803results about "Blood vessel patterns" patented technology

The invention relates to a fundus image blood vessel segmentation method and system, and the method comprises the steps: constructing a fundus image blood vessel segmentation model which comprises a double-branch feature extraction module, a feature fusion and topology constraint module, a multi-task joint optimization module and a post-processing optimization module, wherein the double-branch feature extraction module extracts global features and local features at the same time, the feature fusion and topology constraint module fuses the global features and the local features in a cross-scale mode, a topology constraint mechanism is introduced to maintain the structural integrity of the vascular network, the multi-task joint optimization module comprehensively considers topology loss, segmentation loss and comparison loss, and the overall performance of the vascular network is improved. The post-processing optimization module optimizes the initial segmentation result; and training the constructed fundus image blood vessel segmentation model, and then applying the model to fundus image blood vessel segmentation to obtain a corresponding blood vessel segmentation result. The method and the system are beneficial to improving the accuracy and the reliability of performing blood vessel segmentation on the fundus image.

The present invention relates to systems and methods for integrating real-world functionalities, such as healthcare services, into a virtual environment (metaverse) using digital avatars and data security measures. Users can access services through their avatars, maintaining anonymity and data security. The system securely transmits user requests and data to real-world service providers, who provide services or feedback within the virtual environment. Data security ensures confidential and tamper-proof data transmission and facilitates secure payment transactions. The invention enables the integration of various real-world services into the metaverse, enhancing its functionality and utility while preserving user privacy and security.

The invention discloses a coronary angiography image blood vessel segmentation system based on multi-scale feature fusion. According to the invention, through the innovative design of the adaptive morphological sensing module, the system can dynamically analyze the anatomical structure characteristics of the blood vessel: the differentiable morphological operation layer converts the corrosion expansion operation into a learnable feature extraction process, so that the network can autonomously identify the gradient change and boundary trend of the blood vessel wall; the dynamic nuclear adaptation mechanism adjusts the scale and direction of morphological operation in real time according to the local blood vessel diameter and curvature characteristics, interference of surrounding tissues in a main blood vessel area can be inhibited, and continuous expression can be enhanced for capillary branches. Through deep fusion of dissection driving and data driving, the topological structure integrity of a segmentation result at a blood vessel bifurcation point and a narrow lesion area is remarkably improved, and the common problems of blood vessel fracture and misconnection in a traditional method are effectively avoided.

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 invention discloses a power transmission line constructor identity verification method based on face recognition, and relates to the technical field of electric power engineering safety management, and the method comprises the steps: collecting multispectral face data, and generating a spectral stereo feature matrix; micro blood flow pulsation characteristics, skin texture characteristics and thermal imaging temperature distribution characteristics are extracted from the spectrum stereo characteristic matrix, a three-layer cascade anti-counterfeiting verification mechanism is constructed, and comprehensive living body judgment is carried out; establishing a distributed feature matching network, and matching the spectrum stereo feature matrix with a pre-stored feature library; collecting a geographic position track and an operation behavior mode of a constructor, and carrying out multi-dimensional cross validation on the geographic position track and the operation behavior mode and an identity matching result to generate a multi-level safety evaluation index; and writing the identity verification process and the multi-level security evaluation index into a distributed account book, and generating a verification voucher. According to the invention, cross analysis is carried out on the identity matching result of the constructor and the behavior characteristics, so that the problem that a traditional face recognition system is easily falsely used by the identity is effectively solved.

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 relates to the technical field of ophthalmology medical diagnosis, and discloses a three-dimensional reconstruction-based fundus focus intelligent labeling method and diagnosis system. The method comprises the following steps: receiving multi-modal image data streams such as fundus color photos, OCT images and FFA images of an ophthalmological patient; performing spatial registration and feature fusion by using a pre-trained lesion feature fusion model to generate a three-dimensional lesion probability distribution diagram and a lesion category confidence matrix; constructing an adaptive annotation threshold model to generate a multi-modal annotation instruction set; based on the focus development chain model, focus development is simulated, and instruction set parameters are optimized and labeled; and iteratively optimizing through a distributed reinforcement learning framework, and outputting the focus labeling action sequence to an ophthalmology diagnosis platform. According to the method, multi-modal image information can be integrated, the diagnosis accuracy and efficiency are improved, personalized diagnosis is realized, resources are reasonably utilized, and powerful support is provided for ophthalmic disease diagnosis.

