A section category identification method and device, an ultrasonic equipment and a storage medium

Abstract

The application discloses a section category identification method and device, an ultrasonic equipment and a computer readable storage medium. The method comprises the following steps: obtaining a target ultrasonic image from a video stream, inputting the target ultrasonic image into a classification model to obtain a predicted score of each section category corresponding to the target ultrasonic image; determining a section category corresponding to a maximum value of the predicted score as a target candidate category of the target ultrasonic image; determining a state category of a preset number of frames of ultrasonic images before the target ultrasonic image in the video stream; and if the state category is consistent with the target candidate category, determining the state category as a target section category corresponding to the target ultrasonic image. The section category identification method provided by the application improves the accuracy of section category identification.

Description

Technical Field

[0001] This application relates to the field of ultrasound technology, and more specifically, to a method and apparatus for identifying cross-section categories, an ultrasound device, and a computer-readable storage medium. Background Technology

[0002] During obstetric examinations, doctors need to operate the ultrasound probe while simultaneously pressing buttons on the machine to store ultrasound images from 35 sections. In related technologies, convolutional neural networks (CNNs) can be used to automatically identify the section categories of ultrasound images. This involves inputting the acquired ultrasound images into the CNN, obtaining classification scores, and taking the category corresponding to the highest score as the section category of that ultrasound image. However, in practical applications, some sections have very similar structures, making it difficult for the CNN to accurately distinguish them; in other words, the accuracy of section category recognition is poor.

[0003] Therefore, how to improve the accuracy of section category recognition is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] The purpose of this application is to provide a method, apparatus, ultrasonic device, and computer-readable storage medium for section category identification, thereby improving the accuracy of section category identification.

[0005] To achieve the above objectives, this application provides a method for segment category recognition, comprising:

[0006] The target ultrasound image is obtained from the video stream, and the target ultrasound image is input into the classification model to obtain the prediction score for each section category corresponding to the target ultrasound image;

[0007] The section category corresponding to the maximum predicted score is determined as the target candidate category of the target ultrasound image;

[0008] The state category of ultrasound images in a preset number of frames prior to determining the target ultrasound image in the video stream;

[0009] If the state category matches the target candidate category, then the state category is determined as the target section category corresponding to the target ultrasound image.

[0010] The step of determining the state category of the ultrasound images in the preset number of frames preceding the target ultrasound image includes:

[0011] Obtain the target queue; wherein, the target queue includes the prediction score of each slice category corresponding to each frame of the ultrasound image of a preset number of frames preceding the target ultrasound image in the video stream;

[0012] The section category corresponding to the maximum predicted score in the target queue is determined as the undetermined state category;

[0013] If the number of frames in the preset number of ultrasound images that match the candidate category with the pending state category meets the preset condition, then the pending state category is determined as the state category.

[0014] The predicted scores include standard section scores, basic standard section scores, and non-standard section scores.

[0015] Accordingly, the section category corresponding to the maximum predicted score is determined as the target candidate category of the target ultrasound image, including:

[0016] The section category corresponding to the maximum score among all the standard section scores, all the basic standard section scores, and all the non-standard section scores is determined as the target candidate category of the target ultrasound image.

[0017] After determining the state category as the target section category corresponding to the target ultrasound image, the method further includes:

[0018] If the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to the score threshold, then the target section category is determined as the final category of the target ultrasound image.

[0019] The predicted score includes a standard section score and a basic standard section score. Correspondingly, if the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to a score threshold, then the target section category is determined as the final category of the target ultrasound image, including:

[0020] Determine the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image;

[0021] The joint score of the target section category corresponding to the target ultrasound image is calculated based on the standard section score, the basic standard section score, and the joint score parameters of the target section category corresponding to the target ultrasound image; wherein, the joint score is used to comprehensively describe the standard degree and the basic standard degree of the section.

[0022] If any predicted score is greater than or equal to the corresponding score threshold, or if the combined score is greater than or equal to the combined score threshold, then the target section category is determined as the final category of the target ultrasound image.

[0023] Wherein, determining the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image includes:

[0024] Determine the lower bound and upper bound of the prediction score threshold corresponding to the target section category;

[0025] Based on the number of target frames in the ultrasound images preceding the target ultrasound image that have the same section category as the target section image, an interpolation is performed between the lower bound of the prediction score threshold and the upper bound of the prediction score threshold to calculate the score threshold for the target section category corresponding to the target ultrasound image; wherein, the score threshold is negatively correlated with the number of target frames.

[0026] Determining the lower bound and upper bound of the standard section score threshold corresponding to the target section category includes:

[0027] Obtain standard and non-standard section samples corresponding to the target section category, and determine the standard section score corresponding to the standard and non-standard section samples;

[0028] The standard segment score is used as the target standard segment score threshold. The weighted value of precision and recall is calculated according to the first weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the standard segment score threshold.

[0029] The standard segment score is used as the target standard segment score threshold. The weighted value of precision and recall is calculated according to the second weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the standard segment score threshold.

[0030] Wherein, the first weight ratio and the second weight ratio are the ratios of the weight of precision to the weight of recall, and the first weight ratio is greater than or equal to the second weight ratio.

[0031] Determining the lower bound and upper bound of the basic standard section score threshold corresponding to the target section category includes:

[0032] Obtain basic standard section samples and non-standard section samples corresponding to the target section category, and determine the basic standard section scores corresponding to the basic standard section samples and non-standard section samples;

[0033] The basic standard segment score is used as the target basic standard segment score threshold. The weighted value of precision and recall is calculated according to the third weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the basic standard segment score threshold.

[0034] The basic standard segment score is used as the target basic standard segment score threshold. The weighted value of precision and recall is calculated according to the fourth weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the basic standard segment score threshold.

[0035] Wherein, the third weight ratio and the fourth weight ratio are the ratios of the weight of precision to the weight of recall, and the third weight ratio is greater than or equal to the fourth weight ratio.

[0036] This also includes:

[0037] Determine the lower bound and upper bound of the joint fraction parameter corresponding to the target section category;

[0038] Based on the number of target frames in the ultrasound images preceding the target ultrasound image that have the target candidate section category, interpolation is performed between the lower bound of the joint score parameter and the upper bound of the joint score parameter to calculate the joint score parameter of the target section category corresponding to the target ultrasound image.

