A method and system for processing ultrasound images

Abstract

This application discloses a method and system for processing ultrasound images. An ultrasound device acquires multiple ultrasound images, identifies and processes the ultrasound echo data corresponding to these images, determines the section types and quality scores of the multiple ultrasound images, and then associates the multiple ultrasound images with at least one target section type based on their section types. This significantly improves the efficiency of doctors and optimizes the scanning process. Based on the quality scores of the ultrasound images associated with at least one target section type, the ultrasound device displays a recommended list of ultrasound images corresponding to that target section type. It can recommend ultrasound images based on the quality scores of the ultrasound images associated with the target section type. Doctors can select ultrasound images with high quality scores to replace the target section images, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, and more specifically to a method and system for processing ultrasound images. Background Technology

[0002] During an ultrasound scan, doctors need to perform and save images according to different standard sections. This requires categorizing the ultrasound images into the appropriate standard sections. For example, doctors may save sections sequentially according to the workflow. However, different doctors have their own scanning habits and techniques. Blindly following a specific order can lead to inefficiency. Doctors can manually set the standard sections to be scanned before performing the scans, but this also increases workload and reduces overall ultrasound scanning efficiency.

[0003] Among related technologies, there are also solutions that can automatically identify the corresponding section type of ultrasound image data. In this solution, when multiple ultrasound images correspond to the same section type, the ultrasound image with the highest quality score is usually automatically selected based on the quality score of the ultrasound image. However, when there is a lesion in the target tissue being scanned, it will affect the quality score of the ultrasound image (for example, if a cyst appears in the current ultrasound image, it is considered to have scanned extra tissue, resulting in a low quality score), causing the ultrasound image that the doctor should pay more attention to not to be selected, thereby affecting the doctor's diagnosis of the ultrasound scan results. Summary of the Invention

[0004] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0005] This application provides a method and system for processing ultrasound images, which can not only improve doctors' work efficiency and optimize the scanning process, but also reduce the omission or error of cross-sections caused by human factors, thus ensuring the accuracy and completeness of the examination.

[0006] On one hand, embodiments of this application provide a method for processing ultrasound images, applied to an ultrasound device, the method comprising:

[0007] Acquire multiple ultrasound images;

[0008] The multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images are identified and processed to determine the cross-sectional type and quality score of the multiple ultrasound images.

[0009] Associating multiple ultrasound images with at least one target section type based on the section type of the multiple ultrasound images;

[0010] Based on the quality score of the ultrasound image associated with at least one of the target section types, a recommended ultrasound image list corresponding to at least one of the target section types is displayed, the recommended ultrasound image list including at least one ultrasound image recommended to the user for selection based on the quality score;

[0011] Obtain the user's selection operation in the recommended ultrasound image list, and determine the ultrasound image selected by the user;

[0012] The ultrasound image selected by the user is used as the ultrasound section image of the target section type.

[0013] In one embodiment of this application, the step of displaying a recommended list of ultrasound images corresponding to at least one of the target section types based on the quality score of the ultrasound images associated with at least one of the target section types includes one of the following:

[0014] Display a list of recommended ultrasound images corresponding to at least one of the target section types, and filter the ultrasound images associated with the target section types according to the quality score of the ultrasound images, and display the filtered ultrasound images in the list of recommended ultrasound images;

[0015] Alternatively, display a recommended ultrasound image list corresponding to at least one of the target section types, and sort and display the ultrasound images associated with the target section type in the recommended ultrasound image list according to the quality score.

[0016] Alternatively, a list of recommended ultrasound images corresponding to at least one of the target section types can be displayed, and the ultrasound images associated with the target section type can be filtered according to the quality score of the ultrasound images. The filtered ultrasound images can then be sorted and displayed in the list of recommended ultrasound images according to their quality scores.

[0017] In one embodiment of this application, the step of filtering the ultrasound images associated with the target section type based on the quality score of the ultrasound images includes:

[0018] The quality score of the ultrasound image associated with the target section type is compared with a scoring threshold, and ultrasound images with a quality score lower than the scoring threshold are discarded or stored in the candidate section storage area.

[0019] In one embodiment of this application, before obtaining the user's selection operation from the recommended ultrasound image list, one of the following is also included:

[0020] The quality score corresponding to the ultrasound image is displayed in the recommended ultrasound image list;

[0021] Alternatively, a rating viewing operation can be performed on the ultrasound images in the recommended ultrasound image list, displaying the quality rating of the ultrasound images.

[0022] In one embodiment of this application, the operation of obtaining a rating and viewing the ultrasound images in the recommended ultrasound image list, and displaying the quality rating of the ultrasound images, includes one of the following:

[0023] Obtain a rating view operation for the ultrasound images in the recommended ultrasound image list, and display the quality rating of the ultrasound images in the recommended ultrasound image list;

[0024] Alternatively, a rating viewing operation can be performed on the ultrasound images in the recommended ultrasound image list, and a rating interface can be displayed in response to the rating viewing operation, showing the quality rating of the ultrasound image in the rating interface.

[0025] In one embodiment of this application, the step of displaying a recommended list of ultrasound images corresponding to at least one target section type based on the quality score of the ultrasound images associated with at least one target section type includes...

[0026] Display a list of recommended ultrasound images corresponding to at least one of the target section types, wherein the recommended ultrasound image list displays the ultrasound image associated with the target section type and its corresponding quality score.

[0027] In one embodiment of this application, the ultrasound images in the recommended ultrasound image list are arranged in a horizontal, vertical, matrix, or grid manner.

[0028] In one embodiment of this application, displaying a recommended list of ultrasound images corresponding to at least one target section type based on the quality score of the ultrasound image associated with at least one target section type includes:

[0029] The target section type classification interface is displayed, and the ultrasonic section images or default images corresponding to different target section types are displayed in the target section type classification interface. The default image represents the ultrasonic section image for which the target section type has not been determined.

[0030] Obtain the user's selection operation of the target section type in the target section type classification interface;

[0031] In response to the selection operation, a list of recommended ultrasound images corresponding to the target section type is displayed based on the quality score of the ultrasound image corresponding to the target section type selected by the user.

[0032] In one embodiment of this application, associating the plurality of ultrasound images with at least one target section type based on the section type of the plurality of ultrasound images includes one of the following:

[0033] The multiple ultrasound images are associated with at least one corresponding target section type in a target workflow protocol based on the section type of the multiple ultrasound images, wherein the target workflow protocol is selected by the user from a list of workflow protocols, and the list of workflow protocols is a list stored locally on the ultrasound device or a list obtained from a cloud server;

[0034] Alternatively, based on at least one target section type selected or preset by the user, multiple ultrasound images are associated with at least one target section type according to the section type of the multiple ultrasound images.

[0035] In one embodiment of this application, the step of identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images includes one of the following:

[0036] Run the local recognition program of the ultrasound device to identify and process multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images, and determine the cross-section type and quality score of multiple ultrasound images.

[0037] Alternatively, multiple ultrasound images can be sent to a cloud server, whereby the cloud server can process and identify the multiple ultrasound images to determine the section type and quality score of the ultrasound images; and the section type and quality score of the ultrasound images can be received from the cloud server.

[0038] Alternatively, the ultrasound echo data corresponding to multiple ultrasound images can be sent to a cloud server, so that the cloud server can identify and process the ultrasound echo data corresponding to multiple ultrasound images, thereby determining the section type and quality score of the ultrasound images; and the section type and quality score of the ultrasound images can be received from the cloud server.

[0039] In one embodiment of this application, it further includes:

[0040] Receive the ultrasound image and / or the ID of the ultrasound image whose cross-sectional type has been identified from the feedback of the cloud server.

[0041] In one embodiment of this application, the identification and processing of multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images includes:

[0042] The ultrasound images are compared with at least one target section image corresponding to the target section type to determine the section type of each ultrasound image.

[0043] Anatomical structures in multiple ultrasound images are identified, and the identified anatomical structures are compared with the target anatomical structures in the target cross-sectional image to determine the quality score.

[0044] In one embodiment of this application, identifying anatomical structures in a plurality of ultrasound images includes:

[0045] Identify at least one of the following anatomical structure types, anatomical structure morphology, anatomical structure location, and anatomical structure clarity in multiple ultrasound images;

[0046] The target anatomical structure includes at least one of the following: target anatomical structure type, target structure morphology, target anatomical structure location, and target anatomical structure clarity; the step of comparing the identified anatomical structure with the target anatomical structure in the target cross-sectional image to determine the quality score includes:

[0047] The quality score of the ultrasound image is determined by comparing the type of anatomical structure identified in the ultrasound image with the type of target anatomical structure, and / or comparing the morphology of the anatomical structure with the morphology of the target anatomical structure, and / or comparing the location of the anatomical structure with the location of the target anatomical structure, and / or comparing the clarity of the anatomical structure with the clarity of the target anatomical structure.

[0048] In one embodiment of this application, acquiring multiple ultrasound images includes one of the following:

[0049] Acquire multiple ultrasound images obtained by the user operating the ultrasound device;

[0050] Alternatively, acquire multiple ultrasound images cached by the ultrasound device;

[0051] Alternatively, acquire ultrasound video stream data and obtain multiple ultrasound images from the ultrasound video stream data.

[0052] In one embodiment of this application, the method further includes:

[0053] The ultrasonic section image is stored in the target section storage area corresponding to the target section type;

[0054] Displays a thumbnail of the ultrasonic section image and / or target section type information in the target section storage area;

[0055] After receiving the user's first selection instruction to select the ultrasound section image in the target section storage area, the selected ultrasound section image and its corresponding target section type information are displayed.

[0056] In one embodiment of this application, the method further includes:

[0057] Displays the ultrasonic section image and the target section type information corresponding to the target section type associated with the ultrasonic section image.

[0058] In one embodiment of this application, the method further includes:

[0059] Display a list of sections for the target section type in the target workflow protocol, and highlight the target section type corresponding to the ultrasound section image in the section list, wherein the highlighting includes at least one of the following:

[0060] Annotation display;

[0061] Highlight;

[0062] Color-coded display;

[0063] Fonts are displayed differently.

