121 results about "Vascular network" patented technology

Systems and methods are disclosed for creating an interactive tool for determining and displaying a functional relationship between a vascular network and an associated perfused tissue. One method includes receiving a patient-specific vascular model of a patient's anatomy, including at least one vessel of the patient; receiving a patient-specific tissue model, including a tissue region associated with the at least one vessel of the patient; receiving a selected area of the vascular model or a selected area of the tissue model; and generating a display of a region of the tissue model corresponding to the selected area of the vascular model or a display of a portion of the vascular model corresponding to the selected area of the tissue model, respectively.

The invention discloses an artificial intelligence-based periodontal state evaluation method, which comprises the following steps of: acquiring multi-modal tooth data which comprises a tooth surface image and a local blood vessel network image of an oral cavity; preprocessing the multi-modal tooth data, and performing cross-modal registration by using a registration module to obtain a registered tooth surface image and a registered blood vessel network image; extracting multi-dimensional features of the registered blood vessel network image through a first feature extraction module, wherein the multi-dimensional features comprise spatial morphological features, topological connectivity features and hemodynamic features of blood vessels; extracting macroscopic morphological features and lesion local features of the registered tooth surface image through a second feature extraction module; performing cross-modal fusion on the multi-dimensional features, the macroscopic morphological features, the local lesion features and the clinical data to obtain fusion features; and outputting a periodontal state evaluation result based on the fusion features, thereby facilitating improvement of periodontal state evaluation accuracy and periodontal disease early warning capability.

The invention relates to an intelligent permeation grouting process and device for concrete crack self-repairing. The intelligent permeation grouting process and device are suitable for crack detection and repairing of a newly-built concrete structure. The device comprises a blood vessel network embedded into concrete, a crack detection sensor, a control module, a pumping module and a self-repairing material storage module, and automatic detection and repairing of cracks are achieved. The technological process comprises crack detection, data analysis, grouting parameter determination, material injection and repair monitoring. Through the automation and intelligence technology, the repairing efficiency and precision are remarkably improved, and the device is suitable for large structures such as bridges and tunnels.

The invention discloses an ultrasonic blood flow quantitative analysis method based on a skeletonized image, which comprises the following steps of: firstly, selecting a target analysis area containing a micro blood flow image in a grey-scale map formed by ultrasonic imaging; the method comprises the following steps of: firstly, carrying out normalization processing, nonlinear gray mapping, Gaussian filtering, minimum filtering and high-pass filtering in sequence to realize enhancement, noise suppression and edge enhancement on a target image, and finally, carrying out binaryzation on the image, and carrying out skeleton extraction based on an iterative refinement principle to obtain a final blood flow skeleton. According to the method, preprocessing such as noise suppression and edge enhancement is performed on the ultrasonic micro blood flow image before binarization and skeleton extraction, so that the accuracy and robustness of skeleton extraction are remarkably improved, and a reliable basis is provided for subsequent quantitative analysis and quantitative evaluation of a vascular network structure.

The invention relates to the technical field of medical image processing, and discloses an adversarial generative network model for three-dimensional reconstruction of a brain cell-level vascular network. The model comprises a three-dimensional blood vessel feature extraction module, a dynamic attention generator, a multi-scale discriminator and the like. Characteristics are obtained through multi-modal medical image data (magnetic resonance angiography and confocal microscope scanning data), and high-precision vascular network three-dimensional reconstruction is realized by utilizing cooperative work of all the modules. The dynamic attention generator calculates the topological connection probability of the vascular branches, and the multi-scale discriminator globally and locally evaluates the reconstruction result. Meanwhile, model training is optimized through an adversarial training controller, and the blood vessel network is perfected through a capillary network completion module. According to the method, the advantages of multi-modal data are effectively fused, the reconstruction precision is improved, and powerful support is provided for research and diagnosis of brain vascular diseases.

A lung CT blood vessel image segmentation and reconstruction system based on multiple interpretable features performs further fine-grained segmentation on the basis of main airway features through a feature extraction module to obtain a plurality of connected domains, performs multi-stage feature extraction on the connected domains, and outputs multi-scale feature information; the blood vessel segmentation module generates an information matrix according to the indexes and the pulmonary parenchyma mask, generates a blood vessel network segmentation result through an adversarial network and performs post-processing; and the model reconstruction module performs three-dimensional reconstruction on the blood vessel network segmentation result and the pulmonary parenchyma mask to obtain a 3D model. According to the method, on the basis of anatomical priori knowledge of pulmonary vessels and airways, a large amount of multi-scale feature information is utilized, the information loss of the two-stage segmentation network is reduced, and the network adaptability and the learning ability of the relation between pixels under the complex condition are improved. In the aspect of system design, a modular design and management mode is adopted, efficient resource access is achieved, and meanwhile the operability and usability of the system are improved through end-to-end process design.

