165 results about "Breast tumor" patented technology

The invention discloses a Mama-based spectrum dynamic fusion and double-attention enhancement medical image segmentation method, which comprises the following steps of: firstly, constructing a Mama integrated spectrum domain and attention pyramid module, fusing spectrum dynamic characteristics and a self-attention pooling mechanism, and performing frequency domain information compensation and local characteristic enhancement to obtain a spectrum dynamic fusion image; the spatial correlation loss caused by image blocking processing is relieved; secondly, designing a layered enhanced U-shaped architecture, deploying an MISAP module in a shallow layer of an encoder to capture multi-scale global context features, introducing a bipolar routing attention mechanism in a deep layer, and dynamically allocating sparse attention weights to focus a key pathological region; according to the method, the segmentation precision of complex edge textures and tiny lesions in medical images can be remarkably improved, and the Dice coefficient in breast tumor, polyp and abdominal organ segmentation tasks is averagely improved by 6.5%.

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.

The invention discloses a breast ultrasound image processing method and device, a terminal, a storage medium and a computer program product, and the method comprises the steps: carrying out the data preprocessing of a breast ultrasound image sub-data set, obtaining a breast ultrasound image sample, and dividing the breast ultrasound image sample into a training set and a test set; the method comprises the following steps: constructing a segmentation network model by fusing an OfficientViT encoder, a symmetric U-Net decoder and a CBAM attention enhancement module, and designing a joint loss function; based on the joint loss function, training the segmentation network model by using the training set to obtain a segmentation network training model; and testing the segmentation network training model by using the test set, and optimizing the segmentation network training model according to a test result to obtain a breast tumor region segmentation model. According to the scheme, the breast tumor region segmentation model is set by fusing the codec, the CBAM attention enhancement module and the joint loss function, and the breast tumor region can be accurately segmented from the breast ultrasound image.

The invention discloses a method and a system for visually identifying hemodynamic spatial-temporal heterogeneity in breast tumors, and belongs to the technical field of medical image acquisition and auxiliary detection. The method for visually recognizing the hemodynamic spatial-temporal heterogeneity in the breast tumor comprises the steps that S1, DCE-MRI images of breast benign and malignant tumors are obtained, and region-of-interest sketching is carried out; s2, carrying out TIC automatic classification and coding on each voxel in the tumor by adopting 19 types of TIC curves to obtain a TIC color coding graph; s3, calculating the TIC type and proportion of each focus, and extracting a TIC color coding map to reflect radiomics characteristics of tumor internal heterogeneity; s4, screening the image omics characteristics of the TIC color coding image, and establishing three models including 19 types of TIC curve fusion models; according to the method, the difference of hemodynamics time and space heterogeneity in the breast tumor of the patient can be visually judged and compared, and benign and malignant and biological behaviors of the tumor of the patient are judged.

The invention provides a breast cancer recurrence risk prediction method, system and device based on an ultrasonic image, and relates to the field of intelligent medical treatment, the method uses a deep convolutional neural network to perform deep network feature extraction on a breast ultrasonic image, and uses a deep learning semantic segmentation algorithm to perform accurate positioning and automatic segmentation on a breast tumor region of interest, thereby improving the accuracy of breast cancer recurrence risk prediction. Meanwhile, habitat analysis is carried out on the ultrasonic images to extract tumor heterogeneity features, multi-level and multi-mode features such as deep learning features, radiomics features and habitat analysis features are fused, a breast cancer recurrence risk prediction model is constructed, breast cancer recurrence risk prediction is carried out, and a breast cancer recurrence risk assessment result is output. And a quantitative basis is provided for clinical treatment decisions. The accuracy and robustness of recurrence risk prediction are remarkably improved through multi-feature fusion, and standardization and objectification of breast cancer prognosis evaluation are achieved. In addition, the method further has the advantages of being easy and convenient to operate, low in cost, noninvasive, nonradiative, good in repeatability and the like.

