Image processing method, device, electronic device and storage medium

Abstract

The present disclosure provides an image processing method, device, electronic device, and storage medium, which relate to the field of artificial intelligence, and in particular to the field of image processing based on artificial intelligence. The implementation scheme is as follows: obtaining an ultrasonic image of a first position of a tissue to be tested; obtaining a first detection result based on the ultrasonic image, the first detection result indicating whether to perform a continuous ultrasonic scan on the tissue to be tested, the continuous ultrasonic scan being used to perform an ultrasonic scan on multiple consecutive positions on the tissue to be tested starting from the first position to obtain an ultrasonic video of the tissue to be tested; in response to the first detection result indicating that a continuous ultrasonic scan is to be performed on the tissue to be tested, an ultrasonic video is obtained; and obtaining a target video frame from the ultrasonic video, the multiple target video frames indicating a target section of the tissue to be tested.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence, in particular to the field of image processing based on artificial intelligence, and specifically to an image processing method, device, electronic device, computer-readable storage medium, and computer program product. Background Art

[0002] Artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

[0003] AI-based image processing technology has penetrated various fields. For example, computer processing of ultrasound images can assist doctors in making judgments, improving their accuracy and efficiency.

[0004] The approaches described in this section are not necessarily approaches that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any approach described in this section is prior art simply by virtue of its inclusion in this section. Similarly, unless otherwise indicated, the issues raised in this section should not be considered as having been recognized in any prior art. Summary of the Invention

[0005] The present disclosure provides an image processing method, apparatus, electronic device, computer-readable storage medium, and computer program product.

[0006] According to one aspect of the present disclosure, there is provided an image processing method, comprising: obtaining an ultrasound image of a first position of a tissue to be measured; obtaining a first detection result based on the ultrasound image, the first detection result indicating whether to perform continuous ultrasound scanning on the tissue to be measured, the continuous ultrasound scanning being used to perform ultrasound scanning on a plurality of consecutive positions on the tissue to be measured starting from the first position to obtain an ultrasound video of the tissue to be measured; in response to the first detection result indicating that continuous ultrasound scanning is performed on the tissue to be measured, the ultrasound video is acquired; and obtaining a target video frame from the ultrasound video, the plurality of target video frames indicating a target section of the tissue to be measured.

[0007] According to another aspect of the present disclosure, an image processing device is provided, including: an ultrasound image acquisition unit, configured to obtain an ultrasound image of a first position of a tissue to be measured; a first detection result acquisition unit, configured to obtain a first detection result based on the ultrasound image, the first detection result indicating whether to perform continuous ultrasound scanning on the tissue to be measured, the continuous ultrasound scanning being used to perform ultrasound scanning on a plurality of consecutive positions on the tissue to be measured starting from the first position, so as to obtain an ultrasound video of the tissue to be measured; an ultrasound video acquisition unit, configured to perform continuous ultrasound scanning on the tissue to be measured in response to the first detection result indication, so as to obtain the ultrasound video; and a target video frame acquisition unit, configured to obtain a target video frame from the ultrasound video, the plurality of target video frames indicating a target section of the tissue to be measured.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method according to an embodiment of the present disclosure.

[0009] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to the embodiment of the present disclosure.

[0010] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to the embodiment of the present disclosure.

[0011] According to one or more embodiments of the present disclosure, intelligent guidance can be provided for the ultrasound scanning process to avoid unnecessary continuous scanning processes. At the same time, automatic selection of the measured tissue section image can be achieved to assist doctors in obtaining accurate section images.

[0012] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings illustrate exemplary embodiments and constitute a part of the specification. Together with the description of the specification, they serve to explain exemplary implementation of the embodiments. The illustrated embodiments are for illustrative purposes only and do not limit the scope of the claims. Throughout the drawings, the same reference numerals designate similar, but not necessarily identical, elements.

[0014] Figure 1 A schematic diagram illustrating an exemplary system in which the various methods described herein may be implemented according to an embodiment of the present disclosure;

[0015] Figure 2 A flowchart of an image processing method according to an embodiment of the present disclosure is shown;

[0016] Figure 3 A flowchart showing a process of obtaining a first detection result based on an ultrasound image in an image processing method according to an embodiment of the present disclosure is shown;

[0017] Figure 4 A flowchart illustrating a process of obtaining a first detection result in response to a second detection result indicating that the detected tissue corresponds to a target organ in an image processing method according to an embodiment of the present disclosure is shown;

[0018] Figure 5 A flowchart showing a process of obtaining a first detection result based on an ultrasound image in an image processing method according to an embodiment of the present disclosure is shown;

