MRI medical image segmentation method and system based on deep learning

Abstract

The invention discloses an MRI medical image segmentation method and system based on deep learning, and the method comprises the steps: obtaining a mammary gland MRI image, carrying out the standardization processing of the mammary gland MRI image, and removing a non-region of interest to obtain a target MRI image; performing detail enhancement on the suspected lesion area to reconstruct an image to be preprocessed; performing noise reduction processing, contrast enhancement and edge enhancement preprocessing on the to-be-preprocessed image to generate three versions of input images, and inputting the three versions of input images into a pre-trained deep learning model as three channels to complete breast tumor identification; and carrying out image segmentation on the breast tumor region, and classifying segmented breast tumors. According to the MRI medical image segmentation method and system based on deep learning provided by the invention, accurate and efficient processing of the mammary gland MRI image is realized, efficient identification, accurate segmentation and accurate classification of the mammary gland tumor are realized, the accuracy and robustness of mammary gland tumor detection can be improved, and efficient and reliable auxiliary diagnosis is provided for clinic.

Description

[Technical field]

[0001] The present invention relates to the field of image recognition technology, and in particular to a deep learning-based MRI medical image segmentation method and system. [Background technology]

[0002] With the development of medical imaging technology, early diagnosis and accurate identification of breast cancer have become key links in clinical diagnosis. Breast cancer is one of the most common malignant tumors in women. Early detection and treatment significantly improve the survival rate of patients. The diagnosis of breast cancer usually relies on imaging examinations, especially breast MRI images, which can provide high-resolution images and help detect tiny lesions in breast tissue, especially in dense breast tissue. MRI can provide more accurate information than traditional breast X-ray images.

[0003] However, reading MRI images is not only time-consuming, but also requires radiologists to have extensive professional experience. In addition, in the image analysis of breast cancer diagnosis, the diversity and complexity of lesions may be related to the interaction of dense tissues, making it difficult for radiologists to accurately detect and analyze masses. Therefore, how to achieve breast MRI-assisted diagnosis and improve the accuracy and efficiency of breast tumor identification and classification has become a technical problem that urgently needs to be solved in this field. [Summary of the invention]

[0004] In view of this, an embodiment of the present invention provides an MRI medical image segmentation method and system based on deep learning.

[0005] In a first aspect, an embodiment of the present invention provides an MRI medical image segmentation method based on deep learning, the method comprising:

[0006] S1, obtaining breast MRI images and performing standardization processing, removing non-interested regions to obtain target MRI images;

[0007] S2, performing preliminary target determination on the target MRI image, determining the suspected lesion area according to the preliminary target, and reconstructing the suspected lesion area into an image to be preprocessed by performing detail enhancement;

[0008] S3, performing noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generating three versions of input images, and inputting them into a pre-trained deep learning model as three channels to complete the recognition of breast tumors;

[0009] S4. Perform image segmentation on the breast tumor region and classify the segmented breast tumor.

[0010] According to the above aspects and any possible implementation, an implementation is further provided, wherein S1 specifically includes:

[0011] Acquire raw breast MRI image data from an MRI scanning device, normalize the grayscale values ​​of the MRI image, map the grayscale values ​​to a preset range, and adjust the spatial resolution to unify the pixel size and image size;

[0012] Calculate the global average threshold T based on the overall gray value distribution of the image g , preliminarily distinguish the breast tissue area from the background noise, the global average threshold T g The calculation formula is: Among them, I n (x, y) represents the grayscale value of the normalized image, and N represents the total number of pixels in the image;

[0013] For images with significant local grayscale changes, local contrast adjustment is further introduced to calculate the dynamic threshold T l , the calculation formula of the local contrast adjustment formula is: T l (x,y)=αT g +(1-α)·mean(N(x,y)), where α represents the weighting factor of the global and local thresholds, N(x,y) represents the local neighborhood of pixel (x,y), and mean(N(x,y)) represents the mean grayscale value of the local neighborhood around pixel (x,y);

[0014] According to the dynamic threshold T l Binarize the image and generate a segmentation mask M(x,y) to separate the breast tissue area from other non-interested areas, where M(x,y)=1 is the breast tissue area, M(x,y)=0 is the non-breast tissue area;

[0015] Use Canny edge detection to perform contour detection on the segmentation mask M(x,y), detect edge points in the image, use contour tracking algorithm to generate the complete contour of the breast tissue area, calculate the minimum bounding rectangle containing the breast tissue area based on the complete contour, and generate the breast tissue area mask M based on the minimum bounding rectangle b (x,y), where B b It is represented as the minimum bounding rectangle of the breast tissue area;

[0016] Apply morphological opening operation to the segmentation mask M(x,y) to remove small area noise points and perform dilation operation on the mask boundary;

[0017] According to the breast tissue area mask M b (x,y) inhibits the signals of subcutaneous fat and chest wall muscles, Among them, I f (x, y) represents the target MRI image containing only breast tissue;

[0018] Output the target MRI image.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method for determining the significant local grayscale change is as follows:

[0020] Calculate the variance of the grayscale value in the neighborhood N(x,y) of pixel (x,y):

[0021]

[0022] Calculate the ratio of the maximum and minimum values ​​of the neighborhood grayscale:

[0023]

[0024] Among them, μ(x,y) represents the neighborhood grayscale mean, |N(x,y)| represents the number of pixels in the neighborhood, I(i,j) represents the grayscale value of the pixel in the neighborhood, maxI(i,j) represents the maximum grayscale value in the neighborhood, minI(i,j) represents the minimum grayscale value in the neighborhood, and ε represents a positive number;

[0025] Compare the local variance with the preset variance threshold. If the local variance exceeds the preset variance threshold, there is a significant grayscale change in the area where the pixel (x, y) is located.

[0026] The local contrast is compared with the preset contrast threshold. If the local contrast exceeds the preset contrast threshold, there is a significant grayscale change in the area where the pixel (x, y) is located.

