78 results about "Breast lesion" patented technology

The invention discloses a breast cancer immunohistochemical index and typing prediction method and system. The prediction method comprises the following steps: acquiring a breast ultrasound image and a breast magnetic resonance image of a patient; performing preprocessing and quality control on the mammary gland ultrasonic image and the mammary gland magnetic resonance image; performing breast lesion area segmentation by adopting a deep learning model to obtain segmented lesion areas; based on the segmented lesion area, extracting multi-modal radiomics characteristics of the breast ultrasonic image and the breast magnetic resonance image; fusing the multi-modal radiomics characteristics of the breast ultrasound image and the breast magnetic resonance image, constructing a machine learning model, and performing immunohistochemical index prediction to obtain an immunohistochemical index prediction result; and performing breast cancer molecular typing analysis according to the immunohistochemical index prediction result. By fusing the multi-modal image information, the tumor features can be described more comprehensively, and the accuracy of biomarker prediction is improved.

The invention discloses a DBT reconstruction method based on three-dimensional coordinate displacement, a medium and electronic equipment, and the method comprises the steps: firstly obtaining multi-angle X-ray projection data, and carrying out the preprocessing of the X-ray projection data; then, the preprocessed projection data is corrected in a three-dimensional coordinate system; after interpolation processing is conducted on the projection data, an autoregressive source moving average separation method is adopted for processing, and a cross-sectional image of any depth is obtained; and finally, carrying out image enhancement on the cross-sectional image. According to the method, DBT reconstruction is carried out through three-dimensional displacement by utilizing multi-angle X-ray projection, and the structure in the reconstruction layer is effectively focused through multi-angle projection displacement correction and magnification standardization, so that the structure in the reconstruction layer is clearer, artifacts in the reconstruction layer are reduced, the contrast of the image of the reconstruction layer is improved, and observation of breast lesions is more facilitated.

The invention discloses a breast lesion matching method and system based on an ultrasonic image, and relates to the technical field of breast lesion matching, and the method specifically comprises the following steps: obtaining echo interference analysis information from extracted lesion detection information, and carrying out the analysis after obtaining, judging whether the boundary and the form of the breast lesion can cause inaccurate recognition due to echo interference or not; according to the judgment result, an echo suppression mechanism used for suppressing the influence of echo interference on focus recognition is constructed, and the constructed echo suppression mechanism is applied to an image processing flow; in the application process of the echo suppression mechanism, echo suppression feedback information is obtained in real time, analysis is carried out after obtaining, whether the suppression effect of the echo suppression mechanism can meet the requirement for distinguishing the breast focus and the echo interference area or not is evaluated, and the echo suppression mechanism is adjusted according to the evaluation result. The problem that the mammary gland lesion and the vascular echo interference are difficult to distinguish is solved, and the lesion recognition accuracy is improved.

The invention provides a mammary gland pathological cell image detection method and system based on an improved YOLOv7 model, and the method comprises the steps: S1, collecting mammary gland pathological section images, and generating a data set; s2, carrying out frame labeling on cells obtained in the data set; s3, segmenting the marked data set into a training set and a verification set; step S4, establishing a YOLOv7 network, and respectively adding an ASFF module and a CBAM module in three branches of the YOLOv7 network to obtain an improved YOLOv7 network; and S5, inputting the training set into the improved YOLOv7 network for training, and verifying to obtain the high-precision breast pathological section cell detection model. The improved model can effectively avoid a large semantic gap between non-adjacent levels of mammary gland cell characteristics, greatly enhance the detection capability of multi-scale cell targets such as tiny early cancer cells and large normal mammary gland tissue cells, and improve the recognition precision of early mammary gland lesion cells.