[0039] Wherein, determining the lower bound and upper bound of the joint score parameter corresponding to the target section category includes:

[0040] Obtain target slice samples whose sum of the standard slice score and the basic standard slice score corresponding to the candidate slice category is greater than or equal to a preset value;

[0041] In a coordinate system containing the coordinate points corresponding to the target section sample, a first target linear function satisfying a first condition and a second target linear function satisfying a second condition are determined; wherein, the coordinate points corresponding to the target section sample in the coordinate system are determined based on the standard section fraction and the basic standard section fraction of the target section sample, the first condition includes that the sum of the distances between all the coordinate points corresponding to the target section sample and the first target linear function is minimized and that the basic standard section fractions corresponding to all the non-standard section samples are located at the function value of the first target linear function, and the second condition includes that the sum of the distances between all the coordinate points corresponding to the target section sample and the second target linear function is minimized;

[0042] The parameters of the first objective linear function are determined as the upper bound of the joint fractional parameters, and the parameters of the second objective linear function are determined as the lower bound of the joint fractional parameters.

[0043] The determination of the target section category as the final category of the target ultrasound image includes:

[0044] Determine whether the final category belongs to the category in the list of categories to be output; if so, determine the target section category as the final category of the target ultrasound image.

[0045] After determining the target section category as the final category of the target ultrasound image, the method further includes:

[0046] The standard score corresponding to the target section category is calculated based on the target prediction score of the target section category corresponding to the target ultrasound image; wherein, the target prediction score includes any one or a combination of any of the target standard section score, the target basic standard section score, and the target joint score;

[0047] If the standard score is greater than or equal to the standard score threshold, then the target ultrasound image is determined to be a standard section image of the target section category;

[0048] The calculation of the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image includes: if the section category of the previous frame ultrasound image of the target ultrasound image is the target section category, then the standard score corresponding to the target section category calculated based on the target prediction score of the target section category corresponding to the previous frame ultrasound image of the target ultrasound image is obtained; if the section category of the previous frame ultrasound image of the target ultrasound image is not the target section category, then the standard score corresponding to the target section category is initialized; if any target prediction score of the target section category corresponding to the target ultrasound image is greater than or equal to the corresponding score threshold, then the standard score is accumulated by the corresponding score.

[0049] To achieve the above objectives, this application provides a facet category recognition device, comprising:

[0050] The input module is used to acquire the target ultrasound image from the video stream and input the target ultrasound image into the classification model to obtain the prediction score for each section category corresponding to the target ultrasound image;

[0051] The first determining module is used to determine the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image;

[0052] The second determining module is used to determine the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream.

[0053] The third determining module is used to determine the state category as the target section category corresponding to the target ultrasound image when the state category is consistent with the target candidate category.

[0054] To achieve the above objectives, this application provides an ultrasonic device, comprising:

[0055] Memory, used to store computer programs;

[0056] A processor is used to implement the steps of the facet category recognition method described above when executing the computer program.

[0057] To achieve the above objectives, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the section category recognition method described above.

[0058] As can be seen from the above scheme, the section category recognition method provided in this application includes: acquiring a target ultrasound image from a video stream; inputting the target ultrasound image into a classification model to obtain a predicted score for each section category corresponding to the target ultrasound image; determining the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image; determining the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream; if the state category is consistent with the target candidate category, then determining the state category as the target section category corresponding to the target ultrasound image.

[0059] The section category recognition method provided in this application combines the section category of the target ultrasound image predicted by a neural network with the section categories of ultrasound images from a preset number of previous frames to comprehensively determine the section category of the target ultrasound image. Compared with related technologies that rely solely on image information from the current frame of the target ultrasound image, this application combines image information from previous frames, improving the discrimination accuracy between structurally similar sections and enhancing the accuracy of section category recognition. This application also discloses a section category recognition device, an ultrasound device, and a computer-readable storage medium, which can achieve the same technical effects.

[0060] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. The drawings are used to provide a further understanding of this disclosure and constitute a part of the specification. They are used together with the following detailed description to explain this disclosure, but do not constitute a limitation of this disclosure. In the drawings:

[0062] Figure 1 This is a flowchart illustrating a section category recognition method according to an exemplary embodiment;

[0063] Figure 2 A flowchart illustrating another method for identifying cross-section categories according to an exemplary embodiment;

[0064] Figure 3 This is a schematic diagram illustrating a method for determining the upper and lower bounds of a threshold corresponding to a long axis section of the femur according to an exemplary embodiment;

[0065] Figure 4 This is a structural diagram illustrating a section category recognition device according to an exemplary embodiment;

[0066] Figure 5 This is a structural diagram of an ultrasonic device according to an exemplary embodiment. Detailed Implementation

[0067] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, in the embodiments of this application, "first," "second," etc., are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0068] This application discloses a method for identifying facet categories, which improves the accuracy of facet category identification.

[0069] See Figure 1 A flowchart illustrating a section category recognition method according to an exemplary embodiment is shown below. Figure 1 As shown, it includes:

[0070] S101: Obtain the target ultrasound image from the video stream, and input the target ultrasound image into the classification model to obtain the prediction score for each section category corresponding to the target ultrasound image;

[0071] In this embodiment, the execution subject is an ultrasound device, and the purpose is to automatically identify the cross-sectional category of the acquired target ultrasound image. In specific implementation, multiple cross-sectional categories are predefined, and rules are established for standard cross-sections, basic standard cross-sections, and non-standard cross-sections corresponding to each category. Based on this, multiple standard cross-sections, multiple basic standard cross-sections, and multiple non-standard cross-sections corresponding to each cross-sectional category are determined as training samples to train the classification model. The trained classification model is used to identify the cross-sectional category. The classification model can employ a convolutional neural network or other types of artificial intelligence algorithms.

[0072] In this step, the ultrasound equipment acquires a target ultrasound image, which is then input into a trained classification model. The model outputs a predicted score for each section category. For example, if 13 key section categories are predefined for obstetric screening, the classification model outputs predicted scores for each of the 13 section categories, resulting in 13 predicted scores. Preferably, the predicted scores include standard section scores, basic standard section scores, and non-standard section scores; that is, the classification model outputs standard section scores, basic standard section scores, and non-standard section scores for each section category. In the example above, the classification model outputs standard section scores, basic standard section scores, and non-standard section scores for each of the 13 section categories, resulting in 13 × 3 = 39 predicted scores.