[0027] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S2 specifically includes:

[0028] From the target MRI image I f Extract the left and right unilateral regions from (x,y): I l (x,y)=I f (x,y), x∈[0,W / 2], I r (x,y)=I f (x,y), x∈[W / 2,W], W represents the image width;

[0029] For the left and right regions I l (x,y) and I r (x, y) generates a saliency map S(x, y), S(x, y) = |G x (x,y)|+|G y (x,y)|, where S(x,y) represents the significance value, G x (x,y) and G y(x,y) represents the grayscale gradient of the pixel point (x,y) in the x and y directions respectively;

[0030] Calculate the average significance value of the left and right significance maps, S l (x, y) represents the significance value in the left breast significance map, |S l | and |S r | represents the number of non-zero pixels in the saliency map; compare the difference between the left and right saliency maps, if |mean l -mean r |>T d , T d represents the significant difference threshold, then the side with higher significance is selected as the abnormal side, otherwise, both the left and right unilateral areas are selected as the abnormal side;

[0031] Perform dynamic threshold processing on the saliency map S(x,y) on the abnormal side: T S represents the dynamic threshold, T S =β·mean(s(x,y)), β represents the dynamic adjustment coefficient, according to the mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y);

[0032] For the suspected lesion area image I R The marked rectangular box area of ​​(x, y) is locally enhanced, and the marked rectangular box area image I is decomposed R The marked rectangular box area of ​​(x, y) is Gaussian blurred to extract the base layer, I b (x,y)=Gaussian(I R (x,y),σ), σ represents the standard deviation of Gaussian blur, Gaussian represents Gaussian convolution function; extract detail layer, I d (x,y)=I R (x,y)-I b (x,y),I b (x, y) represents the extraction of low-frequency components by Gaussian blur, I d (x, y) represents the high frequency component; the dynamic enhancement detail layer, I d,e (x,y)=η·I d (x,y), η represents the dynamic enhancement factor, k represents the enhancement scale factor, std represents the standard deviation of the detail layer, and ε represents a positive number; the enhanced detail layer is superimposed on the base layer to generate an enhanced image, I e (x,y)=I b (x,y)+I d,e (x,y);

[0033] Image I of the suspected lesion area after local enhancement based on the marked rectangular frame area R (x,y) generates the image I to be preprocessed o (x,y).

[0034] According to the above aspects and any possible implementation, an implementation is further provided, wherein the mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y), including:

[0035] According to the mask M R (x,y) Determine the minimum bounding rectangle B of the suspected lesion area min , B min =[x min ,x max ,y min ,y max ], x min and x max Indicates the minimum and maximum abscissas of the suspected lesion area, y min and max Indicates the minimum and maximum ordinates of the suspected lesion area;

[0036] Calculate the extended rectangle B e , the minimum bounding rectangle B of the suspected lesion area min To expand, B e =[x min -d x ,x max +d x ,y min -d y ,y max +d y ],d x =φ x ·(x max -x min ), d y =φ y ·(y max -y min ), φ x Indicates the preset horizontal expansion ratio, φ y Indicates the preset vertical expansion ratio;

[0037] According to the expanded rectangular box B e Mark the original image to generate an extended image of the suspected lesion area I R (x,y).

[0038] According to the above aspects and any possible implementation, an implementation is further provided, wherein S3 specifically includes:

[0039] The image to be preprocessed is subjected to noise reduction, contrast enhancement and edge enhancement preprocessing respectively to generate three versions of input images;

[0040] Integrate the denoised image, contrast enhanced image and edge enhanced image into three-channel input;

[0041] Build a deep learning model for breast tumor recognition, use processed and annotated breast MRI images as image datasets, and divide the datasets into training, validation, and test sets;

[0042] Use the cross entropy loss function to optimize model performance, train the deep learning model with the training set, and verify and evaluate it with the validation set and test set;

[0043] Use the trained deep learning model to identify whether a breast tumor is present.

[0044] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the image to be preprocessed is subjected to noise reduction processing, contrast enhancement and edge enhancement preprocessing respectively, specifically comprising:

[0045] Define a pixel neighborhood N(x,y) for the preprocessed image, where N(x,y) is a local window around the center pixel (x,y);

[0046] Calculate the weighted value of each pixel in the neighborhood: w(x,p) represents the grayscale similarity weight between pixel p and the center pixel (x,y), I input (p) represents the gray value of pixel p, and h represents the smoothing parameter;

[0047] Perform weighted averaging of neighborhood pixels according to the weights to generate a denoised image:

[0048]

[0049] Generate denoised image I d (x,y);

[0050] Divide the image to be preprocessed into grid areas of 16×16;

[0051] Calculate the histogram of grayscale values ​​in each grid area and clip the histogram: H c (i) = min(H(i), C), where H(i) represents the frequency of gray level i and C represents the clipping threshold;

[0052] The equalized grayscale values ​​are calculated using the cropped histogram and the grid regions are reassembled to generate a contrast-enhanced image.

[0053] Generate contrast-enhanced image I c (x,y);

[0054] Calculate the Sobel gradient in the horizontal and vertical directions for the preprocessed image:

[0055] Compute the gradient magnitude:

[0056] Superposition Enhanced Edge Features: I e (x,y)=I o (x,y)+λ·G, λ represents the enhancement coefficient;

[0057] Generate edge-enhanced image I e (x,y).

