423results about "Blood vessel patterns" patented technology

Systems and method can be provided to transform input data (e.g., CT imaging data) into structured representations to create interpretable models. Another aspect of the current invention can be generating labels synthetically to apply to real data according to a biologically-based labelling technique to guide the model training with a priori mechanistic knowledge.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The present application relates to the field of image processing, and discloses a method and system for processing an intracranial large vessel image, an electronic device, and a medium. The method comprises: acquiring an intracranial large vessel original image of an object to be identified and an intracranial large vessel original image of a sample subject; on the basis of the intracranial large vessel original images, using a cerebrovascular segmentation model to obtain cerebrovascular mask images; calculating regions of interest and bounding boxes of the cerebrovascular mask images; on the basis of the bounding boxes of the regions of interest, selecting corresponding regions of interest from the intracranial large vessel original images; respectively preprocessing mask image regions of interest and original image regions of interest to obtain images to be processed; marking a target area in the image to be processed of the sample subject; using a training set to train a convolutional neural network model to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of said object into the cerebrovascular occlusion classification model to obtain a target area identification result. The present application can improve the accuracy of target area identification.

The invention belongs to the technical field of medical informatization, and provides an anesthesia medication management system with biological recognition, comprising: a biological recognition module verifies the identities of medical staff and patients through a vein recognition technology; the multi-source data acquisition module acquires vital sign data and equipment parameters of a patient in real time to obtain multi-source data; the intelligent drug management module authorizes to store and take drugs according to the biological recognition result and generates an electronic anesthesia prescription; the processing decision module receives the multi-source data and generates a medicine adjusting instruction through a preset rule and an algorithm in combination with an electronic anesthesia prescription; the execution control module carries out secondary confirmation on the medicine adjusting instruction through biological recognition and then sends the medicine adjusting instruction to anesthetic infusion equipment to be executed; according to the method, through the electroencephalogram double-frequency index, the entropy index and the hemodynamic parameters, in combination with the anesthesia depth evaluation result and the personalized drug metabolism and effect model, the medication scheme is dynamically adjusted, the optimal infusion rate is generated, the anesthesia depth control is more accurate, and the fluctuation range is narrowed.

The invention relates to the technical field of image processing, and discloses a pulmonary embolism focus segmentation method and system based on image recognition, and a storage medium. The method comprises the following steps: extracting a multi-level blood vessel topological structure of a CTPA image through blood vessel diameter gradient analysis; modeling blood vessel density distribution by using a Weibull mixed model to obtain embolism characteristic parameters; the pixel embolism probability is estimated through variational Bayesian reasoning, and a focus distribution diagram is generated; performing multi-scale feature fusion on the lesion probability graph to obtain a segmentation boundary; and obtaining a final embolism focus segmentation result based on the vascular connectivity constraint optimization boundary. The problems that blood vessel level differentiation processing cannot be achieved, and accurate probability modeling and anatomical constraint verification are lacked are solved. The accuracy of pulmonary embolism focus segmentation is improved.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention discloses a human-hat matching method, system and equipment based on an intelligent safety helmet, and relates to the technical field of biological feature recognition, and the human-hat matching method based on the intelligent safety helmet comprises the steps: determining a matching strategy according to a working area where the intelligent safety helmet is located; receiving a human-hat matching instruction, and generating a voice prompt-data acquisition instruction according to the matching strategy and the human-hat matching instruction; the voice prompt-data acquisition instruction is sent to the intelligent safety helmet located in a working electronic fence coverage area, so that the intelligent safety helmet sends a preset voice prompt and feeds back acquired identity data, and the intelligent safety helmet is in a power-on state and a wearing in-place state; and carrying out human-hat matching according to the matching strategy and the identity data to obtain a human-hat matching result. According to the invention, people-hat matching can be accurately carried out in construction areas under various working conditions, and the construction safety management level is effectively improved.

