33 results about "Liver lesion" patented technology

The invention discloses a CT image auxiliary detection method and system for liver focal lesions, and the method comprises the steps: receiving and screening multi-dimensional liver CT image data, and generating a standardized DICOM data set based on a preset layer thickness threshold value, a resolution threshold value and an artifact filtering rule; performing two-stage focus positioning analysis on the standardized DICOM data set, and outputting a focus positioning map containing focus coordinates, sizes and average CT values; starting an interactive image optimization engine based on the focus positioning map, responding to a window width and window level adjustment instruction and measurement tool operation of a physician in real time, generating a dynamically enhanced focus visible range and synchronously updating focus parameters; and integrating the visual field data and the clinical rule base to generate a structured report, automatically filling fields seen by the image by checking a focus list, and outputting a diagnosis proposal meeting the DICOM standard in linkage with an image-text report module. By utilizing the embodiment of the invention, standardization and intelligence of auxiliary diagnosis can be realized, and the accuracy and efficiency of liver lesion detection are improved.

The invention provides a novel macrocyclic compound, a gadolinium chelate thereof, a method for preparing the compound and application of the compound as a magnetic resonance contrast agent. The compound has pertinence on magnetic resonance detection of liver focal lesions and can be used for liver specific imaging.

The invention relates to a computer-implemented method of liver resection surgery planning, comprising the steps of: processing (E2) preoperative computed tomographic images (10) of a patient to generate liver, liver lesion (ss) and venous vessels segmentations; pruning the venous vessels segmentation to retain only major vessels and determining (E3) a primary hepatic zone (PHZ) from the retained major vessels; determining (E4) at least one quantitative feature from the primary hepatic zone, the liver segmentation and the liver lesion(s) segmentation; processing (E5) the determined quantitative features with a classification model to determine a liver resection complexity score for the patient.

The invention discloses a multi-scale anatomical prior and attention-enhanced liver lesion segmentation method, which comprises the following steps of: training a three-dimensional convolution denoising auto-encoder by using an unsupervised training data set of an original image to obtain a trained denoising auto-encoder model; inputting a supervised training data set of an original image into the trained denoising auto-encoder model to obtain prior anatomical features of each image at four different resolution levels; inputting the prior anatomical features and the original image into a 3D attention enhancement segmentation network for network training output to obtain a trained segmentation model; and obtaining a liver focus segmentation result of the three-dimensional image of the to-be-segmented liver region through the trained denoising auto-encoder model segmentation model. Dependence on labeled data is reduced through a semi-supervised learning strategy, global context construction of a focus area is enhanced by introducing a hierarchical three-dimensional attention mechanism, and segmentation precision, robustness and clinical generalization ability of a tiny, low-contrast and boundary fuzzy focus are improved.

The application discloses a liver lesion image description method based on a semantic segmentation network, comprising the following steps: building a Unet semantic segmentation network based on a lightweight network GhostNet, and constructing a liver lesion segmentation model; acquiring a historical liver lesion ultrasound image dataset, inputting the liver lesion segmentation model for training, and obtaining an optimal segmentation model; and describing the features of the lesion image based on the segmentation result. The application segments the liver lesion by constructing a lesion segmentation model, and describes based on the segmentation result, thereby improving the accuracy and reliability of liver disease diagnosis, reducing misjudgment and subjective bias; by combining the advantages of deep learning and traditional image processing, the characteristics of both are fully utilized, the performance and stability of the algorithm are improved; automatic processing and analysis of the ultrasound image are realized, the lesion description result provides accurate disease positioning and type judgment, and a better treatment scheme is provided for doctors, thereby improving the treatment effect and treatment experience of patients.

The application provides an intelligent grading method and system for diffuse liver lesions in ultrasound images, and belongs to the technical field of medical image processing.The method comprises the following steps: multi-dimensional quality evaluation and grading and shunt control of the ultrasound image; liver parenchyma segmentation and positioning of the region of interest; multi-scale parallel texture feature extraction and cross-scale attention fusion; quantitative ultrasound feature calculation and multi-source integration; synchronous output of fatty liver grading and liver fibrosis staging results by a double-task collaborative grading network, training loss containing ordinal consistency constraint to ensure medical rationality; and lesion heat map visualization and longitudinal monitoring trend analysis.The application realizes collaborative grading by utilizing the histological correlation of two lesions, and improves the diagnostic accuracy and objectivity.