[0058] According to the above aspects and any possible implementation, an implementation is further provided, wherein the building of a deep learning model for breast tumor recognition specifically includes:

[0059] Input layer: receives the preprocessed three-channel image I m =[I d ,I c ,I e ], each channel corresponds to the denoised image, contrast enhanced image and edge enhanced image, and the output size is 256×256×3;

[0060] Initial convolution and pooling, using a 7×7 convolution kernel to extract multi-scale features, followed by a 3×3 max pooling layer to reduce the feature size, including: Convolution layer 1: convolution kernel size 7×7, stride 2, number of channels 64, output size 128×128×64, pooling layer: 3×3, stride 2, output size 64×64×64;

[0061] Construct three groups of residual modules, and add channel attention mechanism after each group of residual modules, including: residual block 1: two layers of 3×3 convolution, 64 channels, output size 64×64×64, SE module 1: channel attention mechanism, output dimension 64, output size 64×64×64, residual block 2: two layers of 3×3 convolution, 128 channels, output size 32×32×128, SE module 2: channel attention mechanism, output dimension 128, output size 32×32×128, residual block 3: two layers of 3×3 convolution, 256 channels, output size 16×16×256, SE module 3: channel attention mechanism, output dimension 256, output size 16×16×256;

[0062] Global pooling and classification layer: global average pooling is reduced to 1×1×256, and the output size is 1×1×256;

[0063] Fully connected layer: Sigmoid activation, outputting the probability value of whether a tumor exists;

[0064] The model parameters are optimized using the binary cross entropy loss function.

[0065] According to the above aspects and any possible implementation, an implementation is further provided, wherein S4 specifically includes:

[0066] Perform image segmentation on the breast tumor region to generate a tumor image, and extract features of the tumor image, the features including but not limited to: morphological features, texture features and color features, the morphological features include area, perimeter and aspect ratio, the texture features are extracted by GLCM, including contrast, correlation, energy and entropy, and the color features are color histogram information of RGB and Lab color spaces;

[0067] Perform Z-score standardization on each feature vector to generate a feature vector;

[0068] A first-level SVM classifier is pre-trained for each feature. Multiple pre-trained first-level SVM classifiers are used for first-level classification. The decision boundary of each classifier is based on the RBF kernel function. The feature vector is input and the tumor classification result is output. Category 1 is benign tumor and category 2 is malignant tumor. The final classification result is determined by a voting mechanism.

[0069] If the classification result is category 2, then enter the secondary classification;

[0070] A secondary SVM classifier is pre-trained for each of the four subtypes of malignant tumors. Multiple pre-trained secondary SVM classifiers are used for secondary classification. The output results are the confidence scores of the four categories: Luminal-A, Luminal-B, HER2+, and Basal-like. The confidence weighting strategy is used to determine the final classification result.

[0071] Generate the final classification results of breast tumors.

[0072] In a second aspect, an embodiment of the present invention provides an MRI medical image segmentation system based on deep learning, the system comprising:

[0073] A target image generation module is used to obtain a breast MRI image and perform standardization processing to remove non-interested areas to obtain a target MRI image, wherein the non-interested areas include background noise, subcutaneous fat, and chest wall muscles;

[0074] The module for generating the image to be preprocessed is used to perform preliminary target determination on the target MRI image, determine the suspected lesion area according to the preliminary target, and reconstruct the suspected lesion area into the image to be preprocessed by performing detail enhancement;

[0075] The recognition module is used to perform noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generate three versions of input images, and input them into the pre-trained deep learning model as three channels to complete the recognition of breast tumors;

[0076] The classification module is used to perform image segmentation on the breast tumor area and classify the segmented breast tumor.

[0077] One of the above technical solutions has the following beneficial effects:

[0078] The method of the embodiment of the present invention proposes a deep learning-based MRI medical image segmentation method and system, which realizes accurate and efficient processing of breast MRI images, and realizes efficient identification, accurate segmentation and precise classification of breast tumors, which can improve the accuracy and robustness of breast tumor detection and provide efficient and reliable auxiliary diagnostic tools for clinicians.

Brief Description of the Drawings

[0079] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. 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.

[0080] Figure 1 It is a flowchart of the deep learning-based MRI medical image segmentation method S1-S4 provided in an embodiment of the present invention;

[0081] Figure 2 A schematic diagram of the structure of a deep learning model provided by an embodiment of the present invention;

[0082] Figure 3 This is a functional block diagram of the deep learning-based MRI medical image segmentation system provided in an embodiment of the present invention. [Specific implementation method]

[0083] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0084] Please refer to Figure 1 , which is a flow chart of a deep learning-based MRI medical image segmentation method S1-S4 provided in an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0085] S1, obtaining breast MRI images and performing standardization processing, removing non-interested regions to obtain target MRI images;

[0086] S2, performing preliminary target determination on the target MRI image, determining the suspected lesion area according to the preliminary target, and reconstructing the suspected lesion area into an image to be preprocessed by performing detail enhancement;

[0087] S3, performing noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generating three versions of input images, and inputting them into a pre-trained deep learning model as three channels to complete the recognition of breast tumors;

[0088] S4. Perform image segmentation on the breast tumor region and classify the segmented breast tumor.

[0089] The present invention effectively reduces background and irrelevant area interference by removing non-interest areas including non-breast tissue areas, and focuses on the characteristics of the breast tissue area; the preliminary target determination step helps to identify the abnormal side, then confirms the suspected lesion area in the breast tissue area, and performs detail enhancement processing on the initially identified suspected lesion area, reducing the amount of tasks and processing, further improving the visibility of the lesion area, and improving the recognition accuracy of breast tumors; the deep learning model is used for tumor recognition accurately, quickly and efficiently, the noise reduction processing removes noise in the image, the contrast enhancement makes the tumor area more prominent, the edge enhancement strengthens the edge information of the tumor area, and the noise reduction image, the contrast enhancement image and the edge enhancement image are integrated into three-channel input. This multi-channel input method can provide richer image features for deep learning model analysis, help the model understand the characteristics of the tumor area from different angles, and improve the accuracy and robustness of the model; segmenting the tumor area can provide a clear tumor outline, which is convenient for subsequent tumor classification and further clinical analysis. The model is used to classify the tumor with high precision, combined with the malignancy and subtype classification of the tumor, to provide more comprehensive diagnostic information. The deep learning-based MRI medical image segmentation method and system provided by the present invention realize accurate and efficient processing of breast MRI images, realize efficient identification, accurate segmentation and precise classification of breast tumors, can improve the accuracy and robustness of breast tumor detection, and provide efficient and reliable auxiliary diagnostic tools for clinicians.

