Method of processing an ultrasound image, ultrasound imaging device and readable storage medium

Abstract

The application relates to the technical field of ultrasound images, and discloses an ultrasound image processing method, an ultrasound imaging device and a readable storage medium. The method comprises the following steps: collecting an ultrasound image by using an ultrasound probe; analyzing the ultrasound image to obtain an analysis result; adjusting and guiding the ultrasound probe according to the analysis result, so as to adjust the ultrasound probe to an optimal collection position; in response to the ultrasound probe being adjusted to the optimal collection position, collecting a corresponding target ultrasound image; performing imaging analysis on the target ultrasound image to obtain a detection parameter; and displaying the detection parameter. In the manner, medical staff can intuitively see the detection parameter, the medical staff can avoid using a traditional finger detection method to estimate the parameter, the accuracy of the detection parameter is improved, and the infection risk of a puerpera is reduced.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, and in particular to ultrasound image processing methods, ultrasound imaging equipment, and readable storage media. Background Technology

[0002] During childbirth, the progress of labor needs to be monitored and the delivery method clinically assessed before the pregnant woman enters the delivery room and during labor. Traditional monitoring methods involve internal examination to check the degree of cervical dilation, the position of the presenting head, and the fetal position. This process relies on the midwife's experience and judgment, which is highly subjective. Furthermore, frequent digital examinations can increase the risk of infection and discomfort for the pregnant woman, and reduce her compliance. Summary of the Invention

[0003] The main technical problem addressed by this application is to provide a method for processing ultrasound images, an ultrasound imaging device, and a readable storage medium, which allows medical staff to intuitively see the test parameters, avoid the need for medical staff to use the traditional digital rectal examination method to estimate parameters, improve the accuracy of test parameters, and reduce the risk of infection for postpartum women.

[0004] To address the aforementioned problems, this application provides a method for processing ultrasound images. This method includes: acquiring ultrasound images using an ultrasound probe; analyzing the ultrasound images to obtain analysis results; adjusting the ultrasound probe according to the analysis results to adjust it to the optimal acquisition position; acquiring a corresponding target ultrasound image in response to the ultrasound probe being adjusted to the optimal acquisition position; performing imaging analysis on the target ultrasound image to obtain detection parameters; and displaying the detection parameters.

[0005] The display of detection parameters includes: displaying the target ultrasound image on the display interface and marking the detection parameters on the target ultrasound image.

[0006] The detection parameters include at least one of the following: fetal head progression angle, distance between the pubic symphysis and the fetal head, fetal head progression distance, and distance between the fetal head and the perineum; the method also includes: generating a graph based on the fetal head progression angle and the distance between the fetal head and the perineum; and displaying the graph.

[0007] The method also includes displaying delivery suggestions based on detection parameters.

[0008] The system displays delivery recommendations based on detection parameters, including: obtaining the fetal head progression angle from the detection parameters; and providing delivery recommendations based on the fetal head progression angle, which include vaginal delivery and cesarean section.

[0009] The process of adjusting the ultrasound probe based on the analysis results to the optimal acquisition position includes: adjusting the ultrasound probe if the analysis results do not meet the preset requirements.

[0010] If the analysis results do not meet the preset requirements, the ultrasound probe is adjusted accordingly, including: if the analysis results indicate that the feature information is not in the preset area of ​​the ultrasound image, a first guidance mark is displayed on the display interface to guide the ultrasound probe to move in the direction of the feature information; if the analysis results indicate that the feature information does not meet the requirements of the standard feature information, a second guidance mark is displayed on the display interface to guide the ultrasound probe to be rotated to acquire an ultrasound image from another angle.

[0011] To address the aforementioned problems, another technical solution adopted in this application is to provide an ultrasound imaging device, comprising: an ultrasound probe; a transmitting circuit connected to the ultrasound probe for transmitting ultrasound signals to a target tissue via the ultrasound probe; a receiving circuit connected to the ultrasound probe for acquiring ultrasound echo signals reflected by the target tissue; a processor connected to the receiving circuit for generating an ultrasound image based on the ultrasound echo signals; analyzing the ultrasound image to obtain analysis results; adjusting the ultrasound probe according to the analysis results to adjust the ultrasound probe to the optimal acquisition position; acquiring a corresponding target ultrasound image in response to the ultrasound probe being adjusted to the optimal acquisition position; performing imaging analysis on the target ultrasound image to obtain detection parameters; and a display for displaying the detection parameters.

[0012] The display also shows the target ultrasound image, which is marked with detection parameters.

[0013] The detection parameters include at least two of the following: fetal head progression angle, distance between the pubic symphysis and the fetal head, fetal head progression distance, and distance between the fetal head and the perineum; the processor is also used to generate a graph based on the fetal head progression angle and the distance between the fetal head and the perineum; the display is also used to display the graph.

[0014] The processor is also used to generate delivery recommendations based on the detection parameters; the display is also used to show the delivery recommendations.

[0015] The processor is also used to acquire the fetal head progression angle from the detection parameters; and to generate delivery suggestions based on the fetal head progression angle, including vaginal delivery and cesarean section.

[0016] The processor is also used to guide the adjustment of the ultrasound probe if the analysis results do not meet the preset requirements.

[0017] The processor is further configured to generate a first guidance mark when the analysis result indicates that the feature information is not in a preset area of ​​the ultrasound image; the display is further configured to display the first guidance mark to guide the ultrasound probe to move in the direction of the feature information; or, the processor is further configured to generate a second guidance mark when the analysis result indicates that the feature information does not meet the requirements of the standard feature information; the display is further configured to display the second guidance mark to guide the ultrasound probe to be rotated to acquire an ultrasound image from another angle.

[0018] To address the aforementioned problems, another technical solution adopted in this application is to provide a computer-readable storage medium for storing a computer program, which, when executed by a processor, implements the method provided by the above technical solution.