[0019] Figure 6 A flowchart showing a process of performing continuous ultrasound scanning on a tissue to be detected to acquire an ultrasound video in response to a first detection result indication in an image processing method according to an embodiment of the present disclosure is shown;

[0020] Figure 7 A flowchart showing a process of obtaining a target video frame from an ultrasound video in an image processing method according to an embodiment of the present disclosure is shown;

[0021] Figure 8 A flowchart of an image processing method according to an embodiment of the present disclosure is shown;

[0022] Figure 9 A structural block diagram of an image processing apparatus according to an embodiment of the present disclosure is shown; and

[0023] Figure 10 A structural block diagram of an exemplary electronic device that can be used to implement the embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0024] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be appreciated by those skilled in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0025] In this disclosure, unless otherwise specified, the use of terms such as "first" and "second" to describe various elements is not intended to limit the positional relationship, temporal relationship, or importance relationship of these elements. Such terms are only used to distinguish one element from another. In some examples, the first element and the second element may refer to the same instance of the element, while in some cases, based on the context of the description, they may also refer to different instances.

[0026] The terms used in the descriptions of the various examples described in this disclosure are for the purpose of describing specific examples only and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or more. In addition, the term "and / or" used in this disclosure encompasses any one and all possible combinations of the listed items.

[0027] The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

[0028] Figure 1 FIG2 is a schematic diagram of an exemplary system 100 in which the various methods and apparatuses described herein may be implemented according to an embodiment of the present disclosure. Figure 1 , the system 100 includes one or more client devices 101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120. The client devices 101, 102, 103, 104, 105, and 106 can be configured to execute one or more applications.

[0029] In an embodiment of the present disclosure, the server 120 may run one or more services or software applications that enable execution of the image processing method.

[0030] In some embodiments, server 120 may also provide other services or software applications, which may include non-virtualized environments and virtualized environments. In some embodiments, these services may be provided as web-based services or cloud services, such as provided to users of client devices 101, 102, 103, 104, 105, and / or 106 under a software as a service (SaaS) model.

[0031] exist Figure 1In the configuration shown, the server 120 may include one or more components that implement the functions performed by the server 120. These components may include software components, hardware components, or a combination thereof that can be executed by one or more processors. Users operating client devices 101, 102, 103, 104, 105, and / or 106 may, in turn, utilize one or more client applications to interact with the server 120 to utilize the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from the system 100. Therefore, Figure 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

[0032] The user may use client devices 101, 102, 103, 104, 105 and / or 106 to receive the target video frame. The client device may provide an interface that enables the user of the client device to interact with the client device. The client device may also output information to the user via the interface. Figure 1 Only six client devices are depicted, but one skilled in the art will appreciate that the present disclosure can support any number of client devices.

[0033] Client devices 101, 102, 103, 104, 105, and / or 106 may include various types of computer devices, such as portable handheld devices, general-purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, etc. These computer devices may run various types and versions of software applications and operating systems, such as Microsoft Windows, Apple iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (such as Google Chrome OS); or include various mobile operating systems, such as Microsoft Windows Mobile OS, iOS, Windows Phone, and Android. Portable handheld devices may include cellular phones, smartphones, tablet computers, personal digital assistants (PDAs), etc. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. Gaming systems may include various handheld gaming devices, internet-enabled gaming devices, etc. Client devices are capable of executing a variety of different applications, such as various internet-related applications, communication applications (such as email applications), and short message service (SMS) applications, and may use various communication protocols.

[0034] The network 110 may be any type of network known to those skilled in the art that can support data communications using any of a variety of available protocols, including but not limited to TCP / IP, SNA, IPX, etc. By way of example only, the one or more networks 110 may be a local area network (LAN), an Ethernet-based network, a token ring, a wide area network (WAN), the Internet, a virtual network, a virtual private network (VPN), an intranet, an extranet, a blockchain network, a public switched telephone network (PSTN), an infrared network, a wireless network (e.g., Bluetooth, WIFI), and / or any combination of these and / or other networks.

[0035] Server 120 may include one or more general-purpose computers, specialized server computers (e.g., PC (personal computer) servers, UNIX servers, mid-range servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and / or combination. Server 120 may include one or more virtual machines running virtual operating systems, or other computing architectures involving virtualization (e.g., one or more flexible pools of logical storage devices that may be virtualized to maintain a server's virtual storage device). In various embodiments, server 120 may run one or more services or software applications that provide the functionality described below.