The invention relates to the technical field of breast cancer detection based on multi-modal input, in particular to a breast cancer detection method and system based on multi-modal input. The method comprises the following steps: acquiring breast tumor original clinical data, including breast X-ray image data, medical history text data and clinical index data; preprocessing the obtained original clinical data of the breast tumor; performing feature extraction on the preprocessed breast tumor original clinical data; performing feature fusion on the extracted features to form a fusion feature vector; and performing breast cancer category classification by using the fusion feature vector. According to the application, the related information of the breast cancer is more comprehensively captured by fusing three types of multi-modal input data of the past medical history, the mammary gland X-ray and the clinical data of the patient. According to the multi-modal fusion method, the advantages of different types of data can be fully utilized, richer information is provided, and therefore the accuracy and reliability of detection are improved.

The invention discloses a breast tumor accurate segmentation method based on a space-time fusion network, which adopts the space-time fusion network fusing convolution, LSTM and PK feature fusion to capture space and time features at the same time so as to improve segmentation precision and enhance medical interpretability, and introduces LSTM into each layer of an encoder of the space-time fusion network to capture sequence dependence, so that the segmentation precision is improved. And the model can learn the mode in the whole dynamic enhancement process. In addition, parameters of the classic PK model are fused into network input and training processes, so that the medical interpretability of the model is improved. By comprehensively utilizing a deep learning framework of DCE-MRI spatio-temporal dynamic information and radiomics characteristics, high-precision segmentation of breast tumors is realized, and medically interpretable tumor characteristics are provided, so that doctors can make more accurate diagnosis and treatment plans based on segmentation results.

A breast tumor marker based on a serum protein, used for carrying out serum measurement on suspected breast tumor patients, and effectively distinguishing healthy people, breast nodule patients, and breast cancer patients. A serum protein spectrum classifier has, in an independent verification queue, an accuracy of 0.87, a precision of 0.93, a recall of 0.82, and an F1-score of 0.86. The area under the ROC curve for benign nodule patients is 0.96, the area under the ROC curve for breast cancer patients is 0.96, and the area under the ROC curve for healthy people is 0.96. The marker has high accuracy and good specificity, and has a good application prospect in clinical diagnosis of breast lesions.

The invention relates to a breast tumor lesion part ultrasonic image segmentation method based on an SAM-VMama model. The method comprises the steps of obtaining a breast ultrasonic image set and performing preprocessing; the method comprises the following steps: constructing an SAM-VMama model, wherein the SAM-VMama model comprises a first encoder, a second encoder, a prompt encoder and a mask decoder; obtaining a trained model; obtaining a preprocessed breast ultrasound image to be segmented; and obtaining a segmentation result of the breast lesion tumor. According to the method, the local features are extracted through the first extraction, the global features are extracted through the second encoder, the interactive prompt information processed by the prompt encoder is combined, and the precise segmentation mask is generated through the mask decoder; the method not only retains the flexibility of SAM interactive segmentation, but also improves the long-sequence data processing capability and calculation efficiency through a Mamba structure, effectively adapts to the characteristics of uneven gray scale, multiple artifacts and the like of the ultrasonic image, and achieves the balance of high precision and high efficiency.

The invention relates to a multi-modal image fusion breast tumor diagnosis method and device, equipment and a medium. The method comprises the following steps: acquiring a multi-modal image of a subject, and performing spatial registration processing to obtain a multi-modal image group; the multi-modal image group comprises a molybdenum target image, a B ultrasonic image and an elastic ultrasonic image; performing modal sensitive feature extraction on each modal of the multi-modal image group to obtain molybdenum target image features, B ultrasonic image features and elastic ultrasonic image features; carrying out fusion processing on the molybdenum target image features, the B ultrasonic image features and the elastic ultrasonic image features to obtain global fusion features; performing breast tumor prediction according to the global fusion features to obtain a prediction result; the prediction result comprises the breast tumor focus existence probability and the breast tumor focus type. By adopting the method, cross-modal semantic alignment can be realized while original information of each modal is reserved, and the capability of discriminating a complex breast tumor focus is enhanced through fusion discrimination.

