U-Net-based neck ultrasonic segmentation method and application

Abstract

The invention provides a U-Net-based neck ultrasonic segmentation method and application, and relates to the technical field of medical auxiliary diagnosis. The method comprises the following steps: constructing a U-Net segmentation model; the model comprises a double-encoder structure, a jump connection structure and a decoder structure, a front encoder and a decoder structure in the double-encoder structure respectively and correspondingly use a front fusion module and a rear fusion module of a multi-layer fusion module, and image features are promoted to be transmitted to different layers in the double-encoder structure and the decoder structure; the jump connection structure comprises at least one GAG module, and image features are guaranteed to be transmitted from a rear encoder corresponding layer in the double-encoder structure to a decoder structure corresponding layer; and segmenting and identifying the neck ultrasonic image by using the model, and extracting the image feature of the target tissue in the neck ultrasonic image. According to the invention, automatic segmentation and accurate identification of the target tissue in the neck ultrasonic image can be realized, so that medical auxiliary diagnosis is provided when a doctor carries out ultrasonic guidance.

Description

Technical Field

[0001] The present invention relates to the field of medical auxiliary diagnosis, and in particular to a neck ultrasound segmentation method based on U-Net. Background Art

[0002] Ultrasound is economical, efficient, convenient and real-time, and is one of the core tools for medical diagnosis and treatment. At the same time, ultrasound plays a huge role in guiding radiofrequency ablation for papillary thyroid microcancer, guiding thyroid nodule puncture, diagnosing neuromuscular atrophy, and guiding peripheral nerve (PNB) anesthesia. The ultrasound has the following advantages:

[0003] One of the advantages is that ultrasonic equipment is relatively economical and affordable. Compared with other high-end medical equipment (such as MRI, CT, etc.), its price is more moderate, which also makes ultrasonic technology easier to popularize and apply in primary medical institutions.

[0004] The second advantage is that ultrasound can propagate quickly and vibrate at a high frequency, which enables ultrasound to quickly generate images and provide doctors with accurate diagnostic information or treatment guidance.

[0005] The third advantage is that ultrasound equipment is relatively small and easy to operate, so medical staff can quickly apply it on site. In addition, ultrasound is also relatively portable, making it easy to use in various medical scenarios.

[0006] The fourth advantage is that ultrasound can be transmitted through human tissue without damage, without invasive operations such as cutting or puncture. This means that patients will not feel pain or discomfort when undergoing ultrasound examination or treatment, and there is a lower risk of complications.

[0007] In actual operation, although ultrasound guidance is widely used in clinical practice, efficient and accurate positioning has always been a difficulty faced by anesthesiologists during intubation. During ultrasound guidance, due to different patient positions and different probe tilt angles of doctors, it is easy to cause inconsistent ultrasound tissue imaging. For junior doctors, they are easily misled by inconsistent imaging. For senior doctors, although they can distinguish the target location based on the anatomical features of ultrasound images and the distribution of surrounding tissues, they cannot guarantee the accuracy of recognition.

[0008] Therefore, how to automatically segment and accurately identify the target tissue in the acquired cervical ultrasound image based on the use of ultrasound-guided technology and equipment, and provide auxiliary diagnosis for doctors during ultrasound guidance, is of great clinical significance.

[0009] To this end, the present invention provides a U-Net-based neck ultrasound segmentation method and application to solve the automatic segmentation and accurate identification of target tissue in neck ultrasound images, which is a technical problem that needs to be solved urgently. Summary of the invention

[0010] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a U-Net-based neck ultrasound segmentation method and application. The present invention can automatically segment and accurately identify target tissues in neck ultrasound images, thereby providing medical auxiliary diagnosis when doctors perform ultrasound guidance.

[0011] In order to solve the existing technical problems, the present invention provides the following technical solutions:

[0012] A neck ultrasound segmentation method based on U-Net, comprising:

[0013] Constructing a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a jump connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure respectively correspond to the front fusion module and the back fusion module using a multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure;

[0014] The processed neck ultrasound image is used as sample data for training and testing of the U-Net segmentation model, and the tested U-Net segmentation model is applied to the segmentation and recognition task of the neck ultrasound image;

[0015] Acquire ultrasound images of the neck using an ultrasound probe device;

[0016] The collected cervical ultrasound image is segmented and recognized using the aforementioned U-Net segmentation model to extract image features of the target tissue in the aforementioned cervical ultrasound image.

[0017] Further, the U-Net model is trained using a preset proportion of sample data in the cervical ultrasound dataset;

[0018] After completing the training, the remaining proportion of sample data in the aforementioned neck ultrasound dataset was tested, and the dice score was used as an evaluation indicator to measure the performance of the U-Net model in the ultrasound neck image segmentation task.

[0019] Furthermore, the dual encoder structure is divided into a front encoder and a back encoder, and the front encoder and the back encoder transmit image features through a jump connection; wherein,

[0020] The front encoder is capable of receiving a neck ultrasound image and sequentially executing a SUCK module and a SCAM attention mechanism module to capture image features corresponding to a target tissue; the target tissue comprises at least one of a sternocleidomastoid muscle, anterior scalene muscle, middle scalene muscle, brachial plexus, cervical nerve pathway, thyroid gland, and carotid artery;

[0021] The post-encoder receives the same neck ultrasound image as the front encoder, and while processing the aforementioned neck ultrasound image, receives and fuses the image features transmitted by the front encoder through the jump connection; wherein the post-encoder consists of a three-layer architecture consisting of two 3x3 convolutions and a SCAM attention mechanism module; wherein the SCAM attention mechanism module is used to locate the image features of the aforementioned target tissue, while suppressing other image features other than the image features of the aforementioned target tissue.

[0022] Furthermore, the SUCK module extracts image features of different scales through three different convolution branches; the features of small local areas are obtained by convolution with a small convolution kernel size of 3×3, and the features of large spatial areas are extracted by using 7x7 convolution and 13x13 convolution kernels.