[0064] In one embodiment of this application, the step of identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images includes:

[0065] The anatomical structure information, section type and quality score of the multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images are identified and processed.

[0066] The method further includes: displaying the ultrasound section image and target section type information, anatomical structure information and quality score corresponding to the target section type associated with the ultrasound section image.

[0067] On the other hand, embodiments of this application provide a method for processing ultrasound images, applied to an ultrasound device, the method comprising:

[0068] Displays a recommended list of ultrasound images, which is formed based on image quality scores associated with the target section type;

[0069] Obtain the user's selection operation in the recommended ultrasound image list and determine the ultrasound image selected by the user;

[0070] The ultrasound image selected by the user is used as the ultrasound section image of the target section type.

[0071] On the other hand, embodiments of this application provide an ultrasound imaging system, including:

[0072] Ultrasonic probe;

[0073] The transmitting and receiving circuit is used to excite the ultrasonic probe to emit ultrasonic waves toward the target to be detected, receive the ultrasonic echo of the ultrasonic waves returned from the target to be detected, and obtain the ultrasonic echo signal.

[0074] The beamforming and signal processing module is used to perform beamforming and signal processing on the echo signal to obtain an ultrasound image;

[0075] The human-computer interaction interface is used to receive ultrasonic scanning operation commands input by the user;

[0076] Transmission module;

[0077] Display devices;

[0078] The processor is configured to: determine multiple ultrasound images to be identified according to the ultrasound scanning operation instructions; perform identification processing on the multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images; determine the section type and quality score of the multiple ultrasound images; associate the multiple ultrasound images with at least one target section type according to the section type; control the display device to display a recommended ultrasound image list corresponding to at least one target section type based on the quality score of the ultrasound image associated with at least one target section type, the recommended ultrasound image list including at least one ultrasound image recommended to the user based on the quality score; obtain the user's selection operation in the recommended ultrasound image list through the human-computer interaction interface; determine the ultrasound image selected by the user; and use the ultrasound image selected by the user as the ultrasound section image of the target section type.

[0079] The embodiments of this application include at least the following beneficial effects:

[0080] On one hand, an embodiment of this application provides an ultrasound image processing method applied to an ultrasound device. The ultrasound device acquires multiple ultrasound images, identifies and processes the multiple ultrasound images or their corresponding ultrasound echo data, determines the section type and quality score of the multiple ultrasound images, and then associates the multiple ultrasound images with at least one target section type based on their section types. Based on the quality scores of the ultrasound images associated with the at least one target section type, the ultrasound device displays a recommended ultrasound image list corresponding to the at least one target section type. The recommended ultrasound image list includes at least one ultrasound image recommended to the user based on its quality score, and the device acquires the user's selection operation in the recommended ultrasound image list, determines the ultrasound image selected by the user, and uses the user-selected ultrasound image as the ultrasound section image of the target section type. In this embodiment, the ultrasound device identifies the section type of multiple ultrasound images or their corresponding ultrasound echo data, and then automatically associates the multiple ultrasound images with the target section type based on the section type, which can significantly improve the doctor's work efficiency and optimize the scanning process. Furthermore, in this method, the ultrasound device can recommend ultrasound images to doctors based on the quality score of the ultrasound image associated with the target section type. Doctors can select ultrasound images with high quality scores from the list of ultrasound images recommended by the ultrasound device to replace the target section type with ultrasound section images, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination.

[0081] On the other hand, one embodiment of this application provides an ultrasound image processing method applied to an ultrasound device. The ultrasound device displays a recommended ultrasound image list, which is formed based on image quality scores associated with a target section type. The method also acquires the user's selection operation within the recommended ultrasound image list, determines the ultrasound image selected by the user, and uses the user-selected ultrasound image as the ultrasound section image for the target section type. In this embodiment, the ultrasound device can recommend ultrasound images to doctors based on the quality scores of the ultrasound images associated with the target section type. Doctors can select ultrasound images with high quality scores from the recommended ultrasound image list to replace the original images as the ultrasound section image for the target section type, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination.

[0082] On the other hand, one embodiment of this application provides an ultrasound imaging system that acquires multiple ultrasound images through a processor, performs identification processing on the multiple ultrasound images or the corresponding ultrasound echo data, determines the section type and quality score of the multiple ultrasound images, and then associates the multiple ultrasound images with at least one target section type based on the section type. Based on the quality score of the ultrasound images associated with the at least one target section type, the processor displays a recommended ultrasound image list corresponding to the at least one target section type. The recommended ultrasound image list includes at least one ultrasound image recommended to the user based on the quality score, and the processor acquires the user's selection operation in the recommended ultrasound image list, determines the ultrasound image selected by the user, and uses the user-selected ultrasound image as the ultrasound section image of the target section type. In this embodiment, the ultrasound imaging system identifies the section type of multiple ultrasound images or the corresponding ultrasound echo data through a processor, and then automatically associates the multiple ultrasound images with the target section type based on the section type, which can significantly improve the doctor's work efficiency and optimize the scanning process. Furthermore, this ultrasound imaging system can recommend ultrasound images to doctors based on the quality score of the ultrasound image associated with the target section type. Doctors can select ultrasound images with high quality scores from the list of ultrasound images recommended by the ultrasound equipment to replace the target section type, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination. Attached Figure Description

[0083] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0084] Figure 1 This is a schematic diagram of the structure of an ultrasound imaging system according to an embodiment of this application;

[0085] Figure 2 This is a flowchart of an ultrasound image processing method provided in one embodiment of this application;

[0086] Figure 3 This is a flowchart of an ultrasound image processing method provided in another embodiment of this application;

[0087] Figure 4 This is a schematic diagram of the display interface of an ultrasonic device provided in one embodiment of this application;

[0088] Figure 5 This is a schematic diagram of the display interface of an ultrasound device provided in another embodiment of this application;

[0089] Figure 6This is a flowchart of an ultrasound image processing method provided in another embodiment of this application;

[0090] Figure 7 This is a schematic diagram of the display interface of an ultrasound device provided in another embodiment of this application;

[0091] Figure 8 This is a schematic diagram of the display interface of an ultrasound device provided in another embodiment of this application;

[0092] Figure 9 This is a schematic diagram of the structure of an ultrasound imaging system provided in another embodiment of this application. Figure 10 This is a schematic diagram of the structure of an ultrasound imaging system provided in another embodiment of this application. Detailed Implementation

[0093] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0094] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0095] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0096] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate to describe embodiments of this application, for example, those that can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatuses.

[0097] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0098] It should be understood that in the description of the embodiments of this application, "multiple" means two or more, "greater than", "less than", "exceeding" etc. are understood to exclude the number itself, and "above", "below", "within" etc. are understood to include the number itself.

[0099] During an ultrasound scan, doctors need to perform and save images according to different standard sections. This requires categorizing the ultrasound images into the appropriate standard sections. For example, doctors may save sections sequentially according to the workflow. However, different doctors have their own scanning habits and techniques. Blindly following a specific order can lead to inefficiency. Doctors can manually set the standard sections to be scanned before performing the scans, but this also increases workload and reduces overall ultrasound scanning efficiency.

[0100] Among related technologies, there are also solutions that can automatically identify the corresponding section type of ultrasound image data. In this solution, when multiple ultrasound images correspond to the same section type, the ultrasound image with the highest quality score is usually automatically selected based on the quality score of the ultrasound image. However, when there is a lesion in the target tissue being scanned, it will affect the quality score of the ultrasound image (for example, if a cyst appears in the current ultrasound image, it is considered to have scanned extra tissue, resulting in a low quality score), causing the ultrasound image that the doctor should pay more attention to not to be selected, thereby affecting the doctor's diagnosis of the ultrasound scan results.

[0101] See Figure 1 , Figure 1This is a schematic diagram of the structure of an ultrasound imaging system 10 used to implement the ultrasound image processing method of the present application. The ultrasound imaging system 10 may include an ultrasound device 110 and a cloud server 120. The ultrasound device 110 may include an ultrasound probe 111, a transmitting and receiving circuit 112, a beamforming and signal processing module 113, a processor 114, a transmission module 115, and a display device 116. In the ultrasonic imaging system 10, the ultrasonic device 110 excites the ultrasonic probe 111 to emit ultrasonic waves toward the target to be detected through the transmitting and receiving circuit 112, and receives the ultrasonic echoes returned from the target to obtain ultrasonic echo signals. The echo signals enter the beamforming and signal processing module 113 for beamforming and signal processing to obtain the beamformed ultrasonic image data, which is then output to the processor 114 for image processing. The processor 114 identifies and processes the ultrasonic image or the ultrasonic echo data corresponding to the ultrasonic image. In addition, the processor 114 can also control the transmission module 115 to send the ultrasonic image or the ultrasonic echo data corresponding to the ultrasonic image to the cloud server 120. The cloud server 120 then identifies and processes the ultrasonic image or the ultrasonic echo data corresponding to the ultrasonic image to determine the section type of the ultrasonic image. Furthermore, the processor 114 automatically associates the ultrasonic image with the target section type according to the section type of the ultrasonic image. The target section type can be the type of the corresponding standard section image in the target workflow protocol. Generally, standard section images can be valuable ultrasound section images that meet diagnostic needs and are agreed upon by experts, or ultrasound section images that clearly show specific organ or tissue structures. Ultrasound section images associated with target section types are stored in the local memory of the ultrasound device 110 (not shown) or in the memory of the cloud server 120 (not shown). These ultrasound section images can be displayed on a display device 116 with a human-computer interaction device. Furthermore, the target workflow protocol includes a preset set of target section types, which is selected by the user from a workflow protocol list. This workflow protocol list can be a list stored in the memory of the ultrasound device 110 or a list pre-stored in the memory of the cloud server 120.

[0102] In this embodiment of the application, the processor 114 of the ultrasound device 110 can control the display device 116 to display a recommended ultrasound image list corresponding to at least one target section type based on the quality score of the ultrasound image associated with at least one target section type. The recommended ultrasound image list includes at least one ultrasound image recommended to the user for selection based on the quality score.