[0036] The computing units in the server 120 may run one or more operating systems including any of the operating systems described above as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and / or middle-tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

[0037] In some implementations, server 120 may include one or more applications to analyze and consolidate data feeds and / or event updates received from users of client devices 101, 102, 103, 104, 105, and / or 106. Server 120 may also include one or more applications to display the data feeds and / or real-time events via one or more display devices of client devices 101, 102, 103, 104, 105, and / or 106.

[0038] In some embodiments, server 120 may be a distributed system server or a server integrated with blockchain. Server 120 may also be a cloud server, or an intelligent cloud computing server or intelligent cloud host equipped with artificial intelligence technology. A cloud server is a host product within the cloud computing service system that addresses the management difficulties and poor scalability of traditional physical hosts and virtual private servers (VPS) services.

[0039] The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The databases 130 may reside in a variety of locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and communicate with the server 120 via a network-based or dedicated connection. The databases 130 may be of different types. In some embodiments, the databases used by the server 120 may be, for example, relational databases. One or more of these databases may store, update, and retrieve data to and from the databases in response to commands.

[0040] In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the applications may be different types of databases, such as a key-value store, an object store, or a conventional store backed by a file system.

[0041] Figure 1 The system 100 may be configured and operated in various ways to enable application of the various methods and apparatuses described in accordance with the present disclosure.

[0042] Ultrasound scanning is of great significance for medical diagnosis. Of the 50 million physicians worldwide, only 2% possess ultrasound scanning skills. During an ultrasound scan, images from different cross-sections must be acquired for real-time diagnosis. The entire process, from ultrasound image acquisition to diagnosis, relies heavily on the physician's experience. However, ultrasound scanning is radiation-free, inexpensive, and widely applicable. Ultrasound examinations can be performed from head to toe, making it a preferred option for specialized patients. Ultrasound scanning allows for continuous and dynamic observation of organ movement and function; it can track lesions and display three-dimensional changes without being limited by imaging layers. Ultrasound equipment is also portable and non-invasive, allowing bedside diagnosis for patients with limited mobility. Implementing intelligent guidance during ultrasound scans to assist physicians in obtaining accurate cross-sectional images is a widely sought-after issue in ultrasound scanning.

[0043] In related technologies, when performing ultrasonic scanning on the tissue to be tested, the posture difference between the current section of the ultrasound image and the standard section is calculated based on the ultrasound image obtained during the scanning process. Based on the posture difference, the operation logic is designed to provide guidance for the next ultrasound scan; or by obtaining the current scanning section information and IMU (gyroscope) information, combining the two types of data, and performing information fusion, guidance is provided for the next ultrasound scan from the image perspective and spatial information perspective.

[0044] Because posture includes information from three axes as well as information from different dimensions, such as organs and sections, calculating posture deviations based solely on a single ultrasound image often results in inaccurate guidance. Using an IMU to provide spatial information, however, suffers from inertial drift. Position errors increase with probe movement, further complicating guidance.

[0045] According to one aspect of the present disclosure, a method for image processing is provided. Figure 2 , an image processing method 200 according to some embodiments of the present disclosure includes:

[0046] Step S210: obtaining an ultrasonic image of a first position of the tissue to be measured;

[0047] Step S220: Obtaining a first detection result based on the ultrasound image, the first detection result indicating whether to perform a continuous ultrasound scan on the tissue to be tested, the continuous ultrasound scan being used to perform ultrasound scans on a plurality of consecutive positions on the tissue to be tested starting from the first position to obtain an ultrasound video of the tissue to be tested;

[0048] Step S230: performing continuous ultrasound scanning on the tissue to be tested in response to the first detection result indication to obtain the ultrasound video; and

[0049] Step S240: obtaining target video frames from the ultrasound video, wherein the target video frames indicate target sections of the tissue to be measured.

[0050] A first detection result is obtained based on an ultrasound image obtained by ultrasound scanning the tissue to be tested, and the first detection result indicates whether to perform continuous ultrasound scanning on the tissue to be tested. In response to the indication of the first detection result, the tissue to be tested is continuously ultrasound scanned to obtain an ultrasound video. This can provide intelligent guidance for the continuous ultrasound scanning process and avoid unnecessary continuous ultrasound scanning. At the same time, by obtaining the target video frame from the ultrasound video obtained by continuous ultrasound scanning, automatic selection of standard section images is achieved to assist doctors in obtaining accurate section images.

[0051] In some embodiments, the tissue to be tested can be any biological tissue, such as human or animal tissue. In some examples, the tissue to be tested includes the neck, abdomen, or leg of a human body.