The invention discloses an ultrasonic image and deep learning combined breast lesion automatic identification method, and relates to the technical field of breast lesion identification, and the method comprises the following steps: obtaining an original two-dimensional ultrasonic image of a breast part; inputting the preprocessed ultrasonic image into a feature extraction neural network with a multi-scale attention mechanism; inputting the preprocessed image into a super-resolution reconstruction network for image detail enhancement to generate a super-resolution enhanced image; sample expansion is carried out on the training data based on a generative adversarial network containing a category label condition, and the generative adversarial network is used for generating a composite image corresponding to the lesion category, namely a condition generation image; and inputting the joint features into a subsequent classification sub-network to realize category discrimination and refined classification of the breast lesion, and outputting a breast lesion category prediction result. Training the classification sub-network by adopting a joint loss function; according to the method, the fine-grained classification problem in automatic breast lesion recognition is solved.

The invention provides a tumor real-time tracking and benign and malignant detection method based on a mammary gland ultrasonic video, and the method comprises the steps: collecting the mammary gland ultrasonic video in real time, and inputting the mammary gland ultrasonic video into an UltraNet basic detector to generate a detection result of each frame of ultrasonic image; calculating a similarity probability matrix Si, j to reflect the similarity between the ith target and the jth target; performing cascade matching on the target based on the similarity probability matrix Si, j to form a target track pipeline Ti; and carrying out confidence coefficient updating and bounding box position updating on the detection result in each target track pipeline Ti, and finally outputting a breast lesion detection result of each frame of ultrasonic image. The tumor real-time tracking and benign and malignant detection method based on the breast ultrasonic video has the advantages of high calculation efficiency, high precision and strong robustness, can assist a doctor in accurately positioning a focus and performing diagnosis, and is beneficial to early discovery of breast diseases.

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 is applied to the technical field of medical image segmentation, and discloses a dual-encoder breast cancer medical image segmentation method based on spatial entropy enhancement, and the method takes a U-Net network of a dual-encoder structure as a trunk frame, and fuses the spatial entropy feature of an ultrasonic image and the feature of an original image, so as to improve the segmentation precision of a breast focus area. According to the double-encoder breast cancer medical image segmentation method based on spatial entropy enhancement, the edge feature expression capability is enhanced, and the statistical characteristics and uncertainty of boundary fuzzy regions such as lesion edges in an ultrasonic image are effectively captured and emphasized by calculating and utilizing entropy information of a local region of the image; therefore, the recognition and expression ability of the model to the focus boundary is improved, a dual-path network structure is constructed, depth texture features and entropy-based statistical features are extracted respectively, effective complementation and integration among different dimension features are realized through a designed information fusion module, and the limitation of insufficient single-mode feature extraction is overcome.

The invention discloses a focus intelligent detection and diagnosis system based on multi-modal medical image fusion, and relates to the technical field of medical image processing. A molybdenum target image, an ultrasonic image and a magnetic resonance image of the mammary gland of the patient are acquired through the data acquisition module; a data registration module is used for carrying out partition affine and local refinement registration on the multi-modal image by taking the molybdenum target image as a geometric anchor point and combining parameters such as compression force; extracting registered fusion feature data through a data fusion module; generating a candidate focus set by a focus detection module; finally, the risk assessment module calculates a comprehensive risk score through weighting, correction and consistency verification processes based on a plurality of interpretable feature indexes. And the output module generates a visual diagnosis result. According to the invention, intelligent detection and risk assessment of rechecking of breast lesions are realized, and the diagnosis accuracy and clinical reliability are effectively improved.

The invention belongs to the technical field of focus progress trend prediction, and provides a mammary gland focus progress trend prediction method based on multimode data fusion, and the method comprises the steps: analyzing the local correlation degree between each index and a clinical progress state label and the change acceleration degree of each index in a time window; obtaining the sensitivity degree of each index of each patient in each time window; analyzing the individual physique difference degree of each index according to the sensitivity degree, further analyzing the stage difference degree of each index in each time window, and determining the fluctuation degree of each index of each patient in each time window; based on the sensitivity degree and the fluctuation degree, the fusion weight of each index of each patient in each time window is obtained, fusion of multiple indexes is carried out, and breast focus progress trend prediction is achieved. The method can overcome the defect that a fixed weight index fusion method is insufficient in adaptability in the mammary gland lesion evolution process, and the prediction accuracy of the mammary gland lesion progress trend is improved.