The present invention relates to the classification of liver lesions using liver ultrasound images, and more particularly to an artificial intelligence model for classifying liver lesions using liver ultrasound images, a method for constructing the model, and a method for classifying liver lesions using the artificial intelligence model. To this end, the present invention provides a method for constructing an artificial intelligence model for classifying liver lesions using liver ultrasound images, the method comprising: a preparation step (S100) of excluding images that meet predetermined exclusion criteria from among a plurality of liver ultrasound images; a step (S120) of receiving the prepared plurality of liver ultrasound images as original images; a step (S140) of assigning the original images to a plurality of folds alternately for each liver lesion; a step (S160) of image processing each original image for training; and a step (S180) of training the artificial intelligence model using the image-processed images.

The application discloses a kind of detection Tgfβ1 to hepatolenticular degeneration neutrophil polarization method and application, including the following steps: step 1, by immunohistochemistry and immunofluorescence co-localization determine hepatolenticular degeneration mouse liver fibrosis formation process, hepatocyte high expression Tgfβ1;Step 2, by inhibiting the high expression of Tgfβ1 of hepatolenticular degeneration mouse liver fibrosis formation process, determine the mitigation of neutrophil polarization;Step 3, collect mouse bone marrow neutrophil, by real-time fluorescence quantitative PCR, protein electrophoresis and methylation reaction determine the mechanism of Tgfβ1 promote neutrophil polarization.The application can detect the effect of Tgfβ1 in hepatolenticular degeneration neutrophil polarization, facilitate to determine hepatolenticular degeneration liver lesion mechanism.

The invention relates to an anti-inflammatory and lipid-lowering-based nano preparation for treating fatty liver and a preparation method of the nano preparation. Galactose modified polyethylene glycol-polylactic acid-glycolic acid copolymer (PEG-PLGA) is used as a targeting carrier, and a lipid-lowering drug fenofibrate and an anti-inflammatory drug curcumin are co-loaded; the particle size of the nano preparation is 150-200nm, and the drug encapsulation efficiency is greater than or equal to 85%. Galactose modified PEG-PLGA is adopted as a carrier, galactose can be specifically combined with an asialoglycoprotein receptor (ASGPR) specifically expressed on the surface of liver cells, active targeting of the liver is achieved, the drug enrichment amount of the liver lesion position is remarkably increased, meanwhile, accumulation of drugs in extrahepatic tissue is reduced, extrahepatic toxicity is effectively reduced, and the drug delivery effect is improved. According to the present invention, the anti-inflammatory drug curcumin and the fenofibrate are combined, such that the biological safety of the preparation is improved, the lipid lowering drug fenofibrate and the anti-inflammatory drug curcumin are innovatively co-loaded, and the fenofibrate and the anti-inflammatory drug curcumin form the synergistic effect system: fenofibrate can activate peroxisome proliferator-activated receptor alpha (PPAR alpha), promote liver lipid catabolism, and reduce lipid deposition;

The invention relates to the technical field of medical image processing, and discloses a multi-phase CT (Computed Tomography) image detection method for liver focal lesions, which comprises the following steps of: acquiring four-phase CT images, adjusting the size to unify the size, independently grouping the four-phase CT images, inputting four groups of RetinaNet multi-scale detection networks with shared parameters, extracting a feature map I grouped according to the phase, and extracting a feature map II grouped according to the phase; respectively inputting the four groups of feature maps I into an improved feature pyramid network to output feature maps II grouped according to phase, inputting the four groups of feature maps II into a ConvLSTM time sequence modeling module to carry out time sequence modeling, and finishing screening and outputting a result through a detection head and using non-maximum suppression. According to the method, through joint modeling of the multi-phase CT images, the dynamic evolution characteristics of the lesion in the time dimension are effectively extracted, and the recognition precision of the CT image of the liver lesion is remarkably improved.