[0019] The beneficial effects of this application are as follows: Unlike existing technologies, the ultrasound image processing method provided in this application includes: acquiring ultrasound images using an ultrasound probe; analyzing the ultrasound images to obtain analysis results; adjusting the ultrasound probe according to the analysis results to adjust it to the optimal acquisition position; acquiring the corresponding target ultrasound image in response to the ultrasound probe being adjusted to the optimal acquisition position; performing imaging analysis on the target ultrasound image to obtain detection parameters; and displaying the detection parameters. Through this method, the operator can adjust the ultrasound probe according to the adjustment guidelines to reach the optimal acquisition position and thus acquire the corresponding ultrasound image. This reduces the requirements for the operator's ultrasound instrument operation skills, ultrasound expertise, and clinical expertise, eliminating the need for the operator to blindly adjust the ultrasound probe multiple times, improving acquisition efficiency, and thus improving ultrasound diagnostic efficiency. Furthermore, the display of corresponding detection parameters allows medical staff to intuitively see the detection parameters. During childbirth, this avoids the need for medical staff to use traditional digital rectal examination methods to estimate parameters, improving the accuracy of detection parameters and reducing the risk of infection for the mother. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of an embodiment of the ultrasound image processing method provided in this application;

[0021] Figure 2 This is a schematic diagram of the structure of an embodiment of the ultrasound imaging device provided in this application;

[0022] Figure 3 This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application;

[0023] Figure 4 This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application;

[0024] Figure 5This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application;

[0025] Figures 6-11 This is a schematic diagram illustrating an application scenario of the ultrasound image processing method provided in this application;

[0026] Figure 12 This is a schematic diagram of the trend of changes in labor progress parameters provided in this application;

[0027] Figure 13 This is a schematic diagram of the production probability mapping indicator provided in this application;

[0028] Figure 14 This is a schematic diagram of the display interface provided in this application;

[0029] Figure 15 This is a schematic diagram of the structure of an embodiment of the ultrasound imaging device provided in this application;

[0030] Figure 16 This is a schematic diagram of an embodiment of the computer-readable storage medium provided in this application. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It is understood that the specific embodiments described herein are only for explaining this application and not for limiting it. Furthermore, it should be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all structures. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0032] The terms "first," "second," etc., used in this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0033] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0034] See Figure 1 , Figure 1 This is a schematic flowchart of an embodiment of the ultrasound image processing method provided in this application. The method includes:

[0035] Step 11: Acquire ultrasound images using an ultrasound probe.

[0036] In this embodiment, an ultrasound imaging device can be used to acquire ultrasound images. See also... Figure 2 The ultrasound imaging device 100 includes an ultrasound probe 101, a transmitting circuit 102, a receiving circuit 103, a transmit / receive selection switch 104, a processor 105, a display 106, and a memory 107. The transmitting circuit 102 and the receiving circuit 103 can be connected to the ultrasound probe 101 via the transmit / receive selection switch 104. In some embodiments, the transmitting circuit 102, the receiving circuit 103, and the transmit / receive selection switch 104 can be integrated with the ultrasound probe 101.

[0037] During ultrasound imaging, the transmitting circuit 102 sends a delayed-focused transmission pulse with a certain amplitude and polarity to the ultrasound probe 101 via the transmit / receive selection switch 104 to excite the ultrasound probe 101 to emit ultrasonic waves. After a certain delay, the receiving circuit 103 receives the echo of the ultrasonic wave via the transmit / receive selection switch 104, obtains the ultrasonic echo signal, and performs amplification, analog-to-digital conversion, and beamforming on the echo signal. Then, the processed ultrasonic echo signal is sent to the processor 105 for further processing. The processor 105 processes the ultrasonic echo signal to obtain the corresponding ultrasound image.

[0038] The display 106 is connected to the processor 105. For example, the processor 105 can be connected to the display 106 via an external input / output port. The display 106 can detect user input information, which may include, for example, control commands for ultrasonic wave transmission and reception timing, operation input commands for initiating still image capture, dynamic video capture, and / or dynamic image storage, or other command types. The display 106 may include one or more of the following: keyboard, mouse, scroll wheel, trackball, mobile input device (such as a mobile device with a touch screen, a mobile phone, etc.), multi-function knob, buttons, etc. Therefore, the corresponding external input / output port can be a wireless communication module, a wired communication module, or a combination of both. The external input / output port can also be implemented based on USB, bus protocols such as CAN, and / or wired network protocols.

[0039] The display 106 also includes a screen that can display ultrasound images acquired by the processor 105. Furthermore, while displaying ultrasound images, the screen can also provide a graphical user interface for human-computer interaction. One or more controlled objects can be set on the graphical interface, allowing the user to input operation commands through the display 106 to control these controlled objects and perform corresponding control operations. For example, icons can be displayed on the graphical interface, and the user can operate these icons using a human-computer interaction device to perform specific functions, such as the function of storing dynamic images while simultaneously capturing still images / movie clips. In practical applications, the screen can be a touchscreen display. Furthermore, the display in this embodiment may include one screen or multiple screens.

[0040] In other embodiments of this application, the processor 105 is also configured to receive an instruction to store the ultrasound image, and in response to the instruction to store a dynamic image, a static image, or a short video of the ultrasound image, thereby facilitating a user (e.g., a doctor) to browse and review it for diagnosis.

[0041] The ultrasound imaging device 100 can be of the amplitude modulation type, the spot scanning type, or the grayscale modulation type.

[0042] In this embodiment, the ultrasound imaging device 100 is used during the delivery process to assist medical staff in assisting the pregnant woman with childbirth. Step 11 involves acquiring ultrasound data using an ultrasound probe, which can be a three-dimensional or two-dimensional probe placed at the pregnant woman's perineum or abdomen to collect corresponding intrapartum ultrasound data. The ultrasound data can then be imaged and analyzed to obtain intrapartum ultrasound images. The intrapartum ultrasound image data acquired by the three-dimensional ultrasound probe includes at least one volumetric three-dimensional image, while the ultrasound image data acquired by the two-dimensional ultrasound probe is a segment of intrapartum ultrasound video.

[0043] Step 12: Analyze the ultrasound images and obtain the analysis results.

[0044] In some embodiments, ultrasound data can be subjected to imaging analysis to obtain corresponding ultrasound images. These ultrasound images are then input into an image segmentation model to obtain feature information from the ultrasound images.

[0045] Image segmentation models are trained using training sample images, which are obtained by annotating standard ultrasound images. Specifically, the contours of feature information and the category of that feature information are annotated in the standard ultrasound image, thereby training the image segmentation model.

[0046] In this embodiment, the image segmentation model can be trained based on network structures such as FCN (Fully Convolutional Network), SegNet, Unet, Deeplab, or PSPNet.

[0047] Then, the feature information is analyzed to obtain the analysis results.

[0048] For example, it can be analyzed whether the contour area of ​​the feature information meets the preset contour area requirements. Thus, the analysis result can be the result of whether the contour area of ​​the feature information meets the preset contour area requirements.