The invention discloses a three-dimensional reconstruction and lesion detection system for a mammary gland ultrasonic image, which comprises an ultrasonic data acquisition unit, a mammary gland ultrasonic image preprocessing module, a three-dimensional reconstruction module, a lesion detection and classification module, a data storage and management module and a report generation and display interaction module, the ultrasonic data acquisition unit is used for acquiring breast ultrasonic images, the breast ultrasonic image preprocessing module is used for image preprocessing, the three-dimensional reconstruction module is used for image three-dimensional reconstruction, the lesion detection and classification module is used for detecting and classifying breast lesions, and the data storage and management module is used for storing and managing data and detection reports. And the report generation and display interaction module is used for providing a report and an interaction interface. According to the three-dimensional reconstruction and lesion detection system for the mammary gland ultrasonic image, a mammary gland ultrasonic image three-dimensional modeling algorithm based on tomography is provided for three-dimensional reconstruction of the mammary gland image; and a mammary gland lesion detection algorithm based on the fuzzy neural network is provided to detect and identify mammary gland lesions.

The present invention provides a method and apparatus for segmenting breast ultrasound images. The method comprises: obtaining an ultrasound image of a subject's breast region, the ultrasound image containing a breast lesion; identifying the type of BI-RADS feature of the breast lesion based on the ultrasound image, the BI-RADS feature including at least one of shape feature, direction feature, edge feature, internal echo feature, rear echo feature, calcification feature, and blood flow feature; determining a target segmentation model corresponding to the type of BI-RADS feature from a plurality of segmentation models corresponding to each type of preset BI-RADS feature; and performing breast lesion segmentation on the ultrasound image using the target segmentation model to segment the breast lesion region from the ultrasound image. The characteristics of the breast lesion are fully considered during segmentation, and the accuracy of breast lesion region segmentation is improved by using the target segmentation model corresponding to the type of BI-RADS feature to segment the ultrasound image.

A system for annotating mammography images of a subject using thermal images and mammography images of the subject using thermal and mammography images of the subject by (i) identifying a first breast region in the thermal image and a second breast region in the mammography image, (ii) identifying a block of pixels (Pt) with a high temperature region associated with a breast lesion within the first breast region, (iii) estimating a location (l) of the breast lesion corresponding to the identified block of pixels (Pt), (iv) determining a block of pixels (Pm) corresponding to the location (l) of the block of pixels (Pt) within the second breast region and (v) generating a report with an annotated mammography image with markings of a determined block of pixels (Pm) on the mammography image of the subject corresponding to the block of pixels (Pt) on the thermal image.

The present application relates to a kind of breast lesion three-dimensional reconstruction methods, comprising the following steps: step S1, breast region self-rough scanning is carried out, and the depth image of breast region is acquired, and the three-dimensional model of breast and spatial position are determined;Step S2, fine scanning is carried out to lesion, and AI algorithm is used to realize the segmentation extraction of lesion feature in ultrasonic image;Step S3, with time, ultrasonic rotation angle or ultrasonic moving distance as interval, the image and position information of lesion feature during fine scanning are sequentially recorded;Step S4, the three-dimensional reconstruction of lesion is realized by three-dimensional reconstruction algorithm based on lesion boundary point cloud feature;Step S5, according to the recorded center position of lesion feature, the three-dimensional spatial position of lesion is calculated by weighted average method;Step S6, by breast center coordinates and lesion center coordinates, the relative position relationship of both based on mechanical arm coordinates is determined, and breast lesion three-dimensional reconstruction method is realized.The method can help doctor to determine the shape, size and its position in breast of lesion.