[0103] In this embodiment, the display device 116 of the ultrasound device 110 can be a touch screen, a liquid crystal display, or an independent display device such as a liquid crystal monitor or television, separate from the ultrasound device 110. It can also be a display screen on an electronic device such as a mobile phone or tablet. Furthermore, in this embodiment, the memory of the ultrasound device 110 can be a non-volatile storage medium such as a flash memory card, solid-state memory, or hard disk. In one embodiment, the ultrasound device 110 can interact with the user by integrating with or connecting to an external human-computer interaction device. The human-computer interaction device can include a human-computer interface, for example, for receiving ultrasound scanning operation commands input by the user. Then, the processor 114 obtains the user's selection operation from the recommended ultrasound image list through the human-computer interaction interface, determines the ultrasound image selected by the user, and uses the selected ultrasound image as the ultrasound section image of the target section type. It is understood that the human-computer interaction device is used for human-computer interaction, that is, receiving user input and outputting visual information; it can receive user input using devices such as a keyboard, operation buttons, a mouse, or a trackball, or it can use a touch screen integrated with the display device 116.

[0104] It should be noted that, Figure 1 The structure shown is for illustrative purposes only and may include more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented in hardware and / or software.

[0105] Combination Figure 1 The schematic diagram of the ultrasound imaging system 10 shown is provided below. Figure 2 , Figure 2 A flowchart illustrating a method for processing ultrasound images according to an embodiment of this application is shown. This method can be performed by... Figure 1 The ultrasound device 110 of the ultrasound imaging system 10 shown performs the method, which includes, but is not limited to, the following steps S100 to S600:

[0106] Step S100: Acquire multiple ultrasound images.

[0107] In this step, the multiple ultrasound images acquired by the ultrasound equipment can be multiple ultrasound images identified in real time and stored locally on the ultrasound equipment during the ultrasound scan of the target tissue, or multiple ultrasound images acquired from an external device connected to the ultrasound equipment. Alternatively, the ultrasound equipment can also acquire multiple ultrasound images from a pre-recorded ultrasound scan video, which can be acquired from an external device or recorded by the ultrasound equipment during the scan. It is understood that in real-time ultrasound scanning, the ultrasound equipment can continuously capture multiple ultrasound images of the target tissue. Furthermore, the multiple ultrasound images acquired from the ultrasound scan video can be pre-processed ultrasound images, which may include pre-processing steps such as denoising, enhancement, standardization, and normalization. This improves image quality and the accuracy of feature extraction, and provides clearer data for further ultrasound image analysis.

[0108] Step S200: Identify and process multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images.

[0109] In this step, when multiple ultrasound images are acquired by the ultrasound equipment, the cross-sectional types of the multiple ultrasound images or their corresponding ultrasound echo data are automatically identified and a quality score is obtained through recognition processing. It is understood that ultrasound images can be two-dimensional or three-dimensional. An ultrasound image can be a narrowly defined image or ultrasound echo data of a target tissue after beamforming. The cross-sectional type is a specific anatomical section of an organ or tissue in the ultrasound image; different cross-sectional types can display different structures and features of organs or tissues, such as a heart section or a liver section. It should be noted that in step S200, the automatic identification of cross-sectional types using the recognition processing on the local ultrasound equipment is used as an example. This involves combining a standard cross-sectional image database and using a pre-trained machine learning model or deep learning algorithm to determine the cross-sectional type of the ultrasound image. By distinguishing the characteristics or patterns of different cross-sectional types, the input ultrasound image is then classified and identified based on the learned characteristics or patterns.

[0110] In one embodiment, a standard section image database can be constructed by collecting a large number of standard section image samples. For example, a fully supervised learning method can be used, where each sample consists of a standard ultrasound section image and its corresponding section type. Alternatively, a semi-supervised learning method can be used, where one part of the samples consists of a standard ultrasound section image and its corresponding section type, while another part of the samples only contain standard ultrasound section images. In another embodiment, the standard section image database can also simultaneously store samples suitable for both fully supervised and semi-supervised learning methods.

[0111] It should be noted that in the recognition process, image classification can also be used. Machine learning algorithms or deep learning algorithms can be employed to distinguish the features or patterns of different section types from a standard section image database, thereby achieving section type recognition of ultrasound images. In one embodiment, a machine learning algorithm can be used to identify the section type. Specifically, firstly, feature extraction methods are used to extract image features from the ultrasound image, and then a classification algorithm is used to classify the ultrasound image, thereby obtaining the section type of the ultrasound image. It is understood that feature extraction methods can employ Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Haar features, texture features, etc. In one embodiment, after feature extraction, the cross-sectional features from a standard cross-sectional image database need to be combined for classification to determine the cross-sectional type of the ultrasound image. For example, discriminators such as K-Nearest Neighbor (KNN) classification, Support Vector Machine (SVM) algorithm, random forest, and neural networks can be used to classify the extracted cross-sectional features and determine which cross-sectional type the currently processed ultrasound image belongs to and / or the probability of belonging to that type. Furthermore, the classification can output the probability of the current ultrasound image belonging to each cross-sectional type. It is understood that the KNN algorithm calculates the distance (including Euclidean distance, Hamming distance, etc.) between the ultrasound image and the training set images, and then selects the K images with the smallest distances. The category that appears most frequently among these K images is the cross-sectional type of the ultrasound image. The SVM classifier is mainly used for binary classification problems. A hyperplane is trained using the training set. Images belonging to the training set categories are on one side of the hyperplane, and images not belonging to the training set categories are on the other side. When an ultrasound image is input into the classifier, the classifier determines whether the input ultrasound image belongs to a category in the training set. Multiple SVM classifiers can be used to achieve multi-class classification.

[0112] In one embodiment, the cross-section type can also be identified using a deep learning algorithm. A pre-set deep learning model is trained on cross-section features using a constructed standard cross-section image database, resulting in a trained deep learning model. The ultrasound device then inputs the ultrasound image into the trained deep learning model, which outputs the cross-section type of the ultrasound image. Specifically, the deep learning network used may include, for example, a Convolutional Neural Network (CNN), a ResNet residual network, a MobileNet lightweight convolutional neural network, a VGG convolutional neural network, an Inception network, or a DenseNet network.

[0113] Furthermore, the ultrasound equipment performs quality assessment based on the recognition and processing results of the ultrasound images to determine whether the quality of the ultrasound images meets the standards, thereby determining the quality score of the ultrasound images. Specifically, the quality score of ultrasound images can include a quality score used to characterize the quality of ultrasound images and ultrasound scans. The quality score can be a specific numerical score, such as using a percentage or ten-point scale, or a quality score level, such as low, medium, high, acceptable, unacceptable, etc., thus reflecting the quality of ultrasound images and ultrasound scans.

[0114] It is understandable that ultrasound image quality, or the quality of the ultrasound image itself, can be reflected in parameters such as sharpness, resolution, similarity of anatomical structures, signal-to-noise ratio, and mean square error. In other words, the quality score is influenced by these parameters and can be determined by at least one of the following: ultrasound image sharpness, resolution, similarity of anatomical structures, signal-to-noise ratio, and mean square error. Ultrasound equipment can analyze at least one of these parameters to obtain a quality score characterizing the ultrasound image quality. The better the image quality, the higher the quality score.

[0115] In one embodiment, the ultrasound device can evaluate the image quality of ultrasound images using a deep learning model to obtain a quality score characterizing the ultrasound image quality. Specifically, an ultrasound image database can be pre-built, where each ultrasound image is labeled with a quality score. A deep learning model, such as a MobileNet model, VGG model, or ResNet model, is pre-set. The deep learning model is then trained using the constructed ultrasound image database, resulting in a trained deep learning model. Further, the ultrasound device inputs an ultrasound image into the trained deep learning model, which then outputs the quality score for that ultrasound image. In another embodiment, the ultrasound device can also perform image quality scoring using machine learning, such as first using an SVM algorithm to build a classification model to classify the ultrasound images, and then performing image quality regression on the image to be evaluated to obtain the image quality of the image to be evaluated.

[0116] Understandably, ultrasound equipment can also identify anatomical structures in ultrasound images corresponding to different section types, and obtain a quality score to characterize the quality of the ultrasound scan based on the identified anatomical structures. Ultrasound scan quality reflects the correctness of the physician's technique in operating the ultrasound probe. Experienced physicians typically produce high-quality ultrasound scans, while less experienced physicians may need more time to obtain relatively good-quality ultrasound images. Therefore, the higher the ultrasound scan quality, the higher the quality score.

[0117] In some embodiments, the ultrasound equipment identifies anatomical structures corresponding to section types in ultrasound images through methods such as deep learning-based target detection and image segmentation. Specifically, for deep learning-based target detection and recognition methods, an ultrasound image database can be pre-constructed, where each ultrasound image is labeled with the anatomical structure region corresponding to the section type; specifically, the region where the anatomical structure exists and its location can be marked. A deep learning model for target detection is then pre-set, such as RCNN (Rich Feature Hierarchy Structure Extraction), Fast RCNN, Faster RCNN, YOLO, SSD, etc. This deep learning model is then trained using the constructed ultrasound image database, resulting in a trained deep learning model. Further, the ultrasound image is input into the deep learning model, and the model outputs a result containing the anatomical structure corresponding to the section type, thus revealing the specific location of the anatomical structure in the ultrasound image. Alternatively, for deep learning-based image segmentation and recognition methods, an ultrasound image database can be pre-constructed, where each ultrasound image is labeled with the boundary range of the anatomical structure corresponding to the section type; specifically, the region where the anatomical structure exists and its specific boundary range can be marked. A deep learning model for image segmentation, such as FCN, Unet, SegNet, DeepLab, or Mask R-CNN, is pre-set. This model is then trained using a constructed ultrasound image database. After training, a trained deep learning model is obtained. Furthermore, ultrasound images are input into the deep learning model, which then outputs results including anatomical structures and their specific boundary ranges.

