596results 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 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.

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 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 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.

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.

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 discloses a method for performing micro-hemorrhage focus recognition on brain images by using a deep learning algorithm, which comprises the following steps of: performing brain skull removal operation on magnetic sensitive weighted imaging images and phase images of a plurality of patients by using an HD-BET tool to obtain an intracerebral region map in a 2D image format; the method comprises the following steps of: firstly, firstly, carrying out brain detection on a brain region map, then, carrying out mesh division on each brain region map, dividing the brain region maps into mesh region maps with the same size, and then, training a YOLOv8 target detection model and a 3D-CNN network model by utilizing the mesh region maps, so that micro-hemorrhagic spots can be quickly predicted through the YOLOv8 target detection model, and then false positive micro-hemorrhagic spots are eliminated through the 3D-CNN network model. And a final micro-bleeding point detection result is obtained.

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 discloses a retinal blood vessel image segmentation method based on dynamic weight distribution and index guide optimization. Comprising the steps that a to-be-segmented retinal blood vessel image is input into an LM-TransUNet model, an image segmentation visualization result is obtained, the LM-TransUNet model is obtained based on training of a training set, and the training set comprises the retinal blood vessel image and a corresponding true value; the process of training the LM-TransUNet model based on the training set comprises the following steps: predicting a retinal blood vessel image through an OfficientNet and ResNet hybrid encoder model, calculating segmentation loss and performance index loss according to a prediction result and a truth value, adaptively fusing multi-scale features by using a dynamic weight distribution mechanism, and adjusting network parameters in real time through an index guide optimization module, so as to achieve the purpose of improving the performance of the retinal blood vessel image. And a new prediction result is obtained for iteration until convergence, and a final model is obtained. According to the method, the accuracy of retinal blood vessel image segmentation can be remarkably improved, and particularly, the method is excellent in segmentation of complex blood vessel structures and small blood vessels.

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 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 belongs to the field of medical robots, and relates to a method, device and equipment for adjusting a DSA ray frame rate and a storage medium, and the method comprises the steps: obtaining a current exposure frame rate of a DSA; acquiring operation parameters of an operator end; and generating an updated DSA exposure frame rate according to the current exposure frame rate and the operation parameters, and sending the updated DSA exposure frame rate to the DSA, so that the DSA performs imaging according to the updated DSA exposure frame rate. The DSA ray frame rate can be adjusted according to the related motion parameters of the interventional instrument, so that the radiation dose received by an operation object, an operator and an assistant is reduced, and damage caused by radiation is relieved.

The invention discloses a unet-based longitudinal carotid plaque segmentation method, which comprises the following steps: acquiring a longitudinal carotid ultrasound image, and segmenting the longitudinal carotid ultrasound image through a segmentation model based on a unet structure to obtain a carotid plaque segmentation result; wherein the segmentation model comprises a multi-scale edge enhancement module for down-sampling, an edge focusing attention module for up-sampling and a wavelet frequency domain attention module for performing jump connection on the multi-scale edge enhancement module and the corresponding edge focusing attention module; wherein the multi-scale edge enhancement module is used for performing enhancement extraction on the edge features of the ultrasonic image, and the wavelet frequency domain attention module is used for performing feature optimization on the features subjected to enhancement extraction; and carrying out feature reconstruction on the features after feature optimization according to the distribution weight through an edge focusing attention module, and further carrying out pixel-by-pixel labeling so as to segment the carotid plaque.

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 invention provides a cerebral vessel segmentation method based on geometric knowledge and topological structure fusion, and the method comprises the following steps: S1, inputting a brain image into a vascular skeleton extraction model, and obtaining a vascular skeleton diagram; s2, simplifying a skeleton path of the vascular skeleton diagram to obtain skeleton key points, generating an adjacent matrix, and constructing a sparse structure diagram according to the skeleton key points and the adjacent matrix; and S3, inputting the brain image and the sparse structure diagram into a pre-trained first U-Net model, and segmenting blood vessels in the brain image to obtain a cerebrovascular contour diagram. Through combination of the topological information of the blood vessels and the deep convolutional neural network, the segmentation effect on fine blood vessels and complex blood vessels is improved, and the cerebrovascular segmentation effect is remarkable.