Automatic detection method and detection system for superior mesenteric artery abnormity

Abstract

The invention discloses an automatic detection method and system for superior mesenteric artery abnormity, and belongs to the technical field of medical image processing. The method is based on an abdominal plain-scan CT image, and comprises the following steps: firstly, automatically identifying a superior mesenteric artery region by using a region positioning and segmentation network, and generating a labeling mask; further realizing precise segmentation of the superior mesenteric artery by utilizing a precise segmentation and anomaly prediction network, extracting features of the superior mesenteric artery from a segmentation region, and predicting the probability of anomaly or normality of the superior mesenteric artery; and finally, carrying out classification, identification and diagnosis on the detected abnormal lesions by utilizing an abnormal type diagnosis network. According to the method, radiologists and clinicians can be assisted in large-scale superior mesenteric artery anomaly detection by using the abdominal plain-scan CT image, excellent anomaly prediction performance is achieved, common superior mesenteric artery anomaly classification can be identified, and technical support is provided for early diagnosis of superior mesenteric artery anomaly.

Description

Technical Field

[0001] The invention relates to a superior mesenteric artery abnormality automatic detection method and a detection system, and belongs to the technical field of medical image processing. Background Art

[0002] The superior mesenteric artery, as an important branch of the abdominal aorta, directly nourishes the jejunum, ileum, cecum, ascending colon, and the right half of the transverse colon, maintaining the normal physiological function of the intestines and ensuring the stable operation of the digestive system. When abnormal lesions occur in the superior mesenteric artery, its complexity and severity cannot be ignored. These abnormalities mainly include arterial embolism, vascular wall tears caused by dissection, aneurysms with potential risk of rupture, and vascular inflammatory reactions caused by vasculitis. In clinical manifestations, abnormalities of the superior mesenteric artery are often diverse and non-specific, which makes the diagnosis of the disease more challenging.

[0003] The application of deep learning models in aortic disease, coronary artery disease, and head and cervical vertebral artery disease has achieved remarkable results. These studies not only verified the potential of deep learning technology in the diagnosis of vascular diseases, but also demonstrated its advantages in improving diagnostic accuracy, reducing misdiagnosis rate, and shortening diagnosis time. However, although visceral artery disease also has high clinical importance, the application of deep learning technology in this field is relatively small, especially the research on automatic detection system for superior mesenteric artery anomalies is scarce. Therefore, the development of an automatic detection method for superior mesenteric artery anomalies based on deep learning can not only fill the technical gap in this field, but also provide an efficient and accurate diagnostic tool for clinicians, improve the overall diagnosis and treatment level of visceral artery disease, and provide patients with better medical services. Summary of the invention

[0004] In order to achieve early identification and accurate diagnosis of superior mesenteric artery abnormalities, the present invention provides an automatic detection method and detection system for superior mesenteric artery abnormalities. The technical solution is as follows:

[0005] The present invention provides a method for automatically detecting abnormalities of the superior mesenteric artery, comprising:

[0006] Step 1: Obtain abdominal CT images;

[0007] Step 2: extract features from the abdominal CT image using a feature pyramid backbone architecture and a universal convolution path, and superimpose the features obtained by the two paths to obtain a universal feature map;

[0008] Step 3: Process the general feature map using the target generation network to generate a superior mesenteric artery RoI target frame;

[0009] Step 4: using the target mapping layer, mapping from the general feature map to the target feature map based on the superior mesenteric artery RoI target frame;

[0010] Step 5: The target feature map is processed by the mask expert branch to obtain a global feature map, so as to achieve accurate prediction of the mask of the superior mesenteric artery region; the mask expert branch first expands the dimension of the target feature map through an upsampling convolution layer, then constructs a segmentation backbone architecture, and introduces a parallel segmentation task-specific path, and the output is the superior mesenteric artery global feature map and the predicted mask;

[0011] Step 6: Based on the target feature map and the global feature map, the superior mesenteric artery is classified using the diagnostic expert branch;

[0012] The diagnostic expert branch takes the fused feature map of the superior mesenteric artery target feature map and the global feature map as input, builds a detection backbone architecture, and introduces a parallel path dedicated to the anomaly type detection task.

[0013] Optionally, the target generation network generates a target box of the superior mesenteric artery region using a 3×3 convolution layer and two 1×1 convolution branches.