The present application relates to an image prediction model generation method, device, computer device, and storage medium. The method includes: obtaining a pre-trained multi-view image generation model; obtaining a first training sample, inputting the non-target side medical image of the first sample object into the pre-trained multi-view image generation model to obtain the target side prediction image of the first sample object; performing self-supervised training on the feature extraction model to be trained according to the target side medical image and the target side prediction image of the first sample object to obtain a pre-trained feature extraction model; obtaining a second training sample, using the target side medical image of the second sample object as input and the target side annotation image of the second sample object as supervision information to train the region prediction model to be trained to obtain a pre-trained region prediction model. By using this method, self-supervised adversarial training can be performed based on multi-view images, improving the training efficiency and training accuracy, and achieving an accurate breast lesion segmentation effect.

This invention provides a method for classifying benign and malignant breast lesions, comprising the following steps: Step 1, obtaining N sample pairs; Step 2, constructing a benign and malignant breast lesion classification model; Step 3, training the benign and malignant breast lesion classification model; Step 4, obtaining the benign and malignant breast lesion classification results. This invention can effectively improve the segmentation accuracy of breast lesions in radiological images, providing more accurate and reliable benign and malignant classification results, and has potential clinical application value.

This technology relates to a method of treating breast cancer in a subject and a method of determining whether a breast lesion in a subject is malignant or benign using miRNA panels.

The invention discloses a breast lesion image semi-supervised segmentation method. The method belongs to the technical field of medical image processing and semi-supervised learning. The technical problem of improving the feature extraction capability of a semi-supervised segmentation network in a complex breast lesion segmentation scene under a small number of labeled samples and realizing accurate segmentation of a breast lesion is solved. A dual-scale image of a mammary gland image is generated, so that the network can learn features of mammary gland lesions from different scales, the ability of the network to extract features of mammary gland lesions with large size and form differences is improved, data perturbation is performed on the dual-scale image, and a generator performs mutual generation on segmentation confidence maps of a source image and a perturbation image. And the network stability is improved. Through cascade inverse residual coding feature fusion and decoding feature fusion, more potential features are mined by using existing features, the segmentation performance of the network is improved under a small number of labeled samples, and accurate segmentation of a breast lesion can be realized by inputting a breast image into a final segmentation network.

The present invention provides a method and device for assisting breast cancer identification based on multi-parameter MRI images. The method comprises: respectively acquiring breast MRI images I1 and I2 of a DWI-ADC sequence and a T1 dynamic enhancement sequence of the same patient; extracting a 3D image block I from I1 1‑3D ; Extract 3D image blocks from I2 and stitch them together in time order to form 4D image blocks I containing time information 2‑4D ; Use 3D convolutional neural network module to respectively analyze 3D image blocks I 1‑3D , 4D image block I 2‑4D Feature extraction is performed to obtain features F1 and F2; features F1 and F2 are fused, and the fused features are input into a benign and malignant classifier to obtain the benign and malignant nature of the breast lesion. The present invention improves the recognition accuracy of breast cancer by extracting and fusing features of two different sequences of MRI images.

The invention discloses a multi-degree-of-freedom precise puncture locator for breast lesions, and relates to the technical field of medical instruments, the multi-degree-of-freedom precise puncture locator comprises a locating piece, a lower control part and an upper control part, after a lower pressing plate is loosened, a middle spring, a lower spring and a lower pressure spring are reset, so that an abutting head abuts against the locating piece, a middle abutting column abuts against a folding rod, and the upper control part is connected with the upper control part; the effect of simultaneously controlling the position of the lower control part in the positioning piece and the height and torsion degree of the folding rod in the vertical cylinder is achieved; the control button is rotated to enable the inclined push block to move, when the inclined push block is far away from the inclined plane block, the push frame is separated from the side frame, the side frame is pulled by the front tension spring to be close to the sliding rod, the front abutting rod loses the force of abutting against the pointer shaft, when the inclined push block is close to the inclined plane block, the push frame pushes the side frame, the side frame and the sliding rod jointly abut against the folding rod, and the front abutting rod abuts against the pointer shaft. And the effect of simultaneously controlling the telescopic and circumferential rotation positions of the upper control part on the folding rod and the rotation angle of the positioning ring on the front frame is achieved.

