preset conditions, then a first prompt message is output, and the ultrasound image is re-scanned based on the first prompt message, and the process of automatically identifying the image features of the ultrasound image is returned; if the section quality of the ultrasound image meets the preset conditions, then the ultrasound image is saved as a matched standard section in the workflow protocol.

16. A method for detecting ultrasound images, characterized in that, The method includes: Acquire ultrasound images; Automatically identify the section type of the ultrasound image; Receive at least one standard cross-section to be scanned, manually input by the user; Based on the identified section type of the ultrasound image, the ultrasound image is automatically matched with the at least one standard section: If the ultrasound image does not match the at least one standard section, the ultrasound image is re-scanned, and the section type of the re-scanned ultrasound image is automatically identified. If the ultrasound image matches the at least one frame of standard sections, then the ultrasound image is subjected to image quality detection: if the image quality of the ultrasound image does not meet the preset conditions, then a first prompt message is output, the ultrasound image is re-scanned based on the first prompt message, and the section type of the re-scanned ultrasound image is automatically identified; if the section quality of the ultrasound image meets the preset conditions, then the ultrasound image is saved as a matched standard section in the at least one frame of standard sections.

17. The ultrasound image detection method according to claim 16, characterized in that, The automatic identification of the section type of the ultrasound image includes: Anatomical structure identification is performed on the ultrasound image to obtain at least one anatomical structure in the ultrasound image; Based on the at least one anatomical structure, the section type corresponding to the ultrasound image is determined.

18. The ultrasonic image detection method according to claim 17, characterized in that, The image quality detection of the ultrasound image includes: Based on the section recognition results obtained by identifying the section type of the ultrasound image, the ultrasound image is scored to obtain the total section score corresponding to the ultrasound image. When the total score of the section is greater than or equal to a preset first score threshold, it is determined that the image quality of the ultrasound image meets the preset condition; When the total score of the cross section is less than the first score threshold, it is determined that the image quality of the ultrasound image does not meet the preset condition.

19. The ultrasound image detection method according to claim 16, characterized in that, After performing image quality detection on the ultrasound image, the method further includes: Obtain the total score for each scanned section, where the scanned section refers to the section that has been scanned by the ultrasound equipment; Based on the total score of each scanned section, each scanned section is divided into multiple preset section level groups; The image display page shows the total number of sections in each section level group, as well as the section name label and image number corresponding to each scanned section in each section level group.

20. An ultrasonic device, characterized in that, The ultrasonic device includes: Ultrasonic probe; The transmitting and receiving circuit is used to excite the ultrasonic probe to emit ultrasonic waves and control the ultrasonic probe to receive the echo of the ultrasonic waves in order to obtain an ultrasonic echo signal. The echo processing module is used to perform beamforming and signal processing on the ultrasonic echo signal to obtain an ultrasonic image. A processor for executing the ultrasound image detection method according to any one of claims 1 to 12, or the ultrasound image detection method according to any one of claims 13 to 14, or the ultrasound image detection method according to claim 15, or the ultrasound image detection method according to any one of claims 16 to 19; A display for showing ultrasound images and cross-sectional quality control details of the ultrasound images.

21. An ultrasound imaging network system, characterized in that, The ultrasound imaging network system includes: Ultrasound equipment, used to acquire ultrasound images; An image processing apparatus for performing the ultrasound image detection method according to any one of claims 1 to 12, or the ultrasound image detection method according to any one of claims 13 to 14, or the ultrasound image detection method according to claim 15, or the ultrasound image detection method according to any one of claims 16 to 19.

Images