[0023] Furthermore, the SCAM attention mechanism module can sequentially execute:

[0024] Given an input X∈R B×C×H×W Use global average pooling to split X∈R by rows and columns B×C×H×1 and X∈R B×C×1×W Then, the above X∈R B×C×H×1 and X∈R B×C×1×W Split into 8 parts according to the number of channels, and obtain sub-features X i ∈R B ×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W ;

[0025] The aforementioned sub-feature X i ∈R B×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W After passing the SUCK module, the aforementioned X i ∈RB×C / 8×H×1 With X j ∈R B×C / 8×1×W Aggregate by rows and columns separately and normalize them using group normalization with K groups to obtain X∈R B×C×H×1 and X∈R B×C×1×W , where K is a positive integer;

[0026] After normalization, X∈R B×C×H×1 and X∈R B×C×1×W After passing through the sigmoid function respectively, combined with the above given input X∈R B×C×H×W Generate spatial attention X S ;

[0027] In the aforementioned spatial attention X S After using average pooling and group normalization, the query Q, key K and value V based on self-attention are divided into three branches. After performing a depth-separable convolution operation on each branch, the query attention weight, key attention weight and value attention weight are obtained by calculating the query Q, key K and value V respectively;

[0028] Combined with the query attention weight, key attention weight and value attention weight, after the context-aware self-attention mechanism, average pooling and Sigmoid function, combined with the spatial attention X S Perform multiplication operation to obtain the output feature map.

[0029] Furthermore, the multi-layer fusion module includes a front fusion module corresponding to each layer of the front encoder of the dual encoder structure and a back fusion module corresponding to each layer of the decoder structure; wherein,

[0030] The front fusion module is capable of receiving the image features of each layer input in the front encoder, and after gradually changing the number of channels and the image size through multiple convolutions, performing a residual connection with the output layer of the front encoder;

[0031] The post-fusion module can gradually adjust the number of channels and image size of the image features output by each layer in the decoder structure through multiple deconvolution operations, and then perform residual connection with the image features output by the bottom layer.

[0032] Further, the GAG ​​module includes query embedding, key embedding and value embedding;

[0033] When executing the GAG ​​module, it specifically includes:

[0034] Use the aforementioned key embedding and query embedding to operate on the input image features to obtain the key attention weight and query attention weight;

[0035] The key attention weight and query attention weight are connected with residual connection, and after Gelu function, 1×1 convolution and sigmoid function, they are multiplied with value embedding;

[0036] Among them, when there are multiple GAG ​​modules, the image features output by the upper-layer GAG module are input into the lower-layer GAG module after adjusting the image size through deconvolution operation, and after adjusting the number of channels through 1×1 convolution, residual connection is performed with the result obtained by the above multiplication operation to highlight the global image features of the cervical ultrasound image.

[0037] A cervical ultrasound segmentation device based on U-Net, comprising:

[0038] A data construction unit is used to construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a jump connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure respectively correspond to the front fusion module and the back fusion module using the multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure;

[0039] A model building unit, used to use the processed neck ultrasound image as sample data to train and test the U-Net segmentation model, and apply the tested U-Net segmentation model to the segmentation and recognition task of the neck ultrasound image;

[0040] An image acquisition unit, used for acquiring a neck ultrasound image through an ultrasound probe device;

[0041] The image segmentation and recognition unit is used to segment and recognize the collected cervical ultrasound image using the aforementioned U-Net segmentation model to extract image features of the target tissue in the aforementioned cervical ultrasound image.

[0042] A neck ultrasound segmentation system based on U-Net, comprising:

[0043] A network node for sending and receiving neck ultrasound images;

[0044] An image segmentation module, used to identify the target tissue in the aforementioned cervical ultrasound image and perform segmentation processing;

[0045] A system server, wherein the system server is connected to the network node and the image segmentation module;

[0046] The system server is configured to: construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a skip connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure correspond to the front fusion module and the back fusion module using the multi-layer fusion module, respectively, to promote The image features are respectively transferred to different layers in the dual encoder structure and the decoder structure; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transferred from the corresponding layer of the post encoder in the dual encoder structure to the corresponding layer of the decoder structure; the processed neck ultrasound image is used as sample data in the training and testing of the U-Net segmentation model, and the tested U-Net segmentation model is applied to the segmentation and recognition task of the neck ultrasound image; the neck ultrasound image is collected by an ultrasound probe device; the collected neck ultrasound image is segmented and recognized using the U-Net segmentation model to extract the image features of the target tissue in the neck ultrasound image.

[0047] A computer-readable storage medium stores a computer program, wherein the computer program, when executed by a processor, implements the implementation steps of any of the above methods.

[0048] Based on the above advantages and positive effects, the advantages of the present invention are: an improved U-net segmentation model, which adopts a dual encoder structure with a pre-encoder and a post-encoder to improve the ability to extract image features, wherein the pre-encoder sequentially executes a spatial understanding convolution kernel module and a spatial channel attention mechanism module to capture the image features corresponding to the target tissue to improve the recognition accuracy of small tissues, and introduces a spatial channel attention mechanism module in the post-encoder to locate the image features of the target tissue; and the pre-encoder in the dual encoder structure and the aforementioned decoder structure correspond to the pre-fusion module and the post-fusion module of the multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the post-encoder in the dual encoder structure to the corresponding layer of the decoder structure.

[0049] Furthermore, the improved U-net segmentation model can automatically segment and accurately identify target tissues in neck ultrasound images, especially the sternocleidomastoid muscle, anterior scalene muscle, middle scalene muscle, brachial plexus, cervical nerve pathway, thyroid gland and carotid artery, thereby providing medical auxiliary diagnosis when doctors perform ultrasound guidance. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of a flow chart provided for an embodiment of the present invention.