[0090] In a preferred embodiment of the present invention, the S1 specifically includes:

[0091] Acquire raw breast MRI image data from an MRI scanning device, normalize the grayscale values ​​of the MRI image, map the grayscale values ​​to a preset range, and adjust the spatial resolution to unify the pixel size and image size;

[0092] Calculate the global average threshold T based on the overall gray value distribution of the image g , preliminarily distinguish the breast tissue area from the background noise, the global average threshold T g The calculation formula is: Among them, I n (x, y) represents the grayscale value of the normalized image, and N represents the total number of pixels in the image;

[0093] For images with significant local grayscale changes, local contrast adjustment is further introduced to calculate the dynamic threshold T l , the calculation formula of the local contrast adjustment formula is: T l (x,y)=αT g +(1-α)·mean(N(x,y)), where α represents the weighting factor of the global and local thresholds, N(x,y) represents the local neighborhood of pixel (x,y), and mean(N(x,y)) represents the mean grayscale value of the local neighborhood around pixel (x,y);

[0094] According to the dynamic threshold T l Binarize the image and generate a segmentation mask M(x,y) to separate the breast tissue area from other non-interested areas, where M(x,y)=1 is the breast tissue area, M(x,y)=0 is the non-breast tissue area;

[0095] Use Canny edge detection to perform contour detection on the segmentation mask M(x,y), detect edge points in the image, use contour tracking algorithm to generate the complete contour of the breast tissue area, calculate the minimum bounding rectangle containing the breast tissue area based on the complete contour, and generate the breast tissue area mask M based on the minimum bounding rectangle b (x,y), where B b It is represented as the minimum bounding rectangle of the breast tissue area;

[0096] Apply morphological opening operation to the segmentation mask M(x,y) to remove small area noise points and perform dilation operation on the mask boundary;

[0097] According to the breast tissue area mask M b (x,y) inhibits the signals of subcutaneous fat and chest wall muscles, Among them, I f (x, y) represents the target MRI image containing only breast tissue;

[0098] Output the target MRI image.

[0099] The present invention eliminates the deviation between different images caused by differences in scanning conditions, equipment or image quality by standardizing breast MRI images, thereby ensuring the consistency of subsequent processing steps; by removing background noise in the image, the interference of the model on irrelevant information is significantly reduced; subcutaneous fat and chest wall muscles and the anatomical structure of the tumor easily produce similar grayscale values, which will interfere with the accurate identification of the tumor. The present invention enhances the accuracy and robustness of removing non-interest regions through local contrast adjustment, dynamic threshold segmentation and morphological operations, and by separating the breast tissue area from other non-interest areas, it can effectively reduce interference and focus on the characteristics of the tumor area.

[0100] It should be noted that the method for determining the significant local grayscale change is as follows:

[0101] Calculate the variance of the grayscale value in the neighborhood N(x,y) of pixel (x,y):

[0102]

[0103] Calculate the ratio of the maximum and minimum values ​​of the neighborhood grayscale:

[0104]

[0105] Among them, μ(x,y) represents the neighborhood grayscale mean, |N(x,y)| represents the number of pixels in the neighborhood, I(i,j) represents the grayscale value of the pixel in the neighborhood, maxI(i,j) represents the maximum grayscale value in the neighborhood, minI(i,j) represents the minimum grayscale value in the neighborhood, and ε represents a positive number;

[0106] Compare the local variance with the preset variance threshold. If the local variance exceeds the preset variance threshold, there is a significant grayscale change in the area where the pixel (x, y) is located.

[0107] The local contrast is compared with the preset contrast threshold. If the local contrast exceeds the preset contrast threshold, there is a significant grayscale change in the area where the pixel (x, y) is located.

[0108] The present invention measures the discreteness of the neighborhood grayscale values ​​according to the results of variance calculation, and can determine whether there is a significant grayscale change in the region, and adjust the dynamic threshold accordingly. By calculating the contrast to measure the difference in brightness and darkness in the neighborhood, the edges and feature areas can be highlighted for dynamic adjustment of the threshold. The combination of the two provides a reliable basis for measuring grayscale characteristics for dynamic threshold segmentation, and can effectively distinguish between breast areas and non-areas of interest.

[0109] In a preferred embodiment of the present invention, S2 specifically includes:

[0110] From the target MRI image I f Extract the left and right unilateral regions from (x,y): I l (x,y)=I f (x,y), x∈[0,W / 2], I r (x,y)=I f (x,y), x∈[W / 2,W], W represents the image width;

[0111] For the left and right regions I l (x,y) and I r (x, y) generates a saliency map S(x, y), S(x, y) = |G x (x,y)|+|G y (x,y)|, where S(x,y) represents the significance value, G x (x,y) and G y (x,y) represents the grayscale gradient of the pixel point (x,y) in the x and y directions respectively;

[0112] Calculate the average significance value of the left and right significance maps, S l (x, y) represents the significance value in the left breast significance map, |S l | and |S r | represents the number of non-zero pixels in the saliency map; compare the difference between the left and right saliency maps, if |mean l -mean r |>T d , T d represents the significant difference threshold, then the side with higher significance is selected as the abnormal side, otherwise, both the left and right unilateral areas are selected as the abnormal side;

[0113] Perform dynamic threshold processing on the saliency map S(x,y) on the abnormal side: T S represents the dynamic threshold, T S =β·mean(s(x,y)), β represents the dynamic adjustment coefficient, according to the mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y);

[0114] For the suspected lesion area image I R The marked rectangular box area of ​​(x, y) is locally enhanced, and the marked rectangular box area image I is decomposed R The marked rectangular box area of ​​(x, y) is Gaussian blurred to extract the base layer, I b(x,y)=Gaussian(I R (x,y),σ), σ represents the standard deviation of Gaussian blur, Gaussian represents Gaussian convolution function; extract detail layer, I d (x,y)=I R (x,y)-I b (x,y),I b (x, y) represents the extraction of low-frequency components by Gaussian blur, I d (x, y) represents the high frequency component; the dynamic enhancement detail layer, I d,e (x,y)=η·I d (x,y), η represents the dynamic enhancement factor, k represents the enhancement scale factor, std represents the standard deviation of the detail layer, and ε represents a positive number; the enhanced detail layer is superimposed on the base layer to generate an enhanced image, I e (x,y)=I b (x,y)+I d,e (x,y);

[0115] Image I of the suspected lesion area after local enhancement based on the marked rectangular frame area R (x,y) generates the image I to be preprocessed o (x,y).