[0014] Optionally, the target mapping layer selects corresponding features for the superior mesenteric artery region target frame from the general feature map through bilinear interpolation.

[0015] Optionally, the segmentation backbone architecture of the mask expert branch is based on a ResUnet-like structure, including four downsampling layers, four upsampling layers, and each sampling layer has a residual module.

[0016] Optionally, the segmentation task-specific path takes the target feature map as input and maps the channel dimension from c to through two consecutive 1×1 convolutions. Then, after a global pooling layer, the output is used as the weight of the linear combination of the backbone feature outputs to assist the mask expert branch in generating segmentation task-specific features.

[0017] Optionally, the detection backbone architecture of the diagnostic expert branch is based on a ResNet-like structure, including four sub-modules, each sub-module has a residual module, and each sub-module includes a convolutional layer, a batch normalization layer, a ReLU activation function, a convolutional layer, a batch normalization layer, a ReLU activation function, a maximum pooling layer and a residual connection layer connected in sequence.

[0018] Optionally, step 6 classifies the superior mesenteric artery into normal, dissection, aneurysm and pseudoaneurysm, thromboembolism, vasculitis, and other abnormalities.

[0019] The present invention provides a system for automatically detecting abnormalities of the superior mesenteric artery. The system is applied to the method for automatically detecting abnormalities of the superior mesenteric artery as described in any one of the above items. The system comprises:

[0020] An image acquisition module, configured to acquire an abdominal CT image;

[0021] A spatial domain universal feature perception architecture is configured to extract features from the abdominal CT image using a feature pyramid backbone architecture and a universal convolution path, and to superimpose features obtained from the two paths to obtain a universal feature map;

[0022] A target generation network is configured to process the universal feature map to generate a superior mesenteric artery RoI target box;

[0023] A target mapping layer, configured to implement mapping from the general feature map to a target feature map based on the superior mesenteric artery RoI target box;

[0024] The mask expert branch is configured to process the target feature map to obtain a global feature map to achieve accurate prediction of the superior mesenteric artery region mask; the mask expert branch first expands the dimension of the target feature map through an upsampling convolution layer, then builds a segmentation backbone architecture, and introduces a parallel segmentation task-specific path, and the output is the superior mesenteric artery global feature map and the predicted mask;

[0025] The diagnostic expert branch is configured to classify the superior mesenteric artery based on the target feature map and the global feature map using the diagnostic expert branch; the diagnostic expert branch uses the fused feature map of the superior mesenteric artery target feature map and the global feature map as input to construct a detection backbone architecture and introduces a parallel path dedicated to the abnormality type detection task.

[0026] The present invention provides an electronic device, comprising a memory and a processor;

[0027] The memory is used to store computer programs;

[0028] The processor is used to implement the method for automatically detecting abnormalities of the superior mesenteric artery as described in any one of the above items when executing the computer program.

[0029] The present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for automatically detecting anomalies of the superior mesenteric artery as described in any one of the above items is implemented.

[0030] The beneficial effects of the present invention are:

[0031] The present invention efficiently detects abnormalities of the superior mesenteric artery based on the input of abdominal plain scan CT images; the present invention utilizes a spatial domain general feature perception architecture, a target generation network, a target mapping layer, a mask expert branch, and a diagnosis expert branch to construct an automatic detection method for superior mesenteric artery abnormalities, which not only has excellent abnormality prediction performance, but can also identify common superior mesenteric artery abnormality classifications; after verification and application scenario expansion, the automatic detection method for superior mesenteric artery abnormalities provided by the present invention can provide technical support for the early diagnosis of large-scale superior mesenteric artery abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0033] Figure 1 A schematic diagram of the basic flow of a method for automatic detection of superior mesenteric artery anomalies based on a multi-task dense neural network provided for one embodiment of the present invention.

[0034] Figure 2 A schematic diagram of the overall structure of a method for automatic detection of superior mesenteric artery anomalies based on a multi-task dense neural network provided by an embodiment of the present invention.

[0035] Figure 3 A schematic diagram of the backbone structure of a spatial domain universal feature perception architecture for an automatic detection method for superior mesenteric artery anomalies based on a multi-task dense neural network provided in accordance with an embodiment of the present invention.