[0073] S102: Determine the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image;

[0074] In this step, the section category corresponding to the maximum predicted score is the section category of the target ultrasound image predicted by the classification model, and it is used as the target candidate category for subsequent judgment on whether it can be trusted. If the predicted score includes standard section score, basic standard section score and non-standard section score, then the section category corresponding to the maximum score among all standard section scores, all basic standard section scores and all non-standard section scores output by the classification model is determined as the target candidate category of the target ultrasound image.

[0075] S103: Determine the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream;

[0076] In this embodiment, each frame of ultrasound image acquired by the ultrasound device is input into the classification model to predict the section category. In this step, the state category of ultrasound images of a preset number of frames before the target ultrasound image predicted by the classification model is determined. The preset number of frames is not specifically limited here and can be flexibly set according to the actual situation.

[0077] As a possible implementation, this step may include: obtaining a target queue; wherein the target queue includes the prediction scores of each section category corresponding to each frame of ultrasound images in the video stream preceding the target ultrasound image; determining the section category corresponding to the maximum prediction score in the target queue as the pending state category; if the number of frames in the preset number of ultrasound images whose candidate category matches the pending state category meets a preset condition, then the pending state category is determined as the state category.

[0078] In specific implementation, a target queue is maintained to store the predicted scores corresponding to multiple consecutive ultrasound images predicted by the classification model. Each element in the target queue records the predicted score corresponding to one ultrasound image. The section category corresponding to the highest predicted score in the target queue is determined as the pending state category. Further, it is determined that the number of frames in the preset number of ultrasound images whose candidate category matches the pending state category satisfies a preset condition. If so, the pending state category is determined as the state category of the ultrasound images in the preset number of frames preceding the target ultrasound image. The preset condition in this step may include the number of frames in the preset number of ultrasound images whose candidate category matches the pending state category being greater than or equal to a preset value, or the ratio of the number of frames in the preset number of ultrasound images whose candidate category matches the pending state category to the preset number of frames being greater than or equal to a preset ratio. For example, if the preset number of frames is 5, the preset condition is that the number of frames whose section category matches the candidate category and the pending state category is greater than or equal to 3.

[0079] S104: If the state category is consistent with the target candidate category, then the state category is determined as the target section category corresponding to the target ultrasound image.

[0080] In practice, if the target candidate category of the target ultrasound image is consistent with the state category of the ultrasound images in the previous preset number of frames, it indicates that the classification model has a high consistency in predicting the section category of multiple consecutive frames. The target candidate category is then determined as the target section category corresponding to the target ultrasound image. Otherwise, the output cannot determine the section category of the target ultrasound image.

[0081] In a preferred embodiment, after determining the state category as the target section category corresponding to the target ultrasound image, the method further includes: if the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to a score threshold, then the target section category is determined as the final category of the target ultrasound image. In a specific implementation, when the target candidate category of the target ultrasound image is consistent with the state category of ultrasound images in a preset number of frames preceding the target ultrasound image, the method further determines whether the target section category can be trusted based on the predicted score of the target section category corresponding to the target ultrasound image. If the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to a score threshold, the target section category can be trusted, and the target section category is determined as the final category of the target ultrasound image. If the predicted scores involved in the determination include multiple predicted scores, a corresponding score threshold is set for each predicted score. If any predicted score is greater than or equal to the corresponding score threshold, then the target section category is determined as the final category of the target ultrasound image.

[0082] Furthermore, determining the target section category as the final category of the target ultrasound image includes: determining whether the final category belongs to a category in the list of categories to be output; if so, then determining the target section category as the final category of the target ultrasound image. In specific implementations, a list of categories to be output can be preset, and only categories in the list of categories to be output can be output.

[0083] The section category recognition method provided in this application combines the section category of the target ultrasound image predicted by the neural network with the section category of ultrasound images corresponding to a preset number of frames preceding the target ultrasound image to comprehensively determine the section category of the target ultrasound image. Compared with related technologies that only rely on the image information of the current frame of the target ultrasound image, this application embodiment combines the image information of the previous frames of the target ultrasound image, which improves the discrimination accuracy between structurally similar sections and improves the accuracy of section category recognition.

[0084] This application discloses a method for identifying facet categories. Compared to the previous embodiment, this embodiment further explains and optimizes the technical solution. Specifically:

[0085] See Figure 2 A flowchart illustrating another section category recognition method according to an exemplary embodiment, such as... Figure 2 As shown, it includes:

[0086] S201: Obtain the target ultrasound image from the video stream, and input the target ultrasound image into a classification model to obtain the predicted score for each section category corresponding to the target ultrasound image; wherein, the predicted score includes the standard section score and the basic standard section score;

[0087] In this embodiment, the predicted scores output by the classification model include standard section scores and basic standard section scores. Of course, in order to improve the prediction accuracy of the classification model, non-standard section scores can also be output.

[0088] S202: Determine the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image;

[0089] S203: Determine the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream;

[0090] S204: If the state category is consistent with the target candidate category, then the state category is determined as the target section category corresponding to the target ultrasound image;

[0091] S205: Determine the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image;

[0092] In this embodiment, the predicted scores used to determine whether a target slice category can be trusted may include multiple predicted scores, namely, the standard slice score and the basic standard slice score. In this step, a score threshold corresponding to each predicted score is determined.

[0093] In a preferred embodiment, this step includes: determining the lower bound and upper bound of the prediction score threshold corresponding to the target section category; calculating the score threshold of the target section category corresponding to the target ultrasound image by interpolating between the lower bound and the upper bound of the prediction score threshold based on the number of target frames of ultrasound images preceding the target ultrasound image with the section category of the target section category; wherein the score threshold is negatively correlated with the number of target frames.

[0094] In practice, the upper and lower bounds of the prediction score threshold are pre-determined for each slice category. After determining the target slice category of the target ultrasound image, the ultrasound equipment determines the upper and lower bounds of the prediction score threshold corresponding to the target slice category. Further, based on the number of target frames in the ultrasound images preceding the target ultrasound image that have a candidate slice category, the score threshold is calculated by interpolation between the upper and lower bounds of the prediction score threshold. Linear interpolation can be used here. For example, if the interpolation frame range is 10 frames, the upper bound of the prediction score threshold for slice category A is A0, and the lower bound is A1; the upper bound of the prediction score threshold for slice category B is B0, and the lower bound is B1. If the predicted section category of the first frame of ultrasound image is section category A, then its corresponding score threshold is A0. If the predicted section category of the second frame of ultrasound image is also section category A, then its corresponding score threshold is A0+(A1-A0) / 10. If the predicted section category of the third frame of ultrasound image is also section category A, then its corresponding score threshold is A0+2(A1-A0) / 10, and so on. If the predicted section category of the tenth frame of ultrasound image is also section category A, then its corresponding score threshold is A1. If the predicted section category of the first frame of ultrasound image is section category A, then its corresponding score threshold is A0. If the predicted section category of the second frame of ultrasound image is also section category A, then its corresponding score threshold is A0+(A1-A0) / 10. However, if the predicted section category of the third frame of ultrasound image is section category B, then its corresponding score threshold is B0. If the predicted section category of the fourth frame of ultrasound image is still section category B, then its corresponding score threshold is B0+(B1-B0) / 10, and so on.