[0116] It should be noted that breast lesions are usually concentrated in unilateral areas. Gradient change detection and difference analysis of bilateral breasts can quickly locate the abnormal side. By quickly quantifying the significant differences, only the abnormal side is processed to avoid repeated analysis of the normal side area, reduce the workload of bilateral breast analysis, and improve efficiency. If there is no significant difference, both the left and right unilateral areas are regarded as abnormal sides to avoid missing tumor targets. At the same time, the suspected lesion area is accurately identified to provide a basis for subsequent detail enhancement. Local enhancement improves the contrast and resolution of the suspected lesion area. The enhanced detail layer is superimposed on the base layer to ensure that the enhanced image is clearer. At the same time, the noise will not be excessively amplified, which provides high-quality input for the subsequent deep learning model and improves the accuracy of breast tumor recognition.

[0117] In a preferred embodiment of the present invention, the mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y), including:

[0118] According to the mask M R (x,y) Determine the minimum bounding rectangle B of the suspected lesion area min , B min =[x min ,x max ,ymin ,y max ], x min and x max Indicates the minimum and maximum abscissas of the suspected lesion area, y min and max Indicates the minimum and maximum ordinates of the suspected lesion area;

[0119] Calculate the extended rectangle B e , the minimum bounding rectangle B of the suspected lesion area min To expand, B e =[x min -d x ,x max +d x ,y min -d y ,y max +d y ],d x =φ x ·(x max -x min ), d y =φ y ·(y max -y min ), φ x Indicates the preset horizontal expansion ratio, φ y Indicates the preset vertical expansion ratio;

[0120] According to the expanded rectangular box B e Mark the original image to generate an extended image of the suspected lesion area I R (x,y).

[0121] The present invention expands the local enhancement range by dynamically expanding the rectangular frame to avoid missing tumors. In addition, it adds breast tissue information on the basis of retaining the tumor area and reasonably expands the recognition range. It can capture the boundary features of the tumor and the surrounding normal tissue, which helps the model improve the accuracy of tumor recognition and learn the characteristic differences between lesions and normal tissues.

[0122] In a preferred embodiment of the present invention, S3 specifically includes:

[0123] The image to be preprocessed is subjected to noise reduction, contrast enhancement and edge enhancement preprocessing respectively to generate three versions of input images;

[0124] Integrate the denoised image, contrast enhanced image and edge enhanced image into three-channel input;

[0125] Build a deep learning model for breast tumor recognition, use processed and annotated breast MRI images as image datasets, and divide the datasets into training, validation, and test sets;

[0126] Use the cross entropy loss function to optimize model performance, train the deep learning model with the training set, and verify and evaluate it with the validation set and test set;

[0127] Use the trained deep learning model to identify whether a breast tumor is present.

[0128] The present invention removes noise from MRI images through denoising processing to highlight the main structures and features; contrast enhancement improves the difference between light and dark in the tumor area, making it easier for the model to extract significant features; edge enhancement highlights the tumor boundary and provides clear structured information; and multi-channel input features are learned through residual networks and attention mechanisms to optimize the recognition ability of breast tumors.

[0129] Specifically, the image to be preprocessed is subjected to noise reduction, contrast enhancement and edge enhancement preprocessing, which specifically includes:

[0130] Define a pixel neighborhood N(x,y) for the preprocessed image, where N(x,y) is a local window around the center pixel (x,y);

[0131] Calculate the weighted value of each pixel in the neighborhood: w(x,p) represents the grayscale similarity weight between pixel p and the center pixel (x,y), I input (p) represents the gray value of pixel p, and h represents the smoothing parameter;

[0132] Perform weighted averaging of neighborhood pixels according to the weights to generate a denoised image:

[0133]

[0134] Generate denoised image I d (x,y);

[0135] Divide the image to be preprocessed into grid areas of 16×16;

[0136] Calculate the histogram of grayscale values ​​in each grid area and clip the histogram: H c (i) = min(H(i), C), where H(i) represents the frequency of gray level i and C represents the clipping threshold;

[0137] The equalized grayscale values ​​are calculated using the cropped histogram and the grid regions are reassembled to generate a contrast-enhanced image.

[0138] Generate contrast-enhanced image I c (x,y);

[0139] Calculate the Sobel gradient in the horizontal and vertical directions for the preprocessed image:

[0140] Compute the gradient magnitude:

[0141] Superposition Enhanced Edge Features: I e (x,y)=I o (x,y)+λ·G, λ represents the enhancement coefficient;

[0142] Generate edge-enhanced image I e (x,y).

[0143] The present invention adopts non-local mean filtering, calculates weights by neighborhood grayscale similarity, enhances the protection of tumor boundaries and textures, enhances the ability to retain breast tumor boundary details, and removes random noise from MRI images; dynamic adjustment of clipping threshold, grid division and histogram equalization make contrast enhancement more adaptable, improve the light and dark contrast between the lesion area and normal tissue, and make tumor features more obvious; the present invention superimposes edge features with the original image, enhances the significance of the boundary of the lesion area, and avoids noise amplification caused by excessive enhancement while highlighting the tumor boundary.