The invention belongs to the technical field of microbial fermentation, and particularly relates to a lactobacillus rhamnosus fermentation composition, a preparation method and application of the lactobacillus rhamnosus fermentation composition in the aspects of improving the tumor cell induced inflammation level and preventing and inhibiting breast tumors. The invention provides a lactobacillus rhamnosus fermentation composition. The lactobacillus rhamnosus fermentation composition is prepared from a raw material and lactobacillus rhamnosus NKU ML1-2, the raw materials comprise raspberry, poria cocos, cape jasmine, rehmannia, liquorice and orange peel. The lactobacillus rhamnosus fermentation composition has the effects of nourishing yin and clearing heat, soothing liver and regulating qi, and strengthening spleen and stomach. Results of embodiments show that the lactobacillus rhamnosus fermentation composition can prevent proliferation, invasion and migration of breast cancer tumor cells to a certain extent, and has an improvement effect on the level of inflammation induced by the tumor cells.

This invention relates to the field of optical imaging and detection technology, specifically providing a broadband snapshot-type polarization interferometry imaging detection system and method for breast tumors. A broadband light source illuminates a breast tumor tissue sample. The reflected signal light is collected and collimated by a pre-optical system, then sequentially passes through a first and a second ray shaping unit for two shearing and two polarization state transformations, resulting in two beams of linearly polarized light and two beams of circularly polarized light. These beams are imaged by an imaging lens to an area array detector to obtain a polarization interferogram, which is then demodulated to obtain all Stokes parameters of the breast tumor tissue sample. This invention achieves broadband snapshot imaging of breast tumors with all Stokes parameters without using an analyzer, doubling the light throughput compared to traditional polarization imaging instruments using analyzers. Furthermore, this invention has advantages such as compact structure, no moving parts, easy assembly and adjustment, and no need for image registration.

The invention relates to a breast tumor segmentation method based on a multi-modal ultrasonic image, which aims to solve the problems that artifacts and low-echo regions are easily wrongly recognized as tumors and the tumor boundary segmentation precision is low in the existing breast ultrasonic image segmentation technology, and comprises the following steps of: 1, acquiring a B-type ultrasonic image and a strain elastic image which are paired, and constructing a multi-modal breast ultrasonic image data set; 2, constructing a multi-modal image segmentation model algorithm based on feature correction alignment and boundary uncertainty enhancement; 3, training a multi-modal image segmentation model based on feature correction alignment and boundary uncertainty enhancement by adopting a five-fold cross validation mode, and storing model parameters with optimal performance; and 4, loading optimal model parameters, performing reasoning prediction on images in the verification set, calculating evaluation indexes on the verification set, and comparing the evaluation indexes with other current advanced algorithms. The method can effectively improve the segmentation precision of the breast tumor in the ultrasonic image, and improves the accuracy of network prediction. The invention belongs to the field of computer vision, deep learning and medical image segmentation.

The invention belongs to but not limited to the technical field of medical images and artificial intelligence, and discloses a breast tumor benign and malignant classification method and system based on comparative learning. In the pre-training stage, a self-supervised learning strategy based on contrast learning is used, a comprehensive contrast loss function is designed, and in combination with physiological information of breast tumors, a sample is finely divided, so that the model effectively extracts discriminant features from limited annotation data; a multi-view data enhancement method is adopted, and the diversity of a training set is enhanced through image data at different angles, so that the generalization ability of the model is effectively improved. In the fine tuning classification stage, a three-branch network structure is adopted to respectively process near-infrared images and time sequence images under different wavelengths, and a feature fusion module is designed to fuse various kinds of signal information. The fine tuning stage enhances the sensitivity of the model to tumor characteristics especially aiming at the characteristics of space-time signals in an optical image data set, and especially has outstanding performance in early small tumor detection.

The invention relates to a method for extracting two tocopherol derivatives A and B from epimedium sagittatum and application of the two tocopherol derivatives A and B, which can effectively solve the problem of preparing the tocopherol derivatives A and B from the epimedium sagittatum and realizing the application of the tocopherol derivatives A and B in preparing anti-breast tumor drugs, and the method comprises the following steps: crushing the overground part of the epimedium sagittatum, carrying out reflux extraction with ethanol, carrying out reduced pressure recovery to obtain an extract, extracting, and carrying out chromatographic separation to obtain the two tocopherol derivatives A and B; and carrying out gradient elution, reduced-pressure recovery, thin-layer point plate detection, merging, purification, collection of fractions within retention time, and drying to obtain the product. The raw materials are rich, the preparation method is easy to operate, the obtained product is good in quality and good in use effect, the pharmaceutical composition can be effectively used for treating breast cancer, application in preparation of anti-breast cancer drugs is achieved, the medicinal value and commercial value of epimedium sagittatum are developed, and the pharmaceutical composition has remarkable economic and social benefits.