[0095] It is evident that the score threshold is negatively correlated with the number of target frames in ultrasound images preceding the target ultrasound image that belong to the target section category. That is, when the target section category is first predicted, the score threshold uses the upper bound of the predicted score threshold. As the number of accumulated ultrasound images with the target section category increases, the score threshold gradually decreases until it reaches the lower bound of the predicted score threshold. It is understandable that for section categories with similar structures, the predicted scores output by the classification model are similar and not very high. Therefore, in this embodiment, a score threshold for the target section category corresponding to the target ultrasound image is calculated. Subsequently, the predictive score of the target section category corresponding to the target ultrasound image is compared with this score threshold to determine whether the target section category predicted by the classification model can be trusted. Furthermore, the larger the number of target frames in ultrasound images preceding the target ultrasound image that belong to the target section category, the higher the trust level of the predicted target section category, thus reducing the score threshold.

[0096] It should be noted that the upper and lower bounds of the standard section score threshold and the basic standard section score threshold for each section category can be manually set according to the actual situation, or they can be automatically searched using section samples of each section category. Using the aforementioned target segment categories as an example, determining the lower and upper bounds of the standard segment score threshold corresponding to the target segment category includes: obtaining standard segment samples and non-standard segment samples corresponding to the target segment category, and determining the standard segment scores corresponding to the standard segment samples and non-standard segment samples; using the standard segment scores as the target standard segment score threshold, calculating the weighted value of precision and recall according to a first weight ratio, and determining the target standard segment score threshold corresponding to the maximum weighted value as the upper bound of the standard segment score threshold; using the standard segment scores as the target standard segment score threshold, calculating the weighted value of precision and recall according to a second weight ratio, and determining the target standard segment score threshold corresponding to the maximum weighted value as the lower bound of the standard segment score threshold; wherein, the first weight ratio and the second weight ratio are the ratio of the weight of precision to the weight of recall, and the first weight ratio is greater than or equal to the second weight ratio.

[0097] In practice, for the segment samples corresponding to the target segment category, basic standard segment samples are removed. That is, the upper and lower bounds of the standard segment score threshold are determined using standard and non-standard segment samples. The standard segment score is used as the target standard segment score threshold. The weighted values ​​of precision and recall are calculated according to a first weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the standard segment score threshold. For example, the ratio of the precision weight to the recall weight (i.e., the first weight ratio) is 5:1. Similarly, the standard segment score is used as the target standard segment score threshold. The weighted values ​​of precision and recall are calculated according to a second weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the standard segment score threshold. For example, the ratio of the precision weight to the recall weight (i.e., the second weight ratio) is 1:1.

[0098] Accordingly, determining the lower bound and upper bound of the basic standard segment score threshold corresponding to the target segment category includes: obtaining basic standard segment samples and non-standard segment samples corresponding to the target segment category, and determining the basic standard segment scores corresponding to the basic standard segment samples and non-standard segment samples; using the basic standard segment scores as the target basic standard segment score threshold, calculating the weighted value of precision and recall according to the third weight ratio, and determining the target basic standard segment score threshold corresponding to the maximum weighted value as the upper bound of the basic standard segment score threshold; using the basic standard segment scores as the target basic standard segment score threshold, calculating the weighted value of precision and recall according to the fourth weight ratio, and determining the target basic standard segment score threshold corresponding to the maximum weighted value as the lower bound of the basic standard segment score threshold; wherein, the third weight ratio and the fourth weight ratio are the ratio of the weight of precision to the weight of recall, and the third weight ratio is greater than or equal to the fourth weight ratio.

[0099] In practice, for the segment samples corresponding to the target segment category, standard segment samples are removed. That is, the upper and lower bounds of the basic standard segment score threshold are determined using basic standard segment samples and non-standard segment samples. The basic standard segment score is used as the target basic standard segment score threshold. The weighted values ​​of precision and recall are calculated according to the third weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the basic standard segment score threshold. For example, the ratio of the precision weight to the recall weight, i.e., the third weight ratio, is 10:1. Similarly, the basic standard segment score is used as the target basic standard segment score threshold. The weighted values ​​of precision and recall are calculated according to the fourth weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the basic standard segment score threshold. For example, the ratio of the precision weight to the recall weight, i.e., the fourth weight ratio, is 2:1.

[0100] Understandably, requirements will change constantly during the research and development process, and manually adjusting the upper and lower bounds of each score threshold would be a huge workload. Therefore, the above-mentioned method of automatically determining the upper and lower bounds of each score threshold can improve the algorithm iteration speed.

[0101] S206: Calculate the joint score of the target section category corresponding to the target ultrasound image based on the standard section score, basic standard section score, and joint score parameters of the target section category corresponding to the target ultrasound image; wherein, the joint score is used to comprehensively describe the standard degree and basic standard degree of the section;

[0102] In this embodiment, the predicted score used to determine whether a target slice category can be trusted may further include a joint score calculated based on the standard slice score, the basic standard slice score, and the joint score parameter. The joint score is used to comprehensively describe the standardization and basic standardization of the slice. Specifically, P = kP sp +P bsp +b, where P is the joint fraction, P sp For the standard sectional fraction, P bsp is the basic standard section fraction, and (k,b) is the joint fraction parameter.

[0103] In a preferred embodiment, this embodiment further includes: determining the lower bound and the upper bound of the joint fraction parameter corresponding to the target section category; and calculating the joint fraction parameter of the target section category corresponding to the target ultrasound image by interpolating between the lower bound and the upper bound of the joint fraction parameter based on the number of target frames of ultrasound images preceding the target ultrasound image whose section category is the target candidate section category.