[0051] Figure 2 A schematic diagram of the structure of the U-Net segmentation model provided in an embodiment of the present invention.

[0052] Figure 3 A schematic diagram of the structure of the SUCK module in the U-Net segmentation model provided in an embodiment of the present invention.

[0053] Figure 4 A schematic diagram of the structure of the SCAM attention mechanism module in the U-Net segmentation model provided in an embodiment of the present invention.

[0054] Figure 5 A schematic diagram of the structure of the GAG ​​module in the U-Net segmentation model provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following is a further detailed description of a U-Net-based cervical ultrasound segmentation method and application disclosed in the present invention in conjunction with the accompanying drawings and specific embodiments. It should be noted that the technical features or combinations of technical features described in the following embodiments should not be considered isolated, and they can be combined with each other to achieve better technical effects. In the drawings of the following embodiments, the same reference numerals appearing in each drawing represent the same features or components, which can be applied to different embodiments. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0056] It should be noted that the structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification for people familiar with this technology to understand and read, and are not used to limit the limiting conditions for the implementation of the invention. Any modification of the structure, change of the proportion relationship or adjustment of the size should fall within the scope of the technical content disclosed by the invention without affecting the effects and purposes that can be achieved by the invention. The scope of the preferred embodiments of the present invention includes other implementations, in which the functions can be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, which should be understood by those skilled in the art of the technical field to which the embodiments of the present invention belong.

[0057] Techniques, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the techniques, methods, and devices should be considered part of the authorization specification. In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0058] Example

[0059] See also Figure 1 FIG. 1 is a flow chart of the present invention. The implementation step S100 of the method is as follows:

[0060] S101, construct the neck ultrasound dataset and U-Net segmentation model.

[0061] The neck ultrasound data set includes a plurality of neck ultrasound images that have been processed by data cropping, data labeling and data enhancement operations.

[0062] The neck ultrasound image dataset in this embodiment is obtained by collecting and organizing those skilled in the art. Specifically, neck ultrasound images of 500 patients are collected and organized to construct the neck ultrasound dataset, which includes approximately 3,000 images taken by neck ultrasound examinations.

[0063] In the actual process of constructing the aforementioned cervical ultrasound data set, it is preferred to divide the obtained cervical ultrasound images into multiple categories, and use the Labelme software tool to label the target tissue and surrounding tissue in each cervical ultrasound image. Finally, the construction of the cervical ultrasound data set is completed by using data enhancement. Since data enhancement is a prior art in this field, it will not be described in detail here.

[0064] In actual operation, the original neck ultrasound images were renumbered to protect the patient's privacy information, and the neck ultrasound images with the target structure were selected, totaling 1500 images.

[0065] The target tissue includes at least one of the sternocleidomastoid muscle, the anterior scalene muscle, the middle scalene muscle, the brachial plexus, the cervical nerve pathway, the thyroid gland, and the carotid artery.

[0066] The surrounding tissues are other tissues that can be collected by neck ultrasound except for the aforementioned target tissues.

[0067] Specifically, the surrounding tissues include jugular vein, lymph nodes, esophagus, trachea, vocal cords, laryngeal structures, cervical vertebrae, parathyroid glands, adipose tissue, parathyroid glands, skin and subcutaneous tissue.

[0068] In the neck ultrasound image, when the sternocleidomastoid muscle, anterior scalene muscle, middle scalene muscle, brachial plexus, cervical nerve pathway, thyroid gland and carotid artery are used as target tissues, the jugular vein, lymph nodes, esophagus, trachea, vocal cords, laryngeal structures, cervical vertebrae, parathyroid gland, adipose tissue, parathyroid gland, skin and subcutaneous tissue can be regarded as surrounding tissues to provide background information and anatomical references, thereby helping to locate the target tissues and provide a more comprehensive anatomical structure relationship. Since the above-mentioned division of target tissues and surrounding tissues is a conventional technical means in the art, it will not be described in detail here.

[0069] It is also worth noting that, in the cervical ultrasound image, the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure.

[0070] The small target tissue is preferably a target tissue with a small volume or a thin structure that requires high-resolution ultrasound to be clearly displayed, such as the brachial plexus, cervical nerve pathway, and thyroid gland.

[0071] Among them, the brachial plexus is the terminal branch of the brachial plexus, which is responsible for the movement and sensation of the upper limbs. Since the brachial plexus is small in size and is easily covered by other tissues in ultrasound, a higher resolution is required to display it clearly; the structure of the cervical nerve pathway is also relatively small, and the nerve tissue is thin, so the ultrasound probe needs to be carefully adjusted to accurately locate it; and although the thyroid gland is smaller in overall size than nerves and muscles, it is a very important local small organ relative to the entire neck, and its specific nodules or lesions usually require detailed ultrasound evaluation.

[0072] The large target tissues occupy a large space in the neck area and are relatively easy to identify by ultrasound, for example: sternocleidomastoid muscle, anterior and middle scalene muscles, and carotid artery.

[0073] Among them, the sternocleidomastoid muscle is one of the largest muscles in the neck, running along the side of the neck. It has a prominent position, a large size, and is easy to display on ultrasound. The anterior scalene muscle and the middle scalene muscle occupy a larger area during cross-sectional scanning and are easily distinguished from other structures. The carotid artery is one of the largest blood vessels in the neck, with a deep anatomical position and a large diameter, making it easy to detect and evaluate via ultrasound.

[0074] Dividing the aforementioned target tissues according to the size and range of the anatomical structure can help to more specifically adjust the resolution and scanning mode of the probe during ultrasound evaluation to ensure that target tissues of different sizes can be clearly identified.

[0075] In order to ensure the invariance of the characteristics of the tissue region and reduce the amount of calculation, this embodiment performs cropping and occlusion processing on the originally obtained neck ultrasound image.