The present disclosure provides methods, systems, and devices for coregistering imaging data to form three-dimensional superimposed images of target such as a tumor or a surgical bed. A three-dimensional map can be generated by projecting infrared radiation at a target area, receiving reflected infrared radiation, and measuring depth of the target area. A three-dimensional white light image can be created from a captured two-dimensional white light image and the three-dimensional map. A three-dimensional fluorescence image can be created from a captured two-dimensional fluorescence image and the three-dimensional map. The three-dimensional white light image and the three-dimensional fluorescence image can be aligned using one or more fiducial markers to form a three-dimensional superimposed image. The superimposed image can be used to excise cancerous tissues, for example, breast tumors. Images can be in the form of videos.

The invention provides a high-precision lightweight breast ultrasonic tumor detection method based on Improved-YOLOv8, and the method comprises the steps: obtaining a breast ultrasonic image, carrying out the preprocessing, and obtaining a preprocessed image; performing dynamic data enhancement to obtain an enhanced image; a fast network Faster Net and a self-adaptive wavelet down-sampling layer AWFD are introduced, a backbone network AWFD-Faster Net of an Improved-YOLOv8 network extracts features of an enhanced image, and then three image features are output to a fusion network Fusion; the fusion network Fusion carries out feature fusion on the input image features and then outputs three fusion features to the detection head network Head; the detection head network Head outputs an ultrasonic breast tumor detection result; the method has the advantages of short operation time, high calculation efficiency, high detection precision and the like, and meanwhile, the method is small in network parameter quantity and low in calculation complexity.

The invention provides an ultrasonic breast tumor image segmentation method based on hierarchical cross correlation learning. The method comprises the following steps: performing multi-stage feature extraction on an ultrasonic image; performing alignment and fusion processing on the multi-stage representation features of the ultrasonic image, and performing tumor boundary feature learning in combination with a residual structure; performing alignment and fusion processing on the multi-stage representation features of the ultrasonic image, and performing enhancement and learning by using a residual structure; and performing association modeling on the fused multi-scale tumor boundary features and the fused multi-scale tumor segmentation features by using a hierarchical cross attention mechanism, and performing decoding in combination with the enhanced tumor segmentation features to obtain a tumor segmentation prediction map. According to the invention, the boundary sensing fusion module is utilized to explicitly strengthen the sensing ability to the tumor boundary area in the feature learning process, so that the problems of fuzzy boundary and inaccurate positioning in the traditional method are significantly relieved, and the fineness and coherence of tumor contour extraction are improved.

The invention discloses a postoperative rehabilitation management method for a breast tumor patient and related equipment. The method comprises the following steps: sending an exercise rehabilitation initialization instruction to a target patient under the condition that the intelligent wearable equipment monitors that the operative limb of the target patient is in a natural sagging state; under the condition that the target patient completes the exercise rehabilitation initialization instruction, positioning a shoulder joint on the operative limb side of the target patient based on a movement track collected by an intelligent wearable device; under the condition that the target patient carries out the operation limb movement rehabilitation, the operation limb movement pose of the target patient is predicted through the intelligent wearable device based on the obtained shoulder joint positioning of the target patient, so that movement rehabilitation management is carried out. The multiple problems that postoperative rehabilitation of breast tumors is a key link in continuous treatment, but at present, patient cognition is insufficient, medical resources are not evenly distributed, a rehabilitation path is not clear, and informatization support is insufficient can be solved.

According to the breast cancer prediction model construction system based on the MRI image and the storage medium provided by the invention, the model for predicting the neoadjuvant chemotherapy pathological alleviation condition and prognosis of the breast cancer patient is constructed by combining the dynamic change of the blood vessel characteristics and the clinical pathological characteristics, so that the system has relatively high accuracy and clinical practicability, and is suitable for popularization and application. And making of an individualized adjuvant therapy scheme is facilitated. The method can early predict breast pathology remission and long-term prognosis, and can early and accurately predict neoadjuvant chemotherapy curative effect and long-term prognosis of breast cancer patients. The model predicts whether a patient can achieve complete relieving of breast pathology or not by analyzing changes of breast tumor blood vessel characteristics in the chemotherapy process and correlation with clinical factors, and further predicts the recurrence-free lifetime of the patient, so that a reference basis is provided for individualized treatment.