An AI-based system and method for supporting differential diagnosis and standard of care in healthcare. The method involves receiving patient information from various sources, including patient-reported symptoms, physician notes, and sensor data from medical devices. The patient information is preprocessed and analyzed using deep learning models to generate a ranked list of potential diagnoses, each associated with likelihood scores and key contributing factors. The potential diagnoses are provided to physicians via an interactive interface, and physician feedback is collected to fine-tune the AI models using reinforcement learning. The method aims to enhance physician decision-making, improve diagnostic efficiency, and ensure adherence to the standard of care by leveraging AI's ability to analyze vast amounts of data more effectively than human physicians.

The invention relates to the technical field of ultrasonic detection, and discloses a robot ultrasonic detection method, device and equipment based on three-dimensional perception and a medium, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of a target object; generating a scanning path for detecting the target object based on the three-dimensional point cloud data; controlling a motion execution device to drive the detection probe to move along the scanning path to obtain ultrasonic image data of the internal structure of the target object; in the moving process of the detection probe, the contact force between the detection probe and the target object is maintained within a preset force value range; analyzing the ultrasonic image data, identifying a lumen contour and determining a narrowest position point; and adjusting the posture of the detection probe at the narrowest position point and executing secondary detection to obtain hydrodynamic data. The self-adaptive scanning path is generated through the three-dimensional point cloud data, the movement and the contact force of the detection probe are controlled, the ultrasonic image and the space position are associated, and the problems that the detection path is not attached to the curved surface, the contact force fluctuates, and the image space is not synchronous are solved.

The invention discloses a palm vein image generation method and device, a storage medium and electronic equipment, and relates to the technical field of information. The method comprises the steps that an initial noise image, the number of diffusion steps and condition control information are obtained, and the condition control information comprises an individual identity label, palm posture information used for simulating a real scene and illumination information; generating a condition control embedding vector according to the condition control information; the initial noise image, the diffusion step number and the condition control embedding vector are input into a preset diffusion model to predict noise corresponding to each time step, and the preset diffusion model comprises a linear layer, a cross attention layer and a multi-scale feature layer; and according to the predicted noise corresponding to each time step, gradually updating the image, and finally outputting a target palm vein image under the condition control information. According to the method and the device, the generated palm vein image data can be fit to various complex real scenes, so that the diversity and the practicability of the palm vein image data can be ensured.

The invention relates to the technical field of payment security, and particularly discloses a security payment system and method based on biological recognition. The method is characterized by comprising the following steps: synchronously acquiring fingerprint, finger vein and pressure behavior characteristics through a coaxial integrated sensor; a dynamic encryption engine is adopted to bind the biological characteristics with the transaction parameters to generate a one-time payment token; living body verification is realized based on physiological synchronism of vein pulsation and pressure fluctuation; and establishing a user pressing behavior baseline model to identify abnormal operation. The terminal equipment is provided with a sapphire microlens array and a dynamic pressure-sensitive array. The problems of biological feature forgery, replay attack and living body cheating are solved, the forgery detection rate reaches 99.6%, and the false identification rate is smaller than or equal to 0.0001%.

The invention discloses a dry eye disease course supervision and prediction method and system based on AI image recognition, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining blink behavior video data, meibomian gland infrared imaging data and time sequence ocular surface microvessel image data; analyzing the blink behavior video data, and extracting blink feature data; performing image processing on the infrared imaging data of the meibomian glands, and extracting feature data of the meibomian glands; carrying out binarization and thinning processing on the sequential ocular surface microvascular image data, and extracting blood vessel feature data; standardizing the blood vessel feature data, and fusing to generate a comprehensive feature vector; inputting the comprehensive feature vector into a classification model, and judging a dry eye disease course stage; executing trend analysis based on historical data of the comprehensive feature vector, and generating prediction data and intervention suggestion data; the method has the beneficial effects that through multi-modal image data fusion analysis, accurate monitoring and prediction of the xerophthalmia course are realized, and a scientific basis is provided for clinical intervention.