[0049] For example, one can analyze the types of feature information in the ultrasound image to see if these types match those in a standard ultrasound image. Thus, the analysis result can be the result of whether the types match those in a standard ultrasound image.

[0050] For example, the sharpness of feature information in the ultrasound image can be analyzed to determine whether the sharpness meets the requirements. Therefore, the analysis result can be the result of whether the sharpness meets the requirements.

[0051] The above analysis methods can be performed simultaneously or in any combination. During this process, weights can be set for each analysis method. When multiple methods are combined as analysis requirements, they are multiplied according to their respective weights to obtain the final result, which is then used to determine whether the requirements are met.

[0052] Step 13: Adjust the ultrasound probe according to the analysis results to the optimal acquisition position.

[0053] In some applications, the ultrasound probe can be adjusted based on the analysis results through voice prompts, such as, "Please move it 3 millimeters forward."

[0054] In some application scenarios, the method of adjusting the ultrasound probe based on the analysis results can be demonstrated through animation on the display interface. For example, the ultrasound probe can be modeled, and then a virtual ultrasound probe can be displayed on the display interface. Then, the adjustment methods, such as rotation and movement, can be marked on the display interface.

[0055] In some embodiments, adjusting the ultrasound probe to the optimal acquisition position may involve adjusting the position and / or orientation of the ultrasound probe.

[0056] Step 14: In response to the ultrasound probe being adjusted to the optimal acquisition position, acquire the corresponding target ultrasound image.

[0057] When the operator is familiar with the adjustment guidelines, they should control the ultrasound probe according to the guidelines to bring the ultrasound probe to the designated position and acquire ultrasound data using the designated posture.

[0058] Then, steps 12 and 13 are executed again to obtain the analysis results of the ultrasound data acquired this time. If the analysis results meet the preset requirements, the ultrasound image corresponding to the ultrasound data acquired this time is stored.

[0059] Step 15: Perform imaging analysis on the target ultrasound image to obtain detection parameters.

[0060] The aforementioned detection parameters may include at least one of the following: fetal head progression angle, distance between the pubic symphysis and the fetal head, fetal head progression distance, and distance between the fetal head and the perineum.

[0061] In some embodiments, imaging analysis can be performed on the target ultrasound image to obtain the pubic symphysis contour and the fetal head contour in the ultrasound image. The ultrasound image can be divided into longitudinal section images and transverse section images.

[0062] Therefore, the angle of progression (AOP), the head-Symphysis distance (HSD), and the progress distance (PD) can be calculated using longitudinal section images and the determined pubic symphysis contour and fetal head contour. The distance between the fetal head and the perineum (HPD) can be calculated using transverse section images and the determined pubic symphysis contour and fetal head contour.

[0063] AOP is the angle between the line along the long axis of the pubic symphysis and the tangent at the lower edge of the pubic symphysis and the furthest point of the fetal skull.

[0064] HSD (Head-Side Distance) is the distance between the lowest edge of the pubic symphysis and the fetal skull, and is an indicator of fetal head descent. It is mainly used for fetuses in the occiput anterior position. As the fetal head descends towards the pelvic floor, the HSD gradually shortens.

[0065] HPD is the shortest distance between the outer edge of the fetal skull and the perineum.

[0066] Step 16: Display the detection parameters.

[0067] In some embodiments, step 16 may involve displaying the target ultrasound image on a display interface and marking detection parameters on the target ultrasound image.

[0068] In this embodiment, ultrasound images are acquired using an ultrasound probe; the ultrasound images are analyzed to obtain analysis results; the ultrasound probe is adjusted according to the analysis results to reach the optimal acquisition position; in response to the ultrasound probe being adjusted to the optimal acquisition position, the corresponding target ultrasound image is acquired; the target ultrasound image is analyzed to obtain detection parameters; and the display of detection parameters allows the operator to adjust the ultrasound probe according to the adjustment guidelines to reach the optimal acquisition position and acquire the corresponding ultrasound image. This reduces the requirements for the operator's ultrasound instrument operation skills, ultrasound expertise, and clinical expertise, eliminating the need for the operator to blindly adjust the ultrasound probe multiple times, thus improving acquisition efficiency and ultrasound diagnostic efficiency. Furthermore, the display of corresponding detection parameters allows medical staff to intuitively see the detection parameters. During childbirth, this avoids the need for medical staff to use the traditional digital rectal examination method to estimate parameters, improving the accuracy of detection parameters and reducing the risk of infection for the mother.

[0069] Based on the above detection parameters, the following procedures can be followed during childbirth to facilitate delivery: (See attached document) Figure 3 , Figure 3 This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application. The method includes:

[0070] Step 31: Create a chart based on the fetal head progression angle and the distance between the fetal head and the perineum.

[0071] For example, line graphs or bar charts can be created using data collected at different times, including the fetal head progression angle and the distance between the fetal head and perineum. The horizontal axis of the chart represents time, and the vertical axis represents the specific values ​​of the fetal head progression angle and the distance between the fetal head and perineum.

[0072] In some embodiments, the fetal head progression angle and the distance between the fetal head and the perineum can be generated as separate trend graphs and displayed on the display interface. Alternatively, the fetal head progression angle and the distance between the fetal head and the perineum can be generated as the same trend graph and displayed on the display interface.

[0073] The trend chart visually displays the fetal head progression angle and the distance between the fetal head and perineum. If, within a preset time, the line representing the fetal head progression angle shows an upward trend while the line representing the distance between the fetal head and perineum shows a downward trend, then vaginal delivery is considered possible, and the display screen will suggest vaginal delivery. If, within the preset time, the difference between the lines representing the fetal head progression angle and the distance between the fetal head and perineum does not widen, then the probability of vaginal delivery is considered low, and the display screen will suggest cesarean section.

[0074] Step 32: Display the chart.

[0075] The charts can be displayed on the same screen as the ultrasound images.

[0076] In the above embodiments, by displaying the fetal head progression angle and the distance between the fetal head and the perineum in the form of a chart, medical staff can intuitively see the test parameters. During childbirth, this avoids the need for medical staff to use the traditional digital rectal examination method to estimate the parameters, improves the accuracy of the test parameters, and reduces the risk of infection for the mother.

[0077] In other embodiments, the labor process can be assisted by displaying delivery suggestions on a screen based on detected parameters. Based on this, see [reference needed]. Figure 4 , Figure 4 This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application. The method includes:

[0078] Step 41: Obtain the fetal head progression angle from the detection parameters.