[0052] In some embodiments, the ultrasound image may be an image obtained by performing ultrasound scanning on a first position of the tissue to be measured using an ultrasound probe.

[0053] The ultrasonic probe transmits ultrasonic waves to the measured tissue, receives ultrasonic echoes returned from the measured tissue, obtains ultrasonic echo information, processes the ultrasonic echo signals, and obtains an ultrasonic image of the measured tissue.

[0054] In some embodiments, the image processing method according to the present disclosure further includes the step of determining a target organ to be scanned, for example, obtaining a target organ determined by a doctor, which target organ may be, for example, a thyroid gland.

[0055] In some embodiments, in response to determining the target organ, multiple section types corresponding to the target organ are determined. For example, a section along the long axis (extension direction) of the thyroid gland is a longitudinal section, and a section perpendicular to the long axis of the thyroid gland is a transverse section.

[0056] In some embodiments, as Figure 3 As shown, step S220, obtaining a first detection result based on the ultrasound image, includes:

[0057] Step S310: obtaining a second detection result based on the ultrasound image, where the second detection result indicates whether the detected tissue corresponds to a target organ; and

[0058] Step S320: In response to the second detection result indicating that the detected tissue corresponds to the target organ, the first detection result is obtained.

[0059] The first detection result is obtained by obtaining a second detection result indicating whether the measured tissue corresponds to the target organ, that is, before determining whether to perform continuous ultrasound scanning on the measured tissue, it is judged whether the measured tissue corresponds to the target organ, thereby avoiding empty shots and organ scanning errors.

[0060] For example, when performing an ultrasound scan of the abdomen to obtain an ultrasound image of the kidney as the target organ, since the abdomen also includes the liver, to avoid scanning the liver, a second detection result is obtained based on the ultrasound image to determine whether the ultrasound image acquired at the first location of the measured tissue corresponds to the kidney. If it does not correspond to the kidney, a determination is made as to whether to perform subsequent ultrasound scans, thereby saving ultrasound scanning time.

[0061] In some embodiments, the second detection result is obtained by inputting the ultrasound image into a target organ recognition model, wherein the target organ recognition model is a trained machine learning model that is trained using ultrasound images of various organs as training images.

[0062] In some embodiments, the target organ has corresponding multiple section types, such as Figure 4 As shown, step S320, in response to the second detection result indicating that the detected tissue corresponds to the target organ, obtaining the first detection result includes:

[0063] Step S410: obtaining a third detection result based on the ultrasound image, wherein the third detection result indicates whether the ultrasound image corresponds to one of the multiple slice types; and

[0064] Step S420: In response to the third detection result indicating that the ultrasound image corresponds to one of the plurality of standard section images, the first detection result is obtained.

[0065] The first detection result is obtained by obtaining a third detection result indicating whether the ultrasound image corresponds to one of the multiple section types. That is, before performing continuous ultrasound scanning on the tissue to be tested, it is determined whether the section for scanning the tissue to be tested at the first position meets the requirements, thereby avoiding section scanning errors.

[0066] In some embodiments, the third detection result is obtained by inputting the ultrasound image into a section recognition model. For example, the section recognition model is trained using multiple images corresponding to multiple section types of the target organ.

[0067] In some embodiments, the target organ recognition model and the section recognition model can be the same model. For example, the target organ recognition model is trained using multiple section images of the target organ corresponding to multiple section types. When the target organ recognition model identifies an input image as a target organ, it simultaneously identifies which section type of the target organ the image corresponds to.

[0068] In some embodiments, as Figure 5 As shown, step S220, obtaining a first detection result based on the ultrasound image, includes:

[0069] Step S510: obtaining a target structure in the ultrasound image, where the target structure corresponds to a target section among the multiple target sections; and

[0070] Step S520: In response to the target structure being in a preset area of the ultrasound image, determining the first detection result, wherein the first detection result indicates performing a continuous ultrasound scan on the tissue to be detected.

[0071] In response to the target structure being in a preset area (eg, a middle area), continuous ultrasound scanning is performed, so that the key structure can be more focused during the continuous ultrasound scanning process, and the target structure in the obtained ultrasound video can occupy the center of the field of view.

[0072] In some embodiments, the predetermined region is an area surrounding the center of the ultrasound image, and the proportion of the area occupied by the ultrasound image corresponds to the target structure. For example, when the target structure is the left lobe of the thyroid gland, the proportion of the area occupied by the ultrasound image is 1 / 3-1 / 2.