The invention relates to a computer vision technology, and discloses a laying hen health condition monitoring method and system based on a cockscomb form, and the method comprises the steps: collecting laying hen cockscomb images according to a preset time interval, and forming an image sequence; and carrying out gravity center identification on each image in the sequence, obtaining gravity center sequence data and analyzing gravity center offset. If the center-of-gravity offset exceeds a preset threshold value, color identification is directly carried out on the current image to obtain color data, and if the center-of-gravity offset does not exceed the preset threshold value, the form variation of the cockscomb edge area in the image sequence is analyzed. When the form variation is greater than a preset threshold value, judging that the laying hen is healthy; and if not, returning to execute the color identification step. And finally, performing disease judgment according to the color data to obtain a judgment result. The laying hen health condition monitoring efficiency can be improved.

The invention discloses an OCT image segmentation method and system based on cross-domain attention and multi-level feature fusion, and relates to the technical field of image segmentation. The method comprises the steps that an image segmentation model based on cross-domain attention and multi-level feature fusion is trained based on an obtained OCT blood vessel image training set, and the operation mechanism of the model is that an OCT blood vessel image enters an encoder to be subjected to feature coding and then enters a multi-scale feature extraction module to be subjected to self-adaptive multi-scale feature extraction; the features output by each down-sampling module in the encoder and the features output by the multivariate scale feature extraction module enter a multi-layer dense fusion module together for feature fusion; the features output by the multi-layer dense fusion module and the features output by the multivariate scale feature extraction module enter a decoder together for feature decoding; and then entering a segmentation head to generate a final segmentation image. The method can be used for automatically identifying the blood vessel plaque in the OCT image, and the overall quality and the detail integrity of a segmentation result can be remarkably improved.

The invention discloses a dynamic holographic reconstruction system for CTA and DSA coronary artery image fusion. An image acquisition module transmits acquired image data to a data storage unit for storage; the dynamic registration module is connected with the image acquisition module, a dynamic feature extraction unit and a space-time calibration unit are arranged in the dynamic registration module, and a time synchronization mechanism is established; the image fusion module adopts a fusion algorithm based on deep learning to perform deep fusion on the preprocessed images to obtain fused coronary artery image data; the holographic reconstruction module is connected with the image fusion module, performs three-dimensional holographic reconstruction according to the fused coronary artery image data, and constructs a holographic three-dimensional model of the coronary artery by adopting a three-dimensional reconstruction technology; the display module is connected with the holographic reconstruction module and is used for displaying the coronary artery image after dynamic holographic reconstruction; the control module is used for controlling working parameters and operation processes of all the modules. According to the invention, accurate dynamic fusion and dynamic holographic reconstruction of coronary images can be realized, and a more comprehensive and visual image basis is provided for diagnosis and treatment evaluation of coronary diseases.

The invention relates to the technical field of medical instruments, and discloses an interventional ultrasound method and system for minimally invasive surgery, and the method comprises the steps: obtaining ultrasound image data and operation state parameters; a three-dimensional ultrasonic image model is constructed through multi-layer scanning, and an initial tissue feature evaluation model is analyzed and generated; dynamic blood vessel data and operation constraint conditions are loaded, a comprehensive evaluation model is generated, and puncture point dynamic simulation is carried out; constructing a dynamic blood vessel prediction model, training to obtain a puncture point adjustment model, and generating initial adjustment parameters; obtaining dynamic adjustment simulation information based on the parameters and the model; constructing a multi-path optimization model, and carrying out iterative optimization to obtain an optimal path planning parameter; the safety puncture probability is inferred in combination with real-time images, and a real-time puncture strategy is adjusted to achieve dynamic matching. The system comprises a data acquisition module, an image modeling and analysis module and the like. The safety and accuracy of minimally invasive surgery puncture are improved, and the method is suitable for interventional ultrasound guided minimally invasive surgery scenes.