[0079] The fetal head progression angle can be determined based on the pubic symphysis contour and the fetal head contour. Specifically, it can be obtained directly when marking the detection parameters on the ultrasound image.

[0080] Step 42: Make delivery recommendations based on the fetal head progression angle, including vaginal delivery and cesarean section.

[0081] In some embodiments, if the fetal head progression angle is greater than 110°, the recommended delivery is vaginal delivery.

[0082] If the fetal head progresses to an angle greater than 110° within the preset time, it indicates a high probability of normal delivery, and the delivery interface will display a suggestion for vaginal delivery. The midwife can then proceed with further preparations based on the desired vaginal delivery.

[0083] In some embodiments, if the fetal head progression angle is less than 80°, a cesarean section is recommended for delivery.

[0084] If the fetal head angle is less than 80° within the preset time, it indicates that the mother is unlikely to be able to deliver normally, and the delivery interface will display a recommendation for cesarean section. The midwifery staff can then proceed with the necessary preparations based on the cesarean section requirement.

[0085] In some embodiments, if the fetal head progression angle is greater than 80° and less than 110°, the probability of vaginal delivery and intervention recommendations are displayed.

[0086] If the fetal head progression angle is greater than 80° but less than 110° within the preset time, the probability of vaginal delivery can be calculated based on historical delivery cases. Specifically, the probability of vaginal delivery is calculated in real time based on the actual fetal head progression angle to assess the probability. The probability of vaginal delivery and intervention recommendations are displayed on the interface in real time so that midwives can be informed and make subsequent preparations as needed. Intervention recommendations may include suggesting a cesarean section and requesting medical staff to prepare for the procedure if the probability of vaginal delivery is lower than the preset value. Intervention recommendations may also include suggesting that medical staff consult with the mother's family and relatives regarding their opinion on a cesarean section if the probability of vaginal delivery is lower than the preset value.

[0087] See Figure 5 , Figure 5 This is a schematic flowchart of another embodiment of the ultrasound image processing method provided in this application. The method includes:

[0088] Step 51: Acquire ultrasound images using an ultrasound probe.

[0089] Step 52: Analyze the ultrasound images and obtain the analysis results.

[0090] In some embodiments, an image segmentation model can be used to extract features from ultrasound images to obtain corresponding feature information. The feature information is then compared and analyzed with the feature information of a standard ultrasound image to obtain the analysis results.

[0091] Specifically, the ultrasound image can be input into an image segmentation model corresponding to the current examination mode to obtain feature information in the ultrasound image.

[0092] In one application scenario, if the current examination mode is abdominal mode, the ultrasound image can be input into the image segmentation model corresponding to the abdominal mode to obtain the feature information in the ultrasound image. The feature information includes at least one of the following: fetal eye outline, spine, cervical spine and brain midline.

[0093] In another application scenario, if the current examination mode is perineal mode, the ultrasound image can be input into the image segmentation model corresponding to the perineal mode to obtain the feature information in the ultrasound image. The feature information includes at least one of the following: pubic symphysis contour, fetal head contour, and brain midline.

[0094] Then, the feature information is compared with the standard feature information to obtain the comparison result, which is then used as the analysis result.

[0095] For example, it can be analyzed whether the contour area of ​​the feature information meets a preset contour area requirement. Therefore, the analysis result can be the result of whether the contour area of ​​the feature information meets the preset contour area requirement. For example, the contour area requirement can be set to the ratio of the contour area difference to the standard contour area being less than 5%. Specifically, the comparison method can be to compare the contour of the feature information with the standard contour, determine the area of ​​the non-overlapping region between the two, and this area can represent the contour area difference. Then, the ratio of the contour area difference to the standard contour area is calculated. If this ratio is less than 5%, the contour feature information can be considered to meet the requirement.

[0096] In other embodiments, the contour feature information can be located within the region of the ultrasound image to obtain contour position information. The difference between this contour position information and the standard contour position information can be determined. If the difference is less than a preset value, the position of the contour feature information can be considered to meet the requirements. If the difference is greater than the preset value, the position of the contour feature information can be considered to not meet the requirements.

[0097] For example, the types of feature information in the ultrasound image can be analyzed to determine if these types conform to those in a standard ultrasound image. The analysis result can then be the outcome of whether the types match those in a standard ultrasound image. For instance, if the preset types include the pubic symphysis and fetal head, and the feature information only contains one of these two types, it does not meet the requirements.

[0098] For example, the sharpness of feature information in the ultrasound image can be analyzed to determine whether the sharpness meets the requirements. Therefore, the analysis result can be the result of whether the sharpness meets the requirements.

[0099] The above analysis methods can be performed simultaneously or in any combination. Only when each requirement is met can the analysis results that satisfy the preset requirements be obtained.

[0100] Step 53: If the analysis results do not meet the preset requirements, adjust the ultrasound probe to the optimal acquisition position.

[0101] In some embodiments, if the analysis results indicate that the feature information is not in a preset area of ​​the ultrasound image, a first guidance mark is displayed on the display interface to guide the ultrasound probe to move in the direction of the feature information, thereby guiding the ultrasound probe to be adjusted to the optimal acquisition position.

[0102] The first guiding identifier can be a dynamic mark displayed on the screen, such as a dynamic arrow, whose direction guides the movement of the ultrasound probe. In some embodiments, the arrow can be hollow, and its length can represent the distance the ultrasound probe has moved. During the movement of the ultrasound probe, the hollow arrow is filled. When the arrow is fully filled, it indicates that the ultrasound probe has moved to the target position. In other embodiments, the first guiding identifier can be an arrow and an indicator light. The indicator light uses different colors to indicate whether the ultrasound probe has moved to the target position indicated by the arrow. For example, a red indicator light indicates that the ultrasound probe has not yet moved to the target position indicated by the arrow, while a green indicator light indicates that the ultrasound probe has moved to the target position indicated by the arrow.

[0103] For example, the target information corresponds to the feature information mentioned above, meaning the feature information does not appear in the preset area. The preset area can be a region near the center of the ultrasound image.

[0104] If the analysis results indicate that the feature information is not within the preset area of ​​the ultrasound image, the ultrasound probe is prompted to move in the direction of the feature information. For example, a standard ultrasound image requires the feature information to be located in the area near the center of the entire ultrasound image. If the feature information is located on the left side of the ultrasound image, the ultrasound probe is prompted to move in the corresponding leftward direction.