The invention relates to an automatic hepatic balloon-like deformation cell detection method based on a medical mechanism and deep learning. Comprising the steps of establishing a liver pathology image data set; and constructing a hepatic balloon-like variant cell detection network based on a medical mechanism and a deep learning technology, and training and testing the hepatic balloon-like variant cell detection network. The detection network mainly comprises a backbone network, a segmentation convolution network, a global perception network, a bidirectional multistage aggregation network and a head network, and the segmentation convolution network is mainly used for extracting high-resolution local features such as eosinophilic sphericity, lipid droplet glycogen and loose and transparent cytoplasm; the global sensing network is mainly used for extracting global information such as liver lesion severity, and the bidirectional multi-stage aggregation network is mainly used for extracting multi-scale context information such as relative positions of cell nucleuses and relative volumes of cells. According to the automatic detection method for detecting the hepatic balloon-like deformation cells, the method is based on the medical mechanism that balloon-like deformation is prompted through the image information, the hepatic balloon-like deformation cells can be recognized more accurately, and the method has important clinical application value.

The invention relates to the technical field of image data processing, in particular to a fatty liver lesion image analysis method based on color texture features, which comprises the following steps: extracting a rat liver region through image segmentation, and converting the rat liver region into a CIELAB color space to separate brightness, red-green degree and yellow-blue degree components; calculating a local texture difference feature based on a pixel space neighborhood, and coupling a brightness value to construct a pixel-level validity weight to suppress specular reflection; and weighting and updating a K-Means clustering center by using a pixel-level validity weight, distinguishing a fatty degeneration region from a non-lesion region, and counting the number of pixel points to realize analysis of the fatty liver lesion image. The optical noise interference is accurately inhibited, the clustering accuracy is improved, the fatty liver lesion analysis accuracy is improved, and reliable technical support is provided for drug curative effect evaluation and pathological mechanism research.

The present invention addresses the problem of providing technology that is for preventing or treating the progression of liver conditions leading to liver cancer and targets a phenomenon other than hepatocyte death. This problem is solved by a prophylactic or therapeutic agent that is for at least one disease selected from the group consisting of liver fibrosis, cirrhosis, and liver cancer, contains at least one substance selected from the group consisting of CTGF inhibitors and integrin αV inhibitors, and is for patients suspected of having increased intrasinusoidal pressure.

The present application relates to the technical field of image data processing, and more particularly, to a fat liver lesion image analysis method based on color texture features, comprising: extracting a rat liver region through image segmentation, converting to a CIELAB color space to separate luminance, red-green chroma and yellow-blue chroma components; calculating local texture difference features based on a pixel space neighborhood, coupling luminance values to construct pixel-level effectiveness weights to suppress specular reflection; using pixel-level effectiveness weights to update K-Means clustering centers, distinguishing between steatosis regions and non-lesion regions, and counting pixel points to realize analysis of fat liver lesion images. The present application accurately suppresses optical noise interference, improves clustering accuracy, and improves fat liver lesion analysis accuracy, providing reliable technical support for drug efficacy evaluation and pathological mechanism research.

The invention provides an application of n-butylphthalide. According to the application, n-butylphthalide is used for providing a combination for inducing stem cells to differentiate into fat cells; the invention relates to an application of n-butylphthalide in preparation of a composition. The composition is used for inhibiting white fat accumulation, promoting white fat to be converted into brown fat, inhibiting weight gain, inhibiting obesity, reducing the liver fat content of an individual, reducing the fat content of liver cells, improving the antioxidant activity of the liver and / or reducing the content of triglyceride, glucose and total cholesterol in blood. The present invention relates to a pharmaceutical composition for preventing or treating obesity, preventing metabolic syndrome associated with obesity, preventing or treating fatty liver, improving liver lesion caused by fatty liver, and / or reducing liver damage in an individual, and to a use of n-butylphthalide in the manufacture of a pharmaceutical composition for preventing or treating obesity, preventing metabolic syndrome associated with obesity, preventing or treating fatty liver, and to a use of n-butylphthalide in the manufacture of a pharmaceutical composition for preventing or treating liver lesion caused by fatty liver.