[0076] By way of example and not limitation, Figure 1 For example, for each neck ultrasound image containing the target structure, several physicians with many years of clinical intubation experience in this field segmented the anatomical structures of different target tissues and annotated them using the LabelMe tool.

[0077] As one of the preferred implementations of this embodiment, execute step S102, use the above-mentioned processed cervical ultrasound image as sample data to the training and testing of the U-Net segmentation model, and apply the tested U-Net segmentation model to the segmentation and recognition task of cervical ultrasound images.

[0078] Specifically, the U-Net model is trained using a preset proportion (for example, 80%) of sample data in the cervical ultrasound dataset; after the training is completed, the remaining proportion (for example, 20%) of sample data in the aforementioned cervical ultrasound dataset is tested, and the dice score is used as an evaluation indicator for measuring the performance of the U-Net model in the ultrasound cervical image segmentation task.

[0079] The U-Net segmentation model can retain the image features of the target tissue when extracting image features. In actual operation, the aforementioned image features are specifically determined in combination with the doctor's analysis requirements for the neck ultrasound image.

[0080] See Figure 2 As shown, the U-Net segmentation model preferably includes a dual encoder structure, a jump connection structure and a decoder structure. The front encoder in the dual encoder structure and the aforementioned decoder structure use a front fusion module and a back fusion module of a multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure.

[0081] The U-Net segmentation model in this embodiment preferably adopts a dual encoder structure to improve the ability to extract image features.

[0082] The dual encoder structure is divided into a front encoder and a back encoder, and the front encoder and the back encoder transmit image features through a jump connection.

[0083] Among them, the front encoder is capable of receiving a neck ultrasound image and sequentially executing a spatial understanding convolution kernel module and a spatial channel attention mechanism module to capture image features corresponding to the target tissue.

[0084] The post-encoder receives the same neck ultrasound image as the front-encoder, and while processing the neck ultrasound image, receives and fuses the features transmitted by the front-encoder via the jump connection.

[0085] The post-encoder consists of a three-layer architecture consisting of two 3x3 convolutions and a spatial channel attention mechanism module; the spatial channel attention mechanism module is used to locate the image features of the aforementioned target tissue, while suppressing other image features other than the image features of the aforementioned target tissue.

[0086] It is worth emphasizing that the pre-encoder can capture the image features of small target tissues and large target tissues under the synergistic effect of the spatial understanding convolution kernel module and the spatial channel attention mechanism module. In addition, the pre-encoder is responsible for preliminarily extracting the features of the neck ultrasound image and inputting it to the post-encoder for more refined processing, thereby improving the ability to extract image features in the neck ultrasound image.

[0087] This embodiment takes into account that large target tissues (such as veins or arteries) are more sensitive to semantic information, while small target tissues (such as nerves) are more sensitive to texture information. Therefore, a spatial understanding convolution kernel module is proposed to extract features of different scales.

[0088] The spatial understanding convolution kernel module is called Spatial Understanding Convolution Kernel in full in English, abbreviated as SUCK module.

[0089] The front encoder in the dual encoder structure adopts the SUCK module to improve the segmentation accuracy of small target tissues.

[0090] Specifically, the spatial understanding convolution kernel module, namely the SUCK module, extracts image features of different scales through three different convolution branches; the features of small local areas are obtained by convolution with a small convolution kernel size of 3×3, and the features of large spatial areas are extracted by using 7x7 convolution and 13x13 convolution kernels.

[0091] As an example and not a limitation, the structural diagram of the SUCK module is as follows: Figure 3 As shown in the figure, for a given input feature map, the output result of the SUCK module is composed of the results of three convolution branches, specifically, three different convolution branches are used to extract feature information of different scales.

[0092] For the first branch, the input feature x is directly output after passing through a 3x3 convolution with a dilation rate of 1 and a BN layer (BatchNormailization), that is, the features of the small local area are obtained by convolution with a small convolution kernel size (3×3). For the second branch, the input x is output after residual connection with the first branch after dilation rates of 1 and 2, and for the third branch, the input feature x is concatenated with the residual results of the first and second branches after passing through 3x3 convolution with dilation rates of 1, 2, and 3. That is, the features of the large spatial area are extracted by using simulated 7x7 and 13x13 convolution kernels, respectively.

[0093] Finally, the different features extracted by the three branches are connected with different anatomical structures and input into the SCAM module of the next layer.

[0094] For the SUCK module, since the Gelu activation function is used after each convolution, the expression of the SUCK module is as follows:

[0095] x 0 =Concat(W R1 x i +W R2 (W R1 x i ),W R3 (W R2 (W R1 z i ))+b1)

[0096] The Concat method concatenates the matrices on the left and right sides according to the channel dimension. 0 , x i Represent the output features and input features respectively, W R1 , W R2 , W R3 They represent 3x3 convolutions with expansion rates of 1, 2, and 3 respectively, and b1 is the bias.

[0097] The expression for the dilated convolution with input x, convolution kernel k, and dilation rate r is:

[0098] (x*k r )(p)=∑ s+rt＝p x(s)k(t)

[0099] Among them, x represents the feature map, k r is the convolution kernel with expansion rate r, p is the value at p after the feature map and convolution kernel are operated, t represents the current time point, and is the output (x*k r )(p) time variable.

[0100] The spatial channel attention mechanism module is called Spatial-Channel Attention Module in English, abbreviated as SCAM, that is, SCAM attention mechanism module.

[0101] As another preferred implementation of this embodiment, Figure 4 As shown, the SCAM attention mechanism module can execute step S110 in sequence:

[0102] S111, given input X∈R B×C×H×W Use global average pooling to split X∈R by rows and columns B ×C×H×1 and X∈R B×C×1×W Then, the above X∈R B×C×H×1 and X∈R B×C×1×W Split into 8 parts according to the number of channels, and obtain sub-features X i ∈R B×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W .

