1418 results about "Human Mammary Glands" patented technology

A method for using computer-aided diagnosis (CAD) for digital tomosynthesis mammograms (DTM) including retrieving a DTM image file having a plurality of DTM image slices; applying a three-dimensional gradient field analysis to the DTM image file to detect lesion candidates; identifying a volume of interest and locating its center at a location of high gradient convergence; segmenting the volume of interest by a three dimensional region growing method; extracting one or more three dimensional object characteristics from the object corresponding to the volume of interest, the three dimensional object characteristics being one of a morphological feature, a gray level feature, or a texture feature; and invoking a classifier to determine if the object corresponding to the volume of interest is a breast cancer lesion or normal breast tissue.

The invention provides a method for detecting an X-ray mammary gland lesion image based on feature pyramid network under transfer learning. The method comprises the steps: 1, establishing a source domain data set and a target domain data set; 2, establishing a deformable convolution residual network layer by a deformable convolution and extended residual network module; 3, establishing a multi-scale feature extraction sub-network based on a feature pyramid structure through a feature map up-sampling and feature fusion method in combination with a deformable convolution residual network layer;4, establishing a deformable pooling sub-network sensitive to the focus position; 5, establishing a post-processing network layer to optimize a prediction result and a loss function; and 6, migratingthe training model to a small sample molybdenum target X-ray mammary gland focus detection task so as to improve the detection precision of the network model on the focus in the small sample image. According to the method, a transfer learning strategy is combined to realize focus image processing in a small sample medical image.

Compositions and methods for the therapy and diagnosis of cancer, particularly breast cancer, are disclosed. Illustrative compositions comprise one or more breast tumor polypeptides, immunogenic portions thereof, polynucleotides that encode such polypeptides, antigen presenting cell that expresses such polypeptides, and T cells that are specific for cells expressing such polypeptides. The disclosed compositions are useful, for example, in the diagnosis, prevention and/or treatment of diseases, particularly breast cancer.

The invention discloses a deep residual network-based semantic mammary gland molybdenum target image lump segmentation method. The method comprises the following steps of: labelling pixel categories of lumps and normal tissues corresponding to a collected mammary gland molybdenum target image so as to generate label images, and dividing the mammary gland molybdenum target image and the corresponding label images into training samples and test samples; preprocessing the training samples to form a training data set; constructing a deep residual network, and training the network by utilizing thetraining data set, so as to obtain a deep residual network training model; after a to-be-segmented mammary gland molybdenum target image lump is preprocessed, carrying out binary classification and post-processing on a pixel of the to-be-segmented mammary gland molybdenum target image by utilizing the deep residual network training model, and outputting lump segmentation image to realize semanticsegmentation of the mammary gland molybdenum target image lump. The method is capable of effectively improving the automatic and intelligent levels of mammary gland molybdenum target image lump segmentation, and can be applied to the technical field of assisting radiologists to carry out medical diagnosis.

Methods, systems, and apparatuses for automatically identifying glandular regions and tubule regions in a breast tissue sample are provided. An image of breast tissue is analyzed to detect nuclei and lumen candidates, identify tumor nuclei and true lumen from the candidates, and group tumor nuclei with neighboring tumor nuclei and lumina to define tubule glandular regions and non-tubule glandular regions of the image. Learnt supervised classifiers, such as random forest classifiers, can be applied to identify and classify the tumor nuclei and true lumina. Graph-cut methods can be applied to group the tumor nuclei and lumina and to define the tubule glandular regions and non-tubule glandular regions. The analysis can be applied to whole slide images and can resolve tubule areas with multiple layers of nuclei.

The invention relates to the field of livestock breeding technology, which specifically relates to a forage for sow during lactation period, and the forage is prepared by the following raw materials by weight: 70-80 parts of corn, 20-25 parts of bean cakes, 20-25 parts of millet bran, 15-18 parts of wheat bran, 12-15 parts of pea residues, 10-12 parts of dried white radish, 5-8 parts of black sesame, 3-4 parts of pig heat powder, 6-8 parts of vegetable sponge of luffa, 4-5 parts of fish meals, 2-3 parts of stone flour, 1-2 parts of shell powder, 5-6 parts of laver powder, 8-10 parts of purslane, 1-2 parts of radix Rhapontici, 2-3 parts of cape jasmine, 1-2 parts of pitch, 1-2 parts of Akebia stems, a proper amount of salts and 4-5 parts of feeding promoting agents. The prepared sow forage has balanced and comprehensive nutrition, and can satisfy each nutrition demand of postpartum sow, radix Rhapontici, cape jasmine, pitch, Akebia stems and other Chinese herbal medicine components can free mammary gland, increase milk yield, and the pigling fed by the sow which eats the forage has high survival rate, strong physique and red and bright hair.