The invention discloses a mammary gland ultrasonic video tumor segmentation method and device based on spatial-temporal feature fusion, and aims to realize accurate mammary gland tumor segmentation. The method comprises the following steps: S1, preprocessing breast ultrasound video data, dividing the data into a training set, a verification set and a test set, and processing the data set into breast ultrasound continuous frames; s2, extracting initial features of the breast ultrasonic continuous frames through down-sampling; s3, constructing a Transform module, and extracting global features of the breast ultrasonic video data; s4, constructing a spatial feature extraction module, and extracting spatial features of the initial features; s5, constructing a time feature extraction module, and extracting time features of the initial features; s6, constructing a multi-scale feature fusion module, and performing feature fusion on the time features, the spatial features and the global features to obtain final features; and S7, performing up-sampling on the final features to obtain a breast tumor segmentation map. The method can effectively improve the segmentation precision of the breast tumor, and can be used for tumor segmentation of the breast ultrasonic video.

This invention discloses a medical image segmentation method based on inverse residual attention and multi-scale context. The method inputs the medical image to be segmented into an improved RAS-UNet segmentation network model. This model introduces an inverse residual attention module at the bottleneck feature of the deepest layer of the encoder, which suppresses background through an inverse weighting mechanism and enhances attention to blurred boundaries and weak response regions. Simultaneously, a hollow spatial pyramid pooling module is introduced into the decoding path, utilizing a parallel multi-branch structure to fuse multi-scale contextual information to adapt to lesions or anatomical structures of different sizes. Skip connections between the encoder and decoder achieve efficient fusion of detailed and semantic features. This invention can significantly improve the accuracy and boundary accuracy of segmenting medical targets such as polyps, breast tumors, and the hippocampus, providing a reliable tool for clinical auxiliary diagnosis.

Breast cancer is now the most common cancer in the world, and despite treatment progression in decades in the past, metastatic breast cancer is still a disease which cannot be cured. Novel tumor specific antigens (TSA) and tumor associated antigens (TAA) expressed by breast tumor cells are described herein. Synthetic long peptides, nucleic acids, compositions, cells, TCRs, antibodies, and vaccines derived from these TSAs and TAAs are described. The use of the TSA / TAAs, nucleic acids, compositions, antibodies, cells and vaccines for the prevention or treatment of breast cancer, including triple negative breast cancer (TNBC), is also described.

This invention discloses a hydrogel for tumor treatment based on improving the tumor microenvironment to trigger gas therapy combined with chemotherapy, belonging to the field of biomedical technology. The hydrogel drug delivery system utilizes lipopolysaccharide (LPS) to induce local inflammatory stimulation, thereby altering the tumor microenvironment of breast cancer and promoting nitric oxide (NO) production. The NO donor arginine (L-Arg) and the chemotherapy drug doxorubicin (DOX) are transported using sodium alginate (ALG), achieving combined gas therapy and chemotherapy to inhibit breast tumor growth. The drug delivery system of this invention not only enhances the effect of chemotherapy but also simultaneously achieves precise drug delivery and reduces drug dosage. The preparation method of the drug delivery system of this invention is simple, safe, and without toxic side effects, showing promising application prospects in tumor treatment and possessing significant clinical value.

The present invention discloses a breast tumor cutter, comprising a gripping sleeve, a rotating structure mounted at the rear end of the gripping sleeve, a grasping and cutting structure disposed within the gripping sleeve, the gripping sleeve comprising a sleeve and a movable sleeve, the rotating structure comprising a sleeve column and a rotating seat, and the grasping and cutting structure comprising a straight rod, a plurality of clamping rods, and a cover. The present invention utilizes the rotating structure and the grasping and cutting structure disposed within the gripping sleeve. After peeling back a breast flap and finding the tumor, the rotating seat can be rotated to cause the head end of the clamping rod to pull the cover, deforming the cover and placing it over the tumor. At this point, the grasping and cutting structure can be rotated to cause the cutting head at the head end of the clamping rod to cut along the edge of the tumor, achieving rapid cutting, a complete cutting surface, and a clear cutting edge.