[0144] In a preferred embodiment of the present invention, Figure 2 As shown, the construction of a deep learning model for breast tumor recognition specifically includes:

[0145] Input layer: receives the preprocessed three-channel image I m =[I d ,I c ,I e ], each channel corresponds to the denoised image, contrast enhanced image and edge enhanced image, and the output size is 256×256×3;

[0146] Initial convolution and pooling, using a 7×7 convolution kernel to extract multi-scale features, followed by a 3×3 max pooling layer to reduce the feature size, including: Convolution layer 1: convolution kernel size 7×7, stride 2, number of channels 64, output size 128×128×64, pooling layer: 3×3, stride 2, output size 64×64×64;

[0147] Construct three groups of residual modules, and add channel attention mechanism after each group of residual modules, including: residual block 1: two layers of 3×3 convolution, 64 channels, output size 64×64×64, SE module 1: channel attention mechanism, output dimension 64, output size 64×64×64, residual block 2: two layers of 3×3 convolution, 128 channels, output size 32×32×128, SE module 2: channel attention mechanism, output dimension 128, output size 32×32×128, residual block 3: two layers of 3×3 convolution, 256 channels, output size 16×16×256, SE module 3: channel attention mechanism, output dimension 256, output size 16×16×256;

[0148] Global pooling and classification layer: global average pooling is reduced to 1×1×256, and the output size is 1×1×256;

[0149] Fully connected layer: Sigmoid activation, outputting the probability value of whether a tumor exists;

[0150] The model parameters are optimized using the binary cross entropy loss function.

[0151] The three preprocessing methods of the deep learning model of the present invention respectively extract noise reduction features, contrast features and edge features. Through the three-channel input network, the residual module extracts features of different scales layer by layer. Each group of residual modules contains two layers of convolution. The gradient vanishing problem is alleviated through residual connection, and efficient feature extraction is achieved. Multiple groups of modules gradually extract features of different scales to capture the global and local information of the tumor area. The attention mechanism dynamically adjusts the feature weights of each channel and strengthens the key features. Through the optimization of the binary cross entropy loss function, the prediction result of whether there is a tumor is output, which improves the detection accuracy of breast tumors, strengthens the fusion ability of noise reduction, contrast and edge features, enhances the comprehensive utilization ability of multi-channel features, adapts to the characteristics of breast MRI, and achieves efficient and accurate recognition.

[0152] In a preferred embodiment of the present invention, S4 specifically includes:

[0153] Perform image segmentation on the breast tumor region to generate a tumor image, and extract features of the tumor image, the features including but not limited to: morphological features, texture features and color features, the morphological features include area, perimeter and aspect ratio, the texture features are extracted by GLCM, including contrast, correlation, energy and entropy, and the color features are color histogram information of RGB and Lab color spaces;

[0154] Perform Z-score standardization on each feature vector to generate a feature vector;

[0155] A first-level SVM classifier is pre-trained for each feature. Multiple pre-trained first-level SVM classifiers are used for first-level classification. The decision boundary of each classifier is based on the RBF kernel function. The feature vector is input and the tumor classification result is output. Category 1 is benign tumor and category 2 is malignant tumor. The final classification result is determined by a voting mechanism.

[0156] If the classification result is category 2, then enter the secondary classification;

[0157] A secondary SVM classifier is pre-trained for each of the four subtypes of malignant tumors. Multiple pre-trained secondary SVM classifiers are used for secondary classification. The output results are the confidence scores of the four categories: Luminal-A, Luminal-B, HER2+, and Basal-like. The confidence weighting strategy is used to determine the final classification result.

[0158] Generate the final classification results of breast tumors.

[0159] The features extracted from tumor images by the present invention include morphological features, texture features, and color features, which cover the main information dimensions of the tumor. The voting mechanism enhances the stability of the primary classification and avoids misjudgment caused by the failure of a single classifier. The confidence weighting strategy of the secondary classification combines the results of multiple classifiers and improves the accuracy of malignant tumor subtype classification. By combining the first-level and second-level SVM classifiers, the recognition rate of malignant tumors can be improved, and the subtype classification of malignant tumors can be refined, providing diagnostic value for doctors and helping to make more accurate diagnosis and treatment decisions.

[0160] The embodiments of the present invention further provide device embodiments for implementing the steps and methods in the above method embodiments.

[0161] Please refer to Figure 3 , which is a functional block diagram of a deep learning-based MRI medical image segmentation system provided by an embodiment of the present invention, such as Figure 3 As shown, the deep learning-based MRI medical image segmentation system includes:

[0162] A target image generation module is used to obtain a breast MRI image and perform standardization processing to remove non-interested areas to obtain a target MRI image, wherein the non-interested areas include background noise, subcutaneous fat, and chest wall muscles;

[0163] The module for generating the image to be preprocessed is used to perform preliminary target determination on the target MRI image, determine the suspected lesion area according to the preliminary target, and reconstruct the suspected lesion area into the image to be preprocessed by performing detail enhancement;

[0164] The recognition module is used to perform noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generate three versions of input images, and input them into the pre-trained deep learning model as three channels to complete the recognition of breast tumors;

[0165] The classification module is used to perform image segmentation on the breast tumor area and classify the segmented breast tumor.

[0166] Since each unit module in this embodiment can execute Figure 1 For the method shown in the embodiment, the part not described in detail in this embodiment can be referred to Figure 1 Related instructions.

[0167] At the hardware level, the device may include a processor, and optionally an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. Of course, the device may also include hardware required for other services.

[0168] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0169] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0170] The steps of the method disclosed in the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0171] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0172] For the convenience of description, the above device is described as various units or modules according to their functions. Of course, when implementing the present invention, the functions of each unit or module can be implemented in the same or multiple software and / or hardware.