[0036] Figure 4 A schematic diagram of the backbone structure of a mask expert branch of an automatic detection method for superior mesenteric artery anomalies based on a multi-task dense neural network provided in accordance with an embodiment of the present invention.

[0037] Figure 5 A schematic diagram of the backbone submodule structure of the diagnostic expert branch of the method for automatic detection of superior mesenteric artery anomalies based on a multi-task dense neural network provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0039] Embodiment 1:

[0040] This embodiment provides a method for automatically detecting abnormalities of the superior mesenteric artery, including:

[0041] Step 1: Obtain abdominal CT images;

[0042] Step 2: Use the feature pyramid backbone architecture and the general convolution path to extract features from the abdominal CT image respectively, and superimpose the features obtained by the two paths to obtain a general feature map;

[0043] Step 3: Use the target generation network to process the general feature map and generate the superior mesenteric artery RoI target box;

[0044] Step 4: Using the target mapping layer, the mapping from the general feature map to the target feature map is realized based on the superior mesenteric artery RoI target box;

[0045] Step 5: Use the mask expert branch to process the target feature map to obtain the global feature map, and achieve accurate prediction of the superior mesenteric artery region mask; the mask expert branch first expands the dimension of the target feature map through the upsampling convolution layer, then builds the segmentation backbone architecture, and introduces a parallel segmentation task-specific path. The output is the superior mesenteric artery global feature map and the predicted mask;

[0046] Step 6: Based on the target feature map and the global feature map, the diagnostic expert branch is used to classify the superior mesenteric artery;

[0047] The diagnostic expert branch takes the fused feature map of the superior mesenteric artery target feature map and the global feature map as input, builds the detection backbone architecture, and introduces a parallel path dedicated to the anomaly type detection task.

[0048] This embodiment efficiently detects abnormalities of the superior mesenteric artery based on the input of abdominal plain scan CT images; an automatic detection method for superior mesenteric artery abnormalities is constructed by using a spatial domain general feature perception architecture, a target generation network, a target mapping layer, a mask expert branch, and a diagnosis expert branch. The method not only has excellent abnormality prediction performance, but can also identify common superior mesenteric artery abnormality classifications; after verification and application scenario expansion, the automatic detection method for superior mesenteric artery abnormalities provided in this embodiment can provide technical support for the early diagnosis of large-scale superior mesenteric artery abnormalities.

[0049] Embodiment 2:

[0050] This embodiment provides a method for automatically detecting abnormalities of the superior mesenteric artery. Figure 1-Figure 4 The specific implementation process is as follows:

[0051] Step 1: Obtain abdominal CT images and build a dataset.

[0052] Step 11: All patients underwent multi-phase CT examination, including plain scan, arterial phase, venous phase, and delayed phase. A GEREVOLUTION ACE 64-row 128-layer spiral CT machine was used to scan the entire abdomen. All patients underwent plain CT scan first, followed by enhanced scan.

[0053] Step 12: Load the plain scan CT DICOM image into ITK-SNAP software, and use the corresponding enhanced CT image to accurately label the superior mesenteric artery as a label for subsequent network training. The values ​​of all pixels in the region of interest (RoI) are set to 1, and the values ​​of all pixels outside the RoI are set to 0 to obtain a binary bright field mask image.

[0054] Step 13: Perform spatial interpolation, windowing enhancement and histogram equalization preprocessing on the abdominal CT images, and construct training sample sets and test sample sets.

[0055] Step 2: Input the abdominal CT image obtained in step 1 into the spatial domain general feature perception architecture. Since the abnormal lesion of the superior mesenteric artery is usually a small area in the CT scan, the spatial domain general feature perception architecture is used as a preprocessing tool to extract a general feature map containing the spatial structural characteristics and position information of the superior mesenteric artery, so as to remove irrelevant information for subsequent multi-task analysis.

[0056] The spatial domain general feature-aware architecture is built on top of the feature pyramid backbone architecture and introduces a parallel general convolutional path. The features of the two paths are superimposed to generate a general feature map containing the spatial structural features and location information of the superior mesenteric artery, explicitly constructing all inter-task relationships from a global perspective.