[0104] In practice, the upper and lower bounds of the joint score parameter corresponding to each slice category are predetermined. After determining the target slice category of the target ultrasound image, the ultrasound equipment determines the upper and lower bounds of the joint score parameter corresponding to the target slice category. Based on the number of target frames of ultrasound images preceding the target ultrasound image whose slice category is a candidate slice category, interpolation is performed between the upper and lower bounds of the joint score parameter to calculate the joint score parameter of the target slice category corresponding to the target ultrasound image. The calculation method here is similar to the method of calculating the standard slice score threshold and the basic standard slice score threshold mentioned above, and will not be repeated here.

[0105] It should be noted that the upper and lower bounds of the joint score parameters corresponding to each slice category can be manually set according to the actual situation, or they can be automatically searched using slice samples of each slice category. Using the aforementioned target section categories as an example, determining the lower bound and upper bound of the joint fraction parameter corresponding to the target section category includes: obtaining target section samples whose sum of standard section score and basic standard section score corresponding to the candidate section category is greater than or equal to a preset value; determining a first target linear function satisfying a first condition and a second target linear function satisfying a second condition in a coordinate system containing the coordinate points corresponding to the target section samples; wherein, based on the standard section score and basic standard section score of the target section samples, the coordinate points corresponding to the target section samples in the coordinate system are determined, the first condition includes that the sum of the distances between the coordinate points corresponding to all the target section samples and the first target linear function is minimized and that the basic standard section scores corresponding to all the non-standard section samples are located at the function value of the first target linear function, and the second condition includes that the sum of the distances between the coordinate points corresponding to all the target section samples and the second target linear function is minimized; the parameters of the first target linear function are determined as the upper bound of the joint fraction parameter, and the parameters of the second target linear function are determined as the lower bound of the joint fraction parameter.

[0106] In specific implementation, target slice samples are obtained where the sum of the standard slice score and the basic standard slice score corresponding to the candidate slice category is greater than or equal to a preset value. For example, target samples where the sum of the standard slice score and the basic standard slice score corresponding to the candidate slice category is greater than or equal to 0.4 are obtained. A coordinate system is constructed with the standard slice score on the horizontal axis and the basic standard slice score on the vertical axis. The coordinate points corresponding to all target slice samples and non-standard slice samples are plotted in the coordinate system. A first objective linear function is constructed such that the sum of the distances between the coordinate points corresponding to all target slice samples and the first objective linear function is minimized. Simultaneously, the constraint condition is that the coordinate points corresponding to all non-standard slice samples are located below the first objective linear function. The parameters of the first objective linear function are then solved as the upper bound of the joint score parameters. The objective function is:

[0107]

[0108] The constraint is: subject to [P′] sp 1][kb] T ≥P′ bsp .

[0109] Where k and b are the slope and intercept of the first objective linear function, i.e., the joint fractional parameters, P sp P is the predicted score of the standard section or the basic standard section. bspFor the standard section or the basic standard section, the basic standard section prediction score, P' sp For non-standard sections or other standard sections, predict the score, P' bsp The base standard section prediction score for non-standard sections or other section models.

[0110] Furthermore, an unconstrained second objective linear function is constructed such that the sum of distances between the coordinate points corresponding to all target section samples and the second objective linear function is minimized. The parameters of the second objective linear function are then solved as the lower bound of the joint fraction parameters. The objective function is:

[0111]

[0112] Where k and b are the slope and intercept of the second objective linear function, i.e., the joint fractional parameters.

[0113] Taking the long axis section of the femur as an example, such as Figure 3 As shown, the horizontal axis represents the standard section score (SP Prob), and the vertical axis represents the basic standard section score (BSP Prob). `sp` represents the standard section, `bsp` represents the basic standard section, `nsp` represents the non-standard section, and `other` represents other sections. The two lines perpendicular to the horizontal axis represent the upper and lower bounds of the standard section score threshold, with the left line representing the lower bound and the right line representing the upper bound. The two lines parallel to the horizontal axis represent the upper and lower bounds of the basic standard section score threshold, with the lower line representing the lower bound and the upper line representing the upper bound. The two diagonal lines represent the upper and lower bounds of the joint score parameter, with the upper right diagonal line representing the upper bound and the lower left diagonal line representing the lower bound.

[0114] S207: If any predicted score is greater than or equal to the corresponding score threshold or the joint score is greater than or equal to the joint score threshold, then the target section category is determined as the final category of the target ultrasound image.

[0115] In this embodiment, the predicted scores used to determine whether a target section category can be trusted include the standard section score, the basic standard section score, and the joint score. If the standard section score of the target section category corresponding to the target ultrasound image is greater than or equal to the standard section score threshold, or the basic standard section score is greater than or equal to the basic standard section score threshold, or the joint score is greater than or equal to the joint score threshold, then the target section category is determined as the section category of the target ultrasound image. Here, the joint score threshold can be 0.

[0116] Therefore, this embodiment improves the discrimination accuracy between structurally similar sections and enhances the accuracy of section category recognition by combining image information from the current frame and previous frames of the target ultrasound image. Furthermore, by comparing the predicted score of the target section category corresponding to the target ultrasound image with the score threshold of the target section category corresponding to the target ultrasound image, it determines whether the candidate section category predicted by the classification model can be trusted, further improving the accuracy of section category recognition.

[0117] Based on the above embodiments, as a preferred implementation, after determining the target section category as the final category of the target ultrasound image, the method further includes: calculating the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image; wherein, the target prediction score includes any one or a combination of any of the following: target standard section score, target basic standard section score, and target joint score; if the standard score is greater than or equal to a standard score threshold, then the target ultrasound image is determined to be a standard section image of the target section category; wherein, based on the target prediction score of the target section category corresponding to the target ultrasound image... The calculation of the standard score corresponding to the target section category includes: if the section category of the previous frame ultrasound image of the target ultrasound image is the target section category, then obtaining the standard score corresponding to the target section category calculated based on the target prediction score of the target section category corresponding to the previous frame ultrasound image of the target ultrasound image; if the section category of the previous frame ultrasound image of the target ultrasound image is not the target section category, then initializing the standard score corresponding to the target section category; if any target prediction score of the target section category corresponding to the target ultrasound image is greater than or equal to the corresponding score threshold, then accumulating the standard score with the corresponding score.

[0118] In this embodiment, a standard score is maintained for each target section category. The initial value can be zero. For each frame of ultrasound image acquired by the ultrasound device, if the target prediction score of its corresponding section category is greater than or equal to the corresponding score threshold, the standard score is accumulated. For the currently acquired target ultrasound image, its corresponding section category is the target section category. The more consecutive ultrasound images with the target section category in the previous ultrasound images, the larger the currently accumulated standard score. When the standard score is greater than or equal to the standard score threshold, the target ultrasound image is determined to be a standard section image of the target section category.