The present invention discloses a deep learning-based method for automatic segmentation of intravascular lipid plaques. The method utilizes an improved U-Net++ network architecture to achieve high-precision segmentation of the inner and outer membranes of blood vessels. Based on this, a K-means clustering algorithm based on grayscale values, edge features, and texture features is then employed. By incorporating a memory mechanism, stability and consistency are ensured during the clustering process, enabling accurate classification of plaque types. By using an improved U-Net++ network for inner and outer membrane segmentation in ultrasound and photoacoustic images, the present invention can accurately distinguish between different structures of the vascular wall, thereby achieving accurate identification, high-precision segmentation, and classification of lipid plaques. The K-means clustering algorithm, optimized based on feature selection and a memory mechanism, achieves relatively accurate and stable classification results in virtual histology, providing a reliable foundation for further lesion analysis and clinical application.

The embodiment of the invention provides a pulmonary artery image anomaly classification method and device, equipment and a medium. The pulmonary artery image anomaly classification method comprises the steps that a target CT image and a target ultrasonic image of a pulmonary artery are acquired; respectively coding the target CT image and the target ultrasonic image, and respectively extracting first global and local features of the target CT image and second global and local features of the target ultrasonic image after coding; performing weighted fusion on the first global and local features and the second global and local features through a preset multi-head attention mechanism to obtain multi-modal fusion features; performing decoding processing on the multi-modal fusion features, and performing reconstruction after decoding processing to obtain a target fusion image of the pulmonary artery; and performing anomaly classification based on the target fusion image to obtain an anomaly classification result of the pulmonary artery, thereby improving the accuracy of anomaly classification of the pulmonary artery image.

The invention provides a target detection-based blood vessel feature analysis method and device, equipment and a medium, and the method comprises the steps: carrying out the processing of an input blood vessel image through an initialized feature extraction module, generating a pre-training model, carrying out the feature extraction of the blood vessel image through the pre-training model, and obtaining image feature data; inputting the lesion area into an analysis module, and performing image segmentation on the lesion area through the analysis module to obtain segmented image features; the segmented image features comprise image data of a calcified region and / or a non-calcified region; and respectively inputting the segmented image features into a calcified plaque analysis module and a non-calcified plaque analysis module to obtain the type of a vascular lesion area, further analyzing the density distribution of mixed plaques to determine whether the mixed plaques are in a splicing type or a uniform type, and finally generating an analysis result containing the lesion type and the stenosis degree. According to the invention, accurate positioning, segmentation and classification of vasculopathy can be realized without labeling data, and the automation level and accuracy of vasculopathy diagnosis are improved.

The invention relates to a retinal blood vessel image segmentation method and system based on improved U-Net. The method comprises the following steps: firstly, constructing a data set consisting of a plurality of retinal blood vessel images and corresponding segmentation masks thereof, preprocessing the data set, and dividing the data set into a training set and a verification set; introducing a dilated convolutional layer and an ELA attention mechanism into the U-Net model so as to construct an initial model; training, verifying and adjusting the initial model by using the training set and the verification set to obtain a final model; and finally, inputting a to-be-segmented retinal blood vessel image into the final model, and outputting a segmentation result. Compared with the prior art, the method has the advantages of improving the segmentation precision of the fuzzy boundary and the small target area, improving the segmentation accuracy and the like.

The invention relates to the field of artificial intelligence medical treatment, in particular to a multi-modal feature fusion analysis system and method for gynecological face diagnosis, and aims to improve the early diagnosis precision of gynecological diseases, the system fuses multispectral images and symptom text data, and captures capillary morphology and hemodynamic parameters through a microcirculation feature extraction module; the region mapping analysis module is combined with the face region-gynecological viscera knowledge graph to generate viscera abnormality scores; the endocrine cycle modeling module constructs a dynamic model based on historical data and monitors cycle abnormity; the semantic attention fusion module fuses the symptom text and the microcirculation features by using a bidirectional cross attention mechanism to form a fusion feature vector; the knowledge reasoning feedback module is used for generating a diagnosis result and a personalized conditioning scheme based on dynamic knowledge graph multi-path reasoning, and optimizing a diagnosis and treatment path. The system overcomes the limitation of a traditional two-dimensional RGB image, effectively captures the subepidermal microcirculation state, and provides an important basis for early diagnosis of gynecological diseases.