[0118] In some embodiments, ultrasound equipment can identify anatomical structures corresponding to section types in ultrasound images using machine learning-based target detection and image segmentation methods. Specifically, for machine learning-based target detection and identification methods, a set of candidate bounding boxes of interest (ROIs) can be obtained in the ultrasound image first using a sliding window or selective search algorithm. Then, feature extraction is performed on each candidate ROI, such as traditional features like PCA, LDA, Haar, and texture features, or features extracted by a neural network. The extracted features are then matched with features extracted from marked ROIs in a pre-defined ultrasound image database, and classified using a linear classifier, SVM, or a simple neural network to determine whether the current candidate ROI contains the anatomical structure corresponding to the section type. In addition, for image segmentation and recognition methods using machine learning, specifically, the ultrasound image can be pre-segmented using image segmentation algorithms such as threshold segmentation and edge detection to obtain a set of candidate target structure boundary ranges in the ultrasound image; then, feature extraction can be performed on the region surrounded by each candidate target structure boundary range, such as traditional features like PCA, LDA, Haar, and texture features, or features extracted by neural networks; then, the extracted features are matched with the features extracted from the boundary ranges marked in a preset database, and classification is performed using methods such as linear classifiers, SVM, or simple neural networks, thereby determining whether the current candidate boundary range contains the anatomical structure corresponding to the section type.

[0119] It is understandable that the anatomical structures of the target tissue in a standard cross-sectional image are known. Therefore, the number of anatomical structures corresponding to the cross-sectional type in the ultrasound image determines the quality of the ultrasound scan, and thus the quality score of the ultrasound scan can be determined by the number of anatomical structures. For example, if a standard cross-sectional image is known to contain three anatomical structures (X, Y, and Z), and the ultrasound equipment identifies all three anatomical structures in the ultrasound image, then the ultrasound scan quality can be considered satisfactory.

[0120] In one embodiment, the anatomical structures corresponding to different section types of the target tissue can be pre-set with different weights. After the ultrasound device identifies each anatomical structure in the ultrasound image, it can assign different weights to each anatomical structure, with important anatomical structures having higher weights and less important anatomical structures having lower weights. Furthermore, the weighted anatomical structures are summed to obtain a quality score for the ultrasound scan. If the doctor's ultrasound scanning technique is skilled and precise, and all important anatomical structures of the target tissue are captured, the corresponding quality score will be higher. Conversely, if the doctor's ultrasound scanning technique is poor, resulting in fewer identified anatomical structures, the corresponding quality score will be lower.

[0121] Step S300: Associating multiple ultrasound images with at least one target section type based on the section type of the multiple ultrasound images.

[0122] In this step, after determining the section types of multiple ultrasound images, the ultrasound device can automatically associate the multiple ultrasound images with at least one corresponding target section type based on their section types. The target section type can be a representative standard section image type, which may include a set of ultrasound section images defined according to anatomical and clinical needs, or ultrasound section images certified by medical experts that clearly display a specific organ or target tissue. Specifically, if the section type of an identified ultrasound image matches a target section type, the ultrasound image is automatically associated with that target section type. This reduces the time doctors spend analyzing ultrasound images and improves the accuracy and reliability of diagnosis.

[0123] Step S400: Based on the quality score of the ultrasound image associated with at least one target section type, display a recommended ultrasound image list corresponding to at least one target section type. The recommended ultrasound image list includes at least one ultrasound image recommended to the user for selection based on the quality score.

[0124] In this step, the ultrasound device can generate at least one recommended ultrasound image list corresponding to the target section type based on the quality scores of the ultrasound images associated with that type. This list is then displayed on the display screen. The recommended ultrasound image list, as a collection of multiple ultrasound images, recommends high-quality images for the user to choose from based on the quality scores. Furthermore, the ultrasound device can also display the quality scores of each ultrasound image on the display screen, allowing the user to intuitively see the image quality and ultrasound scan quality assessment results. This means the user can select high-quality ultrasound images from this list for further analysis, reducing image selection time and improving diagnostic efficiency.

[0125] Step S500: Obtain the user's selection operation in the recommended ultrasound image list and determine the ultrasound image selected by the user.

[0126] In this step, the ultrasound equipment can also obtain the user's selection operation from the recommended ultrasound image list through a human-computer interaction device, thereby determining the ultrasound image selected by the user. Specifically, the user can make a selection operation from the recommended ultrasound image list using a mouse, keyboard, or other input devices. By clicking to select an ultrasound image with a high quality score, the time for recognizing ultrasound cross-sectional images can be reduced, which helps to quickly perform ultrasound scans.

[0127] Step S600: Use the ultrasound image selected by the user as the ultrasound section image of the target section type.

[0128] In this step, the ultrasound equipment determines the ultrasound image selected by the user and uses it as the ultrasound section image for the target section type. That is, after the user sees multiple high-quality ultrasound images displayed in the recommended ultrasound image list, they can select the ultrasound image of interest or the one with the highest quality score as the ultrasound section image corresponding to the target section type and store it. This simplifies the operation of ultrasound section image matching and improves the efficiency of ultrasound examination.

[0129] The ultrasound image processing method provided in this application involves an ultrasound device acquiring multiple ultrasound images, identifying and processing the multiple ultrasound images or the corresponding ultrasound echo data, determining the section type and quality score of the multiple ultrasound images, and then associating the multiple ultrasound images with at least one target section type based on the section type. Based on the quality score of the ultrasound images associated with the at least one target section type, the ultrasound device displays a recommended ultrasound image list corresponding to the at least one target section type. The recommended ultrasound image list includes at least one ultrasound image recommended to the user based on the quality score. The device also acquires the user's selection operation in the recommended ultrasound image list, determines the ultrasound image selected by the user, and uses the user-selected ultrasound image as the ultrasound section image of the target section type.

[0130] In this embodiment, the ultrasound device identifies the section type of multiple ultrasound images or their corresponding ultrasound echo data. Then, it automatically associates the multiple ultrasound images with the target section type based on the section type, significantly improving the doctor's work efficiency and optimizing the scanning process. Furthermore, the ultrasound device can recommend ultrasound images to the doctor based on the quality score of the ultrasound images associated with the target section type. The doctor can select an ultrasound image with a high quality score from the recommended list to replace the original image with the ultrasound section image of the target section type, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination.

[0131] In one embodiment, step S400 above, displaying a recommended list of ultrasound images corresponding to at least one target section type based on the quality score of the ultrasound image associated with at least one target section type, includes one of the following steps:

[0132] Step S410: Display a list of recommended ultrasound images corresponding to at least one target section type, and filter the ultrasound images associated with the target section type according to the quality score of the ultrasound images, and display the filtered ultrasound images in the list of recommended ultrasound images.

[0133] In this step, a recommended ultrasound image list corresponding to the target section type is displayed on the display device. The displayed ultrasound images associated with the target section type can be ultrasound images that have been pre-filtered and processed by the ultrasound device according to the quality score of the ultrasound images. Specifically, if the quality score uses a specific numerical score, the ultrasound device can compare the quality score of the ultrasound image associated with the target section type with a scoring threshold, such as comparing the quality score with a first scoring threshold, to determine whether the quality score reaches the first scoring threshold, i.e., equal to or exceeding the first scoring threshold. If so, the quality of the ultrasound image is determined to meet the standard and displayed in the recommended ultrasound image list; otherwise, the quality of the ultrasound image is determined to be substandard. Alternatively, if the quality score uses a quality level, the ultrasound device can compare the quality score with a first scoring level to determine whether the quality score reaches the first scoring level, i.e., equal to or exceeding the first scoring level. If so, the quality of the ultrasound image is determined to meet the standard and displayed in the recommended ultrasound image list; otherwise, the quality of the ultrasound image is determined to be substandard. It should be understood that the first scoring threshold and the first scoring level in this embodiment can be set in advance according to user needs. Further, the ultrasound device retains ultrasound images with a quality score that reaches the first scoring threshold or the first scoring level in the recommended ultrasound image list. Furthermore, doctors can see on the display device that the ultrasound images in the recommended ultrasound image list are filtered and have high quality scores, thereby helping doctors reduce the time spent manually screening ultrasound images and improve diagnostic efficiency.

[0134] Step S420: Display a list of recommended ultrasound images corresponding to at least one target section type, and sort and display the ultrasound images associated with the target section type in the list of recommended ultrasound images according to their quality scores.

[0135] In this step, a recommended list of ultrasound images corresponding to the target section type is displayed on the display device. The displayed ultrasound images, associated with the target section type, can be sorted by quality score before being displayed. For example, the ultrasound image with the highest quality score can be prioritized and displayed at the top of the recommended ultrasound image list, allowing users to view or select high-quality images according to priority. This enables quick access to high-quality ultrasound images and improves the efficiency of ultrasound scanning.

[0136] Step S430: Display a list of recommended ultrasound images corresponding to at least one target section type, filter the ultrasound images associated with the target section type according to their quality scores, and sort and display the filtered ultrasound images in the list of recommended ultrasound images according to their quality scores.

[0137] In this step, the ultrasound images displayed in the recommended ultrasound image list corresponding to the target section type can be ultrasound images that have undergone preliminary screening. The specific screening process can be found in the embodiment of step S410. The ultrasound device can retain ultrasound images with quality scores reaching a first score threshold or a first score level in the recommended ultrasound image list. Then, the screened ultrasound images are sorted according to their quality scores and displayed in the recommended ultrasound image list. This reduces the workload for doctors in screening high-quality ultrasound images from numerous ultrasound images associated with the target section type, allowing for quick viewing of high-quality ultrasound images and improving the efficiency of ultrasound scanning.

[0138] In one embodiment, the above steps S410 and S430, which involve filtering ultrasound images associated with the target section type based on the quality score of the ultrasound images, include the following steps:

[0139] The quality score of the ultrasound image associated with the target section type is compared with a scoring threshold. Ultrasound images with a quality score lower than the scoring threshold are discarded or stored in the candidate section storage area.