The invention discloses a lightweight eye fundus blood vessel image segmentation model based on Mama. The model comprises the following steps: an encoder and a decoder comprise at least four layers of SlimPVM Blocks; gradually downsampling the input feature map through maximum pooling operation; the CCA Bottleneck module is used for calculating the self-attention of the cross-channel in the height dimension and the width dimension of the feature map; the encoder and the decoder carry out feature fusion through selective jump connection, and the selective jump connection carries out weighted selection on features through a channel attention mechanism; the decoder uses SlimPVM Block to refine the features after up-sampling, the model recovers lost spatial dimensions through convolution transpose operation, and coding features from selective jump connection are combined; and the decoder finally outputs a final segmentation mask generated through a 1 * 1 convolution layer, and the segmentation mask can accurately describe the boundary of the target area in the medical image. The method is specially used for retinal vessel segmentation tasks, model parameters are reduced, calculation overhead is reduced, and meanwhile segmentation performance is kept and even 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 provides a blood vessel image segmentation method and device based on multi-mode cooperative enhancement and dynamic alignment, and relates to the technical field of deep learning and image processing. According to the method, 3D blood vessel original image sets of different modalities are obtained, and after segmentation and modal feature mark fusion, a diffusion model is used for denoising and enhancement to generate a modal embedded graph; inputting the modal embedded graph into an improved 3D UNet encoder to extract multi-scale low-level features; performing global feature enhancement and dynamic alignment on the low-level features based on a graph neural network to generate aligned high-level features; calculating feature information entropies of the aligned advanced features and performing dynamic weight fusion to obtain modal cooperation features; based on the comparative learning of the positive and negative sample pairs, optimizing the difference representation between the modal cooperation features, and obtaining a fusion feature; and inputting the fusion features into a decoder to generate a blood vessel segmentation result. According to the method, the morphological characteristics of small blood vessels in different modes can be efficiently captured, and the segmentation precision and robustness of the multi-mode blood vessel image are remarkably improved.

The invention relates to the field of medical image processing, and discloses a coronary artery stenosis detection method and system based on an angiography image. The method comprises the following implementation steps: collecting a coronary sequence video, sampling and labeling blood vessel frames in the coronary sequence video under the guidance of a professional doctor, and obtaining a coronary angiography image set and a coronary angiography background data set; using a fine-tuned diffusion model DDPM to extract a potential feature map of the blood vessel, and performing channel remodeling on the potential feature map and an original coronary artery image; performing dimension feature extraction on the coronary angiography image, and extracting features of a corresponding layer in the backbone network; then, narrow feature enhancement and multi-scale Transform operation are carried out on the extracted feature mapping; and finally carrying out feature fusion on the feature mapping of different layers and then sending to a detection head module for prediction. According to the method, the detection effect of coronary artery stenosis can be improved, a foundation is established for subsequent quantitative detection of vascular stenosis, and the method has a great application prospect.

The invention discloses a palm vein and palm print feature fusion intelligent identity verification method and system, and relates to the technical field of biological recognition, and the method comprises the steps: collecting a palm image and identity data of a to-be-authenticated user, and carrying out the identity coding of the palm image based on the identity data, and obtaining a palm identity coding image set; then, through region marking and feature extraction, multi-dimensional features of palm veins and palm prints are obtained; then, performing spatial alignment fusion on the features of the two to generate a palm code fusion feature data set; and then, through encryption processing and network training, a self-adaptive palm identity recognition network is obtained. And finally, acquiring a palm image of a target user, and performing identity verification by using the network. The technical problems of insufficient safety, low accuracy and poor anti-interference capability caused by dependence on a single biological characteristic in an existing identity authentication method are solved, and the technical effect of improving the safety, the accuracy and the anti-interference capability of identity authentication is achieved.