This invention relates to a self-supervised liver lesion detection method based on frequency-aware image restoration, belonging to the field of image processing technology. First, this invention designs a self-supervised task based on synthetic anomalies to synthesize a wider range of suitable pseudo-anomaly images, thereby alleviating the problem of insufficient anomaly data during algorithm model training. Second, to suppress the sensitivity of the reconstruction network to synthetic liver anomalies, a module for extracting high-frequency information from the image is designed. By recovering the image from its high-frequency components, the unfavorable generalization of anomalies by the reconstruction network is mitigated. Finally, a weight decay training strategy is employed to train the segmentation sub-network, reducing the trivial solution problem in the early stages of training and achieving the detection of local, subtle lesions. Extensive experiments on real-world public datasets demonstrate that the method of this invention achieves leading performance in liver lesion detection.

The invention belongs to the technical field of pharmaceutical preparations, and particularly relates to a hypecoum erectum composition for treating a non-alcoholic fatty liver disease and a preparation method of the hypecoum erectum composition. The hypecoum erectum composition is prepared from the following components: hypecoum erectum ketone and juglantin, wherein the mass ratio of the hypecoum erectum ketone to the juglantin is 1: (0.3-0.5). Experimental results show that the hypecoum erectum composition can improve liver function and liver lesion degree by reducing blood fat level, liver lipid accumulation and slowing down liver inflammation.

The invention discloses a hepatic lesion grading method and device based on an ultrasound contrast video, and belongs to the field of hepatic lesion grading, and the method comprises the steps: obtaining to-be-graded ultrasound contrast video data, and obtaining arterial phase key frame data, portal phase key frame data and delay phase key frame data through an interval sampling method; according to a preset region division rule, performing region division on each frame of image in each period of key frame data to obtain arterial phase input data, portal pulse phase input data and delay phase input data; inputting the input data of each period into a first preset multi-task multi-classification model to obtain classification results of each period; and according to the classification result of each stage, performing classification by adopting a preset LI-RADS decision rule, and outputting a hepatic lesion classification result. Therefore, by implementing the method and the device, the problem that the hepatic lesion grading efficiency is difficult to improve while the hepatic lesion grading accuracy is ensured in the prior art can be solved.

The application discloses a preparation method of carboxymethyl mulberry leaf polysaccharide with improved antioxidant activity, which comprises the following steps: (1) crude mulberry leaf polysaccharide is dissolved in an alkali solution, acid is added under stirring, and then the reaction is continuously stirred; (2) the reaction is terminated by adjusting to neutral with acid, and the reaction liquid is dialyzed in a dialysis bag; (3) after centrifugation, alcohol precipitation is carried out in an ethanol system, and freeze-drying is carried out, so that the carboxymethyl mulberry leaf polysaccharide with improved antioxidant activity is prepared. The carboxymethyl mulberry leaf polysaccharide product prepared by the application has excellent solubility and increased antioxidant activity. After the mouse liver injury symptoms are obviously improved, the decrease of the liver index is relieved, and the liver lesion is reduced. Therefore, the carboxymethyl mulberry leaf polysaccharide can effectively prevent liver injury and oxidative stress caused by aflatoxin B1, has a wide application prospect in the antioxidant preparation research of the mulberry leaf polysaccharide, and provides a train of thought for subsequent research and development and optimization and innovation of the polysaccharide.

The invention discloses a GAN-based medical image enhancement method. The method comprises the following steps: designing a CNN architecture based on hepatic lesion classification; performing traditional medical image data enhancement; generating a hepatic lesion image through the GAN; and carrying out data enhancement and model training on the medical image based on the GAN. According to the invention, the generative adversarial network is utilized to generate a high-quality synthetic medical image, especially a hepatic lesion image, so that the problems of small sample size and unbalanced category of a data set are solved. A training data set is expanded by generating a realistic lesion image in combination with a classical data enhancement method (such as rotation, flipping, translation and zooming). The liver lesion classification CNN architecture design comprises three convolutional layers, two full-connection layers and a Dropout layer to extract effective features and prevent overfitting. A comprehensive data enhancement and model training strategy comprises the combination of a GAN generated image and a traditional enhanced image, the selection of training parameters and the setting of monitoring indexes, and the effectiveness of model training and the improvement of classification performance are ensured.