[0119] Therefore, the standard section recognition method provided in this application maintains a corresponding standard score for each section category to describe the accumulated standard degree. It uses the continuity information in the ultrasound video to calculate the accumulated standard score of the current target ultrasound image, avoids detecting occasional standard section images, reduces the false alarm rate of non-standard sections and the false negative rate of standard sections, and improves the accuracy of standard section recognition.

[0120] The following describes a facet category recognition device provided in an embodiment of this application. The facet category recognition device described below and the facet category recognition method described above can be referred to each other.

[0121] See Figure 4 A structural diagram of a section category recognition device is shown according to an exemplary embodiment, as follows: Figure 4 As shown, it includes:

[0122] The input module 401 is used to acquire a target ultrasound image from a video stream and input the target ultrasound image into a classification model to obtain a prediction score for each section category corresponding to the target ultrasound image.

[0123] The first determining module 402 is used to determine the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image;

[0124] The second determining module 403 is used to determine the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream.

[0125] The third determining module 403 is used to determine the state category as the target section category corresponding to the target ultrasound image when the state category is consistent with the target candidate category.

[0126] The section category recognition device provided in this application combines the section category of the target ultrasound image predicted by the neural network with the section category of the ultrasound images corresponding to a preset number of frames preceding the target ultrasound image to comprehensively determine the section category of the target ultrasound image. Compared with related technologies that only rely on the image information of the current frame of the target ultrasound image, this application embodiment combines the image information of the previous frames of the target ultrasound image, which improves the discrimination accuracy between structurally similar sections and improves the accuracy of section category recognition.

[0127] Based on the above embodiments, as a preferred implementation, the second determining module 403 is specifically used for: acquiring a target queue; wherein, the target queue includes the prediction scores of each section category corresponding to each frame of the ultrasound images of a preset number of frames preceding the target ultrasound image in the video stream; determining the section category corresponding to the maximum prediction score in the target queue as the pending state category; if the number of frames in the preset number of ultrasound images whose candidate category is consistent with the pending state category meets a preset condition, then the pending state category is determined as the state category.

[0128] Based on the above embodiments, as a preferred implementation, the predicted score includes a standard section score, a basic standard section score, and a non-standard section score;

[0129] Accordingly, the first determining module 402 is specifically a module that determines the section category corresponding to the maximum value of all the standard section scores, all the basic standard section scores and all the non-standard section scores as the target candidate category of the target ultrasound image.

[0130] Based on the above embodiments, as a preferred embodiment, it further includes:

[0131] The fourth determining module is used to determine the target section category as the final category of the target ultrasound image when the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to the score threshold.

[0132] Based on the above embodiments, as a preferred implementation, the predicted score includes a standard section score and a basic standard section score; correspondingly, the fourth determining module includes:

[0133] The first determining submodule is used to determine the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image;

[0134] The calculation submodule is used to calculate the joint score of the target section category corresponding to the target ultrasound image based on the standard section score, basic standard section score, and joint score parameters of the target section category corresponding to the target ultrasound image; wherein, the joint score is used to comprehensively describe the standard degree and basic standard degree of the section;

[0135] The first determining submodule is used to determine the target section category as the final category of the target ultrasound image when any predicted score is greater than or equal to the corresponding score threshold or the joint score is greater than or equal to the joint score threshold.

[0136] Based on the above embodiments, as a preferred implementation, the first determining submodule includes:

[0137] A determining unit is used to determine the lower bound and the upper bound of the prediction score threshold corresponding to the target section category;

[0138] The calculation unit is configured to calculate the score threshold of the target section category corresponding to the target ultrasound image by interpolating between the lower bound of the prediction score threshold and the upper bound of the prediction score threshold based on the number of target frames of ultrasound images preceding the target ultrasound image that have the same section category as the target section image; wherein the score threshold is negatively correlated with the number of target frames.

[0139] Based on the above embodiments, as a preferred implementation, the determining unit is specifically used for: acquiring standard and non-standard segment samples corresponding to the target segment category, and determining the standard segment scores corresponding to the standard and non-standard segment samples; using the standard segment score as a target standard segment score threshold, calculating the weighted value of precision and recall according to a first weight ratio, and determining the target standard segment score threshold corresponding to the maximum weighted value as the upper bound of the standard segment score threshold; using the standard segment score as a target standard segment score threshold, calculating the weighted value of precision and recall according to a second weight ratio, and determining the target standard segment score threshold corresponding to the maximum weighted value as the lower bound of the standard segment score threshold; wherein, the first weight ratio and the second weight ratio are the ratio of the weight of precision to the weight of recall, and the first weight ratio is greater than or equal to the second weight ratio.

[0140] Based on the above embodiments, as a preferred implementation, the determining unit is specifically used for: acquiring basic standard segment samples and non-standard segment samples corresponding to the target segment category, and determining the basic standard segment scores corresponding to the basic standard segment samples and non-standard segment samples; using the basic standard segment scores as the target basic standard segment score threshold, calculating the weighted value of precision and recall according to the third weight ratio, and determining the target basic standard segment score threshold corresponding to the maximum weighted value as the upper bound of the basic standard segment score threshold; using the basic standard segment scores as the target basic standard segment score threshold, calculating the weighted value of precision and recall according to the fourth weight ratio, and determining the target basic standard segment score threshold corresponding to the maximum weighted value as the lower bound of the basic standard segment score threshold; wherein, the third weight ratio and the fourth weight ratio are the ratio of the weight of precision to the weight of recall, and the third weight ratio is greater than or equal to the fourth weight ratio.

[0141] Based on the above embodiments, as a preferred embodiment, it further includes:

[0142] The fifth determining module is used to determine the lower bound and upper bound of the joint fraction parameter corresponding to the target section category;

[0143] The first calculation module is used to calculate the joint score parameter of the target section category corresponding to the target ultrasound image by interpolating between the lower bound of the joint score parameter and the upper bound of the joint score parameter based on the number of target frames of ultrasound images preceding the target ultrasound image whose section category is the target candidate section category.