The invention relates to the technical field of traditional Chinese medicines, in particular to application of a traditional Chinese medicine composition in preparation of medicines for preventing and treating hyperplasia of mammary glands. Research finds that the traditional Chinese medicine composition prepared from rhizoma arisaematis, radix aconiti, clove, cinnamon, radix angelicae, asarum, ligusticum wallichii, cynanchum paniculatum, frankincense, myrrh, camphor and borneol can obviously reduce the diameter, height and uterine coefficient of nipples of rats with hyperplasia of mammary glands, improve the lesion state of mammary glands, and can obviously improve the levels of sex hormones E2, P, LH and FSH of serum, which indicates that the traditional Chinese medicine composition can be used for treating hyperplasia of mammary glands. The traditional Chinese medicine composition can effectively improve the sex hormone level and the breast lesion state, and has a good treatment effect on rat mammary gland hyperplasia induced by estrogen and progesterone.

The present invention discloses a breast tomographic ultrasound elastography device, comprising: a water tank for containing water to facilitate ultrasonic wave propagation and imaging, and an annular ultrasonic probe moving up and down is arranged inside it; a squeezing mechanism arranged at the inner bottom end of the water tank; a grip arranged below the water tank; a brake handle arranged inside the grip, and the brake handle is slidably connected to the grip. This breast tomographic ultrasound elastography device can be used for breast lesion examination to detect possible lesion tissues, so as to achieve early detection and early treatment of breast cancer. The device uses ultrasonic waves to detect the breast in all directions, and at the same time uses a sound penetration sheet to squeeze the breast 360 degrees, and forms an image in a computer. Then, through the comparison of the body elasticity, the position of the lesion is detected, so as to realize breast lesion examination. The device has the characteristics of low examination cost and no harm to the body, and elastography can assist tomographic imaging in the diagnosis of the affected area.

The embodiment of the invention provides a multi-dimensional data processing method and system for emergency data quality control analysis. The method comprises the following steps: acquiring photoacoustic blood flow imaging data and emergency treatment parameters of a patient, and generating multi-channel time-space associated data; constructing a laser pulse energy density correction module, generating an ultrasonic receiver sensitivity adjustment parameter through a dynamic coupling mechanism of the pain index grade and the capillary density distribution, and synchronously establishing a phase synchronous compensation network of the laser pulse repetition frequency and the heart rate variability; generating a real-time three-dimensional photoacoustic blood flow dynamic image, and dividing a difference region into a high-perfusion core region, a transition region and a low-perfusion marginal region based on blood flow velocity gradient mutation point spatial distribution; and generating an emergency treatment quality control score based on the dynamic correlation function, the mapping relation and the coupling coefficient. According to the technical scheme provided by the embodiment of the invention, real-time three-dimensional blood flow visualization and accurate partition diagnosis of mammary gland lesions are realized, and the pathological relevance of emergency treatment quality control scores is remarkably improved.

The present invention discloses a method, device, equipment and medium for rapid segmentation of breast lesion features in medical videos, and relates to the field of artificial intelligence technology. The method comprises: aligning the features of a first feature map to obtain a second feature map; collecting local context features of the second feature map based on a first deep convolutional layer and a first point-by-point convolutional layer to obtain a third feature map; and enhancing the features of the third feature map based on a second point-by-point convolutional layer and a third point-by-point convolutional layer to obtain a fourth feature map. Based on the deep convolutional layer and the point-by-point convolutional layer, the amount of data during feature collection can be reduced, thereby improving the reasoning speed. At the same time, after feature alignment, the local context features of the second feature map are collected by combining the first deep convolutional layer and the first point-by-point convolutional layer; the features of the third feature map are enhanced by combining the second point-by-point convolutional layer and the third point-by-point convolutional layer, which can significantly improve the accuracy of recognition.