The invention relates to the technical field of medical image processing, and aims to provide a dynamic ultrasonic breast nodule real-time segmentation and recognition method based on deep learning. The method comprises the following steps: collecting ultrasonic mammary gland images and videos with nodules and case data with operative pathology results, constructing a data set, constructing a static image nodule segmentation network, and training the static image nodule segmentation model on an original image; predicting an intermediate frame nodule probability by using an LSTM layer, constructing a video dynamic segmentation network, and training a dynamic segmentation model; constructing a benign and malignant identification network structure by using a basic network, and training a benign and malignant identification model; and outputting nodule position information in real time, using the benign and malignant recognition model for recognizing benign and malignant nodules of each frame, and outputting the number of output nodules and the comprehensive benign and malignant probability after examination is finished. Information incompleteness of a single image can be avoided, error detection is reduced, missing small nodules are reduced, and the nodule benign and malignant identification accuracy is improved.

The invention relates to a plaster used for treating rheumatism, rheumatoid disease and various hyperosteogeny, which consists of 35 kinds of Chinese medicine materials, including nux vomica, unprocessed radix aconiti, unprocessed wild aconite, clematis chinensis, Chuanmugua, radix cyathulae, common culbmoss herb, Chuanduan, divaricate sapshnikovia root, cassia twig and ephedra, etc. The plaster has the functions of eliminating dampness, dispelling cold, activateing collaterals, promoting blood circulation by removing blood stasis, reducing swelling and resolving masses. Therefore, the plaster has the good effects on rheumatism, rheumatoid disease, prolapse of lumbar intervertebral disc, cervical spondylosis, gonarthritis, hyperosteogeny and hyperplasia of mammary glands and no toxicity and side effects. In addition, curative effect is quick, the course of treatment is short and cure rate is high.

Provided are systems using compression devices for a mammography unit, and methods of using the same, for example, in conjunction with imaging of a patient's breast. The instant mammography units can comprise at least one x-ray transparent inflatable chamber for containing a fluid. When fluid is introduced into the chamber, at least one surface of the chamber expands, breast motion is limited, and the breast and its vasculature are compressed. Fluid may also be released from the chamber, and as the chamber deflates, blood flow to the breast is restored, producing Korotkoff sounds that may be detected by a sound detection device. The detected sounds may be used to assist a radiologist in identifying regions of interest on a mammogram, and additionally or alternatively may be used to a enhance a computer-assisted detection (CAD) process by contributing an additional data parameter.

The invention provides an MRI image-based axillary lymph gland metastasis prediction system and relates to the technical field of computer aided diagnosis. The system comprises an input module, an area-of-interest extraction module, a lump segmentation module, a sub-visualization module, a feature extraction module, a feature dimensionality reduction module, a classification and diagnosis module,and an output module. The input module receives a to-be-diagnosed mammary gland DCE-MR image sequence input by a user. The area-of-interest extraction module extracts an area of interest from the mammary gland DCE-MR image sequence. The lump segmentation module segments a lump in the area of interest. The sub-visualization module carries out the visual display on each segmented image and extractsthe edge of a focus. The feature extraction module extracts relevant feature values according to the lump information and transmits the relevant feature values to the feature dimensionality reductionmodule. The feature dimensionality reduction module carries out feature dimensionality reduction on an extracted feature set. The classification and diagnosis module inputs each lump feature value into a classifier. After that, the automatic classification and recognition is carried out by a computer for judging whether a lymph gland has already been transferred or not. The output module displaysa transfer prediction result and a transfer probability. According to the invention, the accurate segmentation of breast lesions can be realized. The accurate diagnosis of mammary axillary lymph glandmetastasis can be effectively assisted.