[0103] It is worth noting here that for the aforementioned X∈R B×C×H×W , this embodiment preferably uses global average pooling for each dimension, thereby creating two unidirectional one-dimensional sequence structures, namely X∈R B×C×H×1 and X∈R B×C×1×W Then, the above X∈R B ×C×H×1 and X∈R B×C×1×W Split into 8 parts according to the number of channels, and obtain sub-features X i ∈R B×C / 8×H×1 and sub-feature X j ∈R B ×C / 8×1×W This operation enables the model to learn more spatial distribution and contextual connections. In addition, since the SUCK module can obtain different receptive fields, S112 is executed.

[0104] S112, the aforementioned sub-feature X i ∈R B×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W After passing the SUCK module, the aforementioned X i ∈R B×C / 8×H×1 With X j ∈R B×C / 8×1×W Aggregate by rows and columns respectively, and use group normalization (GN) with K groups to normalize them, and get X∈R B×C×H×1 and X∈R B×C×1×W .

[0105] Wherein, K is a positive integer. In this embodiment, it is preferred to set the value of K to 8, that is, for the aforementioned X i With X j Aggregate by rows and columns separately and use group normalization with 8 groups to transform the above X i With X j Normalize them separately.

[0106] In addition, it is worth emphasizing that the aforementioned X i With X j After passing through the SUCK module, the operations of aggregation are performed according to rows and columns, that is, under the concat connection operation and the group normalization of GN-8, the 8 sub-features X i and 8 molecular features X j Re-aggregate into a complete feature X∈R B×C×H×1 And 1 complete feature X∈R B×C×1×W .

[0107] S113, after normalization, X∈R B×C×H×1 and X∈R B×C×1×W After passing through the sigmoid function respectively, combined with the above given input X∈R B×C×H×W Generate spatial attention X S .

[0108] The described spatial attention extracts multi-semantic spatial information from each feature, thus providing spatial priors for channel attention computation.

[0109] The spatial attention X S It is expressed as:

[0110]

[0111] Among them, σ is the Sigmoid function, and They represent the group normalization of 8 groups of sub-features separated along the height and width directions respectively. Concat means concatenating the matrices on the left and right sides according to the channel dimension.

[0112] S114, in the aforementioned spatial attention X S After using average pooling and group normalization, the query Q, key K and value V based on self-attention are divided into three branches. After performing a depth-separable convolution operation on each branch, the query attention weight, key attention weight and value attention weight are obtained by calculating the query Q, key K and value V respectively.

[0113] Among them, group normalization GN can divide a feature into several groups according to different channels, and then normalize the channels in each group. This can reduce the amount of model calculation while maintaining the normalization effect.

[0114] The depthwise separable convolution (DWConv) is a convolution operation that can reduce the computational complexity and number of parameters of the model.

[0115] Specifically, the depthwise separable convolution operation includes a depthwise convolution step and a pointwise convolution step.

[0116] In the depthwise convolution step, convolution is performed on each channel of the input separately. That is, for each input channel, a separate convolution kernel is used for the operation instead of convolving all input channels together. For example, if the input feature map has N channels (N is a positive integer), the depthwise convolution will use a convolution kernel for each channel. Assuming that the size of each convolution kernel is k×k (k is a positive integer), there are N k×k convolution kernels in the depthwise convolution operation.

[0117] In the pointwise convolution (also called 1x1 convolution) step, the information of each channel after the depth convolution is fused. Specifically, the output channels at each position are linearly combined through a 1×1 convolution kernel to adjust the number of channels. Then, the results of the depth convolution are combined into the final output channel number.

[0118] S115, combining the query attention weight, key attention weight and value attention weight, after passing through the context-aware self-attention mechanism, after average pooling and Sigmoid function, combined with the spatial attention X S Perform multiplication operation to obtain the output feature map.

[0119] Considering that channel attention focuses on the feature correlation between channels and ignores the information of spatial dimension, in this embodiment, it is preferred to use self-attention Q,K,V∈R calculated along the channel dimension. B×C×N , where N = WH, the segmentation performance of the model is enhanced by combining channel attention and spatial attention.

[0120] Specifically, Q, K, and V represent query, key, and value, respectively, which are the three core elements in the self-attention mechanism, and are used to calculate attention weights and weighted output information.

[0121] B represents the batch size, that is, the number of samples in the batch; C represents the number of feature channels, which is used to refer to the depth or dimension of the input features; N is the number of spatial positions of each feature map, which is equal to W×H, that is, the product of the width and height of the feature map.

[0122] The above structure applies the context-aware self-attention mechanism in the channel dimension. By jointly calculating the attention weights of Q, K, and V, it can capture the information relationship between different channels in the global context, thereby enhancing the segmentation performance of the model, especially in pixel-level tasks (such as image segmentation), improving the ability to capture detailed information in feature maps.

[0123] Among them, the context-aware multi-head self-attention mechanism (CA-MHSA), that is, the multi-head self-attention mechanism based on channel attention, is used to enhance the context understanding ability of the self-attention model. It calculates attention on multiple heads and combines context information to further improve the representation ability of the model, especially when processing inputs with complex dependencies.

[0124] Based on this, it can be determined that the output feature map Y of the SCAM attention mechanism module is expressed as:

[0125] Among them, σ represents the Sigmoid function, Pool preferably represents 7X7 average pooling, are mapping functions for generating queries, keys, and values, respectively, and the CA-MHSA attention mechanism is represented by Att, so that

[0126]

[0127] It is worth noting that in this embodiment, 7x7 average pooling is preferably used, which downsamples the resolution from (H, W) to (H', W') by taking the average of each 7x7 region in the input feature map. This operation helps to reduce the spatial dimension of the feature map while retaining important contextual information in the image.