The invention discloses a novel graph contrast learning framework with mutual information maximization, and relates to the technical field of graph contrast learning, multi-modal learning and mutual information maximization. The framework comprises the following steps: constructing a nearest neighbor graph, spreading similar samples to data of a missing mode, optimizing a loss function, and performing clustering optimization. The existing method has two main problems: (1) the existing method is mostly suitable for complete data and has a poor processing effect on data of a missing mode; and (2) the current palmprint and palm vein fusion recognition technology does not completely capture the complex relationship between the palmprint and palm vein features, resulting in inaccurate clustering result. According to the method, the modal loss in the incomplete palm print and palm vein fusion recognition task is effectively reduced, and the clustering performance is enhanced. According to the method, the mutual information maximization loss function is introduced, the adaptability and robustness of the model to incomplete data are effectively enhanced, and the method has high recognition precision and clustering performance when processing incomplete multi-modal data.

The invention provides a photoelectric volume pulse wave identity recognition method and system, and relates to the technical field of identity recognition, and the method comprises the steps: obtaining a to-be-recognized PPG signal; and inputting the PPG signals into a trained identification model, firstly extracting linear features and nonlinear features, then projecting the features fused by the two features into a feature space by using a learned discriminant projection matrix to obtain multi-view features, and finally classifying the PPG signals by using the multi-view features to obtain an identity identification result. According to the method, manifold regularization and inter-class-error double sparse constraints are combined, the problems of intra-class discretization and inter-class overlapping of a linear model are solved, a graph structure learning method for adaptive local density adjustment is provided, and the manifold modeling precision of non-stationary PPG signals is improved.

The invention belongs to the technical field of medical consumable management, and particularly relates to an intelligent management system and method for medical high-value consumables and a storage medium, and the system comprises a management cabinet body, an authority management module, a bioelectricity recognition module, a short-term early warning module and a low-inventory early warning module. By introducing the authority management module, the bioelectricity identification module, the low-inventory early warning module and the like, automatic recording of consumable delivery and warehousing, automatic calculation of real-time inventory and automatic sending of early warning information are realized. For example, the low-inventory early warning module automatically calculates the current inventory according to the replenishment and use conditions of the consumables, and when the inventory is lower than the set minimum inventory, the low-inventory early warning is automatically sent to the warehouse keeper, so that the warehouse keeper can replenish the consumables in time, medical operation delay caused by insufficient inventory is avoided, and the management efficiency is greatly improved.

A detection device includes a substrate, a plurality of photodiodes that are arranged in a detection region of the substrate, a plurality of first terminals that are arranged in a first direction in a peripheral region outside the detection region of the substrate, an insulating film that covers the first terminals, and an anisotropic conductive film that is located above the insulating film, and covers the first terminals.

The invention relates to the field of medical image processing, in particular to a gastric cancer precancerous lesion progress risk assessment system based on time sequence image analysis, which comprises a time sequence gastroscope image registration module, a lesion curved surface dynamic representation module, a multi-dimensional feature manifold construction module, a risk dynamic prediction module and a virtual pigment endoscope enhancement module, according to the method, a differential geometry theory is introduced, the gastric mucosa surface is modeled as a Riemannian curved surface, and Gaussian curvature, average curvature and other characteristics are extracted; constructing a feature manifold space by adopting a manifold learning method, and analyzing a lesion state evolution trajectory through geodesic distance; and predicting the time for the lesion to reach a high-risk state based on the probability density distribution and the evolution vector field on the manifold. According to the method, accurate characterization of gastric cancer precancerous lesion morphological characteristics, quantitative analysis of dynamic evolution and accurate prediction of risk progress are realized, a scientific basis is provided for clinical follow-up visit decisions, and the method has the remarkable advantages of improving the early intervention rate, optimizing medical resource allocation and the like.

