61 results about "Nodulations" patented technology

The invention discloses a thyroid ultrasound image nodule segmentation method based on spatio-temporal characteristics and frequency enhancement, and relates to the technical field of medical image processing, and the method comprises the following specific steps: multi-scale characteristic extraction: carrying out the gray normalization and median filtering preprocessing of a thyroid ultrasound image, and carrying out the segmentation of the thyroid ultrasound image nodule; extracting multi-scale features under different resolutions by using a pre-trained backbone network, obtaining the multi-scale features under different resolutions by the backbone network through multilayer convolution and pooling operation, and generating basic and advanced features through convolution operation; by designing an intra-frame feature extraction architecture based on frequency feature fusion and an efficient spatial-temporal feature aggregation mechanism, intra-frame and inter-frame collaborative optimization is realized, intra-frame and inter-frame high and low frequency features are solved by means of octave convolution, the perceptual ability of a model to ultrasonic image details and semantic information is improved, inter-frame and inter-frame feature fusion is realized, and the perceptual ability of the model to ultrasonic image details and semantic information is improved. A local circulation neighborhood propagation mechanism is adopted, and interference of thyroid movement on time sequence feature extraction is effectively restrained.

The invention belongs to the technical field of medicine, and discloses a pulmonary nodule intraoperative positioning system based on a flexible array sensor, which comprises the flexible array sensor arranged at a to-be-detected part of a subject and used for collecting a compression signal and obtaining stress distribution; the nodule area detection unit is used for determining a nodule area through clustering analysis according to the stress distribution; the morphological optimization unit is used for performing morphological optimization on the determined nodule region; the feature extraction unit is used for extracting related geometric features based on the optimized nodule region; and the visualization unit is used for visually displaying the stress distribution, the optimized nodule region and related geometric features. The method can assist doctors in accurately positioning the pulmonary nodules, and meanwhile, quantitative features of the nodules are provided.

Disclosed in the present invention is a physician-interpretable ultrasound-based breast cancer pathological subtype detection system, comprising: a segmentation module, which uses a U-Net network and incorporates a foreground optimization network to segment a breast and a nodule; a texture channel, which extracts a texture channel feature; an edge channel, which extracts boundary information; a position channel, which extracts position information, so as to quantize the overall relationship between the nodule and the boundary information of the breast to extract a breast nodule growth position feature; a shape channel, which calculates an edge aspect ratio on the basis of a segmented nodule region, so as to obtain a predicted interval value as a shape channel feature; an echo channel, which calculates the echo difference between the interior and exterior of the nodule to serve as an echo channel feature value; and a joint prediction module, which uses a multinomial logistic regression model to classify and predict breast cancer pathological subtypes on the basis of output features of the texture channel, the edge channel, the echo channel, the shape channel and the position channel. An artificial intelligence diagnosis tool that is interpretable to physicians is built, thereby providing an automated theoretical basis for clinical diagnosis and decision-making.

The present invention provides a composition for restoration of tissue including a biodegradable polymeric copolymer which is obtained by polymerizing a hydrophobic biodegradable polymer and a hydrophilic biodegradable polymer. The composition of the present invention has excellent tissue restoration effects because of high collagen production rate without occurrence of nodules even when injected directly into the dermis, unlike the conventional composition for restoration of tissue.

The invention provides an enhanced UNet + + nodule ultrasound image segmentation method based on a dual-path attention mechanism, the method is based on a DPAM-UNet + + network model, and the DPAM-UNet + + network model comprises an encoder, a decoder, a jump connection and a DPAM module integrated into the jump connection; the method comprises the following steps: step 1, an encoder extracts features in an ultrasonic image to obtain an input feature F1; step 2, obtaining an enhanced feature map after the input feature F1 passes through a DPAM module; 3, performing feature splicing on the enhanced feature map and the input feature F1 to obtain an initial fusion feature map; and step 4, a decoder performs feature recovery on the initial fusion feature map to obtain a recovered feature map, and performs feature splicing on the recovered feature map and the initial fusion feature map to form a final fusion feature map. According to the method, the dual-path attention module is introduced and integrated into the jump connection of the UNet + + network, and meanwhile, a novel integrated loss function is introduced, so that the nodule segmentation precision is improved.