[0144] Based on the above embodiments, as a preferred implementation, the fifth determining module is specifically used for: obtaining target section samples whose sum of standard section score and basic standard section score corresponding to the candidate section category is greater than or equal to a preset value; determining a first target linear function satisfying a first condition and a second target linear function satisfying a second condition in a coordinate system containing the coordinate points corresponding to the target section samples; wherein, the coordinate points corresponding to the target section samples in the coordinate system are determined based on the standard section score and basic standard section score of the target section samples, the first condition includes that the sum of the distances between the coordinate points corresponding to all the target section samples and the first target linear function is the smallest and that the basic standard section scores corresponding to all the non-standard section samples are located at the function value of the first target linear function, the second condition includes that the sum of the distances between the coordinate points corresponding to all the target section samples and the second target linear function is the smallest; the parameters of the first target linear function are determined as the upper bound of the joint fraction parameters, and the parameters of the second target linear function are determined as the lower bound of the joint fraction parameters.

[0145] Based on the above embodiments, as a preferred implementation, the fourth determining module is specifically used to: when the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to the score threshold, determine whether the final category belongs to the category in the list of categories to be output; if so, determine the target section category as the final category of the target ultrasound image.

[0146] Based on the above embodiments, as a preferred embodiment, it further includes:

[0147] The second calculation module is used to calculate the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image; wherein, the target prediction score includes any one or a combination of any of the target standard section score, the target basic standard section score, and the target joint score;

[0148] The determination module is used to determine that the target ultrasound image is a standard section image of the target section category when the standard score is greater than or equal to the standard score threshold.

[0149] Specifically, the second calculation module is used to: if the section category of the previous frame ultrasound image of the target ultrasound image is the target section category, then obtain the standard score corresponding to the target section category calculated based on the target prediction score of the target section category corresponding to the previous frame ultrasound image of the target ultrasound image; if the section category of the previous frame ultrasound image of the target ultrasound image is not the target section category, then initialize the standard score corresponding to the target section category; if any target prediction score of the target section category corresponding to the target ultrasound image is greater than or equal to the corresponding score threshold, then accumulate the standard score according to the corresponding score.

[0150] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0151] Based on the hardware implementation of the above program modules, and in order to implement the method of the embodiments of this application, the embodiments of this application also provide an ultrasonic device. Figure 5 This is a structural diagram illustrating an ultrasonic device according to an exemplary embodiment, such as... Figure 5 As shown, the ultrasound equipment includes:

[0152] Communication interface 1 enables information exchange with other devices, such as network devices;

[0153] Processor 2 is connected to communication interface 1 to enable information interaction with other devices. When running a computer program, it executes the aspect category recognition method provided by one or more of the above-mentioned technical solutions. The computer program is stored in memory 3.

[0154] Of course, in practical applications, the various components of the ultrasonic equipment are coupled together through bus system 4. It can be understood that bus system 4 is used to achieve communication and connection between these components. In addition to the data bus, bus system 4 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 5 The general will label all buses as Bus System 4.

[0155] The memory 3 in this embodiment is used to store various types of data to support the operation of the ultrasound device. Examples of such data include any computer program used to operate the ultrasound device.

[0156] It is understood that memory 3 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), ferromagnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), SyncLink Dynamic Random Access Memory (SLDRAM), and Direct Rambus Random Access Memory (DRRAM).The memory 3 described in the embodiments of this application is intended to include, but is not limited to, these and any other suitable types of memory.

[0157] The methods disclosed in the embodiments of this application can be applied to processor 2, or implemented by processor 2. Processor 2 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 2 or by instructions in the form of software. The processor 2 may be a general-purpose processor, DSP, or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 2 can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor may be a microprocessor or any conventional processor, etc. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium, which is located in memory 3. Processor 2 reads the program in memory 3 and completes the steps of the aforementioned method in combination with its hardware.

[0158] When processor 2 executes the program, it implements the corresponding processes in the various methods of the embodiments of this application. For the sake of brevity, these will not be described in detail here.

[0159] In an exemplary embodiment, this application also provides a storage medium, namely a computer storage medium, specifically a computer-readable storage medium, such as a memory 3 that stores a computer program, which can be executed by a processor 2 to complete the steps described in the aforementioned method. The computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, Flash Memory, magnetic surface memory, optical disc, or CD-ROM.

[0160] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0161] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, 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 an ultrasound device (which may be a personal computer, server, or network device, etc.) to execute all or part 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 mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0162] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for identifying the cross-sectional category of ultrasound images, characterized in that, include: The target ultrasound image is obtained from the video stream, and the target ultrasound image is input into the classification model to obtain the prediction score for each section category corresponding to the target ultrasound image; The section category corresponding to the maximum predicted score is determined as the target candidate category of the target ultrasound image; The state category of ultrasound images in a preset number of frames prior to determining the target ultrasound image in the video stream; If the state category is consistent with the target candidate category, then the state category is determined as the target section category corresponding to the target ultrasound image; If the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to the score threshold, then the target section category is determined as the final category of the target ultrasound image; Calculate the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image; If the standard score is greater than or equal to the standard score threshold, then the target ultrasound image is determined to be a standard section image of the target section category; The calculation of the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image includes: if the section category of the previous frame ultrasound image of the target ultrasound image is the target section category, then the standard score corresponding to the target section category calculated based on the target prediction score of the target section category corresponding to the previous frame ultrasound image of the target ultrasound image is obtained; if the section category of the previous frame ultrasound image of the target ultrasound image is not the target section category, then the standard score corresponding to the target section category is initialized; if any target prediction score of the target section category corresponding to the target ultrasound image is greater than or equal to the corresponding score threshold, then the standard score is accumulated by the corresponding score.

2. The section category identification method according to claim 1, characterized in that, The state categories of ultrasound images in a preset number of frames prior to determining the target ultrasound image include: Obtain the target queue; wherein, the target queue includes the prediction score of each slice category corresponding to each frame of the ultrasound image of a preset number of frames preceding the target ultrasound image in the video stream; The section category corresponding to the maximum predicted score in the target queue is determined as the undetermined state category; If the number of frames in the preset number of ultrasound images that match the candidate category with the pending state category meets the preset condition, then the pending state category is determined as the state category.

3. The section category identification method according to claim 1, characterized in that, The predicted scores include standard section scores, basic standard section scores, and non-standard section scores; Accordingly, the section category corresponding to the maximum predicted score is determined as the target candidate category of the target ultrasound image, including: The section category corresponding to the maximum score among all the standard section scores, all the basic standard section scores, and all the non-standard section scores is determined as the target candidate category of the target ultrasound image.