[0140] In this step, after acquiring the quality score of the ultrasound image associated with the target section type, the ultrasound device compares the quality score with a scoring threshold. This allows it to identify ultrasound images with high quality scores and discard those with scores below the threshold, or store them in a candidate section storage area for later comparison. Specifically, if the quality score uses a numerical value, it compares the quality score of the ultrasound image associated with the target section type with a scoring threshold, such as a first scoring threshold. If the quality score is lower than the first threshold, the corresponding ultrasound image is discarded or stored in the candidate section storage area. Alternatively, if the quality score uses a quality level, the ultrasound device compares the quality score with a first scoring level. If the quality score is lower than the first scoring level, the corresponding ultrasound image is discarded or stored in the candidate section storage area. It is understandable that by comparing the ultrasound image quality score with a scoring threshold to filter out ultrasound images with high quality scores corresponding to the target section type, the time and effort required for doctors to analyze ultrasound images can be reduced, improving diagnostic efficiency.

[0141] In one embodiment, prior to the user's selection of the recommended ultrasound image list in step S500, one of the following steps is further included:

[0142] Step S510: Display the quality score corresponding to the ultrasound image in the recommended ultrasound image list.

[0143] In this step, the ultrasound device can display ultrasound images in the recommended ultrasound image list, and automatically display the corresponding quality score directly next to or below the ultrasound image. Users can intuitively see the quality scores of the ultrasound images after filtering and sorting. It should be understood that the quality score in this embodiment can be displayed as a numerical value, a quality grade, or other suitable method.

[0144] Step S520: Obtain the rating and view operation for the ultrasound images in the recommended ultrasound image list, and display the quality rating of the ultrasound images.

[0145] In this step, the ultrasound device can also obtain the user's rating and viewing operation of the ultrasound images in the recommended ultrasound image list through the human-computer interaction device, and in response to the rating and viewing operation, display the ultrasound image selected by the user and its corresponding quality score on the display device connected to the ultrasound device or an integrated display device. It is understood that the recommended ultrasound image list displayed on the display device does not show the quality score of the ultrasound images by default. When a doctor needs to assess the quality of the ultrasound image, they can obtain and view the quality score of the ultrasound image by clicking on the ultrasound image or clicking the "View Score" button next to the ultrasound image. For users who only need to view the ultrasound image itself, not displaying the quality score reduces visual interference and improves the user experience.

[0146] In one embodiment, the above steps of obtaining a rating and viewing ultrasound images from the recommended ultrasound image list, and displaying the quality rating of the ultrasound images, include one of the following steps:

[0147] This feature allows users to view the quality ratings of ultrasound images in the recommended ultrasound image list.

[0148] In this step, the ultrasound equipment can also obtain the user's rating and viewing operation of the ultrasound images in the recommended ultrasound image list through the human-computer interaction device, and in response to the rating and viewing operation, display the ultrasound image selected by the user and its corresponding quality rating in the recommended ultrasound image list. For example, the corresponding quality rating can be displayed directly next to or below the ultrasound image, allowing the user to intuitively see the quality rating of the ultrasound images after filtering and sorting.

[0149] Get a rating view operation for ultrasound images in the recommended ultrasound image list, and display a rating interface in response to the rating view operation, and display the quality rating of the ultrasound image in the rating interface.

[0150] In this step, the ultrasound device can also obtain the user's rating and viewing operation of the ultrasound images in the recommended ultrasound image list through a human-computer interaction device. In response to the rating and viewing operation, a rating interface is displayed on a display device connected to the ultrasound device or an integrated display device, showing the quality score of the ultrasound image. Understandably, when a user needs to view the quality score of an ultrasound image, they can click on the ultrasound image or the "View Rating" button next to it. The display device will then show a dedicated rating interface displaying the ultrasound image's quality score, showing the ultrasound image selected by the user and its corresponding quality score. Displaying the quality score only when needed avoids unnecessary information interference when viewing ultrasound images, improving the user experience.

[0151] In one embodiment, step S400 above, which displays a recommended list of ultrasound images corresponding to at least one target section type based on the quality score of the ultrasound image associated with at least one target section type, further includes the following steps:

[0152] Step S440: Display a list of recommended ultrasound images corresponding to at least one target section type. The list of recommended ultrasound images displays ultrasound images associated with the target section type and their corresponding quality scores.

[0153] In this step, the ultrasound device can generate at least one recommended ultrasound image list corresponding to the target section type based on the quality score of the ultrasound image associated with the target section type, and display it on the display interface of the display device. The recommended ultrasound image list, as a collection of multiple ultrasound images, can simultaneously display ultrasound images associated with the target section type and their corresponding quality scores, allowing users to intuitively see the image quality and ultrasound scan quality assessment results of the ultrasound images. In other words, doctors can directly select ultrasound images from this list with reference to the quality scores for further analysis, reducing the time spent analyzing ultrasound images and improving diagnostic efficiency.

[0154] In one embodiment, the ultrasound images in the recommended ultrasound image list of any of the above step embodiments are arranged in a horizontal, vertical, matrix, or grid manner.

[0155] Understandably, ultrasound images in the recommended ultrasound image list can be arranged in different ways to suit different display needs and user preferences. In one embodiment, the ultrasound images in the recommended ultrasound image list are arranged horizontally, meaning the ultrasound images are displayed along the horizontal direction of the display interface, and users can view the ultrasound images by scrolling horizontally. Alternatively, the ultrasound images can be arranged vertically, meaning the ultrasound images are displayed along the vertical direction of the display interface, and users can view the ultrasound images by scrolling vertically. Specifically, in either horizontal or vertical arrangement, users can switch between viewing more ultrasound images by sliding the scroll bar control on the display interface. In another embodiment, the ultrasound images in the recommended ultrasound image list are arranged in a matrix, meaning the ultrasound images are arranged in rows and columns on the display interface to form a matrix layout, where each ultrasound image occupies a cell in the matrix. The number of ultrasound images displayed in rows and columns can be fixed, and multiple ultrasound images of the same size can be displayed simultaneously. Alternatively, the ultrasound images can also be arranged in a grid, similar to the matrix arrangement, consisting of rows and columns. The difference is that the size of the grid occupied by each ultrasound image can be adjusted according to user needs to accommodate ultrasound images of different sizes.

[0156] In one embodiment, see Figure 3 As shown, step S400 above, based on the quality score of the ultrasound image associated with at least one target section type, displays a recommended list of ultrasound images corresponding to at least one target section type, including the following steps S401 to S403:

[0157] Step S401: Display the target section type classification interface. The target section type classification interface displays ultrasound section images or default images corresponding to different target section types. The default image represents an ultrasound image that is not associated with a target section type.

[0158] Step S402: Obtain the user's selection operation of the target aspect type in the target aspect type classification interface.

[0159] In step S403, in response to the selection operation, a list of recommended ultrasound images corresponding to the target section type is displayed based on the quality score of the ultrasound image corresponding to the target section type selected by the user.

[0160] It is understandable that a target section type classification interface can also be displayed on the display device. This interface can then display ultrasound images corresponding to different target section types. Multiple ultrasound images corresponding to the same target section type can be grouped into a set for display, and a representative ultrasound image can be selected from this set for display, such as the one with the highest quality score. It should be noted that ultrasound images not associated with a target section type can be displayed using a default image. Furthermore, the ultrasound device can obtain the user's selection of the target section type in the classification interface through a human-computer interaction device, and in response to the selection, display a recommended list of ultrasound images corresponding to the selected target section type based on the quality score of the ultrasound image. Specifically, for example... Figure 4 As shown, the interface for classifying target section types can display ultrasound images or default images corresponding to different target section types at the top, for example, in a matrix arrangement. Each cell for a target section type can display not only the ultrasound image with the highest quality score, but also the name and sequence number of the target section type. Figure 4 The system displays target section types such as the portal vein long-axis section, the right anterior axillary line longitudinal section, and the first hepatic hilum section. After a user selects any target section type, a list of recommended ultrasound images corresponding to that selected section type is displayed below the target section type classification interface. Furthermore, the ultrasound images in the recommended ultrasound image list can be filtered and arranged according to their quality scores. For example, if the recommended ultrasound images are displayed horizontally, their quality scores can gradually decrease from left to right. By default, the first ultrasound image on the far left of the recommended ultrasound image list has the highest quality score, thus prioritizing the recommendation of ultrasound images with higher quality scores. This provides doctors with more valuable image options to aid in analysis and diagnosis, improving the efficiency of ultrasound scanning.

[0161] It should be noted that doctors can browse different target section types, such as heart, liver, and kidney, through the target section type classification interface. Each cell for a target section type not only displays the corresponding ultrasound image but also shows the image data identifiers for that target section type, including the number of manually saved images and the number of automatically recognized images. For example, Figure 4 The cell for the long axis section of the portal vein displays "M1 / A10", which means "Manually saved image: 1" and "Automatically recognized images: 10". This indicates that the user obtains 1 ultrasound image of the long axis section of the portal vein by manually saving it, and the ultrasound equipment automatically recognizes and saves 10 ultrasound images of the long axis section of the portal vein.

[0162] In one embodiment, for ultrasound images in the recommended ultrasound image list that have been matched to the target slice type, the user can select the ultrasound image of interest by clicking to view detailed slice recognition results. Specifically, the ultrasound device obtains the user's selection operation of the ultrasound image in the recommended ultrasound image list through the human-computer interaction device, and in response to the selection operation, displays the slice recognition result interface of the selected ultrasound image through the display device. The user can input operation commands through the buttons, knobs, or touch on the human-computer interaction interface of the human-computer interaction device, for example... Figure 4 The recommended ultrasound image list displayed on the target section type classification interface also includes a "View Match" control. The ultrasound device responds to the user's selection of a matched ultrasound image and clicking the "View Match" command, thereby displaying the recognition results of that ultrasound image on the display device. For example... Figure 5 As shown, Figure 5 The current display shows the section recognition results of the long-axis section of the portal vein in an adult abdominal ultrasound scan. The corresponding ultrasound image is displayed in the left area of ​​the recognition results interface, while the target section type information, anatomical structure information, and quality score are displayed in the right area. The target section type name is "Portal Vein Long-Axis Section," and the anatomical structure information recognized by this ultrasound image includes the structure name "Portal Vein" and its highlighted marking method in the ultrasound image, as well as a quality score of "9 points" (out of 10). In another embodiment, in Figure 5 The left side of the recognition results interface displays not only the currently viewed ultrasound image, but also a list of recommended ultrasound images containing that image. Users can click to select and view other ultrasound images that are associated with and match the same target section type, along with their section recognition results, thus improving the user experience.