[0073] In some embodiments, in response to the first detection result indicating continuous ultrasound scanning of the tissue to be measured, the ultrasound probe automatically scans the tissue to be measured to obtain an ultrasound video, wherein the tissue to be measured is automatically scanned along a path starting from a first position.

[0074] In some embodiments, as Figure 6 As shown, step S230, in response to the first detection result indicating continuous ultrasound scanning of the tissue to be tested, obtaining the ultrasound video includes:

[0075] Step S610: in response to the first detection result indicating the continuous ultrasonic scanning of the tissue to be tested, outputting prompt information, wherein the prompt information is used to prompt the execution personnel to perform the continuous ultrasonic scanning; and

[0076] Step S620: In response to the continuous ultrasound scanning being performed, obtaining the ultrasound video.

[0077] By outputting prompt information, the execution personnel are prompted to perform continuous ultrasonic scanning, thereby realizing the execution of continuous ultrasonic scanning.

[0078] In some embodiments, the prompt information is a text prompt information displayed on a display. In other embodiments, the prompt information is a voice instruction.

[0079] In some embodiments, the operator starts from a first position and scans the tissue to be measured along a path to achieve continuous ultrasound scanning, thereby obtaining an ultrasound video.

[0080] In some embodiments, in response to the continuous ultrasound scanning being performed, the target structure is tracked in the ultrasound video obtained during the continuous ultrasound scanning.

[0081] During continuous ultrasound scanning, by tracking the target structure, the operator can be guided to correctly move the ultrasound probe so that the acquired ultrasound video focuses on the target structure, and further the target structure in the acquired ultrasound video can occupy the center of the field of view.

[0082] In some embodiments, the target structure is highlighted on the display during tracking, so that the operator can adjust the scanning path according to the displayed target structure.

[0083] In some embodiments, the target video frame in the ultrasound video is obtained by inputting the ultrasound video into a target section image recognition model. In fact, the target video frame is a video frame corresponding to the target section of the measured tissue.

[0084] In some embodiments, as Figure 7 As shown, step S240, obtaining a target video frame from the ultrasound video, includes:

[0085] Step S710: obtaining image features of each video frame in the ultrasound video; and

[0086] Step S720: Compare the image features of each video frame in the ultrasound video with the image features of each image in a preset image library to obtain the target video frame, wherein the preset image library includes multiple standard section images, and the image features of the target video frame have the greatest similarity with the image features of the first standard section image among the multiple standard section images, and the similarity is greater than a preset similarity threshold.

[0087] By comparing the image features of each video frame in the ultrasound video with the image features of each image in the preset image library, the target video frame is obtained, and the unsupervised algorithm is used to ensure the robustness of key frame recognition.

[0088] In some embodiments, the plurality of standard cross-section images are ultrasound images obtained by performing ultrasound scanning on target cross-sections of multiple tissues corresponding to the multiple organs. In some embodiments, each tissue includes multiple target cross-sections, and the plurality of standard cross-section images include ultrasound images corresponding to the multiple target cross-sections of each tissue.

[0089] In some embodiments, after acquiring the target video frame, the target video frame is stored in a preset queue. Simultaneously, steps S210-S240 are performed for a second position of the measured tissue to obtain a target video frame corresponding to another target section of the measured tissue, thereby completing a comprehensive ultrasound scan of the measured tissue.

[0090] See Figure 8 In one embodiment of the present disclosure, the image processing method of the present disclosure is implemented by executing steps S801-S818.

[0091] like Figure 8 As shown, first, step S801 is performed to determine the organ to be scanned, for example, the organ to be scanned is the thyroid gland. In one example, the organ to be scanned can be determined by receiving an instruction input by a staff member via an input / output device, wherein the instruction indicates the organ to be scanned.

[0092] Next, step S802 is executed to determine an organ scanning rule. In one example, the organ scanning rule includes the section type of the organ and the section image to be scanned.

[0093] Next, step S803 is performed to scan the tissue to be measured using an ultrasound probe to obtain an ultrasound image. In one example, a scanning instruction may be issued to instruct the operator to operate the ultrasound probe to scan the tissue to be measured.

[0094] Next, step S804 is performed to determine, based on the ultrasound image, whether the tissue to be measured is the target organ and whether the current scanning position meets the scanning rules. For example, whether the current scanning position meets the scanning rules can be determined by determining whether a section in the ultrasound image corresponds to one of multiple section types of the organ.

[0095] When it is determined that the tissue to be measured is the target organ and the current scanning position meets the scanning rule, step S805 is then executed to obtain the key structure in the ultrasound image.