The invention discloses an anti-nodule composition containing ganoderma lucidum spore oil as well as a preparation method and application of the anti-nodule composition. The anti-nodule composition is prepared from the following components in parts by mass: 15 to 35 parts of garden balsam stem, 30 to 40 parts of radix astragali seu hedysari, 4 to 16 parts of rhizoma dioscoreae nipponicae and 6 to 14 parts of ganoderma lucidum spore. Ganoderma lucidum spores, garden balsam stems and rhizoma dioscoreae nipponicae have a synergistic effect on inhibition of pulmonary nodule swelling, and the medicine obtained according to the formula can effectively reduce pulmonary nodules; the formula is matched with an atomization preparation to improve the targeting property, further improve the drug effect and reduce the side effect.

The invention discloses recombinant streptomyces nodosus for producing 8-deoxyampholactone as well as a construction method and application of the recombinant streptomyces nodosus. A primer D3U.F with a sequence as shown in SEQ ID NO1 and a primer D3U.R with a sequence as shown in SEQ ID NO2 are adopted to amplify an amph DIII gene upstream homologous arm, and a primer D3D.F with a sequence as shown in SEQ ID NO3 and a primer D3D.R with a sequence as shown in SEQ ID NO4 are adopted to amplify an amph DIII gene downstream homologous arm; the method comprises the following steps: amplifying a kanamycin resistance gene by adopting a primer kan.F as shown in a sequence SEQ ID NO5 and a primer kan.R as shown in a sequence SEQ ID NO6; inserting the fragment into a vector by using a homologous recombination technology to construct a recombinant plasmid; and introducing the recombinant plasmid into streptomyces nodosus through conjugational transfer, and screening a double-exchange mutant strain. According to the method, an amphDIII gene is knocked out through a homologous recombination technology, and glycosylation modification of trehalose amine at a C19 site is blocked, so that a mutant strain delta amphDIII with high yield of 8-deoxyampholactone is obtained; the product has clear nuclear magnetic resonance and mass spectrum characteristics, and can be used as a low-toxicity antifungal drug precursor.

The present disclosure provides a method for reducing, removing, or dispersing a dermal filler aggregate in a subject. The method includes contacting a target area of the subject’s skin with a device configured to apply vibrational or mechanical energy, and applying vibrational energy to the target area for a duration sufficient to facilitate reduction, removal, or dispersion of the dermal filler aggregate. The vibrational energy may be applied while the device is held stationary over the aggregate or moved across the target area in vertical, horizontal, circular, or sweeping motions. In certain embodiments, the device is configured to contact the skin, and visible light is optionally applied during treatment. Application of vibrational energy promotes mechanical disruption and redistribution of dermal filler material within the surrounding tissue, thereby reducing the palpability or appearance of filler nodules, clumps, or irregularities.

The invention belongs to the field of health care, and particularly relates to essential oil for relieving pain caused by fascia nodules and a preparation method and application thereof.The essential oil is prepared from, by weight, 15%-20% of core active essential oil, 0.5%-1.5% of a heat sensitizer, 1%-3% of an antioxidant, 0.5%-1.5% of trace functional components, 0.05%-0.1% of essence and the balance base oil. According to the invention, the raw materials are specifically selected and scientifically proportioned, and the effective components of the specific raw materials are extracted, so that the synergistic interaction among the components is realized. The prepared essential oil can quickly and effectively soften fascia nodules and body hardened tissues and relieve pain caused by the fascia nodules, has mild and comfortable warm feeling, greatly improves the efficiency and comfort of subsequent massage, physical therapy and other operations, and has wide application prospects in the fields of body physical therapy, exercise rehabilitation, daily health care and the like.

The present application discloses a method and related device for detecting thyroid cancer or breast cancer, the method comprising acquiring a region of interest of a target image to obtain an image of interest; determining a number of weighted feature maps and contour feature maps of the target image based on the image of interest; determining the benign and malignant nodule category and nodule feature category corresponding to the target image based on the number of weighted feature maps; and determining the nodule position and size information of the target image based on the contour feature map. The present application implements fully automatic output from an image to classification and segmentation results, and the segmentation results include attribute information such as benign and malignant nodule category, nodule feature category, and location and size, which can improve the detection speed of thyroid cancer or breast cancer. At the same time, information such as nodule feature category and location and size is conducive to the classification of nodule benign and malignant categories, thereby improving the reliability of the detection results of thyroid cancer or breast cancer.