[0057] The structure of the general feature perception architecture in the spatial domain is as follows Figure 2 and Figure 3 As shown in Figure 1, the feature pyramid backbone architecture is based on a ResNet-like structure, including four upsampling layers and four downsampling layers. Through 1×1 convolutional layers and 2× upsampling layers, the downsampled features are fused layer by layer with the upsampled features to obtain a feature map with high semantic information; the general convolution path contains a simple 3×3 convolutional layer, whose weight matrix is The bias matrix is Where c represents the feature dimension. Since all task features come from the common feature map, it will be optimized simultaneously by the gradients of different tasks, which helps to extract common features in all tasks.

[0058] Step 3: Input the general feature map into the target generation network to generate the superior mesenteric artery RoI target box.

[0059] Specifically, the target generation network uses a window sliding on the general feature map to generate 9 target boxes with preset aspect ratios and areas for each position through a 3×3 convolutional layer. A 1×1 convolutional layer is used to exclude target boxes that exceed the boundary of the feature map through Softmax loss training to determine whether the target box covers the superior mesenteric artery RoI. Another 1×1 convolutional layer is used for training through SmoothL1 loss to correct the coordinates of the target box belonging to the superior mesenteric artery RoI.

[0060] Step 4: Use the target mapping layer to select the corresponding features for the superior mesenteric artery RoI target box through bilinear interpolation of the general feature map, complete the mapping of the target box from the general feature map to the target feature map, and then realize the precise segmentation and abnormality prediction of the superior mesenteric artery through two branches, namely the mask expert branch and the diagnosis expert branch. Each branch runs different task-specific paths in parallel, and each path learns different task features.

[0061] Step 5: In order to predict the superior mesenteric artery RoI mask more accurately, the mask expert branch first expands the dimension of the superior mesenteric artery target feature map through an upsampling convolutional layer, then builds a segmentation backbone architecture and introduces a parallel segmentation task-specific path. The output is the superior mesenteric artery global feature map and the predicted mask.

[0062] Specifically, refer to Figure 4 The segmentation backbone architecture is based on a ResUnet-like structure, including four downsampling layers and four upsampling layers. Each sampling layer has a residual module. Each upsampling layer includes a convolutional layer, a ReLU activation function, a convolutional layer, a ReLU activation function, a maximum pooling layer, and a residual connection layer connected in sequence. Specifically, the nth downsampling layer The structure can be expressed as:

[0063]

[0064] Each downsampling layer consists of a copy cropping layer, a convolution layer, a ReLU activation function, a convolution layer, a ReLU activation function, a deconvolution layer, a ReLU activation function and a residual connection layer connected in sequence, and the nth upsampling layer f is symmetrical to the nth downsampling layer. Un The structure can be expressed as:

[0065]

[0066] The segmentation task-specific path takes the target feature map as input and maps the channel dimension from c to 2 through two consecutive 1×1 convolutions. This is followed by a global pooling layer, whose output is used as the weight of the linear combination of the backbone feature outputs to assist the mask expert branch in generating segmentation task-specific features.

[0067] Step 6: The diagnostic expert branch is used to implement the diagnostic classification of the superior mesenteric artery, including six categories of normal superior mesenteric artery, dissection, aneurysm and pseudoaneurysm, thromboembolism, vasculitis and other abnormalities. The fusion feature map of the superior mesenteric artery target feature map and the global feature map is used as input to build the detection backbone architecture, and a parallel path dedicated to the abnormality type detection task is introduced.

[0068] The detection backbone architecture is based on a ResNet-like structure, which includes four submodules. Each submodule has a residual module. Each submodule includes a convolutional layer, a batch normalization layer, a ReLU activation function, a convolutional layer, a batch normalization layer, a ReLU activation function, a maximum pooling layer and a residual connection layer connected in sequence. Specifically, refer to Figure 5 , the nth submodule f n The structure can be expressed as:

[0069] X n =ReLU(BN(Conv(ReLU(BN(Conv(f n-1 ))))))

[0070] f n =MaxPool(X n )+f n-1

[0071] The dedicated path for the anomaly type detection task contains a weight matrix: and the bias matrix is A 3×3 convolution is performed, followed by a weight matrix of and the bias matrix is 1×1 convolution, where r represents the rank number and r<<c, indicating that these two convolutions are low-rank forms of ordinary convolutions. Low-rank convolution can reduce the computational cost of path training while retaining sufficient model expression capabilities.