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 invention provides a palm vein feature extraction and depth adaptive matching method, which relates to the technical field of biological recognition, and is characterized in that a three-dimensional space-time convolutional neural network and a graph neural network are combined to extract space depth features, time blood flow dynamic features and blood vessel topological features of palm veins, and information bottleneck is utilized to optimize and screen a feature subset with the most distinguishing property; the method breaks through the limitation of traditional two-dimensional texture feature extraction, obtains abundant palm vein information through multi-source data acquisition (such as near-infrared imaging, a light field camera and a spectrum sensor), overcomes the interference of environmental factors such as illumination, angle and noise, achieves the high-precision modeling and stable extraction effects of palm vein features, and can achieve the real-time extraction of the palm vein features in a complex scene. A high recognition rate is kept under different illumination conditions or user hand posture changes, the robustness of the palm vein recognition system is remarkably improved, and the method is suitable for scenes with high safety requirements.

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 relates to the technical field of image analysis, in particular to a three-dimensional hepatic duct, hepatic artery, portal vein and hepatic vein image segmentation method and system. The method comprises the following steps: acquiring multi-phase medical image data of a liver region; performing region identification on the image data, and extracting hepatic duct, hepatic artery, portal vein and hepatic vein regions; executing structure tracking on the hepatic duct area to obtain a hepatic duct positioning result; carrying out improved contrast extraction on the hepatic artery region to obtain hepatic artery positioning data; performing directional segmentation analysis on the portal vein region to obtain a portal vein positioning result; performing branch angle extraction on the hepatic vein region in combination with the portal vein direction to obtain hepatic vein positioning data; taking the hepatic artery as a reference structure, performing elastic registration on each blood vessel structure, and generating registration data under a unified coordinate; and completing three-dimensional visualization based on a registration result. According to the method, multi-stage image joint segmentation and registration are realized, and the method has the technical advantage of high structural recognition integrity.

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

The invention provides a method for automatically segmenting a pulmonary artery tree and a tracheal tree on a CTPA image, and relates to the technical field of image processing, and the method comprises the steps: carrying out the data preprocessing of a pulmonary artery angiography image, and obtaining a preprocessed image; refining the preprocessed image to obtain a center line and a tubular structure radius; constructing three-dimensional Gaussian distribution according to the center line and the radius of the tubular structure, and obtaining a Gaussian distribution thermodynamic diagram according to the three-dimensional Gaussian distribution; inputting the preprocessed image into a first initial network to obtain an initial prediction result, and training the first initial network through the initial prediction result and a Gaussian distribution thermodynamic diagram to obtain a first segmentation network; inputting the preprocessed image into a first segmentation network to obtain a first segmentation result; and inputting the first segmentation result and the preprocessed image into a second segmentation network to obtain a final segmentation result. According to the method, the blood vessel segmentation technology is optimized through the Gaussian distribution thermodynamic diagram of the center line, and the recognition accuracy and segmentation precision of the model are improved.

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 discloses a digestive tract tumor endoscope image multi-scale detection system. The system comprises an endoscope image acquisition module, an image preprocessing module, a feature extraction module, a focus identification module, a diagnosis report generation module and a rehabilitation plan making module. The invention belongs to the technical field of digestive tract detection, and particularly relates to a digestive tract tumor endoscope image multi-scale detection system, which adopts an improved U-net architecture, a dynamic connection rule and a self-adaptive multi-scale convolution kernel bidirectional feature pyramid fusion technology to realize multi-level feature extraction and high-precision semantic segmentation of a digestive tract image; through multi-scale feature extraction, channel spatial recombination, spatial-temporal feature fusion, attention enhancement output and weight optimization, multi-dimensional accurate recognition and feature expression of the nidus of the digestive tract are realized.