The invention relates to the technical field of biological medicines, and discloses an exosome coupling medicine for treating non-alcoholic steatohepatitis. According to the exosome coupled drug delivery system, the liver targeting of the exosome is utilized, the drug is accurately delivered to the liver lesion position, the side effect of non-target tissue is reduced, meanwhile, the exosome plays a role in protecting the drug, the stability and bioavailability of the drug are improved, the treatment effect is remarkably enhanced, the advantages of targeting and long-term effect are shown, and the exosome coupled drug delivery system has a good application prospect. An innovative path is provided for the treatment of the non-alcoholic steatohepatitis. The preparation method of the exosome coupling drug is simple and feasible, and is suitable for further industrial mass production.

The application discloses a liver image visualization method and system based on three-dimensional reconstruction, which comprises the following steps: acquiring original liver image data to construct an initial three-dimensional model; accurately positioning and labeling lesions and blood vessels of the model, detecting coordinate deviation and real-time calibration to obtain a positioning model; judging defects of the model and adopting defect repair to obtain a complete model; extracting tissue density characteristics to adaptively stratify to obtain an optimized model; applying densification technology to interpolate and restore small blood vessels to obtain a dense model; if the data volume is over limit, compression encryption is carried out to obtain a transmission data packet; and the transmission data packet is remotely transmitted to a medical center to be decompressed and loaded to obtain a visualization result. The application realizes safe and efficient transmission of large-scale three-dimensional model data and remote consultation visualization support, so that the accuracy of liver lesion diagnosis of doctors and the remote medical cooperation efficiency are comprehensively improved.

The invention discloses a lesion development early warning method and system based on an ultrasonic image, a medium and equipment, and the method comprises the steps: based on the ultrasonic image containing the liver and the kidney, extracting a first-stage ultrasonic image feature, and determining the lesion development rate of the liver, the first-stage ultrasonic image feature comprising a liver echo attenuation coefficient and a liver-kidney ratio; secondly, secondary ultrasonic image features are extracted, liver morphological change evaluation results are determined, the secondary ultrasonic image features comprise liver morphologies, and the liver morphological change evaluation results comprise anomalies and normalities; and when the determined lesion development rate of the liver is greater than or equal to a preset lesion development rate early warning threshold value, and / or the liver morphological change evaluation result is abnormal, triggering to generate a lesion development early warning signal. The liver lesion development rate and the morphological change result are quantitatively evaluated on the basis of ultrasonic image extraction features, early warning can be triggered in time, and early discovery and intervention of liver abnormal conditions are facilitated.

The invention belongs to the technical field of traditional Chinese medicine for treating diabetic liver injury, and particularly discloses a preparation method of an anaphalis fasciculata extract and application of the anaphalis fasciculata extract in treating diabetic liver injury, and the preparation method comprises the following steps: drying and crushing anaphalis fasciculata, extracting by adopting an ethanol hot reflux method, and drying after vacuum concentration to obtain an anaphalis fasciculata extract; the preparation method comprises the following steps: firstly, preparing a crude extract of the anaphalis cladonioides, drying to obtain the crude extract of the anaphalis cladonioides, then carrying out adsorption purification and impurity removal on the crude extract of the anaphalis cladonioides, eluting with ethanol, collecting fractions with better activity by taking aldose reductase inhibitory activity as an index, and drying to obtain the extract of the anaphalis cladonioides. Tests show that the anaphalis fasciculata extract obtained by the method has a remarkable protection effect on improvement of hepatic lesion induced by high-glucose injury of diabetes, provides a new way for liver injury induced by the high-glucose injury, has the advantages of no toxicity, safety and low cost, and can be widely applied to the field of diabetes mellitus high-glucose injury induced hepatic lesion treatment and treatment of diabetes mellitus high-glucose injury induced hepatic lesion treatment. And the method has important significance and value for further deep research of the anaphalis cladonioides.