[0163] In another embodiment, such as Figure 4 As shown, the recommended ultrasound image list displayed on the target section type classification interface can also be configured with an "Image Reorder" control. The ultrasound device responds to the user's selection of the ultrasound image to be re-associated and clicking the "Image Reorder" command, thereby associating the ultrasound image with another target section type. This allows the user to manually review and adjust the ultrasound section recognition results. It should be understood that after an ultrasound image is associated with another target section type, it will be added to the recommended ultrasound image list corresponding to that target section type for re-filtering. The order of display will be updated based on the quality scores of each ultrasound image. The user can view the section recognition results in the recommended ultrasound image list corresponding to the re-associated target section type.

[0164] In one embodiment, step S300 above, associating multiple ultrasound images with at least one target section type according to the section type of multiple ultrasound images, includes one of the following steps:

[0165] Step S310: Associating multiple ultrasound images with at least one corresponding target section type in the target workflow protocol according to the section type of the multiple ultrasound images, wherein the target workflow protocol is selected by the user from the workflow protocol list, and the workflow protocol list is a list stored locally on the ultrasound device or a list obtained from the cloud server.

[0166] In this step, the ultrasound device automatically associates multiple ultrasound images with at least one corresponding target section type in the target workflow protocol based on the section types of the currently identified ultrasound images. The target workflow protocol includes a pre-defined set of target section types. This target workflow protocol can be a standard protocol guiding the user to complete a certain type of ultrasound examination, such as abdominal ultrasound, echocardiography, or fetal ultrasound. The target workflow protocol specifies the basic sections and optional sections that should be included in a particular examination. Furthermore, the user can operate the ultrasound device or cloud server to select a suitable target workflow protocol from a list of workflow protocols stored locally on the ultrasound device or obtained from a list of workflow protocols from the cloud server for section identification processing, thereby improving the efficiency of ultrasound scanning. If the identified ultrasound image belongs to a target section type in the target workflow protocol, the ultrasound image is matched with the target section type in the target workflow protocol, which reduces the influence of human factors on the scanning results and improves the accuracy and reliability of the diagnosis.

[0167] In one embodiment, if the identified ultrasound image does not belong to the target section type in the target workflow protocol, a matching failure message is returned.

[0168] Step S320: Based on at least one target section type selected or preset by the user, associate multiple ultrasound images with at least one target section type according to the section type of the multiple ultrasound images.

[0169] In this step, the ultrasound equipment can also respond to the user's manual selection operation command through the human-computer interaction device. The user can select or specify a preset target section type, and associate multiple ultrasound images with the user-selected or specified preset target section type according to the section type of multiple ultrasound images. This simplifies the operation of ultrasound section image matching, reduces the workload of doctors, and effectively improves the efficiency of ultrasound examination.

[0170] In one embodiment, step S200 above involves identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images, including one of the following steps:

[0171] Step S210: Run the local recognition program of the ultrasound device to identify and process multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images, and determine the cross-sectional type and quality score of the multiple ultrasound images.

[0172] Step S220: Send multiple ultrasound images to the cloud server so that the cloud server can identify and process the multiple ultrasound images to determine the section type and quality score of the ultrasound images; receive the section type and quality score of the ultrasound images fed back by the cloud server.

[0173] Step S230: Send the ultrasound echo data corresponding to multiple ultrasound images to the cloud server so that the cloud server can identify and process the ultrasound echo data corresponding to multiple ultrasound images, thereby determining the section type and quality score of the ultrasound images; receive the section type and quality score of the ultrasound images fed back by the cloud server.

[0174] It is understandable that when an ultrasound device acquires multiple ultrasound images or corresponding ultrasound echo data, the local recognition program of the ultrasound device can be run to perform recognition processing, thereby determining the section type and quality score of the multiple ultrasound images. Alternatively, the ultrasound device can send multiple ultrasound images or corresponding ultrasound echo data to a cloud server, where the cloud server can perform recognition processing to automatically identify the section type of the multiple ultrasound images and determine the corresponding quality score, which is then fed back to the ultrasound device. For example, a trained recognition processing model can be deployed on the cloud server. The cloud server receives and inputs multiple ultrasound images or corresponding ultrasound echo data into the trained recognition processing model, thereby obtaining the section type and quality score of the ultrasound images output by the recognition processing model. By utilizing the abundant computing resources of the cloud server for recognition processing, the accuracy and efficiency of ultrasound section image recognition can be improved. Furthermore, the recognition processing steps S210 to S230 can be referred to in any embodiment of step S200 above, and will not be repeated here.

[0175] In one embodiment, the ultrasound image processing method of this application further includes the following steps:

[0176] Receive ultrasound images and / or ultrasound image IDs that have been identified by the cross-sectional type from the cloud server.

[0177] In this step, the ultrasound device receives ultrasound images or their IDs that have been identified by the cloud server, indicating the identified section type. It then automatically associates multiple ultrasound images with at least one corresponding target section type based on their section types. Alternatively, it can extract the ID information of each standard section image from the standard section image database, and then match the IDs of the ultrasound images with their identified section types to automatically associate multiple ultrasound images with at least one corresponding target section type. It should be understood that the standard section image database in this embodiment can be a standard section image database deployed in the ultrasound device or cloud server. Its construction principle and method can be found in the description of any embodiment in step S200, and will not be repeated here.

[0178] In one embodiment, see Figure 6 As shown, the above step S210, which involves identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images, includes the following steps S211 to S212:

[0179] Step S211: Compare multiple ultrasound images with at least one target section image corresponding to a target section type to determine the section type of each ultrasound image.

[0180] Step S212: Identify anatomical structures in multiple ultrasound images, compare the identified anatomical structures with the target anatomical structures in the target cross-sectional image, and determine the quality score.

[0181] Understandably, ultrasound equipment can more accurately determine the section type of each ultrasound image by comparing multiple ultrasound images with a target section image corresponding to at least one target section type. It should be noted that the target section image can be a standard section image pre-stored in the ultrasound equipment as a reference, capable of displaying the type, shape, location, and clarity of anatomical structures in the target section image. Furthermore, the ultrasound equipment can identify anatomical structures in multiple ultrasound images and determine a quality score by comparing the identified anatomical structures with the target anatomical structures in the target section image, which can reduce the time and effort doctors spend processing ultrasound images and improve diagnostic efficiency. Specific implementation methods for identifying anatomical structures in multiple ultrasound images can be found in any embodiment of step S200 above, and will not be repeated here.

[0182] In one embodiment, the identification of anatomical structures in multiple ultrasound images in step S212 includes:

[0183] Identify at least one of the following in multiple ultrasound images: anatomical structure type, anatomical structure morphology, anatomical structure location, and anatomical structure clarity.

[0184] It is understood that ultrasound equipment can identify anatomical structures in multiple ultrasound images separately. For example, it can use machine learning or deep learning algorithms, or it can use object detection based on machine learning or deep learning combined with image segmentation methods for identification. This allows it to identify the type of anatomical structure in the ultrasound image, such as the first hepatic hilum and the second hepatic hilum. Furthermore, it can identify the morphology of the anatomical structure, including its size and outline, as well as its location, such as the relative position of the first hepatic hilum to other liver structures. It can also identify the clarity of the anatomical structure, i.e., whether the anatomical structure is clearly visible in the ultrasound image. Identifying anatomical structures in multiple ultrasound images can provide detailed anatomical information, improving the accuracy and completeness of ultrasound scanning. Specific identification processing can be found in any embodiment of step S200 above, and will not be repeated here.

[0185] In one embodiment, the target anatomical structure in step S212 above includes at least one of target anatomical structure type, target structure morphology, target anatomical structure location, and target anatomical structure clarity.

[0186] It should be noted that the target anatomical structure to be compared with the identified anatomical structure can include the type, morphology, location, and clarity of the target anatomical structure. This information is derived from medical literature, expert consensus, or high-quality reference image databases. By comparing with the target anatomical structure, the accuracy and completeness of the identified anatomical structures in the ultrasound image can be assessed, enabling quality control of the ultrasound image and ensuring that the image meets diagnostic requirements.

[0187] In one embodiment, step S212 above, comparing the identified anatomical structure with the target anatomical structure in the target cross-sectional image to determine a quality score, includes the following steps:

[0188] The quality score of the ultrasound image is determined by comparing the type of anatomical structure identified in the ultrasound image with the type of target anatomical structure, and / or comparing the morphology of the anatomical structure with the morphology of the target anatomical structure, and / or comparing the location of the anatomical structure with the location of the target anatomical structure, and / or comparing the clarity of the anatomical structure with the clarity of the target anatomical structure.

[0189] In this step, the ultrasound equipment can specifically compare the identified anatomical structure type with the target anatomical structure type, the anatomical structure morphology with the target anatomical structure morphology, the anatomical structure location with the target anatomical structure location, or the anatomical structure clarity with the target anatomical structure clarity. It can also combine any number of comparison results to jointly determine the ultrasound image quality score. Specifically, the closer the match between the identified anatomical structure type and the target anatomical structure type, the identified anatomical structure morphology and the target anatomical structure morphology, the identified anatomical structure location and the target anatomical structure location, or the identified anatomical structure clarity and the target anatomical structure clarity, the higher the ultrasound image quality score.

[0190] In one embodiment, acquiring multiple ultrasound images in any of the above embodiments includes one of the following:

[0191] Acquire multiple ultrasound images obtained by the user operating the ultrasound device;

[0192] Acquire multiple ultrasound images cached by the ultrasound device;

[0193] Acquire ultrasound video stream data and obtain multiple ultrasound images from the ultrasound video stream data.

[0194] It is understood that there are various ways to acquire ultrasound images. For example, it can be a collection of current multi-frame ultrasound images saved by the user through real-time ultrasound scanning imaging, or multiple frames of ultrasound images imported into the ultrasound device and stored in advance. It can also be stored video stream data, where the video stream data includes multiple frames of ultrasound images. Regarding the import method, it can be, for example, using a CD to import image data and video stream data, or using a USB flash drive to import image data and video stream data, or receiving image data and video stream data from a network, etc. This application does not limit the scope of the embodiments in this paper.