[0096] Next, step S806 is executed to determine whether the position of the key structure in the ultrasound image meets the scanning requirements, for example, whether the key structure is in a preset area (eg, the middle area) of the ultrasound image.

[0097] When the judgment result in step S806 is "No", step S807 is executed to move the ultrasound probe to continue scanning. In one example, a movement prompt is output to prompt the operator to move the ultrasound probe to continue scanning.

[0098] When the judgment result in step S806 is "yes", step S808 is executed, and a recording prompt is output, which indicates to start recording the ultrasound video.

[0099] Next, step S809 is executed to move the ultrasound probe to continue scanning. In one example, a movement prompt is output to prompt the operator to move the probe to continue scanning.

[0100] Next, step S810 is executed to locate the key structure in the video frame obtained during the ultrasound video recording process.

[0101] Next, step S811 is executed to track the key structure in the video frames obtained during the ultrasound video recording process.

[0102] Next, step S812 is executed to determine whether the video frame is a target video frame.

[0103] When the judgment result in step S812 is "yes", step S813 is executed to add the video frame to the storage queue, and after step S813 is executed, step S814 is continued to determine whether the target number of frames in the storage queue meets the requirement.

[0104] When the determination result in step S812 is “No”, step S814 is executed to determine whether the target number of video frames in the storage queue meets the requirement.

[0105] If the result of the determination in step S814 is "yes," step S815 is executed, where a completion prompt is output, indicating that the scanning of the current portion of the tissue under examination has been completed. For example, when the scanning rule includes multiple slice types, the scanning of the current portion of the tissue under examination is determined to be complete when a target video frame corresponding to a slice type is obtained.

[0106] When the judgment result in step S814 is "No", the process returns to step S809 to continue moving the probe to scan the measured tissue, thereby completing the scanning of the current portion of the measured tissue.

[0107] Next, step S816 is executed to determine whether the entire scan of the tissue to be tested is completed.

[0108] If the result of the determination in step S816 is "yes," step S817 is executed, and a completion prompt is output, indicating that the entire scan of the tissue under examination has been completed. For example, if the scan rule includes a slice type, upon obtaining a target video frame corresponding to the slice type, the completion of the scan of the current portion of the tissue under examination is determined, and the completion of the entire scan of the tissue under examination is also determined.

[0109] When the judgment result in step S816 is "no", step S818 is executed to continue scanning the measured tissue to obtain an ultrasound image of another position of the measured tissue. After completing step S818, step S804 is then executed to complete the entire scan of the measured tissue.

[0110] According to another aspect of the present disclosure, an image processing device is also provided. Figure 9The device 900 includes: an ultrasonic image acquisition unit 910, configured to obtain an ultrasonic image of a first position of the tissue to be measured; a first detection result acquisition unit 920, configured to obtain a first detection result based on the ultrasonic image, wherein the first detection result indicates whether to perform continuous ultrasonic scanning on the tissue to be measured, and the continuous ultrasonic scanning is used to perform ultrasonic scanning on multiple consecutive positions on the tissue to be measured starting from the first position to obtain an ultrasonic video of the tissue to be measured; an ultrasonic video acquisition unit 930, configured to perform continuous ultrasonic scanning on the tissue to be measured in response to the first detection result indication to obtain the ultrasonic video; and a target video frame acquisition unit 940, configured to obtain a target video frame from the ultrasonic video, wherein the multiple target video frames indicate a target section of the tissue to be measured.

[0111] In some embodiments, the first detection result acquisition unit 920 includes: a second detection result acquisition unit, configured to obtain a second detection result based on the ultrasound image, the second detection result indicating whether the measured tissue corresponds to the target organ; and a first acquisition subunit, configured to obtain the first detection result in response to the second detection result indicating that the measured tissue corresponds to the target organ.

[0112] In some embodiments, the first acquisition subunit includes: a third detection result acquisition unit, configured to obtain a third detection result based on the ultrasound image, the third detection result indicating whether the ultrasound image corresponds to one of the multiple section types; and a second acquisition subunit, configured to obtain the first detection result in response to the third detection result indicating that the ultrasound image corresponds to one of the multiple standard section images.

[0113] In some embodiments, the first detection result acquisition unit includes: a target structure acquisition unit, configured to obtain a target structure in the ultrasound image, wherein the target structure corresponds to one of the multiple target sections; and a determination unit, configured to determine the first detection result in response to the target structure in a preset area of the ultrasound image, wherein the first detection result indicates continuous ultrasound scanning of the tissue to be tested.