[0173] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0174] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0175] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0176] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0177] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0178] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0179] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0180] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0181] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0182] The present invention may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0183] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0184] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A deep learning-based MRI medical image segmentation method, characterized in that: The method comprises: S1, obtaining breast MRI images and performing standardization processing, removing non-interested regions to obtain target MRI images; S2, performing preliminary target determination on the target MRI image, determining the suspected lesion area according to the preliminary target, and reconstructing the suspected lesion area into an image to be preprocessed by performing detail enhancement; S3, performing noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generating three versions of input images, and inputting them into a pre-trained deep learning model as three channels to complete the recognition of breast tumors; S4. Perform image segmentation on the breast tumor region and classify the segmented breast tumor.

2. The method according to claim 1, characterized in that The S1 specifically includes: Acquire raw breast MRI image data from an MRI scanning device, normalize the grayscale values ​​of the MRI image, map the grayscale values ​​to a preset range, and adjust the spatial resolution to unify the pixel size and image size; Calculate the global average threshold T according to the overall gray value distribution of the image g , preliminarily distinguish the breast tissue area from the background noise, the global average threshold T g The calculation formula is: Among them, I n (x, y) represents the grayscale value of the normalized image, and N represents the total number of pixels in the image; For images with significant local grayscale changes, local contrast adjustment is further introduced to calculate the dynamic threshold T l , the calculation formula of the local contrast adjustment formula is: T l (x,y)=αT g +(1-α)·mean(N(x,y)), where α represents the weighting factor of the global and local thresholds, N(x,y) represents the local neighborhood of pixel (x,y), and mean(N(x,y)) represents the mean grayscale value of the local neighborhood around pixel (x,y); According to the dynamic threshold T l Binarize the image and generate a segmentation mask M(x,y) to separate the breast tissue area from other non-interested areas, where M(x,y)=1 is the breast tissue area, M(x,y)=0 is the non-breast tissue area; Use Canny edge detection to perform contour detection on the segmentation mask M(x,y), detect edge points in the image, use contour tracking algorithm to generate the complete contour of the breast tissue area, calculate the minimum bounding rectangle containing the breast tissue area based on the complete contour, and generate the breast tissue area mask M based on the minimum bounding rectangle b (x,y), where B b It is represented as the minimum bounding rectangle of the breast tissue area; Apply morphological opening operation to the segmentation mask M(x,y) to remove small area noise points and perform dilation operation on the mask boundary; According to the breast tissue area mask M b (x,y) inhibits the signals of subcutaneous fat and chest wall muscles, Among them, I f (x, y) represents the target MRI image containing only breast tissue; Output the target MRI image.

3. The method according to claim 2, characterized in that The method for determining the significant local grayscale change is as follows: Calculate the variance of the grayscale value in the neighborhood N(x,y) of pixel (x,y): Calculate the ratio of the maximum and minimum values ​​of the neighborhood grayscale: Among them, μ(x,y) represents the neighborhood grayscale mean, |N(x,y)| represents the number of pixels in the neighborhood, I(i,j) represents the grayscale value of the pixel in the neighborhood, maxI(i,j) represents the maximum grayscale value in the neighborhood, minI(i,j) represents the minimum grayscale value in the neighborhood, and ε represents a positive number; Compare the local variance with the preset variance threshold. If the local variance exceeds the preset variance threshold, there is a significant grayscale change in the area where the pixel (x, y) is located. The local contrast is compared with the preset contrast threshold. If the local contrast exceeds the preset contrast threshold, there is a significant grayscale change in the area where the pixel (x, y) is located.

4. The method according to claim 2, characterized in that: The S2 specifically includes: From the target MRI image I f Extract the left and right unilateral regions from (x,y): I l (x,y)=I f (x,y), x∈[0,W / 2], I r (x,y)=I f (x,y), x∈[W / 2,W], W represents the image width; For the left and right regions I l (x,y) and I r (x, y) generates a saliency map S(x, y), S(x, y) = |G x (x,y)|+|G y (x,y)|, where S(x,y) represents the significance value, G x (x,y) and G y (x,y) represents the grayscale gradient of the pixel point (x,y) in the x and y directions respectively; Calculate the average significance value of the left and right significance maps, S l (x, y) represents the significance value in the left breast significance map, |S l | and |S r | represents the number of non-zero pixels in the saliency map; compare the difference between the left and right saliency maps, if |mean l -mean r |>T d , T d represents the significant difference threshold, then the side with higher significance is selected as the abnormal side, otherwise, both the left and right unilateral areas are selected as the abnormal side; Perform dynamic threshold processing on the saliency map S(x,y) on the abnormal side: T S represents the dynamic threshold, T S =β·mean(s(x,y)), β represents the dynamic adjustment coefficient, according to the mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y); For the suspected lesion area image I R The marked rectangular box area of ​​(x, y) is locally enhanced, and the marked rectangular box area image I is decomposed R The marked rectangular box area of ​​(x, y) is Gaussian blurred to extract the base layer, I b (x,y)=Gaussian(I R (x,y),σ), σ represents the standard deviation of Gaussian blur, Gaussian represents Gaussian convolution function; extract detail layer, I d (x,y)=I R (x,y)-I b (x,y),I b (x, y) represents the extraction of low-frequency components by Gaussian blur, I d (x, y) represents the high frequency component; the dynamic enhancement detail layer, I d,e (x,y)=η·I d (x,y), η represents the dynamic enhancement factor, k represents the enhancement scale factor, std represents the standard deviation of the detail layer, and ε represents a positive number; the enhanced detail layer is superimposed on the base layer to generate an enhanced image, I e (x,y)=I b (x,y)+I d,e (x,y); Image I of the suspected lesion area after local enhancement based on the marked rectangular frame area R (x,y) generates the image I to be preprocessed o (x,y).