[0072] Step 7: Train the automatic detection model for superior mesenteric artery anomalies. The training loss function can be described as:

[0073] L=L box +αL mask +βL cls

[0074] Among them, L box Refers to SmoothL1 loss, L cls Refers to the six-category cross entropy loss, L maskIt refers to the mean of Dice loss and binary cross entropy loss. α and β are weight factors to balance the relationship between losses. The specific formula is as follows:

[0075]

[0076] Among them, T represents the number of categories, y is a 1*T vector, the value of the position corresponding to the true label is 1, and the others are 0, feature a is a T*1 vector, S j It represents the probability that the input sample of the model belongs to the jth category, x is the numerical difference between the predicted box and the true box, and v = (v x ,v y ,v w ,v h ) is the coordinate of the real frame, are the coordinates of the predicted box, x, y represent the center coordinates / upper left corner coordinates of the box, w, h represent the width and height respectively; n represents the number of samples, that is, the number of pixels in each image, p and q represent the predicted pixel values ​​and their corresponding true values ​​(Ground Truth, GT); m = 6.

[0077] Step 8: Input the unlabeled plain scan CT images in the test sample set into the trained superior mesenteric artery abnormality automatic detection model, and output the segmentation mask of the superior mesenteric artery predicted by the model and the classification diagnosis result.

[0078] Embodiment three:

[0079] This embodiment provides a system for automatically detecting abnormalities of the superior mesenteric artery. The system is applied to the method for automatically detecting abnormalities of the superior mesenteric artery described in the first or second embodiment. The system includes:

[0080] An image acquisition module, configured to acquire an abdominal CT image;

[0081] The spatial domain universal feature perception architecture is configured to use the feature pyramid backbone architecture and the universal convolution path to extract features from the abdominal CT image respectively, and the features obtained by the two paths are superimposed to obtain a universal feature map;

[0082] The target generation network is configured to process the universal feature map and generate the superior mesenteric artery RoI target box;

[0083] The target mapping layer is configured to realize the mapping from the general feature map to the target feature map based on the superior mesenteric artery RoI target box;

[0084] The mask expert branch is configured to process the target feature map to obtain a global feature map to achieve accurate prediction of the superior mesenteric artery region mask. The mask expert branch first expands the dimension of the target feature map through an upsampling convolution layer, then builds a segmentation backbone architecture and introduces a parallel segmentation task-specific path. The output is the superior mesenteric artery global feature map and the predicted mask.

[0085] The diagnostic expert branch is configured to classify the superior mesenteric artery based on the target feature map and the global feature map. The diagnostic expert branch takes the fused feature map of the superior mesenteric artery target feature map and the global feature map as input to construct the detection backbone architecture and introduces a parallel path dedicated to the abnormality type detection task.

[0086] Embodiment 4:

[0087] In order to verify and illustrate the technical effect of the method of the present invention, this example compares the manual and traditional clinical model evaluation methods with the method of the present invention to verify the real effect of the method.

[0088] The traditional clinical model evaluation method screens out clinical predictive factors by performing univariate and multivariate logistic regression analysis on the patient's clinical data, and constructs single logistic regression and nested logistic regression prediction models based on these factors.

[0089] The manual assessment method detected potential SMA anomalies by instructing 5 vascular radiologists with more than 10 years of experience in vascular radiology to independently review the abdominal plain CT images (the entire range of slices for each patient); the window level and width could be freely adjusted, but the radiologists were blinded to any other clinical and radiological information; if at least 1 slice was considered to have SMA anomaly, the case was diagnosed as having SMA anomaly.

[0090] The comparison results of the method of the present invention with the manual and traditional clinical model evaluation methods are shown in Table 1 below:

[0091] Table 1: Comparison results between the present invention and the traditional solution

[0092]

[0093] It can be seen from the above comparison data that the method of the present invention is higher in accuracy and efficiency than the manual and traditional clinical model evaluation methods. The traditional clinical model evaluation method is only applicable to specific images and cannot be promoted on a large scale. The manual evaluation method and the method of the present invention have a wide range of applications, but the manual evaluation method is inferior to the method of the present invention in other data. The comparison results show that the method of the present invention can quickly, efficiently, accurately and automatically detect abnormalities in the superior mesenteric artery.