The computer aided method of predicting mammary cancer risk includes the following steps: partitioning the mammary gland tissue area in X-ray image regularly into small areas and calculating the density characteristic value of the small areas; selecting the initial suspicious small areas with the preset density characteristic value for further research; judging the final suspicious small areas based on the information in the corresponding areas and the adjacent areas on two frames of images; and judging the near term mammary cancer risk of the testee based on the existence of the marked final suspicious small areas and predicting the mammary cancer position in case of existing final suspicious small areas. The present invention has high objectivity, high accuracy and high effectiveness in predicting mammary cancer risk.

The invention discloses a breast tumor partition method. The breast tumor partition method includes the steps of building a coupled framework of classifications and biased field correcting of breast tissue nuclear magnetic resonance images, enhancing the breast areas and the peripheral areas, and partitioning the breast tumor images in cooperation with the shape prior. By means of the breast tumor partition method, biased field information is fused into classification models, the biased field information and the classification models are combined to be the unified framework, and the classifications and a corrected biased field of the breast tumor nuclear magnetic resonance images are solved with the fast energy minimization method at the same time, and use information of each other in the model evolution process for finally achieving accurate solving of the classifications and the corrected biased field; the shapes of blood vessels and tumors are analyzed, differences of the blood vessels and the tumors in shape are caught, the shape prior is built through parameters such as the characteristic values and the characteristic vectors, and the level set driving force based on the shapes is built and combined with a level set method based on local information, so that a level set overcomes the interference of a tubular structure during evolution and only captures the breast tumor area.

The invention relates to a dairy cattle feed additive containing selenium-enriched yeast, iron-enriched yeast and zinc-enriched yeast. The dairy cattle feed additive comprises selenium-enriched yeast powder, iron-enriched yeast powder, zinc-enriched yeast powder, mycotoxin adsorbent, compound vitamin, compound probiotics and a biological antioxidant. The dairy cattle feed additive is reasonable in formula, contains various organic microelements and probiotics and is balanced in nutrition, antibiotics are not added, the number of somatic cells in milk can be effectively reduced, the occurring rate of recessive mastitis of dairy cattle is reduced, a good developmental mechanism of a mammary gland system is maintained, milk yield and milk quality are improved, white muscle disease of the dairy cattle can be prevented, the requirements of vast farmers at present can be met, and the dairy cattle feed additive is suitable for development of green healthy cultivation and production of pollution-free ruminant products.

The invention discloses an early-stage breast cancer nondestructive screening and imaging system, which mainly comprises circular gears, an annular ultrasonic array, a bowl-shaped annular shell, ultrasonic coupling liquid, a protective film, a horn antenna, a waveguide tube, a microwave generator, a frequency divider, a data acquisition circuit, a preprocessing circuit, a stepping motor, a driver, a digital I/O card, a computer and a display. The working process of the system is that: detected mammary glands are radiated by pulse microwaves to generate thermo-acoustic signals which are received by the annular ultrasonic array and are finally acquired by the computer; the driver drives the annular ultrasonic array to rotate to a next position around detected biological tissues; and the steps of acquisition and rotation are repeated until the thermo-acoustic signals of enough positions are received, and the computer re-establishes three-dimensional thermo-acoustic images of the detectedtissues. The early-stage breast cancer nondestructive screening and imaging system can quickly and nondestructively realize the three-dimensional thermo-acoustic imaging of the detected mammary glands to provide one of important bases for the diagnosis of early-stage breast cancer.

The invention provides a mammary gland ultrasonic image quality evaluation method based on auxiliary diagnosis and subjective aesthetics. The method comprises the following steps: firstly, constructing an ultrasonic image database of a small amount of strong supervision annotation information and a large amount of weak supervision annotation information; secondly, training an automatic segmentation deep network model DNN-1 of the focus ROI by adopting various small sample learning strategies; then, inputting the ROI image and the original image together, and performing training to obtain an ultrasonic auxiliary diagnosis network DNN-2; and finally, migrating a convolutional layer of the auxiliary diagnosis network, performing training to obtain an aesthetic evaluation convolutional neuralnetwork DNN-3 in combination with the aesthetic score of the doctor, and obtaining a breast ultrasonic image quality evaluation result in combination with DNN-2 based on an action mechanism.