[0195] In one embodiment, the ultrasound image processing method of this application further includes the following steps:

[0196] Step S700: Store the ultrasound section image in the target section storage area corresponding to the target section type.

[0197] Step S800: Display thumbnails of ultrasound section images and / or target section type information in the target section storage area.

[0198] Step S900: After obtaining the first selection instruction from the user to select the ultrasound section image in the target section storage area, the selected ultrasound section image and its corresponding target section type information are displayed.

[0199] It should be noted that after the ultrasound device stores the ultrasound section image corresponding to the target section type in the target section storage area, the ultrasound section image or target section type information in the standard section storage area can be displayed as a thumbnail on the display device. Alternatively, both the ultrasound section image thumbnail and the target section type information can be displayed simultaneously. Furthermore, the human-computer interaction device of the aforementioned embodiment can detect the user's first selection instruction to select an ultrasound section image in the target section storage area, and display the selected ultrasound section image and its corresponding target section type information on the display interface or human-computer interaction interface of the display device. Specifically, when the ultrasound device receives the aforementioned first selection instruction, it responds to the instruction and displays the selected ultrasound section image and its corresponding target section type information through the display device, such as... Figure 7 As shown, Figure 7 This diagram illustrates the ultrasound cross-sectional images included in an adult abdominal scan, including the long-axis section of the intrahepatic portal vein, the longitudinal section at the junction of the left hepatic vein and the inferior vena cava, the longitudinal section of the left lobe of the liver along the abdominal midline, and the long-axis section of the gallbladder. The displayed target cross-section type is the intrahepatic portal vein section. Thumbnails of the ultrasound cross-sectional images and target cross-section type information, or one of them, can be displayed in a list on the right side of the display interface, and the cross-section name can also be displayed simultaneously on the thumbnails.

[0200] It should be understood that users can select the corresponding ultrasound section image and its corresponding target section type information by using buttons, knobs on the human-computer interaction device or by touching the human-computer interaction interface. The ultrasound section image and its corresponding target section type information can be displayed at a normal size in the center of the display interface or the human-computer interaction interface. The present application embodiment does not limit the display position of the ultrasound section image and the target section type information.

[0201] In one embodiment, the ultrasound image processing method of this application further includes the following steps:

[0202] Step S1000: Display the ultrasound section image and the target section type information corresponding to the target section type associated with the ultrasound section image.

[0203] In this step, when a doctor needs to view a certain ultrasound section image and the target section type information associated with the ultrasound section image, the ultrasound device can load the corresponding data from the memory according to the received operation instructions. This data is then displayed on a display device connected to the ultrasound device or an integrated display device, which automatically associates the ultrasound section image with its corresponding target section type information. This effectively enhances the intuitiveness of the ultrasound section image display and improves the efficiency of ultrasound scanning.

[0204] In one embodiment, the ultrasound image processing method of this application further includes the following steps:

[0205] Display a list of target section types in the target workflow protocol, and highlight the target section type corresponding to the ultrasound section image in the section list. The highlighting includes at least one of the following: annotation display, highlighting display, color differentiation display, or font differentiation display.

[0206] In this step, such as Figure 8 As shown, a display area for the aspect list can be partitioned off on the display device's interface. This area displays a list of aspects of the target aspect type in the target workflow protocol. For example, all defined target aspect types can be listed in the left-hand area of ​​the display interface. Figure 8 The "Smart Abdomen" in the context refers to the current target workflow protocol, which can include target section types such as the portal vein long-axis section, the right anterior axillary line longitudinal section, and the first hepatic hilum section. This section list can be sorted by section name or other logic, allowing doctors to quickly view and identify the target section type corresponding to the current ultrasound image. Furthermore, the target section type corresponding to the ultrasound image can be highlighted in the section list. For example, the target section type corresponding to the already matched ultrasound images can be marked with a "√" or other symbols, and the target section type corresponding to the currently selected ultrasound image can be displayed with a box selection annotation. Alternatively, the name of the selected target section type can be highlighted, the name font bolded, or the name text color changed to distinguish it from other unselected target section types, enabling doctors to quickly identify the target section type corresponding to the current ultrasound image. By displaying the section list and highlighting matching sections, the difficulty of ultrasound scanning operations can be reduced, making it easier for doctors to use. In another embodiment, multiple highlighting methods can be used simultaneously to highlight the target section type corresponding to the ultrasonic section image in the section list.

[0207] In one embodiment, the above-described embodiments involve identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images, including the following steps:

[0208] Multiple ultrasound images or their corresponding ultrasound echo data are processed to identify and determine the anatomical structure information, section type, and quality score of the multiple ultrasound images. In this step, when multiple ultrasound images are acquired by the ultrasound equipment, the anatomical structure information and section type of the multiple ultrasound images or their corresponding ultrasound echo data are automatically identified through identification processing. A quality score characterizing the quality of the ultrasound scan is obtained based on the identified anatomical structure information. The anatomical structure information may include anatomical structure type, anatomical structure morphology, anatomical structure location, and anatomical structure clarity, etc. For specific identification processing, please refer to any embodiment of step S200 above, which will not be repeated here.

[0209] In one embodiment, after determining the anatomical structure information, section type, and quality score of multiple ultrasound images, the method further includes the following step: displaying the ultrasound section image and the target section type information, anatomical structure information, and quality score corresponding to the target section type associated with the ultrasound section image. In this step, after the ultrasound device determines the anatomical structure information, section type, and quality score of multiple ultrasound images, the information can be displayed on a display device connected to the ultrasound device or an integrated display device, effectively enhancing the intuitiveness of ultrasound image recognition and improving the efficiency of ultrasound scanning.

[0210] See Figure 9 , Figure 9 A flowchart illustrating a method for processing ultrasound images according to an embodiment of this application is shown. This method can be performed by... Figure 1 The ultrasound device 110 of the ultrasound imaging system 10 shown performs the method, which includes, but is not limited to, the following steps S1100 to S1300:

[0211] Step S1100: Display a recommended list of ultrasound images, which is formed based on image quality scores associated with the target section type.

[0212] Step S1200: Obtain the user's selection operation in the recommended ultrasound image list and determine the ultrasound image selected by the user.

[0213] Step S1300: Use the ultrasound image selected by the user as the ultrasound section image of the target section type.

[0214] The ultrasound image processing method provided in this application is applied to an ultrasound device. In this method, the ultrasound device can recommend ultrasound images to doctors based on the quality score of the ultrasound image associated with the target section type. Doctors can select ultrasound images with high quality scores from the ultrasound image list recommended by the ultrasound device to replace the ultrasound images with ultrasound section images of the target section type, thereby reducing the omission or error of sections caused by human factors and ensuring the accuracy and completeness of the examination.

[0215] It should be noted that the above Figure 9 For a detailed explanation of the execution principles of each step, please refer to the section above. Figure 2 The description of any embodiment of the ultrasound image processing method shown is omitted here.

[0216] See Figure 10 As shown, one embodiment of this application also provides an ultrasound imaging system 20, including: an ultrasound probe 210, a transmitting and receiving circuit 220, a beamforming and signal processing module 230, a processor 240, a transmission module 250, and a display device 260. In an ultrasound imaging system, the transmitting and receiving circuits can excite the ultrasound probe to emit ultrasound waves toward the target to be detected, and receive the ultrasound echoes returning from the target to obtain ultrasound echo signals. The beamforming and signal processing module can perform beamforming and signal processing on the echo signals to obtain ultrasound images. The display device provides a human-machine interface to receive ultrasound scanning operation commands input by the user. The processor 240 can determine multiple ultrasound images to be identified according to the ultrasound scanning operation commands, and perform identification processing on the multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images to determine the section type and quality score of the multiple ultrasound images; associate the multiple ultrasound images with at least one standard section according to the section type of the multiple ultrasound images; based on the quality score of the ultrasound images associated with at least one standard section, control the display device to display a recommended ultrasound image list corresponding to at least one standard section, the recommended ultrasound image list including at least one ultrasound image recommended to the user for selection based on the quality score; obtain the user's selection operation in the recommended ultrasound image list through the human-machine interface, determine the ultrasound image selected by the user, and use the ultrasound image selected by the user as the ultrasound section image of the standard section.

[0217] In this embodiment, the ultrasound imaging system uses a processor to identify the section type of multiple ultrasound images or the corresponding ultrasound echo data. Then, based on the section type, it automatically associates the multiple ultrasound images with a standard section, significantly improving the doctor's work efficiency and optimizing the scanning process. Furthermore, this ultrasound imaging system can recommend ultrasound images to the doctor based on the quality score of the ultrasound images associated with the standard section. The doctor can select an ultrasound image with a high quality score from the recommended list to replace the standard section image, thereby reducing the omission or error of sections due to human factors and ensuring the accuracy and completeness of the examination.

[0218] It should be noted that the ultrasound image processing method in this application embodiment can also be based on Figure 10The ultrasound imaging system 20 shown is implemented. The implementation of each component of the ultrasound imaging system 20 can refer to any of the aforementioned embodiments of the ultrasound imaging system 10, and will not be described in detail here.

[0219] On the other hand, embodiments of this application provide an electronic device, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the ultrasound image processing method described above.

[0220] On the other hand, embodiments of this application provide a computer storage medium storing a computer program applied to an ultrasonic imaging device. When the computer program is executed by a processor, it implements the ultrasonic image processing method described above.

[0221] On the other hand, embodiments of this application provide a computer program product, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the ultrasound image processing method described above.

[0222] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between apparatuses or units, and may be electrical, mechanical, or other forms.

[0223] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0224] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0225] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0226] It should also be understood that the various implementation methods provided in this application can be combined arbitrarily to achieve different technical effects.

[0227] The above provides a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of this application.