The application discloses a breast lesion image semi-supervised segmentation method, and belongs to the technical field of medical image processing and semi-supervised learning. The technical problem of improving the feature extraction capability of a semi-supervised segmentation network in a complex breast lesion segmentation scene and realizing accurate segmentation of a breast lesion is solved. A double-scale image of a breast image is generated, so that the network can learn the features of the breast lesion from different scales, and the feature extraction capability of the network for the breast lesion with a large size and shape difference is improved. Meanwhile, data disturbance is performed on the double-scale image, the segmentation confidence maps of the source image and the disturbed image are generated by the generator, and the stability of the network is improved. Through cascading inverse residual coding feature fusion and decoding feature fusion, more potential features are mined from existing features, the segmentation performance of the network is improved under a small amount of labeled samples, and accurate segmentation of the breast lesion can be realized by inputting the breast image into the final segmentation network.

The invention discloses an automatic prompting lightweight ultrasound image breast lesion segmentation method and system, and the method comprises the steps: obtaining breast lesion ultrasound image data, and carrying out the data preprocessing, and obtaining a preprocessed breast lesion ultrasound image; an encoder module, an automatic prompt module and a decoder module are introduced, and an ultrasonic image mammary gland lesion segmentation model is constructed; and based on the ultrasonic image mammary gland lesion segmentation model, performing segmentation processing on the preprocessed mammary gland lesion ultrasonic image to obtain a mammary gland lesion ultrasonic image segmentation result. Complex structures and weak boundaries can be captured more finely in a breast ultrasound image segmentation task, and the segmentation precision is improved. The lightweight ultrasonic image breast lesion segmentation method and system with the automatic prompting function can be widely applied to the technical field of ultrasonic image segmentation.

The invention discloses a system for multispectral transmission image breast lesion segmentation, and belongs to the technical field of medical image processing and optical imaging. The system comprises an image preprocessing subsystem and a semi-supervised segmentation network subsystem. The preprocessing subsystem adopts modulation and demodulation, frame accumulation and a scattering correction technology based on spatial pyramid matching, so that the signal-to-noise ratio and the contrast ratio of an image are remarkably improved; the segmentation network subsystem proposes a multi-head consistency semi-supervised network model, integrates a multi-head decomposition module and a cross supervision module, and effectively utilizes unlabeled data to carry out uncertainty modeling and consistency constraint. Under the condition that only 20% of labeled data is used, the segmentation Dice coefficient on the self-built mammary gland phantom data set reaches 91.47% and is obviously superior to that of an existing method, and a complete solution from image enhancement to intelligent segmentation is provided for early diagnosis of the breast cancer.

The invention discloses a multifunctional radiofrequency ablation system for treating breast lesions, which comprises a radiofrequency host, an ablation electrode, a pumping device and a controller, the radiofrequency host is electrically connected with the controller, both the ablation electrode and the pumping device are electrically connected with the radiofrequency host, and the pumping device is communicated with the ablation electrode; the ablation electrode comprises a needle rod assembly and a handle assembly, a first channel is formed in the needle rod assembly, a second channel communicated with the first channel is formed in the handle assembly, and a quick-release connecting piece communicated with the second channel is arranged outside the handle assembly. The device integrates the five functions of radiofrequency ablation, cyst fluid suction, sampling biopsy, smoke exhaust and flushing, cyst fluid suction, sampling biopsy, flushing operation and inner cyst wall ablation in the normal saline environment are achieved through the ablation electrode, instruments do not need to be replaced repeatedly in the ablation process, smoke generated in the ablation process can be exhausted, and the ablation efficiency is improved. And the operation efficiency and the ablation accuracy are effectively improved.

The present invention provides a method for segmenting breast lesions from ultrasound videos that integrates spatiotemporal information, relating to the field of image processing technology. The method extracts temporal information from ultrasound video sequences in parallel through interval sampling, and adds the extracted temporal information sequence to the original sequence, using this as input information for spatial information extraction. During the spatial information extraction process, the temporal dimension and the channel dimension are merged to streamline the data structure, further reducing computational complexity through window grouping. Furthermore, through attention learning with a moving window, more comprehensive information learning and feature extraction can be performed. This method achieves effective extraction of spatiotemporal information, has better performance, and significantly reduces computational complexity, cost, and time, enabling end-to-end segmentation of ultrasound videos.