[0105] In some embodiments, if the analysis results indicate that the feature information does not meet the requirements of the standard feature information, a second guidance mark is displayed on the display interface to guide the rotation of the ultrasound probe to acquire an ultrasound image from another angle.

[0106] The second guidance identifier can be the same as the first guidance identifier mentioned above.

[0107] For example, if the analysis results indicate that the feature information does not meet the requirements of the standard feature information, a second guidance mark will be displayed on the display interface to guide the rotation of the ultrasound probe to acquire ultrasound data from another angle.

[0108] For example, if the area of ​​the feature information does not conform to the standard contour area (usually the area of ​​the feature information is smaller than the standard contour area), the operator can be prompted to rotate the ultrasound probe to acquire ultrasound data from a different angle. Specifically, the rotation method can be determined based on the contour of the feature information. For instance, the contour of the feature information can be distinguished as upper, lower, left, and right. If the left or right contour differs significantly from the standard contour, the operator is prompted to rotate the ultrasound probe clockwise. If the upper or lower contour differs significantly from the standard contour, the operator is prompted to change the incident angle of the ultrasound waves on the probe.

[0109] Step 54: In response to the ultrasound probe being adjusted to the optimal acquisition position, acquire the corresponding target ultrasound image.

[0110] During step 54, the position of the ultrasound probe can be acquired in real time. Appropriate prompts, such as voice reminders, are given when the ultrasound probe is adjusted to the optimal acquisition position. Then, at the optimal acquisition position, the ultrasound probe is used to acquire the corresponding target ultrasound image.

[0111] Step 55: Perform imaging analysis on the target ultrasound image to obtain detection parameters.

[0112] Step 56: Display the detection parameters.

[0113] In one application scenario, the display interface can show options for the corresponding acquisition mode. For example, the user can first select the position of interest for scanning. For intrapartum ultrasound diagnosis, ultrasound data can generally be acquired in the abdomen or perineum. Therefore, before data acquisition, either abdominal mode or perineal mode can be selected. Specifically, the abdominal or perineal mode can be selected in the display interface, or a corresponding button can be set on the ultrasound probe. When the button for the corresponding mode is triggered, the corresponding mode is transmitted to the ultrasound system connected to the ultrasound probe, so that the system operates according to the currently selected mode.

[0114] After selecting the observation position, the operator can hold the ultrasound probe and adjust it to any desired position and orientation to acquire ultrasound images. Similarly, ultrasound images are acquired for each adjusted position and orientation. The ultrasound probe can generally be a convex array probe or a 3D ultrasound probe.

[0115] The ultrasound data collected during the above process are analyzed and evaluated using a network model or analysis module with analytical functions. The results of the ultrasound probe adjustment are then fed back and the adjustment instructions are displayed on the display interface of the display device.

[0116] The specific process is as follows:

[0117] 1. For ultrasound data acquired by an ultrasound probe, preprocessing is performed first, including noise reduction, filtering, smoothing, edge enhancement, ESRI, and other operations, to improve the image quality after ultrasound data imaging.

[0118] 2. The preprocessed image is fed into the AI ​​(Artificial Intelligence) algorithm module for image processing. This module contains two trained image segmentation models: one for abdominal features and the other for perineal features. The selection of the appropriate feature segmentation network model is determined based on a pre-selected mode. For example, if the perineal mode is pre-selected, ultrasound images acquired in this mode are input into the perineal feature segmentation network model; if the abdominal mode is pre-selected, ultrasound images acquired in this mode are input into the abdominal feature segmentation network model.

[0119] For ultrasound images acquired in abdominal mode, a trained abdominal feature segmentation network model is used to segment the tissues in the incoming ultrasound images, identifying features such as the fetal eye outline, spine, cervical vertebrae, and midline brain echoes. For ultrasound images acquired in perineal mode, a trained perineal feature segmentation network model is used to segment the tissues in the incoming ultrasound images, identifying features such as the pubic symphysis, fetal head, and midline brain echoes. If a convex array probe is used, the ultrasound image of the two-dimensional section is segmented directly; if volumetric data is acquired using a 3D probe, the ultrasound image of all two-dimensional sections contained within the volumetric data is segmented.

[0120] In some embodiments, the dataset used to train the image segmentation model in the AI ​​algorithm module consists of two-dimensional standard-section ultrasound images of full-term pregnant women acquired clinically. These ultrasound images are categorized according to the maternal position scanned by the ultrasound probe, generally falling into two main types: abdominal ultrasound images and perineal ultrasound images. For the abdominal position, a large number of standard-section ultrasound images, including features such as fetal eye contours, spine, cervical vertebrae, and midline brain echoes, are acquired to train the abdominal feature segmentation network model. For the perineal position, standard-section ultrasound images, including features such as the pubic symphysis, fetal head contours, and midline brain echoes, are acquired to train the perineal feature segmentation network model. These training ultrasound images are acquired by experienced physicians, who then select the acquired images and annotate each feature on the selected images that meet the requirements. Furthermore, these annotated ultrasound images can be transformed through mirroring, rotation, cropping, scaling, etc., to obtain more training samples for data augmentation.

[0121] In other embodiments, the dataset types are not limited to the two types shown in this embodiment. In practice, they can be further divided according to the body position and features of greater interest, such as ultrasound images of the abdomen when the pregnant woman is lying on her side, and ultrasound images of the perineum when lying on her side. The network model used for training can be a network model such as FCN, Segnet, U net, or Deeplab.

[0122] Then, the feature information segmented by the network model is analyzed and evaluated to determine whether the ultrasound image meets the requirements of the standard section ultrasound image. If it does not meet the requirements, the operator is guided to adjust the ultrasound probe, thereby quickly and accurately locating the optimal scanning state of the ultrasound probe and obtaining the best scanning section data.

[0123] For example, in the abdominal mode, the input ultrasound image is processed by the abdominal feature segmentation network model to extract feature information, and the segmentation results of various features in the ultrasound image can be obtained. For the segmentation results, preset requirements for standard section ultrasound images are set in terms of feature type, feature segmentation region integrity, feature location, and feature orientation. The system compares the segmentation feature results with the preset requirements of the standard section ultrasound image and, combined with biological experience, provides guidance for ultrasound probe adjustment.