[0114] In some embodiments, the ultrasound video acquisition unit includes: an output unit, configured to output prompt information in response to the first detection result indicating continuous ultrasound scanning of the tissue to be tested, wherein the prompt information is used to prompt the executor to perform the continuous ultrasound scan; and an acquisition subunit, configured to obtain the ultrasound video in response to the continuous ultrasound scan being performed.

[0115] In some embodiments, the apparatus 900 further includes: a tracking unit configured to track the target structure in the ultrasound video obtained during the continuous ultrasound scanning in response to the continuous ultrasound scanning being performed.

[0116] In some embodiments, the target video frame acquisition unit includes: a feature extraction unit, configured to obtain image features of each video frame in the ultrasound video; and a comparison unit, configured to compare the image features of each video frame in the ultrasound video with the image features of each image in a preset image library to obtain the target video frame, wherein the preset image library includes multiple standard section images, and the similarity between the image features of the target video frame and the image features of the first standard section image among the multiple standard section images is the largest, and the similarity is greater than a preset similarity threshold.

[0117] In some embodiments, the measured tissue includes the neck, abdomen or legs of the human body.

[0118] According to an embodiment of the present disclosure, an electronic device, a readable storage medium, and a computer program product are also provided.

[0119] refer to Figure 10 , a block diagram of an electronic device 1000 that can serve as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0120] like Figure 10 As shown, the electronic device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. Various programs and data required for the operation of the electronic device 1000 can also be stored in the RAM 1003. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0121] Multiple components in the electronic device 1000 are connected to the I / O interface 1005, including: an input unit 1006, an output unit 1007, a storage unit 1008, and a communication unit 10010. The input unit 1006 can be any type of device that can input information to the electronic device 1000. The input unit 1006 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a trackpad, a trackball, a joystick, a microphone, and / or a remote control. The output unit 1007 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 1008 can include but is not limited to a magnetic disk and an optical disk. The communication unit 10010 allows the electronic device 1000 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks, and may include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device and / or the like.

[0122] The computing unit 1001 may be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 1001 performs the various methods and processes described above, such as method 200. For example, in some embodiments, method 200 may be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the method 200 described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the method 200 in any other appropriate manner (eg, by means of firmware).

[0123] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0127] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0128] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0129] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0130] Although the embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. In addition, the steps may be performed in an order different from that described in this disclosure. Further, the various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced by equivalent elements that appear after this disclosure.

Claims

1. An image processing method, comprising: obtaining an ultrasound image of a first position of a tissue to be measured; Obtaining a first detection result based on the ultrasound image, the first detection result indicating whether to perform a continuous ultrasound scan on the tissue to be tested, the continuous ultrasound scan being used to perform ultrasound scans on a plurality of consecutive positions on the tissue to be tested starting from the first position to obtain an ultrasound video of the tissue to be tested, wherein the target organ has a plurality of corresponding section types, and obtaining the first detection result based on the ultrasound image includes: obtaining a second detection result based on the ultrasound image, wherein the second detection result indicates whether the tissue to be detected corresponds to a target organ; and In response to the second detection result indicating that the tissue to be tested corresponds to the target organ, obtaining the first detection result includes: obtaining a third detection result based on the ultrasound image, the third detection result indicating whether the ultrasound image corresponds to one of the plurality of slice types; and In response to the third detection result indicating that the ultrasound image corresponds to one of a plurality of standard section images, the first detection result is obtained, wherein the second detection result and the third detection result are obtained based on a target organ recognition model, the target organ recognition model is trained using a plurality of section images of the target organ corresponding to a plurality of section types as training images, and the operations performed by the target organ recognition model include: while recognizing the input ultrasound image as the target organ, identifying the section type corresponding to the ultrasound image from the plurality of section types of the target organ; In response to the first detection result indicating performing continuous ultrasound scanning on the tissue to be tested, acquiring the ultrasound video; and A target video frame is obtained from the ultrasound video, where the target video frame indicates a target section of the tissue to be measured.

2. The method according to claim 1, wherein Obtaining a first detection result based on the ultrasound image includes: obtaining a target structure in the ultrasound image, where the target structure corresponds to one of the multiple target slices; and In response to the target structure being in a preset area of the ultrasound image, a first detection result is determined, wherein the first detection result indicates that continuous ultrasound scanning is to be performed on the tissue to be detected.