5. The method according to claim 4, characterized in that The mask M R (x, y) determines the minimum bounding rectangle of the suspected lesion area, marks the rectangle to generate the suspected lesion area image I R (x,y), including: According to the mask M R (x,y) Determine the minimum bounding rectangle B of the suspected lesion area min , B min =[x min ,x max ,y min ,y max ], x min and x max Indicates the minimum and maximum abscissas of the suspected lesion area, y min and max Indicates the minimum and maximum ordinates of the suspected lesion area; Calculate the extended rectangle B e , the minimum bounding rectangle B of the suspected lesion area min To expand, B e =[x min -d x ,x max +d x ,y min -d y ,y max +d y ],d x =φ x ·(x max -x min ), d y =φ y ·(y max -y min ), φ x Indicates the preset horizontal expansion ratio, φ y Indicates the preset vertical expansion ratio; According to the expanded rectangular box B e Mark the original image to generate an extended image of the suspected lesion area I R (x,y).

6. The method according to claim 1, characterized in that The S3 specifically includes: The image to be preprocessed is subjected to noise reduction, contrast enhancement and edge enhancement preprocessing respectively to generate three versions of input images; Integrate the denoised image, contrast enhanced image and edge enhanced image into three-channel input; Build a deep learning model for breast tumor recognition, use processed and annotated breast MRI images as image datasets, and divide the datasets into training, validation, and test sets; Use the cross entropy loss function to optimize model performance, train the deep learning model with the training set, and verify and evaluate it with the validation set and test set; Use the trained deep learning model to identify whether a breast tumor is present.

7. The method according to claim 6, characterized in that The preprocessing of the image to be preprocessed includes respectively performing noise reduction processing, contrast enhancement and edge enhancement preprocessing, specifically comprising: Define a pixel neighborhood N(x,y) for the preprocessed image, where N(x,y) is a local window around the center pixel (x,y); Calculate the weighted value of each pixel in the neighborhood: w(x,p) represents the grayscale similarity weight between pixel p and the center pixel (x,y), I input (p) represents the gray value of pixel p, and h represents the smoothing parameter; Perform weighted averaging of neighborhood pixels according to the weights to generate a denoised image: Generate denoised image I d (x,y); Divide the image to be preprocessed into grid areas of 16×16; Calculate the histogram of grayscale values ​​in each grid area and clip the histogram: H c (i) = min(H(i), C), where H(i) represents the frequency of gray level i and C represents the clipping threshold; The equalized grayscale values ​​are calculated using the cropped histogram and the grid regions are reassembled to generate a contrast-enhanced image. Generate contrast-enhanced image I c (x,y); Calculate the Sobel gradient in the horizontal and vertical directions for the preprocessed image: Compute the gradient magnitude: Superposition Enhanced Edge Features: I e (x,y)=I o (x,y)+λ·G, λ represents the enhancement coefficient; Generate edge-enhanced image I e (x,y).

8. The method according to claim 6, characterized in that The construction of a deep learning model for breast tumor recognition specifically includes: Input layer: receives the preprocessed three-channel image I m =[I d ,I c ,I e ], each channel corresponds to the denoised image, contrast enhanced image and edge enhanced image, and the output size is 256×256×3; Initial convolution and pooling, using a 7×7 convolution kernel to extract multi-scale features, followed by a 3×3 max pooling layer to reduce the feature size, including: Convolution layer 1: convolution kernel size 7×7, stride 2, number of channels 64, output size 128×128×64, pooling layer: 3×3, stride 2, output size 64×64×64; Construct three groups of residual modules, and add channel attention mechanism after each group of residual modules, including: residual block 1: two layers of 3×3 convolution, 64 channels, output size 64×64×64, SE module 1: channel attention mechanism, output dimension 64, output size 64×64×64, residual block 2: two layers of 3×3 convolution, 128 channels, output size 32×32×128, SE module 2: channel attention mechanism, output dimension 128, output size 32×32×128, residual block 3: two layers of 3×3 convolution, 256 channels, output size 16×16×256, SE module 3: channel attention mechanism, output dimension 256, output size 16×16×256; Global pooling and classification layer: global average pooling is reduced to 1×1×256, and the output size is 1×1×256; Fully connected layer: Sigmoid activation, outputting the probability value of whether a tumor exists; The model parameters are optimized using the binary cross entropy loss function.

9. The method according to claim 1, characterized in that: The S4 specifically includes: Perform image segmentation on the breast tumor region to generate a tumor image, and extract features of the tumor image, the features including but not limited to: morphological features, texture features and color features, the morphological features include area, perimeter and aspect ratio, the texture features are extracted by GLCM, including contrast, correlation, energy and entropy, and the color features are color histogram information of RGB and Lab color spaces; Perform Z-score standardization on each feature vector to generate a feature vector; A first-level SVM classifier is pre-trained for each feature. Multiple pre-trained first-level SVM classifiers are used for first-level classification. The decision boundary of each classifier is based on the RBF kernel function. The feature vector is input and the tumor classification result is output. Category 1 is benign tumor and category 2 is malignant tumor. The final classification result is determined by a voting mechanism. If the classification result is category 2, then enter the secondary classification; A secondary SVM classifier is pre-trained for each of the four subtypes of malignant tumors. Multiple pre-trained secondary SVM classifiers are used for secondary classification. The output results are the confidence scores of the four categories: Luminal-A, Luminal-B, HER2+, and Basal-like. The confidence weighting strategy is used to determine the final classification result. Generate the final classification results of breast tumors.

10. A deep learning-based MRI medical image segmentation system using the method of claim 1, characterized in that: include: A target image generation module is used to obtain a breast MRI image and perform standardization processing to remove non-interested areas to obtain a target MRI image, wherein the non-interested areas include background noise, subcutaneous fat, and chest wall muscles; The module for generating the image to be preprocessed is used to perform preliminary target determination on the target MRI image, determine the suspected lesion area according to the preliminary target, and reconstruct the suspected lesion area into the image to be preprocessed by performing detail enhancement; The recognition module is used to perform noise reduction, contrast enhancement and edge enhancement preprocessing on the image to be preprocessed, generate three versions of input images, and input them into the pre-trained deep learning model as three channels to complete the recognition of breast tumors; The classification module is used to perform image segmentation on the breast tumor area and classify the segmented breast tumor.

Images