[0128] As another preferred implementation of this embodiment, in order to enable the information extracted by each layer of the encoder to flow better, the U-Net segmentation model in this embodiment preferably uses a multi-layer fusion module to enable the upper and lower layers to preserve more image features, and uses the GAG ​​attention mechanism to improve the model segmentation accuracy.

[0129] Preferably, the multi-layer fusion module includes a front fusion module arranged corresponding to each layer of the front encoder of the dual encoder structure and a back fusion module arranged corresponding to each layer of the aforementioned decoder structure.

[0130] The front fusion module can receive the image features of each layer input in the front encoder, and after gradually changing the number of channels and the image size through multiple convolutions, perform a residual connection with the output layer of the front encoder.

[0131] Specific combination Figure 2 As shown, the front fusion module can receive the image features of each layer input, and after gradually changing the number of channels and the image size through four convolutions, perform a residual connection with the output layer of the front encoder.

[0132] In this embodiment, it is preferred that in the 5-layer structure of the front encoder, the SUCK module, the SCAM attention mechanism module and the activation function Gelu are used sequentially for each layer structure, so that the features obtained by residual connection of all layers include the image features of the first four layers, and are residually connected with the last layer.

[0133] The post-fusion module can gradually adjust the number of channels and image size of the image features output by each layer in the decoder structure through multiple deconvolution operations, and then perform residual connection with the image features output by the bottom layer.

[0134] Specific, combined Figure 2 As shown, the post-fusion module can receive the image features of each layer output. In this embodiment, four deconvolution operations are preferably used to gradually change the number of channels, and finally a residual connection is performed with the bottom layer output of the decoder structure.

[0135] As another preferred implementation of this embodiment, the post-fusion module can gradually adjust the number of channels and image size of the image features output by each layer in the decoder structure through multiple deconvolution operations and convolution operations, and then perform a residual connection with the image features output by the bottom layer.

[0136] It is worth noting that the convolution operation and deconvolution operation in the post-fusion module preferably have different configuration requirements according to different layers, wherein the deconvolution operation can adjust the image size, for example, the deconvolution operation is used in each layer of the post-fusion module, so that the height and width of the image size are expanded by twice the original. For example, the height and width of the image size are adjusted from 32×32 to 512×512 through 4 deconvolution operations, and the convolution operation can adjust the number of channels and refine the image features. In the post-fusion module, it is preferred to set the convolution operation for each deconvolution operation, so as to gradually change the number of channels, and ensure that the image size and the number of channels match the image size and the number of channels output in the jump connection structure when performing a residual connection with the bottom output of the decoder structure, thereby completing the residual connection.

[0137] Among them, the convolution operation can be one convolution or multiple convolutions. The convolution operation in the post-fusion module preferably uses multiple convolutions. This is because the gradual transformation of channels by multiple convolutions will be smoother. This avoids the problems of gradient disappearance, gradient explosion or information loss in the U-Net segmentation model designed in this embodiment when performing image processing, while enhancing the model stability.

[0138] Preferably, the GAG ​​(Global Attention Gate, GAG for short) module is a mechanism for enhancing the model's ability to perceive global information during feature learning. The GAG ​​module adjusts and optimizes the selection of local image features by capturing global image features, so that the model can focus on global relevant information more effectively.

[0139] In this embodiment, the GAG ​​module includes query embedding, key embedding and value embedding. Figure 5 As shown, step S120 is included:

[0140] S121, use the aforementioned key embedding and query embedding to operate on the input image features to obtain the key attention weight and the query attention weight.

[0141] S122, a residual connection is adopted for the aforementioned key attention weight and query attention weight, and after passing through the Gelu function, 1×1 convolution and sigmoid function, multiplication is performed with the value embedding.

[0142] Among them, when there are multiple GAG ​​modules, the image features output by the upper-layer GAG module are input into the lower-layer GAG module after adjusting the image size through deconvolution operation, and after adjusting the number of channels through 1×1 convolution, residual connection is performed with the result obtained by the above multiplication operation to highlight the global image features of the cervical ultrasound image.

[0143] In addition, combined Figure 2 As shown, two GAG modules are added to the skip connection structure, and the GAG ​​module can receive the image features output by the post encoder in the dual encoder structure and the image features output by the decoder structure, and perform the aforementioned step S120, so as to adjust the attention weights calculated between the local image features and the global image features to determine the importance of the local image features in the final decision. Furthermore, the GAG ​​module can suppress non-relevant features while showing relevant features, and the latter GAG module can simultaneously receive the image features output by the previous GAG module, and further highlight the global image features of the cervical ultrasound image.

[0144] In actual operation, steps S103 and S104 are specifically performed.

[0145] S103, collecting a neck ultrasound image using an ultrasound probe device.

[0146] S104, using the aforementioned U-Net segmentation model to segment and identify the acquired cervical ultrasound image to extract image features of the target tissue in the aforementioned cervical ultrasound image.

[0147] In addition, to ensure that the U-Net segmentation model is suitable for the recognition of cervical ultrasound images in actual operation, the performance of the improved U-Net segmentation model in this embodiment on the cervical ultrasound image dataset is verified, and the generated test results are analyzed. Specifically, the following operations are included:

[0148] First, the improved U-Net segmentation model is trained on the cervical ultrasound image dataset personalized in this embodiment, and the performance of the model is evaluated. Secondly, the experimental results are compared and analyzed with the U-Net segmentation algorithm before the improvement. Finally, the improved model is compared and evaluated with the mainstream segmentation algorithm.

[0149] Taking the improved U-Net segmentation model in this embodiment, the classic Unet model, and advanced models (such as MACUnet, MEGANet, ACCUnet, MultiResUnet, and Unet++) as examples, 100 rounds of iterative training were performed on the neck ultrasound image dataset personalized in this embodiment, and then the DSC evaluation index of the segmented and identified target tissue was compared, and finally the Mean DSC (MDSC) and Mean IOU (MIOU) results were given, as shown in Table 1.