The application discloses a liver ultrasound contrast lesion classification method based on multi-modal information and perfusion kinetics, and the method comprises the following steps: acquiring video image data with perfusion contrast images and standard gray-scale images and preprocessing, and structuring a clinical data vector of a patient; identifying a liver lesion ROI region and obtaining a TIC curve for analysis to obtain blood flow kinetics key phase characteristics; based on a double-head input time sequence classification model of the gray-scale images and the contrast images, first spatial characteristics and second spatial characteristics are extracted and fused to obtain visual fusion characteristics; a clinical data vector is projected to an embedding space aligned with image characteristics to obtain a clinical feature vector; finally, an explainability heat map is obtained, blood flow kinetics key phase characteristics are marked on the TIC curve, the most important clinical indicators in classification decision are listed according to category probability, and the summary is used as an auxiliary diagnosis result.

The application belongs to the technical field of biological medicine, and particularly relates to POFUT1 The application discloses an application of a gene function inhibitor in preparation of a drug for treating cholestatic liver disease. POFUT1 It is found for the first time that inhibition of the function of the gene can significantly reduce the degree of liver lesion of cholestatic liver disease. POFUT1 The deletion of the gene can significantly reduce serum liver enzyme, bile acid and bilirubin levels, reduce liver inflammatory cell infiltration and collagen deposition. Further verification by AAV8-mediated RNA interference technology shows that targeted silencing of the gene in liver cells can effectively alleviate liver injury and liver fibrosis. POFUT1 The application provides a novel treatment target and strategy for cholestatic liver disease, and has a wide clinical application prospect.

The application provides a 3D liver lesion detection method and device. The method comprises the following steps: acquiring multi-phase CT images to be detected, constructing 3D data including upper and lower layers of each slice layer to be detected as the center; inputting the 3D data into a detection model, extracting 3D context features of the images, and detecting 2D lesions obtained by center cutting; combining 2D lesions of continuous layer slices belonging to the same lesion into a 3D lesion; inputting the 3D lesion cut in the local image of multiple phases output by the detection model into a false positive identification model to obtain a detection result of the 3D lesion. The application can extract 3D context features by constructing 3D data, can fully utilize lesion information of each phase for lesion detection, and thus improves the lesion detection accuracy; and the false positive identification model is set to filter false positives of the 3D lesion, which can further improve the lesion detection accuracy.

Provided are: an agent for ameliorating a liver lesion caused by a lesion in the anterior pituitary gland and / or for preventing and / or treating diseases accompanied by said liver lesion; and a screening method for an agent for ameliorating a liver lesion caused by a lesion in the anterior pituitary gland and / or for preventing and / or treating diseases accompanied by said liver lesion. This agent for ameliorating a liver lesion caused by a lesion in the anterior pituitary gland and / or for preventing and / or treating diseases accompanied by said liver lesion comprises one protein or polypeptide among the following (a)-(c): (a) a protein or polypeptide consisting of the amino acid sequence set forth in SEQ ID NO: 1 of the sequence listing; (b) a protein or polypeptide consisting of an amino acid sequence in which one or more amino acids are deleted, substituted, and / or added in the amino acid sequence set forth in SEQ ID NO: 1 of the sequence listing, and having an insulin-like action and a growth-promoting action; or (c) a protein or polypeptide consisting of an amino acid sequence that shares a sequence identity of at least 95% with the amino acid sequence set forth in SEQ ID NO: 1 of the sequence listing, and having an insulin-like action and a growth-promoting action.

The invention relates to the technical field of medicine application, and discloses mulberry leaf polysaccharide, a preparation method and application of the mulberry leaf polysaccharide in treatment of AFB1-induced mouse liver injury. The mulberry leaf polysaccharide can be independently used, and can also be combined with other traditional Chinese medicine extracts to prepare a pharmaceutical composition together. Experimental results show that the liver injury symptom of the mouse treated by the mulberry leaf polysaccharide is obviously improved, the liver index decline trend is relieved, and the liver lesion degree is obviously relieved. Therefore, the mulberry leaf polysaccharide disclosed by the invention can effectively treat liver injury caused by AFB1 and accompanied oxidative stress, and has a wide application prospect in the research field of mulberry leaf polysaccharide antioxidant preparations. The discovery not only provides powerful support for subsequent research and development of the polysaccharide, but also provides a new thought and direction for further optimization and innovation.