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.

In various examples, infrared image data may be used to detect a subcutaneous characteristic(s) (e.g., a palm vein topology) of a person (e.g., a person requesting entry to a vehicle, a vehicle occupant) and authenticate the user based on the detected subcutaneous characteristic(s). For example, infrared image data representing one or more acquired subcutaneous characteristics (e.g., a topology of veins and / or other blood vessels in a region of the authenticating user's palm, hand, neck, forearm, face, fingertip, eye, etc.) may be generated. Hand and / or palm detection may be applied to detect a region depicting the user's hand or palm, and that region (or some subset thereof) may be segmented to generate a representation of an acquired vein topology. The acquired vein topology may be compared with one or more reference vein topologies stored in a database to determine whether the acquired vein topology matches one of the reference vein topologies.

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.

The invention provides a blood vessel image segmentation method, device and equipment based on a differential learning model, and relates to the field of neural network and image processing. According to the method, 3D blood vessel original image sets of different modalities are obtained and input into a blood vessel image segmentation model, and in the segmentation model, a reconstruction image embedded with modal features is obtained by using a modal difference marking method; inputting the reconstructed image into a 3D UNet encoder to extract low-level features; according to the low-level features, high-level features are generated through a position encoder and a self-attention mechanism; expanding the advanced features into one-dimensional vectors through a linear layer, and optimizing the one-dimensional vectors by adopting a Transform encoder to generate optimized features; secondly, calculating difference features between modals by means of element-by-element subtraction of the features after Transform optimization, so as to obtain difference fusion features; the difference fusion features are remodeled through a Transform encoder, and a final blood vessel segmentation result is obtained. According to the invention, morphological characteristics of small blood vessels can be accurately captured, and the blood vessel image segmentation precision is effectively improved.

According to the ultra-wide-angle fundus image analysis method and system, an ultra-wide-angle fundus image is divided into a macular region, a posterior pole region and a peripheral region, the focus of each region is automatically recognized, and then auxiliary diagnosis of various fundus diseases of the whole image is achieved. According to the method and the system, efficient, accurate and automatic fundus disease analysis and evaluation are realized through region partitioning and focus analysis of the ultra-wide-angle fundus image, particularly, the method and the system have remarkable advantages in retina peripheral region lesion detection, and the system can be applied to screening and auxiliary diagnosis of clinical ophthalmic diseases on the basis of a deep learning algorithm model. Along with the rapid development of deep learning, a convolutional neural network (CNN) obtains a significant result in the field of image classification, and the method carries out the partitioning of an ultra-wide-angle fundus image based on a deep learning model, and carries out the analysis and evaluation of common fundus diseases and lesions of the whole image, and optic nerve diseases including the lesions of glaucoma. The invention provides an automatic and accurate fundus image auxiliary diagnosis method.

The invention discloses a living body face detection method based on a near-infrared and visible light binocular camera, and belongs to the technical field of face recognition, and the method comprises the steps: collecting binocular visible light and near-infrared images, and carrying out the cross-modal alignment; subcutaneous microvascular pulsation time sequence signals in the near-infrared image are extracted, texture boundary features in the visible light image sequence are enhanced in combination with the near-infrared image sequence, and an enhanced texture feature map is obtained; analyzing 3D deformation continuity and physiological motion consistency in the expressions and actions based on the enhanced texture feature map; and in combination with time-frequency domain disturbance detection, carrying out living body determination. According to the method, a multi-modal time sequence synchronous material reflection gradient analysis and microvascular pulsation time sequence tracking technology is adopted, and in combination with facial dynamic 3D deformation biomechanical model constraint and time-frequency combined anti-interference verification, the accuracy and robustness of in-vivo detection can be effectively improved, and a powerful guarantee is provided for the safety of a face recognition technology.

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.