The invention discloses a non-contact palm vein multi-mode identification system based on deep learning. The system comprises a multi-mode image acquisition and processing terminal, a mode matching and identification decision terminal, a safety protection and anti-counterfeiting terminal and an iterative optimization terminal. The multi-modal image acquisition and processing terminal is used for acquiring palm print and palm vein images, performing dynamic calibration, image quality enhancement and preprocessing, and outputting multi-modal images; the mode matching and identification decision terminal is used for carrying out feature extraction, multi-modal feature fusion, mode matching and identification decision and outputting a final matching result; the safety protection and anti-counterfeiting terminal is used for carrying out living body detection, anti-counterfeiting monitoring and data encryption; and the iterative optimization terminal is used for realizing model lightweight, knowledge distillation, end-to-end optimization and continuous learning. According to the invention, through deformation invariant feature extraction and cross-modal attention fusion, the recognition accuracy and environmental adaptability in a complex state are improved.

The invention provides a palm vein high-security identity authentication system based on spectral analysis and deep learning, and relates to the technical field of biological recognition, the system comprises a hardware layer, a data processing layer, an algorithm layer and an application layer, through an antagonistic living body detection module in the algorithm layer, in combination with physical modeling and a GAN (Generative Adversarial Network), the high-security identity authentication of a complex imitation is realized. The method comprises the following steps: carrying out effective identification of a high-biomimetic material, carrying out physical modeling to analyze the absorption characteristics of palm veins in a hyperspectral range, such as the difference between oxyhemoglobin and oxyhemoglobin and periodic spectrum micro-interference caused by heart rate, and providing living body evidence based on physiological dynamics; meanwhile, a high-simulation template sample generated by the GAN is used for training a discriminator, the insight ability of the system to a template means is further enhanced, and compared with single traditional texture or static signal detection, the system combines physical characteristics with deep learning, and the confidence coefficient of in-vivo detection reaches up to 99.99%.

The invention provides a registration method and device in a cardiac surgery and computer equipment, which are used for improving the safety and accuracy of the surgery. The registration method comprises the following steps: acquiring an electrocardiosignal and multi-modal physiological data of a surgical patient, and performing cardiac time phase division on a cardiac cycle based on the electrocardiosignal and the multi-modal physiological data; collecting multi-angle image data of the heart under each cardiac time phase, and synchronously collecting physiological parameter data; performing three-dimensional reconstruction based on the multi-angle image data corresponding to each cardiac time, and mapping the physiological parameter data corresponding to each cardiac time to a corresponding three-dimensional reconstruction result to obtain a multi-temporal three-dimensional heart model library; the method comprises the following steps: acquiring real-time electrocardiosignals and real-time multi-modal physiological data of a surgical patient in a surgical process to identify a current cardiac time phase, selecting a current time phase three-dimensional heart model from a multi-time phase three-dimensional heart model library according to the current cardiac time phase, and performing surgical registration by using the current time phase three-dimensional heart model.

The invention discloses a DSA image coronary artery interlayer automatic identification system based on U-KAN and anatomy prior. The DSA image coronary artery interlayer automatic identification system comprises a data preprocessing module, an anatomy prior information extraction module, a U-KAN model construction module, a model training module, an interlayer area automatic identification module and a result processing and evaluation module. According to the method, the KAN layer is introduced, so that the expression capability and the recognition precision of the U-Net model on complex medical images (such as DSA images) are remarkably enhanced. According to the method, anatomical prior information (such as geometric morphology of coronary artery, vascular branches and the like) is creatively fused into the deep learning model, so that the sensitivity of the model to a lesion area is improved. According to the invention, through the optimized U-KAN network, the identification precision is ensured, and the calculation efficiency is high. The U-KAN model provided by the invention has strong expansibility, can be applied to automatic identification of coronary artery interlayer, and can also be applied to other types of medical image analysis tasks, brain CT and automatic detection and segmentation of tumor MRI images.