[0124] Specifically, the method for adjusting based on the index attributes of segmentation features is as follows:

[0125] For example Figure 6 As shown, Figure 6 The ultrasound image is formed from ultrasound data acquired through a transverse scan of the lower abdomen. An abdominal feature segmentation network model is used to segment the ultrasound image, identifying characteristic regions, such as... Figure 6 The system identifies the outlines of the eyes and nose; then it automatically analyzes the types and completeness of the segmented features, such as... Figure 6 If the eye outline in the upper right corner does not meet the requirement of a complete outline, the system will adjust the probe to the upper right corner based on biological experience. Figure 7 As shown, after segmenting the characteristics of the fetal spine, the system will prompt the probe to be adjusted to the right based on the direction of the spine characteristics and big data experience in biology. At this time, the corresponding guidance mark can be displayed on the display interface.

[0126] For example, in the perineal mode, after the input ultrasound image is processed by the perineal feature segmentation network model to extract feature information, the segmentation results of various features in the ultrasound image can be obtained. For the segmentation results, preset requirements for standard section ultrasound images are set in terms of feature type, feature segmentation region completeness, feature location, and feature direction. The system compares the segmentation feature results with the preset requirements of the standard section ultrasound image and, combined with biological experience, provides guidance for ultrasound probe adjustment.

[0127] The preset requirements for standard section ultrasound images are: a complete, clear, and comprehensive two-dimensional image of the target tissue or feature. Specifically, the preset requirements for standard section ultrasound images are: First, the most diverse range of segmented target features, specifically, as many features as possible from the pubic symphysis, fetal head contour, and fetal midline of the brain appear in the acquired image. Second, the highest classification probability of the target features, meaning that features such as the pubic symphysis, fetal head contour, and fetal midline of the brain in the ultrasound image are most similar to the standard features. Third, the feature regions such as the pubic symphysis, fetal head contour, and fetal midline of the brain in the ultrasound image are as complete as possible, meaning the contour area of ​​the target features is the largest.

[0128] For example, see Figure 8 For ultrasound images acquired via a longitudinal section of the perineum, a perineal feature segmentation network model is used to segment the image, ensuring that the pubic symphysis and fetal head contours appear simultaneously in the segmentation results, and that both shapes are closest to the standard feature shapes. Furthermore, the model ensures that the two feature contours are as complete as possible, i.e., with the largest possible area. For example... Figure 9 As shown, the ultrasound image was segmented using a perineal feature segmentation network model to ensure that the fetal head contour and midline brain echo appeared simultaneously in the segmentation result, and that both shapes were closest to the standard feature shape. It also ensured that the two feature contours were as complete as possible, i.e., with the largest area.

[0129] Specifically, the method for guiding probe adjustment based on the segmentation results and the preset requirements of standard section ultrasound images is as follows:

[0130] like Figure 10 The image shown is an ultrasound image acquired via a transverse section of the perineum. The analysis system uses a perineal feature segmentation network model to segment the incoming ultrasound image and then compares it with preset requirements for standard transverse ultrasound images. For ultrasound images that do not meet the requirements, adjustments are made based on biological experience. (See above.) Figure 10 If the feature is not complete, the system will prompt the probe to adjust the image acquisition so that the longitudinal section at that location is facing upwards.

[0131] Based on the analysis of the segmentation results of the acquired images, the system display interface will prompt probe adjustment information, such as... Figure 11 As shown. When the segmentation result meets the requirements, the "OK indicator" on the display device turns green, indicating that the optimal scanning state has been found. The scanned image output in this state is the optimal image. At this time, the "OK indicator" on the display is green, which can be equivalent to the fourth guidance indicator in the above embodiment. If the segmentation result does not meet the requirements, the "OK indicator" flashes red, indicating that the optimal scanning state and optimal scanning section have not been found, and prompts the direction for adjusting the ultrasound probe. The ultrasound probe adjustment guidance includes the direction of ultrasound probe movement, such as... Figure 10As shown, four movement direction indicator lights are set on the display interface, such as "Up", "Down", "Left", and "Right". The indicator light illuminates in the desired direction of movement. Two rotation direction indicator lights are also set on the display interface, such as "A - Clockwise Rotation" and "B - Counterclockwise Rotation". The indicator light illuminates in the desired direction of rotation.

[0132] Specifically, the ultrasound probe is adjusted according to the adjustment instructions until the system indicates that the ultrasound probe adjustment has met the requirements, which is the optimal acquisition position. At this position, the optimal cross-sectional ultrasound image is acquired.

[0133] Specifically, the operator follows the rules as follows: Figure 11 Follow the on-screen instructions to continuously adjust the ultrasound probe until the "OK indicator" turns green. At this point, the probe's data acquisition is optimal, and the ultrasound images acquired in this state are also optimal.

[0134] Once the optimal scanning position and orientation are found, the image segmentation results of the ultrasound image acquired in that scanning state are directly input into the system for parameter calculation. The calculation of these parameters is primarily based on the definition of labor progression parameters. A two-dimensional coordinate model is established using extracted features to calculate the values ​​of each parameter in the ultrasound image. For example, based on the segmented pubic symphysis features and fetal head contour, the following intrapartum labor progression parameters can be calculated:

[0135] A. The angle of progression (AOP) can be expressed as the angle between the long axis of the pubic symphysis and the tangent of the fetal head drawn from the lower end of the pubic symphysis.

[0136] B. Head direction (HD) can be represented as the angle between the longest axis of the identifiable fetal head in the image and the long axis of the pubic symphysis.

[0137] C. The progression distance (PD) can be expressed as the minimum distance between the subpubic line, which passes through the lower end of the pubic symphysis and is perpendicular to the long axis of the pubic symphysis, and the tangent of the fetal head.

[0138] D. The head-symphysis distance (HSD) can be expressed as: the distance between the fetal head and the lower end of the pubic symphysis line, which passes through the lower end of the pubic symphysis and is perpendicular to the long axis of the pubic symphysis.

[0139] Specifically, the parameters that can be calculated and obtained vary depending on the location scanned by the ultrasound probe. Parameters such as AOP, HD, PD, and HSD can all be calculated based on transperineal ultrasound images. In addition to the above parameters, other parameters related to labor progress, such as head-perineum distance (HPD), midline angle (MLA), and headstation, can also be calculated. The calculation process is the same as in this embodiment and will not be detailed here.

[0140] To provide more diagnostic and decision-making information, the following model can be established based on the results of parameter calculations:

[0141] A) Processing model.

[0142] The processing model is primarily established to provide auxiliary evidence for diagnostic analysis. It is based on statistical analysis of a large number of samples to obtain diagnostic patterns. The parameter values ​​calculated by the system are input into the processing model for comparative analysis, and then output auxiliary diagnostic results. A specific implementation of the processing model is as follows:

[0143] 1. Analyze and study a large number of samples, and record the delivery conditions and parameters of each sample.