[0150] The target tissues for segmentation and identification include the thyroid gland represented by T, the carotid artery represented by CA, the cervical nerve pathway represented by CNP, the brachial plexus represented by BP, the middle scalene muscle represented by MSM, the anterior scalene muscle represented by ASM, and the sternocleidomastoid muscle represented by SCM.

[0151] Table 1 Experimental comparison results of multiple groups of models

[0152]

[0153] The experimental results show that the improved U-Net segmentation model in this embodiment can accurately segment the target tissue, with a Mean DSC of 0.933 and a Mean IOU of 0.874, which has a high confidence level. Moreover, for the rotated neck ultrasound image, the segmentation and recognition operations can be performed more accurately for small target tissues (such as cervical nerve pathways, brachial plexus, etc.).

[0154] For other technical features, please refer to the previous embodiments and will not be described in detail here.

[0155] In addition, the present invention also provides an embodiment, which provides a neck ultrasound segmentation device based on U-Net, including:

[0156] A data construction unit is used to construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a jump connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure respectively correspond to the front fusion module and the back fusion module using a multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure.

[0157] The model building unit is used to input the processed neck ultrasound image as sample data into the training and testing of the U-Net segmentation model, and apply the tested U-Net segmentation model to the segmentation and recognition task of the neck ultrasound image.

[0158] The image acquisition unit is used to acquire the neck ultrasound image through the ultrasound probe device.

[0159] The image segmentation and recognition unit is used to segment and recognize the collected cervical ultrasound image using the aforementioned U-Net segmentation model to extract image features of the target tissue in the aforementioned cervical ultrasound image.

[0160] For other technical features, please refer to the previous embodiments and will not be described in detail here.

[0161] In addition, the present invention also provides an embodiment, which provides a cervical ultrasound segmentation system based on U-Net, including:

[0162] Network node, used to send and receive neck ultrasound images.

[0163] The image segmentation module is used to identify the target tissue in the aforementioned cervical ultrasound image and perform segmentation processing.

[0164] A system server is connected to the network node and the image segmentation module.

[0165] The system server is configured to: construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a skip connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure correspond to the front fusion module and the back fusion module using the multi-layer fusion module, respectively, to promote The image features are respectively transferred to different layers in the dual encoder structure and the decoder structure; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transferred from the corresponding layer of the post encoder in the dual encoder structure to the corresponding layer of the decoder structure; the processed neck ultrasound image is used as sample data in the training and testing of the U-Net segmentation model, and the tested U-Net segmentation model is applied to the segmentation and recognition task of the neck ultrasound image; the neck ultrasound image is collected by an ultrasound probe device; the collected neck ultrasound image is segmented and recognized using the U-Net segmentation model to extract the image features of the target tissue in the neck ultrasound image.

[0166] For other technical features, please refer to the previous embodiments and will not be repeated here.

[0167] In addition, an embodiment of the present invention further provides a computer-readable storage medium having a program stored thereon for use in the aforementioned U-Net-based cervical ultrasound segmentation system. When the program is executed by a processor, the steps of any of the aforementioned U-Net-based cervical ultrasound segmentation methods can be implemented.

[0168] For other technical features, please refer to the previous embodiments and will not be repeated here.

[0169] In the above description, within the scope of the target protection of the present disclosure, the components can be selectively and operatively combined in any number. In addition, terms such as "including", "comprising" and "having" should be interpreted as inclusive or open by default, rather than exclusive or closed, unless they are explicitly defined to the contrary. All technical, scientific or other terms have the meaning understood by those skilled in the art unless they are defined to the contrary. Common terms found in dictionaries should not be interpreted too idealistically or too impractically in the context of relevant technical documents, unless the present disclosure explicitly defines them as such.

[0170] Although the exemplary aspects of the present disclosure have been described for illustrative purposes, those skilled in the art should appreciate that the above description is only a description of the preferred embodiments of the present invention and is not intended to limit the scope of the present invention. The scope of the preferred embodiments of the present invention includes other implementations in which functions may not be performed in the order in which they appear or are discussed. Any changes or modifications made by those skilled in the art based on the above disclosure are within the scope of the claims.

Claims

1. A neck ultrasound segmentation method based on U-Net, characterized in that: include: Constructing a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a jump connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure respectively correspond to the front fusion module and the back fusion module using a multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure; The processed neck ultrasound image is used as sample data for training and testing of the U-Net segmentation model, and the tested U-Net segmentation model is applied to the segmentation and recognition task of the neck ultrasound image; Acquire ultrasound images of the neck using an ultrasound probe device; The collected cervical ultrasound image is segmented and recognized using the aforementioned U-Net segmentation model to extract image features of the target tissue in the aforementioned cervical ultrasound image.

2. The method according to claim 1, characterized in that The U-Net model is trained using a preset proportion of sample data in the cervical ultrasound dataset; After completing the training, the remaining proportion of sample data in the aforementioned neck ultrasound dataset was tested, and dicescore was used as an evaluation indicator to measure the performance of the U-Net model in the ultrasound neck image segmentation task.

3. The method according to claim 1, characterized in that The dual encoder structure is divided into a front encoder and a back encoder, and the front encoder and the back encoder transmit image features through a jump connection; wherein, The front encoder is capable of receiving a neck ultrasound image and sequentially executing a SUCK module and a SCAM attention mechanism module to capture image features corresponding to a target tissue; the target tissue comprises at least one of a sternocleidomastoid muscle, anterior scalene muscle, middle scalene muscle, brachial plexus, cervical nerve pathway, thyroid gland, and carotid artery; The post-encoder receives the same neck ultrasound image as the front encoder, and while processing the aforementioned neck ultrasound image, receives and fuses the image features transmitted by the front encoder through the jump connection; wherein the post-encoder consists of a three-layer architecture consisting of two 3x3 convolutions and a SCAM attention mechanism module; wherein the SCAM attention mechanism module is used to locate the image features of the aforementioned target tissue, while suppressing other image features other than the image features of the aforementioned target tissue.