The invention discloses a polyester material composite microsphere, a leather filling agent and a preparation method, and the prepared filling agent forms controllable sequential degradation in vivo by controlling the compounding proportion and preparation process parameters of polyester materials with different molecular weights; the problems of microsphere aggregation and nodule reaction in the use process of the existing filling agent are solved, meanwhile, local accumulation of acidic degradation products is remarkably reduced, and inflammatory reaction is avoided.

A brush includes a body, and a plurality of nodules disposed on at least one surface of the body and at least partially protruding outward from the body. The plurality of nodules include a plurality of first nodules. Each nodule of the plurality of first nodules includes a housing including an open side and an accommodating space, and a rotating ball including a portion accommodated inside the accommodating space of the housing and a remaining portion exposed to an outside of the housing. The rotating ball is rotatable.

The present disclosure relates to a nodule recognition method, device, storage medium and electronic device. The nodule recognition method obtains a scanned image to be recognized; inputs the scanned image to be recognized into a preset nodule recognition model to obtain nodule category information output by the preset nodule recognition model, wherein the nodule category information includes nodule category and non-nodule category; because the preset nodule recognition model can fuse global features and local features of feature maps of multiple scales through multiple serially connected global and local feature fusion modules to obtain target fusion features, it can effectively fuse multi-scale global features and local features; and then determines the nodule category information in the scanned image to be recognized according to the target fusion features through a classifier, which can effectively improve the accuracy and reliability of the obtained nodule category information.

The present disclosure provides an image-based intraoperative positioning method for lung nodules, comprising: predicting distances between an intraoperative lung nodule and a plurality of lung feature points based on a neural network model; drawing circles with each lung feature point as the center and the predicted distance between the intraoperative lung nodule and the corresponding lung feature point as the radius, to obtain a plurality of intersection points; screening a plurality of positioning intersection points from the plurality of intersection points; and calculating the geometric center of the plurality of positioning intersection points to obtain the intraoperative position of the lung nodule. The method can automatically locate the lung nodule on the lung model after intraoperative collapse, and does not require additional devices during the operation. The present disclosure also provides an image-based intraoperative positioning device for lung nodules, equipment and media.

The invention discloses a traditional Chinese medicine prescription for treating nodules and application thereof. The traditional Chinese medicine prescription comprises the following raw material medicines: selfheal, bupleurum chinense, rhizoma pinellinae praeparata, thunberg fritillary bulb, radix scrophulariae, oyster, curcuma zedoary, spina gleditsiae, turtle shell and astragalus membranaceus. According to the traditional Chinese medicine prescription disclosed by the invention, the characteristics of the raw material medicines are utilized, the formula is reasonable, the proportioning is scientific, and the synergistic efficacy is achieved. The traditional Chinese medicine prescription disclosed by the invention takes'qi regulating, phlegm reducing, blood circulation promoting, blood stasis removing, hardness softening and stagnation dissipating 'as a treatment principle, and has a good effect on treating various nodules.

The present invention discloses a lung nodule detection method based on deep learning, comprising the following steps: target detection of lung nodules, edge segmentation of lung nodules, and three-dimensional reconstruction of lung nodules. The lung nodule detection method based on deep learning of the present invention proposes a fast two-stage nodule detection, segmentation and classification CAD system based on 2DCNN, which can not only detect the malignancy degree of nodules, but also detect their morphological features; introduces a segmentation attention module to improve the interpretability of the model and enhance the feature extraction of small nodules; replaces the original backbone network with a lightweight network to simplify the network, reduce the number of model parameters, and lower the computational cost; after obtaining a complete nodule slice, crops the nodules to be trained, solving the problem of unbalanced positive and negative sample distribution.

The invention relates to a composition intended to improve plant growth comprising at least one strain of bacteria belonging to the species Mitsuaria noduli, preferably the Mitsuaria noduli strain filed with the CNCM on July 30, 2021 under No. I-5724 or I-5725; and the use of such a strain as a biostimulant.