[0144] 2. Analyze the statistical relationship between parameter values ​​and the probability of vaginal delivery to obtain empirical patterns, which are then used as preset diagnostic thresholds for the system's analysis and diagnosis. For example, taking the parameter AOP as an example, based on a large number of sample statistics, empirical patterns are obtained: for AOP > 110°, vaginal delivery is recommended, and information such as the probability of vaginal delivery is provided; for AOP < 80°, cesarean section is recommended, and information such as the probability of vaginal delivery and other interventional information is provided; for parameter values ​​within the range of (80°, 110°), the probability of vaginal delivery and interventional recommendations can be provided. Besides the parameter AOP, other parameters also have the same function.

[0145] 3. When using this system for assisted diagnosis, the system will automatically calculate the values ​​of the detection parameters and provide actual delivery suggestions based on the parameter diagnosis thresholds and parameter diagnosis ranges.

[0146] 4. Utilizing data sharing and cloud computing technologies, cloud computers can conduct broader statistical analysis and research on clinical sample data to obtain more accurate parameter diagnostic thresholds. Specifically, after each diagnosis by an independent terminal machine, the system uploads case information, delivery results, and parameter information to the cloud computer. The cloud computing machine, based on existing research conclusions and combined with the latest incoming diagnostic sample information, will perform further statistical analysis and calculations to obtain more accurate parameter diagnostic thresholds and ranges. These latest thresholds are then shared with each independent terminal machine to update the parameter diagnostic thresholds.

[0147] B) A model showing the trend of changes in labor progress parameters.

[0148] Specifically, the values ​​of detection parameters calculated at different time points can be connected to form a labor guidance curve, which can then be used to help analyze and predict the trend of labor progress. For example... Figure 12 As shown, this is a graph depicting the changing trends of labor progress parameters constructed based on the AOP and HPD parameter values ​​calculated at different times.

[0149] C) Production probability mapping indicator graph model.

[0150] The quantified values ​​of indicators such as the probability of vaginal delivery or cesarean section are mapped to corresponding color ranges, and then the relevant decision probabilities are displayed through the corresponding colors, such as... Figure 13 As shown.

[0151] In addition to establishing the models mentioned above, we can also establish, for example, two-dimensional dynamic models and three-dimensional dynamic models to help display the progress of labor from various aspects and help doctors to make intuitive analysis and diagnosis.

[0152] The display device for the display interface can be an accessory to the ultrasound device, or it can be various data-sharing display devices, such as multidisciplinary joint diagnostic display devices, telemedicine display devices, etc. The display module is responsible for receiving, managing, and displaying image data, segmentation features, parameter diagrams, various model illustrations, and diagnostic results input from the intelligent image acquisition system and intelligent image processing system. For example... Figure 14 As shown, Figure 14 It displays parameter diagrams, feature outlines, parameter sizes and information, probability of vaginal delivery, probability of cesarean section, intervention opinions, and other information.

[0153] In addition to the information shown above, the display interface can also include electronic production process diagrams, two-dimensional dynamic model illustrations, three-dimensional dynamic model illustrations, etc.

[0154] The above methods can greatly simplify the workflow, reduce the requirements for ultrasound expertise and clinical experience of medical staff, and improve diagnostic efficiency, objectivity, and accuracy.

[0155] See Figure 15 , Figure 15 This is a schematic diagram of an embodiment of the ultrasonic device provided in this application. The ultrasonic imaging device 100 includes: an ultrasonic probe 101, a transmitting circuit 102, a receiving circuit 103, a processor 105, and a display 106.

[0156] The transmitting circuit 102 is connected to the ultrasonic probe 101 and is used to transmit ultrasonic signals to the target tissue through the ultrasonic probe 101.

[0157] The receiving circuit 103 is connected to the ultrasonic probe 101 and is used to acquire the ultrasonic echo signal reflected by the target tissue.

[0158] The processor 105 is connected to the receiving circuit 103 and is used to generate an ultrasound image based on the ultrasound echo signal; analyze the ultrasound image to obtain analysis results; adjust the ultrasound probe according to the analysis results to adjust the ultrasound probe to the optimal acquisition position; acquire the corresponding target ultrasound image in response to the ultrasound probe being adjusted to the optimal acquisition position; and perform imaging analysis on the target ultrasound image to obtain detection parameters.

[0159] The display 106 is connected to the processor 105 and is used to display the detection parameters.

[0160] In other embodiments, the display is also used to display a target ultrasound image, wherein detection parameters are marked on the target ultrasound image.

[0161] The above-mentioned detection parameters include at least two of the following: fetal head progression angle, distance between the pubic symphysis and the fetal head, fetal head progression distance, and distance between the fetal head and the perineum;

[0162] In other embodiments, the processor 105 is also configured to generate a graph based on the fetal head progression angle and the distance between the fetal head and the perineum.

[0163] Display 106 is also used to display charts.

[0164] In other embodiments, the processor 105 is also used to generate delivery recommendations based on detection parameters.

[0165] The display 106 is also used to display delivery advice.

[0166] In other embodiments, the processor 105 is also configured to acquire the fetal head progression angle from the detection parameters; and to generate a delivery recommendation based on the fetal head progression angle, wherein the delivery recommendation includes vaginal delivery and cesarean section.

[0167] In other embodiments, the processor 105 is also configured to provide adjustment guidance for the ultrasound probe if the analysis results do not meet preset requirements.

[0168] In other embodiments, the processor 105 is also configured to generate a first guidance identifier when the analysis results indicate that the feature information is not in a preset area of ​​the ultrasound image.

[0169] The display 106 is also used to display a first guidance mark to guide the ultrasound probe to move in the direction of the feature information.

[0170] In other embodiments, the processor 105 is also configured to generate a second guidance identifier when the analysis result indicates that the feature information does not meet the requirements of the standard feature information.

[0171] The display 106 is also used to display a second guide mark to guide the rotation of the ultrasound probe to acquire an ultrasound image from another angle.

[0172] The ultrasound imaging device 100 also includes a memory (not shown) for storing computer programs.

[0173] It is understood that the processor 105 is also used to execute computer programs to implement the methods of any of the above embodiments, as detailed in any of the above embodiments, which will not be repeated here.