The invention discloses a medical image segmentation method and system based on SAM fine tuning and automatic prompt generation, and belongs to the field of medical image processing, and the method comprises the steps: employing a pixel-level feature adapter to enhance the feature representation of a medical image, and then employing low-rank adaptation LoRA to carry out the efficient fine tuning of parameters of an SAM image encoder; secondly, an automatic bounding box generator based on a Transform decoder is designed, and a target bounding box is directly regressed from image features to serve as a space prompt; and finally, constructing a two-stage mask decoder, in the first stage, generating a coarse segmentation mask by using the frozen native SAM decoder in combination with the bounding box prompt, and in the second stage, inputting the coarse mask as an additional prompt into a trainable decoder for fine segmentation, and outputting a final result. According to the method, full-automatic and high-precision medical image segmentation is realized, and the segmentation performance on various medical images such as skin lesions, breast tumors and lung nodules is remarkably improved while the original architecture advantages of the SAM are reserved.

The invention relates to the field of target grading and classification, and discloses a breast tumor grading method and device based on an ultrasonic radio frequency signal and a medium, and the method comprises the steps: obtaining a breast tumor ultrasonic original echo signal; performing multi-mode feature extraction on the original echo signal to obtain a multi-mode feature; processing the multi-mode features by adopting a dynamic feature gating unit DFG to obtain enhanced multi-mode features; and inputting the enhanced multi-mode features into a gated multi-mode Transform unit GMT for processing, and obtaining a breast tumor grading result. According to the method, the five-classification precision of 3, 4A, 4B, 4C and 5 levels of (BI-RADS) can be improved, deployment is convenient, and the classification efficiency is improved.

This invention relates to a method for precise localization of breast tumors based on multi-level double-layer coding, comprising the following steps: acquisition of scattered microwave signals; signal processing and feature construction; multi-level data label coding based on a breast model containing the tumor target: each level contains double-layer coding, the first layer of coding is coarse-grained localization, and the second layer of coding is fine-grained localization; the localization interval of the initial level is determined based on the breast model containing the tumor target; given the constraints of the termination coding, the localization interval of the next level is determined based on the current level's localization interval after data label double-layer coding; model training and coding prediction output.

The invention discloses application of the activity of a histone succinylation modification enzyme of NCOA family protein as a drug target in a drug for preventing / treating / diagnosing tumors, and belongs to the technical field of tumor biology. The level of the NCOA family protein catalyzed histone succinylation modification is obviously higher in various tumor tissues than the level of the NCOA family protein catalyzed histone succinylation modification in para-tumor tissues, which suggests that the NCOA family protein catalyzed histone succinylation modification can be used as a biomarker for occurrence and development of tumors. A cell model research result shows that the activity of the NCOA family protein catalyzed histone succinylation modification enzyme plays an important role in proliferation of various tumor cells. A mouse model shows that the activity of the histone succinylation modification enzyme of the NCOA family protein NCOA3 plays an important role in occurrence and development of breast tumors and pulmonary metastasis.

The invention provides a breast cancer ultrasonic image segmentation method and system, and relates to the technical field of image processing, and the method comprises the steps: obtaining an original breast ultrasonic image; performing data enhancement processing on the original breast ultrasound image; a breast tumor segmentation model is constructed, and the breast tumor segmentation model comprises an MGM encoder module, a multi-scale cross attention module, an edge feature refining module and an EMCAD decoder module which are connected in sequence; inputting the original breast ultrasound image after data enhancement processing into the breast tumor segmentation model, and training the breast tumor segmentation model; acquiring a real-time breast ultrasonic image; and inputting the real-time breast ultrasonic image into the trained breast tumor segmentation model, and outputting a final segmented image. The method depends on a multi-module collaborative architecture, fuses multi-scale semantic features, accurately refines tumor edges, and greatly improves tumor segmentation precision and edge positioning accuracy.