Claims

1. A method for processing ultrasound images, applied to ultrasound equipment, characterized in that, The method includes: Acquire multiple ultrasound images; The multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images are identified and processed to determine the cross-sectional type and quality score of the multiple ultrasound images. Associating multiple ultrasound images with at least one target section type based on the section type of the multiple ultrasound images; Based on the quality score of the ultrasound image associated with at least one of the target section types, a recommended ultrasound image list corresponding to at least one of the target section types is displayed, the recommended ultrasound image list including at least one ultrasound image recommended to the user for selection based on the quality score; Obtain the user's selection operation in the recommended ultrasound image list, and determine the ultrasound image selected by the user; The ultrasound image selected by the user is used as the ultrasound section image of the target section type.

2. The method for processing ultrasound images according to claim 1, characterized in that, The quality score of the ultrasound image associated with at least one of the target section types is used to display a recommended list of ultrasound images corresponding to at least one of the target section types, including one of the following: Display a list of recommended ultrasound images corresponding to at least one of the target section types, and filter the ultrasound images associated with the target section types according to the quality score of the ultrasound images, and display the filtered ultrasound images in the list of recommended ultrasound images; Alternatively, display a recommended ultrasound image list corresponding to at least one of the target section types, and sort and display the ultrasound images associated with the target section type in the recommended ultrasound image list according to the quality score. Alternatively, a list of recommended ultrasound images corresponding to at least one of the target section types can be displayed, and the ultrasound images associated with the target section type can be filtered according to the quality score of the ultrasound images. The filtered ultrasound images can then be sorted and displayed in the list of recommended ultrasound images according to their quality scores.

3. The ultrasound image processing method according to claim 2, characterized in that, The step of filtering ultrasound images associated with the target section type based on the quality score of the ultrasound images includes: The quality score of the ultrasound image associated with the target section type is compared with a scoring threshold, and ultrasound images with a quality score lower than the scoring threshold are discarded or stored in the candidate section storage area.

4. The ultrasound image processing method according to claim 2, characterized in that, Before obtaining the user's selection action from the recommended ultrasound image list, one of the following is also included: The quality score corresponding to the ultrasound image is displayed in the recommended ultrasound image list; Alternatively, a rating viewing operation can be performed on the ultrasound images in the recommended ultrasound image list, displaying the quality rating of the ultrasound images.

5. The method for processing ultrasound images according to claim 4, characterized in that, The operation of obtaining and viewing the quality rating of the ultrasound images in the recommended ultrasound image list, and displaying the quality rating of the ultrasound images, includes one of the following: Obtain a rating view operation for the ultrasound images in the recommended ultrasound image list, and display the quality rating of the ultrasound images in the recommended ultrasound image list; Alternatively, a rating viewing operation can be performed on the ultrasound images in the recommended ultrasound image list, and a rating interface can be displayed in response to the rating viewing operation, showing the quality rating of the ultrasound image in the rating interface.

6. The method for processing ultrasound images according to claim 1, characterized in that, The quality score of the ultrasound image associated with at least one of the target section types is used to display a recommended list of ultrasound images corresponding to at least one of the target section types, including... Display a list of recommended ultrasound images corresponding to at least one of the target section types, wherein the recommended ultrasound image list displays the ultrasound image associated with the target section type and its corresponding quality score.

7. The method for processing ultrasound images according to any one of claims 1 to 6, characterized in that, The ultrasound images in the recommended ultrasound image list are arranged in a horizontal, vertical, matrix, or grid manner.

8. The method for processing ultrasound images according to any one of claims 1 to 6, characterized in that, The quality score of the ultrasound image associated with at least one of the target section types, based on the quality score of the ultrasound image, displays a recommended list of ultrasound images corresponding to at least one of the target section types, including: The target section type classification interface is displayed, and the ultrasound images or default images corresponding to different target section types are displayed in the target section type classification interface. The default image represents the ultrasound image that is not associated with the target section type. Obtain the user's selection operation of the target section type in the target section type classification interface; In response to the selection operation, a list of recommended ultrasound images corresponding to the target section type is displayed based on the quality score of the ultrasound image corresponding to the target section type selected by the user.

9. The method for processing ultrasound images according to claim 1, characterized in that, Associating multiple ultrasound images with at least one target section type based on the section type of the multiple ultrasound images includes one of the following: The multiple ultrasound images are associated with at least one corresponding target section type in a target workflow protocol based on the section type of the multiple ultrasound images, wherein the target workflow protocol is selected by the user from a list of workflow protocols, and the list of workflow protocols is a list stored locally on the ultrasound device or a list obtained from a cloud server; Alternatively, based on at least one target section type selected or preset by the user, multiple ultrasound images are associated with at least one target section type according to the section type of the multiple ultrasound images.

10. The method for processing ultrasound images according to any one of claims 1 to 6 and 9, characterized in that, The step of identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images includes one of the following: Run the local recognition program of the ultrasound device to identify and process multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images, and determine the cross-section type and quality score of multiple ultrasound images. Alternatively, multiple ultrasound images can be sent to a cloud server, whereby the cloud server can process and identify the multiple ultrasound images to determine the section type and quality score of the ultrasound images; and the section type and quality score of the ultrasound images can be received from the cloud server. Alternatively, the ultrasound echo data corresponding to multiple ultrasound images can be sent to a cloud server, so that the cloud server can identify and process the ultrasound echo data corresponding to multiple ultrasound images, thereby determining the section type and quality score of the ultrasound images; and the section type and quality score of the ultrasound images can be received from the cloud server.

11. The method for processing ultrasound images according to claim 10, characterized in that, Also includes: Receive the ultrasound image and / or the ID of the ultrasound image whose cross-sectional type has been identified from the feedback of the cloud server.

12. The method for processing ultrasound images according to claim 9, characterized in that, The process of identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images includes: The ultrasound images are compared with at least one target section image corresponding to the target section type to determine the section type of each ultrasound image. Anatomical structures in multiple ultrasound images are identified, and the identified anatomical structures are compared with the target anatomical structures in the target cross-sectional image to determine the quality score.

13. The method for processing ultrasound images according to claim 12, characterized in that, The identification of anatomical structures in the plurality of ultrasound images includes: Identify at least one of the following anatomical structure types, anatomical structure morphology, anatomical structure location, and anatomical structure clarity in multiple ultrasound images; The target anatomical structure includes at least one of the following: target anatomical structure type, target structure morphology, target anatomical structure location, and target anatomical structure clarity; the step of comparing the identified anatomical structure with the target anatomical structure in the target cross-sectional image to determine the quality score includes: The quality score of the ultrasound image is determined by comparing the type of anatomical structure identified in the ultrasound image with the type of target anatomical structure, and / or comparing the morphology of the anatomical structure with the morphology of the target anatomical structure, and / or comparing the location of the anatomical structure with the location of the target anatomical structure, and / or comparing the clarity of the anatomical structure with the clarity of the target anatomical structure.

14. The method for processing ultrasound images according to any one of claims 1 to 6 and 9, characterized in that, The acquisition of multiple ultrasound images includes one of the following: Acquire multiple ultrasound images obtained by the user operating the ultrasound device; Alternatively, acquire multiple ultrasound images cached by the ultrasound device; Alternatively, acquire ultrasound video stream data and obtain multiple ultrasound images from the ultrasound video stream data.

15. The method for processing ultrasound images according to claim 1, characterized in that, The method further includes: The ultrasonic section image is stored in the target section storage area corresponding to the target section type; Displays a thumbnail of the ultrasonic section image and / or target section type information in the target section storage area; After receiving the user's first selection instruction to select the ultrasound section image in the target section storage area, the selected ultrasound section image and its corresponding target section type information are displayed.

16. The method for processing ultrasound images according to claim 1, characterized in that, The method further includes: Displays the ultrasonic section image and the target section type information corresponding to the target section type associated with the ultrasonic section image.

17. The method for processing ultrasound images according to claim 9, characterized in that, The method further includes: Display a list of target section types in the target workflow protocol, and highlight the target section type corresponding to the ultrasound section image in the list, wherein the highlighting includes at least one of the following: Annotation display; Highlight; Color-coded display; Fonts are displayed differently.

18. The method for processing ultrasound images according to claim 1 or 12, characterized in that, The step of identifying and processing multiple ultrasound images or ultrasound echo data corresponding to multiple ultrasound images to determine the cross-sectional type and quality score of the multiple ultrasound images includes: The anatomical structure information, section type and quality score of the multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images are identified and processed. The method further includes: displaying the ultrasound section image and target section type information, anatomical structure information and quality score corresponding to the target section type associated with the ultrasound section image.

19. A method for processing ultrasound images, applied to ultrasound equipment, characterized in that, The method includes: Displays a recommended list of ultrasound images, which includes at least one ultrasound image and is formed based on an image quality score associated with a target section type. Obtain the user's selection operation in the recommended ultrasound image list and determine the ultrasound image selected by the user; The ultrasound image selected by the user is used as the ultrasound section image of the target section type.

20. An ultrasound imaging system, characterized in that, include: Ultrasonic probe; The transmitting and receiving circuit is used to excite the ultrasonic probe to emit ultrasonic waves toward the target to be detected, receive the ultrasonic echo of the ultrasonic waves returned from the target to be detected, and obtain the ultrasonic echo signal. The beamforming and signal processing module is used to perform beamforming and signal processing on the echo signal to obtain an ultrasound image; The human-computer interaction interface is used to receive ultrasonic scanning operation commands input by the user; Transmission module; Display devices; The processor is configured to determine multiple ultrasound images to be identified according to the ultrasound scanning operation instructions, and to perform identification processing on the multiple ultrasound images or the ultrasound echo data corresponding to the multiple ultrasound images to determine the section type and quality score of the multiple ultrasound images; and to associate the multiple ultrasound images with at least one target section type according to the section type of the multiple ultrasound images. Based on the quality score of the ultrasound image associated with at least one of the target section types, the display device is controlled to display a recommended ultrasound image list corresponding to at least one of the target section types, the recommended ultrasound image list including at least one ultrasound image recommended to the user for selection based on the quality score; The human-computer interaction interface is used to obtain the user's selection operation in the recommended ultrasound image list, determine the ultrasound image selected by the user, and use the ultrasound image selected by the user as the ultrasound section image of the target section type.

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