4. The section category identification method according to claim 1, characterized in that, The predicted score includes a standard section score and a basic standard section score. Accordingly, if the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to a score threshold, then the target section category is determined as the final category of the target ultrasound image, including: Determine the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image; The joint score of the target section category corresponding to the target ultrasound image is calculated based on the standard section score, the basic standard section score, and the joint score parameters of the target section category corresponding to the target ultrasound image; wherein, the joint score is used to comprehensively describe the standard degree and the basic standard degree of the section. If any predicted score is greater than or equal to the corresponding score threshold, or if the combined score is greater than or equal to the combined score threshold, then the target section category is determined as the final category of the target ultrasound image.

5. The section category identification method according to claim 4, characterized in that, The step of determining the score threshold corresponding to each predicted score of the target section category corresponding to the target ultrasound image includes: Determine the lower bound and upper bound of the prediction score threshold corresponding to the target section category; Based on the number of target frames in the ultrasound images preceding the target ultrasound image that have the same section category as the target section image, an interpolation is performed between the lower bound of the prediction score threshold and the upper bound of the prediction score threshold to calculate the score threshold for the target section category corresponding to the target ultrasound image; wherein, the score threshold is negatively correlated with the number of target frames.

6. The section category identification method according to claim 5, characterized in that, Determining the lower bound and upper bound of the standard section score threshold corresponding to the target section category includes: Obtain standard and non-standard section samples corresponding to the target section category, and determine the standard section score corresponding to the standard and non-standard section samples; The standard segment score is used as the target standard segment score threshold. The weighted value of precision and recall is calculated according to the first weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the standard segment score threshold. The standard segment score is used as the target standard segment score threshold. The weighted value of precision and recall is calculated according to the second weight ratio. The target standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the standard segment score threshold. Wherein, the first weight ratio and the second weight ratio are the ratios of the weight of precision to the weight of recall, and the first weight ratio is greater than or equal to the second weight ratio.

7. The section category identification method according to claim 5, characterized in that, Determining the lower bound and upper bound of the basic standard section score threshold corresponding to the target section category includes: Obtain basic standard section samples and non-standard section samples corresponding to the target section category, and determine the basic standard section scores corresponding to the basic standard section samples and non-standard section samples; The basic standard segment score is used as the target basic standard segment score threshold. The weighted value of precision and recall is calculated according to the third weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the upper bound of the basic standard segment score threshold. The basic standard segment score is used as the target basic standard segment score threshold. The weighted value of precision and recall is calculated according to the fourth weight ratio. The target basic standard segment score threshold corresponding to the maximum weighted value is determined as the lower bound of the basic standard segment score threshold. Wherein, the third weight ratio and the fourth weight ratio are the ratios of the weight of precision to the weight of recall, and the third weight ratio is greater than or equal to the fourth weight ratio.

8. The section category identification method according to claim 4, characterized in that, Also includes: Determine the lower bound and upper bound of the joint fraction parameter corresponding to the target section category; Based on the number of target frames in the ultrasound images preceding the target ultrasound image that have the target candidate section category, interpolation is performed between the lower bound of the joint score parameter and the upper bound of the joint score parameter to calculate the joint score parameter of the target section category corresponding to the target ultrasound image.

9. The section category identification method according to claim 8, characterized in that, Determining the lower bound and upper bound of the joint score parameter corresponding to the target section category includes: Obtain target slice samples whose sum of the standard slice score and the basic standard slice score corresponding to the candidate slice category is greater than or equal to a preset value; In a coordinate system containing the coordinate points corresponding to the target section sample, a first target linear function satisfying a first condition and a second target linear function satisfying a second condition are determined; wherein, the coordinate points corresponding to the target section sample in the coordinate system are determined based on the standard section fraction and the basic standard section fraction of the target section sample, the first condition includes minimizing the sum of distances between all coordinate points corresponding to the target section sample and the first target linear function and all basic standard section fractions corresponding to non-standard section samples being located at the function value of the first target linear function, and the second condition includes minimizing the sum of distances between all coordinate points corresponding to the target section sample and the second target linear function; The parameters of the first objective linear function are determined as the upper bound of the joint fractional parameters, and the parameters of the second objective linear function are determined as the lower bound of the joint fractional parameters.

10. The section category identification method according to claim 1, characterized in that, Determining the target section category as the final category of the target ultrasound image includes: Determine whether the final category belongs to the category in the list of categories to be output; if so, determine the target section category as the final category of the target ultrasound image.

11. The section category identification method according to claim 1, characterized in that, The target prediction score includes any one or a combination of any of the following: standard section score, basic standard section score, and joint score; wherein the joint score is used to comprehensively describe the standard degree and basic standard degree of the section.

12. A device for identifying the cross-sectional category of an ultrasound image, characterized in that, include: The input module is used to acquire the target ultrasound image from the video stream and input the target ultrasound image into the classification model to obtain the prediction score for each section category corresponding to the target ultrasound image; The first determining module is used to determine the section category corresponding to the maximum predicted score as the target candidate category of the target ultrasound image; The second determining module is used to determine the state category of ultrasound images in a preset number of frames preceding the target ultrasound image in the video stream. The third determining module is used to determine the state category as the target section category corresponding to the target ultrasound image when the state category is consistent with the target candidate category; The fourth determining module is used to determine the target section category as the final category of the target ultrasound image when the predicted score of the target section category corresponding to the target ultrasound image is greater than or equal to the score threshold. The second calculation module is used to calculate the standard score corresponding to the target section category based on the target prediction score of the target section category corresponding to the target ultrasound image; The determination module is used to determine that the target ultrasound image is a standard section image of the target section category when the standard score is greater than or equal to the standard score threshold. Specifically, the second calculation module is used to: if the section category of the previous frame ultrasound image of the target ultrasound image is the target section category, then obtain the standard score corresponding to the target section category calculated based on the target prediction score of the target section category corresponding to the previous frame ultrasound image of the target ultrasound image; if the section category of the previous frame ultrasound image of the target ultrasound image is not the target section category, then initialize the standard score corresponding to the target section category; if any target prediction score of the target section category corresponding to the target ultrasound image is greater than or equal to the corresponding score threshold, then accumulate the standard score according to the corresponding score.

13. An ultrasonic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to implement the steps of the section category recognition method as described in any one of claims 1 to 11 when executing the computer program.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of the section category recognition method as described in any one of claims 1 to 11.

Images