4. The method according to claim 3, characterized in that The SUCK module extracts image features of different scales through three different convolution branches; the features of small local areas are obtained by convolution with a small convolution kernel size of 3×3, and the features of large spatial areas are extracted by using 7x7 convolution and 13x13 convolution kernels.

5. The method according to claim 3, characterized in that: The SCAM attention mechanism module can perform the following steps in sequence: Given an input X∈R B×C×H×W Use global average pooling to split X∈R by rows and columns B×C×H×1 and X∈R B ×C×1×W Then, the above X∈R B×C×H×1 and X∈R B×C×1×W Split into 8 parts according to the number of channels, and obtain sub-features X i ∈R B ×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W ; The aforementioned sub-feature X i ∈R B×C / 8×H×1 and sub-feature X j ∈R B×C / 8×1×W After passing the SUCK module, the aforementioned X i ∈R B ×C / 8×H×1 With X j ∈R B×C / 8×1×W Aggregate by rows and columns separately and normalize them using group normalization with K groups to obtain X∈R B×C×H×1 and X∈R B×C×1×W , where K is a positive integer; After normalization, X∈R B×C×H×1 and X∈R B×C×1×W After passing through the sigmoid function respectively, combined with the above given input X∈R B×C×H×W Generate spatial attention X S ; In the aforementioned spatial attention X S After using average pooling and group normalization, the query Q, key K and value V based on self-attention are divided into three branches. After performing a depth-separable convolution operation on each branch, the query attention weight, key attention weight and value attention weight are obtained by calculating the query Q, key K and value V respectively; Combined with the query attention weight, key attention weight and value attention weight, after the context-aware self-attention mechanism, average pooling and Sigmoid function, combined with the spatial attention X S Perform multiplication operation to obtain the output feature map.

6. The method according to claim 1, characterized in that The multi-layer fusion module includes a front fusion module corresponding to each layer of the front encoder of the dual encoder structure and a back fusion module corresponding to each layer of the decoder structure; wherein, The front fusion module is capable of receiving the image features of each layer input in the front encoder, and after gradually changing the number of channels and the image size through multiple convolutions, performing a residual connection with the output layer of the front encoder; The post-fusion module can gradually adjust the number of channels and image size of the image features output by each layer in the decoder structure through multiple deconvolution operations, and then perform residual connection with the image features output by the bottom layer.

7. The method according to claim 1, characterized in that The GAG ​​module includes query embedding, key embedding and value embedding; When executing the GAG ​​module, it specifically includes: Use the aforementioned key embedding and query embedding to operate on the input image features to obtain the key attention weight and query attention weight; The key attention weight and query attention weight are connected with residual connection, and after Gelu function, 1×1 convolution and sigmoid function, they are multiplied with value embedding; Among them, when there are multiple GAG ​​modules, the image features output by the upper-layer GAG module are input into the lower-layer GAG module after adjusting the image size through deconvolution operation, and after adjusting the number of channels through 1×1 convolution, residual connection is performed with the result obtained by the above multiplication operation to highlight the global image features of the cervical ultrasound image.

8. A U-Net-based neck ultrasound segmentation device according to any one of claims 1 to 7, characterized in that include: A data construction unit is used to construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a jump connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure respectively correspond to the front fusion module and the back fusion module using the multi-layer fusion module to promote the image features to be transmitted to different layers in the dual encoder structure and the decoder structure respectively; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transmitted from the corresponding layer of the back encoder in the dual encoder structure to the corresponding layer of the decoder structure; A model building unit, used to use the processed neck ultrasound image as sample data to train and test the U-Net segmentation model, and apply the tested U-Net segmentation model to the segmentation and recognition task of the neck ultrasound image; An image acquisition unit, used for acquiring a neck ultrasound image through an ultrasound probe device; The image segmentation and recognition unit is used to segment and recognize the collected cervical ultrasound image using the aforementioned U-Net segmentation model to extract image features of the target tissue in the aforementioned cervical ultrasound image.

9. A U-Net-based neck ultrasound segmentation system according to any one of claims 1 to 7, characterized in that include: A network node for sending and receiving neck ultrasound images; An image segmentation module, used to identify the target tissue in the aforementioned cervical ultrasound image and perform segmentation processing; A system server, wherein the system server is connected to the network node and the image segmentation module; The system server is configured to: construct a cervical ultrasound data set and a U-Net segmentation model; the cervical ultrasound data set includes a number of cervical ultrasound images processed by data cropping, data labeling and data enhancement operations; the U-Net segmentation model can retain the image features of the target tissue when extracting image features; the target tissue is divided into small target tissue and large target tissue according to the size and range of the anatomical structure; the U-Net segmentation model includes a dual encoder structure, a skip connection structure and a decoder structure, wherein the front encoder in the dual encoder structure and the aforementioned decoder structure correspond to the front fusion module and the back fusion module using the multi-layer fusion module, respectively, to promote The image features are respectively transferred to different layers in the dual encoder structure and the decoder structure; the jump connection structure includes at least one GAG ​​module to ensure that the image features are transferred from the corresponding layer of the post encoder in the dual encoder structure to the corresponding layer of the decoder structure; the processed neck ultrasound image is used as sample data in the training and testing of the U-Net segmentation model, and the tested U-Net segmentation model is applied to the segmentation and recognition task of the neck ultrasound image; the neck ultrasound image is collected by an ultrasound probe device; the collected neck ultrasound image is segmented and recognized using the U-Net segmentation model to extract the image features of the target tissue in the neck ultrasound image.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 7 are implemented.

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