[0174] See Figure 16 , Figure 16 This is a schematic diagram of an embodiment of the computer-readable storage medium provided in this application. The computer-readable storage medium 160 is used to store a computer program 71, which, when executed by a processor, implements the following methods:

[0175] The system acquires ultrasound images using an ultrasound probe; analyzes the ultrasound images to obtain analysis results; adjusts the ultrasound probe according to the analysis results to the optimal acquisition position; in response to the ultrasound probe being adjusted to the optimal acquisition position, acquires the corresponding target ultrasound image; performs imaging analysis on the target ultrasound image to obtain detection parameters; and displays the detection parameters.

[0176] It is understood that when the computer program 71 is executed by the processor, it is also used to implement the method of any of the above embodiments. For details, please refer to any of the above embodiments, which will not be repeated here.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules or 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.

[0178] 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, depending on actual needs.

[0179] 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.

[0180] If the integrated units in the other embodiments described above are implemented as software functional units and sold or used as independent products, they 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.) or processor to execute all or part of the steps of the methods described in 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.

[0181] The above description is merely an embodiment of this application and does not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for processing ultrasound images, characterized in that, The method includes: Acquiring ultrasound images using an ultrasound probe; The ultrasound images were analyzed to obtain the analysis results; If the analysis result indicates that the feature information in the ultrasound image is not in the preset area of ​​the ultrasound image, a first guidance mark is displayed on the display interface to guide the ultrasound probe to move in the direction of the feature information; If the analysis result indicates that the feature information does not meet the requirements of the standard feature information, a second guidance mark is displayed on the display interface to guide the rotation of the ultrasound probe to acquire an ultrasound image from another angle. The step of displaying the second guide mark on the display interface includes: distinguishing the contour of the feature information into upper, lower, left, and right sides; if the left or right contour differs significantly from the standard contour, then the second guide mark is displayed on the display interface to rotate the ultrasonic probe clockwise; if the upper or lower contour differs significantly from the standard contour, then the second guide mark is displayed on the display interface to change the ultrasonic wave incident angle of the ultrasonic probe. If the current examination mode is abdominal mode, the feature information includes at least one of the following: fetal eye outline, spine, cervical spine, and midline of the brain; if the current examination mode is perineal mode, the feature information includes at least one of the following: pubic symphysis outline, fetal head outline, and midline of the brain. In response to the ultrasound probe being adjusted to the optimal acquisition position, the corresponding target ultrasound image is acquired; Imaging analysis is performed on the target ultrasound image to obtain detection parameters; the detection parameters include the fetal head progression angle and the distance between the fetal head and the perineum; The step of performing imaging analysis on the target ultrasound image to obtain detection parameters includes: classifying the target ultrasound image into longitudinal section images and transverse section images; calculating the fetal head progression angle using the pubic symphysis contour and fetal head contour determined in the longitudinal section image and the target ultrasound image; and calculating the distance between the fetal head and the perineum using the pubic symphysis contour and fetal head contour determined in the transverse section image and the target ultrasound image. A trend chart is created using the fetal head progression angle and the distance between the fetal head and perineum collected at different times. The trend chart shows the changing trends of these parameters. If, within a preset time period, the line representing the fetal head progression angle shows an upward trend and the line representing the distance between the fetal head and perineum shows a downward trend, then vaginal delivery is recommended. If, within a preset time period, the difference between the lines representing the fetal head progression angle and the distance between the fetal head and perineum does not widen, then a cesarean section is recommended. The detection parameters are displayed.

2. The method according to claim 1, characterized in that, The display of the detection parameters includes: The target ultrasound image is displayed on the display interface, and the detection parameters are marked on the target ultrasound image.

3. An ultrasonic imaging device, characterized in that, The ultrasound imaging device includes: Ultrasonic probe; A transmitting circuit, connected to the ultrasound probe, is used to transmit ultrasound signals to the target tissue through the ultrasound probe; A receiving circuit, connected to the ultrasound probe, is used to acquire the ultrasound echo signal reflected by the target tissue; A processor, connected to the receiving circuit, is used to generate an ultrasound image based on the ultrasound echo signal; and to analyze the ultrasound image to obtain analysis results; If the analysis result indicates that the feature information in the ultrasound image is not in the preset area of ​​the ultrasound image, a first guidance mark is displayed on the display interface to guide the ultrasound probe to move in the direction of the feature information; If the analysis result indicates that the feature information does not meet the requirements of the standard feature information, a second guidance mark is displayed on the display interface to guide the rotation of the ultrasound probe to acquire an ultrasound image from another angle. The step of displaying the second guidance mark on the display interface includes: distinguishing the contour of the feature information into upper, lower, left, and right sides; if the left or right contour differs significantly from the standard contour, the second guidance mark is displayed on the display interface to rotate the ultrasound probe clockwise; if the upper or lower contour differs significantly from the standard contour, the second guidance mark is displayed on the display interface to change the ultrasonic wave incident angle of the ultrasound probe. If the current examination mode is abdominal mode, the feature information includes at least one of the following: fetal eye outline, spine, cervical spine, and midline of the brain; if the current examination mode is perineal mode, the feature information includes at least one of the following: pubic symphysis outline, fetal head outline, and midline of the brain. In response to the ultrasound probe being adjusted to the optimal acquisition position, the corresponding target ultrasound image is acquired; Imaging analysis is performed on the target ultrasound image to obtain detection parameters; the detection parameters include the fetal head progression angle and the distance between the fetal head and the perineum; The step of performing imaging analysis on the target ultrasound image to obtain detection parameters includes: classifying the target ultrasound image into longitudinal section images and transverse section images; calculating the fetal head progression angle using the pubic symphysis contour and fetal head contour determined in the longitudinal section image and the target ultrasound image; and calculating the distance between the fetal head and the perineum using the pubic symphysis contour and fetal head contour determined in the transverse section image and the target ultrasound image. A trend chart is created using the fetal head progression angle and the distance between the fetal head and perineum collected at different times. The trend chart shows the changing trends of these parameters. If, within a preset time period, the line representing the fetal head progression angle shows an upward trend and the line representing the distance between the fetal head and perineum shows a downward trend, then vaginal delivery is recommended. If, within a preset time period, the difference between the lines representing the fetal head progression angle and the distance between the fetal head and perineum does not widen, then a cesarean section is recommended. A display for showing the detection parameters.

4. The ultrasonic imaging device according to claim 3, characterized in that, The display is also used to display the target ultrasound image, wherein the detection parameters are marked on the target ultrasound image.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program, which, when executed by a processor, is used to implement the method as described in any one of claims 1-2.

Images