The invention discloses a nodule detection method suitable for an ultrasonic image. The method comprises the following steps: carrying out feature extraction on an input ultrasonic image by utilizing a backbone network of an integrated space self-adaptive deformation enhancement module, and introducing deformable convolution to improve the perception capability on an irregular marginal region; constructing a feature enhancement module fusing global and local information, and performing multi-scale fusion on feature maps extracted by the backbone network based on a double-branch network of Fourier transform and large kernel convolution; and inputting the enhanced feature map into an encoder and an interactive learning decoder to realize global context modeling and regression decoding, and outputting a nodule detection result through a composite loss function training model of joint classification and regression loss. The method can be widely applied to ultrasonic image nodule detection tasks of various organs, the radiograph reading burden of ultrasonic doctors can be effectively reduced, objective and quantitative auxiliary diagnosis indexes are provided, and the accuracy and efficiency of clinical diagnosis are improved.

The invention relates to a target contour uncertainty error quantification method based on conditional constraint probability generation, and is used for solving the problems that in the prior art, intuitive interpretation of a nodule ultrasonic diagnosis result cannot be provided, and clinical viewpoints from multiple experts are difficult to learn at the same time. According to the method, a target contour uncertainty error quantification method based on conditional constraint probability generation is provided, semantic and structural coding information in a nodule ultrasound image is captured by establishing a deep learning model, and sampling is performed after the coding information is mapped to hidden space distribution; and based on a sampling result, obtaining a target contour segmentation result and uncertainty quantification. According to the method, label distribution from multiple experts is learned through the model, uncertainty quantification of the prediction result is output, the interpretability of the model is effectively improved, a doctor is helped to recognize a nodule high-risk area, and therefore transparency and reliability of the method in actual clinical application are enhanced.

A method for producing biomass from a microalgae includes culturing the microalgae in an effluent diluted in seawater. A method for bioremediating an effluent includes culturing a microalgae in the effluent diluted in seawater. The microalgae is at least one of a strain of the genus Nodularia, a strain of the genus Chrysoreinhardia, a strain of the genus Halochlorella, or combinations thereof. At the beginning of culturing, the diluted effluent exhibits concentrations of total nitrogen (N) in the range of 30-150 mg / l and concentrations of total phosphorus (P) in the range of 1-15 mg / l. The N / P quotient is in the range of 5-40.

The present invention discloses a lymph node nodule ultrasound image small target segmentation system based on attention self-distillation, which relates to the field of image segmentation technology. A segmentation model including a U-net network, a single-layer convolutional neural network and a self-distillation supervision module is constructed, and attention-guided calculation is performed on nodule features encoded by the model and nodule areas in the original image through the self-distillation supervision module, and feature information of large nodules is used to compensate for information loss caused by reduced feature map resolution. Due to the similarity of nodule area imaging, nodule image mapping can guide the feature alignment relationship of nodule feature mapping, quantify the relationship between each input image and the corresponding feature map, and distill the feature knowledge learned from the large nodule area to the small nodule area, thereby improving the segmentation performance of small nodules in lymph node nodule segmentation, improving the robustness and generalization of the model for nodule detection of different sizes, and better adapting to data sets and actual clinical scenarios in different fields.

Textured polyvinyl alcohol (PVA) brush nodules and brush assemblies are disclosed for wet cleaning of sensitive substrates such as semiconductor wafers. A cylindrical brush includes a core configured for rotation and a plurality of PVA nodules disposed on the core. Each nodule has a nodule face including molded-in surface geometry (e.g., ridges and grooves) configured to disrupt and redistribute liquid at a brush / substrate interface, promote interface shear, and reduce hydroplaning-like fluid lift to help maintain contact under wet conditions. In certain embodiments, a fluid management arrangement directs cleaning liquid toward the nodule faces so that liquid exits predominantly at or through the nodule faces, improving purging and reducing water and chemistry consumption. Modular rail embodiments and manufacturing methods, including planar tray molds with CNC-machined cavity face surface geometry for forming textured nodule faces, are also described.

The invention belongs to the technical field of biological medicines, and particularly relates to an application of Chipercumin D in preparation of a medicine for treating calcified aortic valve diseases. The core purpose of the invention is to provide a large-scale drug screening method based on Dot-blotting, and the drug is searched for inhibiting osteogenic differentiation of aortic valve interstitial cells and reducing expression of osteogenic specific genes ALP and RUNX2 and formation of calcified nodules, so that the purpose of preventing or treating calcified aortic valve diseases (CAVD) is achieved.