[0094] Some steps in the embodiments of the present invention may be implemented using software, and the corresponding software program may be stored in a readable storage medium, such as a CD or a hard disk.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for automatically detecting abnormalities of the superior mesenteric artery, characterized in that: The method comprises: Step 1: Obtain abdominal CT images; Step 2: extract features from the abdominal CT image using a feature pyramid backbone architecture and a universal convolution path, and superimpose the features obtained by the two paths to obtain a universal feature map; Step 3: Process the general feature map using the target generation network to generate a superior mesenteric artery RoI target frame; Step 4: using the target mapping layer, mapping from the general feature map to the target feature map based on the superior mesenteric artery RoI target frame; Step 5: The target feature map is processed by the mask expert branch to obtain a global feature map, so as to achieve accurate prediction of the mask of the superior mesenteric artery region; the mask expert branch first expands the dimension of the target feature map through an upsampling convolution layer, then constructs a segmentation backbone architecture, and introduces a parallel segmentation task-specific path, and the output is the superior mesenteric artery global feature map and the predicted mask; Step 6: Based on the target feature map and the global feature map, the superior mesenteric artery is classified using the diagnostic expert branch; The diagnostic expert branch takes the fused feature map of the superior mesenteric artery target feature map and the global feature map as input, builds a detection backbone architecture, and introduces a parallel path dedicated to the anomaly type detection task.

2. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The target generation network generates a target box of the superior mesenteric artery region using a 3×3 convolution layer and two 1×1 convolution branches.

3. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The target mapping layer selects corresponding features for the superior mesenteric artery region target frame by bilinear interpolation of the general feature map.

4. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The segmentation backbone architecture of the mask expert branch is based on a ResUnet-like structure, including four downsampling layers, four upsampling layers, and each sampling layer has a residual module.

5. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The segmentation task-specific path takes the target feature map as input and maps the channel dimension from c to Then, after a global pooling layer, the output is used as the weight of the linear combination of the backbone feature outputs to assist the mask expert branch in generating segmentation task-specific features.

6. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The detection backbone architecture of the diagnostic expert branch is based on a ResNet-like structure, including four sub-modules, each of which has a residual module, and each sub-module includes a convolutional layer, a batch normalization layer, a ReLU activation function, a convolutional layer, a batch normalization layer, a ReLU activation function, a maximum pooling layer and a residual connection layer connected in sequence.

7. The method for automatically detecting abnormalities of the superior mesenteric artery according to claim 1, characterized in that: The step 6 classifies the superior mesenteric artery into normal, dissection, aneurysm and pseudoaneurysm, thromboembolism, vasculitis, and other abnormalities.

8. An automatic detection system for abnormalities of the superior mesenteric artery, characterized in that: The system is applied to the method for automatically detecting abnormalities of the superior mesenteric artery according to any one of claims 1 to 7, and the system comprises: An image acquisition module, configured to acquire an abdominal CT image; A spatial domain universal feature perception architecture is configured to extract features from the abdominal CT image using a feature pyramid backbone architecture and a universal convolution path, and to superimpose features obtained from the two paths to obtain a universal feature map; A target generation network is configured to process the universal feature map to generate a superior mesenteric artery RoI target box; A target mapping layer, configured to implement mapping from the general feature map to a target feature map based on the superior mesenteric artery RoI target box; The mask expert branch is configured to process the target feature map to obtain a global feature map to achieve accurate prediction of the superior mesenteric artery region mask; the mask expert branch first expands the dimension of the target feature map through an upsampling convolution layer, then builds a segmentation backbone architecture, and introduces a parallel segmentation task-specific path, and the output is the superior mesenteric artery global feature map and the predicted mask; The diagnostic expert branch is configured to classify the superior mesenteric artery based on the target feature map and the global feature map using the diagnostic expert branch; the diagnostic expert branch uses the fused feature map of the superior mesenteric artery target feature map and the global feature map as input to construct a detection backbone architecture and introduces a parallel path dedicated to the abnormality type detection task.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the method for automatically detecting abnormalities of the superior mesenteric artery as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for automatically detecting abnormalities of the superior mesenteric artery as described in any one of claims 1 to 7 is implemented.

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