3. The method according to claim 1, wherein In response to the first detection result indicating that the tissue to be tested is continuously ultrasonically scanned, obtaining the ultrasonic video includes: In response to the first detection result indicating that the tissue to be tested should be continuously ultrasonically scanned, outputting prompt information, wherein the prompt information is used to prompt an executor to perform the continuous ultrasonic scanning; and In response to the continuous ultrasound scan being performed, the ultrasound video is obtained.

4. The method according to claim 3, further comprising: In response to the continuous ultrasound scanning being performed, a target structure is tracked in the ultrasound video obtained during the continuous ultrasound scanning.

5. The method according to claim 1, wherein Obtaining a target video frame from the ultrasound video includes: Obtaining image features of each video frame in the ultrasound video; and The image features of each video frame in the ultrasound video are compared with the image features of each image in a preset image library to obtain the target video frame, wherein the preset image library includes multiple standard section images, and the image features of the target video frame have the greatest similarity with the image features of the first standard section image among the multiple standard section images, and the similarity is greater than a preset similarity threshold.

6. The method according to claim 1, wherein The tissue to be tested includes the neck, abdomen or legs of the human body.

7. An image processing device comprising: an ultrasound image acquisition unit, configured to acquire an ultrasound image of a first position of a tissue to be measured; A first detection result acquisition unit is configured to obtain a first detection result based on the ultrasound image, wherein the first detection result indicates whether to perform a continuous ultrasound scan on the tissue to be tested, wherein the continuous ultrasound scan is used to perform an ultrasound scan on a plurality of consecutive positions on the tissue to be tested starting from the first position to obtain an ultrasound video of the tissue to be tested, wherein the target organ has a corresponding plurality of section types, and the first detection result acquisition unit includes: a second detection result obtaining unit configured to obtain a second detection result based on the ultrasound image, wherein the second detection result indicates whether the tissue to be detected corresponds to a target organ; and The first acquisition subunit is configured to obtain the first detection result in response to the second detection result indicating that the tissue to be detected corresponds to the target organ, and the first acquisition subunit includes: a third detection result obtaining unit configured to obtain a third detection result based on the ultrasound image, wherein the third detection result indicates whether the ultrasound image corresponds to one of the multiple slice types; and a second acquisition subunit configured to obtain the first detection result in response to the third detection result indicating that the ultrasound image corresponds to one of the plurality of standard section images, wherein the second detection result and the third detection result are obtained based on a target organ recognition model, the target organ recognition model being trained using a plurality of section images of the target organ corresponding to a plurality of section types as training images, the target organ recognition model performing the following operations: simultaneously recognizing the input ultrasound image as the target organ, and identifying a section type corresponding to the ultrasound image from the plurality of section types of the target organ; an ultrasound video acquisition unit, configured to perform continuous ultrasound scanning on the tissue to be tested in response to the first detection result indication, to acquire the ultrasound video; and The target video frame acquisition unit is configured to obtain target video frames from the ultrasound video, wherein the multiple target video frames indicate target sections of the tissue to be measured.

8. The device according to claim 7, wherein The first detection result obtaining unit includes: a target structure acquisition unit configured to obtain a target structure in the ultrasound image, where the target structure corresponds to one of the multiple target slices; and The determining unit is configured to determine the first detection result in response to the target structure being in a preset area of the ultrasound image, wherein the first detection result indicates performing continuous ultrasound scanning on the tissue to be detected.

9. The device according to claim 7, wherein The ultrasound video acquisition unit includes: an output unit configured to output prompt information in response to the first detection result indicating that the tissue to be tested should be continuously ultrasonically scanned, wherein the prompt information is used to prompt an executor to perform the continuous ultrasonic scanning; and The acquisition subunit is configured to obtain the ultrasound video in response to the continuous ultrasound scanning being performed.

10. The apparatus according to claim 9, further comprising: A tracking unit is configured to track a target structure in the acquired ultrasound video during the continuous ultrasound scanning in response to the continuous ultrasound scanning being performed.

11. The device according to claim 7, wherein The target video frame acquisition unit includes: a feature extraction unit configured to obtain image features of each video frame in the ultrasound video; and A comparison unit is configured to compare image features of each video frame in the ultrasound video with image features of each image in a preset image library to obtain the target video frame, wherein the preset image library includes multiple standard section images, and the image features of the target video frame have the greatest similarity with the image features of the first standard section image among the multiple standard section images, and the similarity is greater than a preset similarity threshold.

12. The device according to claim 7, wherein The tissue to be tested includes the neck, abdomen or legs of the human body.

13. An electronic device comprising: at least one processor; as well as a memory communicatively coupled to the at least one processor; in The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer program product comprising a computer program, wherein When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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