The invention discloses a blood stasis dispersing and stagnation eliminating health care cream and a preparation method thereof. The blood stasis dispersing and stagnation eliminating health care cream is composed of active components such as safflower general flavone, frankincense-myrrh co-extract and pericarpium citri reticulatae volatile oil and an emulsion matrix. Targeted delivery and long-acting release of blood activating components are achieved through a supercritical combined extraction-micro-capsule wrapping-gradient emulsification technology, the effects of remarkably reducing the size and improving the hardness on qi and blood stasis type nodules are achieved, and the transdermal efficiency is improved by 180% compared with that of a traditional ointment.

The present invention provides a composition for restoration of tissue including a biodegradable polymeric copolymer which is obtained by polymerizing a hydrophobic biodegradable polymer and a hydrophilic biodegradable polymer. The composition of the present invention has excellent tissue restoration effects because of high collagen production rate without occurrence of nodules even when injected directly into the dermis, unlike the conventional composition for restoration of tissue.

The application belongs to the technical field of medicine, and discloses a lung nodule intraoperative positioning system based on a flexible array sensor, which comprises a flexible array sensor, is arranged at a detection site of a subject, is used for collecting a pressure signal, and obtains a stress distribution; a nodule region detection unit, which is used for determining a nodule region through cluster analysis according to the stress distribution; a morphological optimization unit, which is used for performing morphological optimization on the determined nodule region; a feature extraction unit, which is used for extracting relevant geometric features based on the optimized nodule region; and a visualization unit, which is used for visually displaying the stress distribution, the optimized nodule region and the relevant geometric features. The application can assist doctors to realize accurate positioning of lung nodules and provide quantitative features of the nodules.

The present application relates to a kind of lung nodule segmentation method and device based on target consistent multi-resolution fusion, which comprises the following steps: S1: collecting lung CT image data and using the object-consistent multi-resolution input strategy (OCMIS), high-resolution and low-resolution data are generated;S2: by small nodule fine-grained enhanced encoder (SNFGEE) and multi-granularity enhanced encoder (MGEE), high and low resolution data are respectively extracted features, and high and low resolution features of small nodule are obtained;S3: by muti-resolution lossless feature fusion module (MLFF), high and low resolution features are fused, and fusion features are generated;S4: the fusion features are input into the decoder, and segmentation results are generated.The present application can realize the accurate segmentation of complex morphology lung nodule.The present application is a kind of lung nodule segmentation method and device based on target consistent multi-resolution fusion, which can be widely applied in the field of medical image segmentation.

An electronic device according to various embodiments may: obtain a first input image set obtained by capturing an image of at least a part of a user's body at a first time point; detect at least one first nodule with respect to the first input image set; determine similarity between the at least one first nodule and at least one second nodule, wherein the at least one second nodule is obtained on the basis of a second input image set stored in a memory; and obtain risk information of the at least one first nodule by inputting the first input image set and the second input image set to a risk determination model when the similarity has a value equal to or greater than a pre-configured value, wherein the risk determination model is an artificial intelligence model trained to output risk information of the at least one first nodule on the basis of first size information of the at least one first nodule, first type information of the at least one first nodule, and variation information of the at least one first nodule and the at least one second nodule.

The application discloses a lung nodule medical image segmentation method based on nodule symmetry constraint, and relates to the technical field of medical image processing. The method comprises the following steps: after the CT data is preprocessed, features are extracted and a nodule center is predicted by using a deep network; based on the center, differentiable radial sampling is performed, a radial feature sequence is constructed, and boundary radii in each direction are predicted; a radius consistency loss and an opposite profile consistency loss are calculated, a symmetry mask is constructed, and the symmetry mask is fused with a basic segmentation response; finally, thresholding and center-based connected domain extraction are performed to output a final mask. By explicitly embedding a geometric morphological prior in the network, the radial symmetry of the nodule is utilized to effectively distinguish blood vessels from nodules, the problems of lacking geometric constraints, being prone to misjudgment of blood vessels and relying on complex post-processing in the prior art are solved, and end-to-end high-precision lung nodule segmentation is realized.