The invention provides a vascularized bone organ and a construction method thereof, and a vascularized bone organ model containing a hollow vascular network is constructed by combining a suspension 3D biological printing method, a cell-carrying printing strategy and a sacrificial biological ink material. The vascularized bone organ model has excellent structural stability, mechanical performance, growth and development capability and osteogenesis capability, selected materials and a manufacturing method are simple in process, engineering stable production can be achieved, cells can be blended for a long time for three-dimensional culture, and the vascularized bone organ model has high applicability and research potential.

An integrated modular microphysiological system is provided which includes a two or more chambers and a vascular network which includes at least one channel. The chamber can be configured for culturing a tissue and includes a layer of endothelial cells which forms an endothelial barrier within the well. The endothelial barrier can be in fluid contact with at least one of the at least one channels in the vascular network. The endothelial barrier can also be in fluid contact with a fluid in the chamber.

The invention discloses a cardiovascular data monitoring method for the cardiovascular medicine department, and relates to the technical field of cardiovascular monitoring, and the method comprises the steps: collecting and preprocessing multi-source cardiovascular data of a patient, and building a cardiovascular data set in combination with a conditional generative adversarial network; constructing a cardiovascular network framework based on the established cardiovascular data set, and optimizing cardiovascular network parameters by using a meta-learning algorithm to capture feature data in the cardiovascular data set; analyzing a causal relationship between the feature data and the cardiovascular event by using a causal discovery algorithm, and constructing a causal relationship graph based on an analysis result; and a fuzzy membership function and an entropy weight algorithm are fused, nodes in the causal relationship graph are adjusted, and a hierarchical monitoring strategy is generated in combination with a multi-objective evolutionary algorithm. According to the method, the multi-source cardiovascular data of the patient is collected and preprocessed, and the conditional generative adversarial network is introduced to generate high-quality and diversified synthetic data, so that the data scale is effectively expanded, and the balance and representativeness of data distribution are improved.

The invention relates to the field of medical image processing and artificial intelligence, in particular to an inflammation active retinopathy prediction system and method based on artificial intelligence, and the system comprises an image processing module which comprises an image acquisition unit, a blood vessel density analysis unit, a blood-retina barrier integrity imaging analysis unit and a data integration module. The core innovation of the invention lies in that a blood vessel density analysis unit constructs a blood vessel structure analysis framework by introducing topology and differential geometry theories, comprises a topological manifold representation module, a curvature flow dynamic analysis module and a non-European interactive network module, maps fundus image data into a topological manifold structure, and extracts topological features of a blood vessel network; curvature flow evolution analysis is carried out based on the blood vessel network topology features, and blood vessel form dynamic features are generated; a non-European metric space of a vascular network and inflammatory factors is constructed, an interaction relationship between the vascular network and the inflammatory factors is analyzed, and vascular-inflammation interaction characteristics and blood-retina barrier integrity index data are integrated.

The invention relates to a postoperative complication prediction method and device based on a two-way map neural network, and the method comprises the steps: collecting ICG video images before and after cerebrovascular bypass surgery, and obtaining the clinical feature data of a patient; preprocessing the collected ICG video image, extracting a blood vessel network by adopting an image segmentation algorithm, and generating corresponding blood vessel graph structures before and after bridging; extracting blood vessel features, blood flow dynamic features and clinical features from the collected ICG video images and clinical feature data, and constructing a multi-modal high-dimensional feature set; and taking the multi-modal high-dimensional feature set and the corresponding vascular graph structures before and after bridging as input, and generating a complication prediction score through a pre-trained prediction model based on a two-way graph neural network. Compared with the prior art, the method has the advantages that the multi-modal feature information is fused, the graph neural network structure is optimized, the model performance is stable, the accuracy is high, and the method has important clinical application value.

Provided are a vascularized bone organoid and a construction method therefor. By combining a suspension-based three-dimensional (3D) bioprinting method, a cell-laden printing strategy, and a sacrificial bioink material, a vascularized bone organoid model containing a hollow vascular network is constructed. The vascularized bone organoid model has excellent structural stability, mechanical properties, growth and development capability, and osteogenic capability, involves simple selected materials and fabrication process, implements engineered stable production, allows for long-term co-culture of cells for 3D cultivation, and has high applicability and research potential.

The invention provides a dynamic bionic robot skin, a control method thereof and a robot. The dynamic bionic robot skin is characterized in that a bionic blood vessel network channel is arranged in a dynamic bionic layer; the bionic blood vessel network channel is used for simulating dynamic changes of blood vessels; the bionic blood vessel network channel is an elastic cavity embedded into the dynamic bionic layer; the dynamic blood vessel regulation module is communicated with the bionic blood vessel network through a pipeline, and the dynamic blood vessel regulation module is used for changing the filling degree and / or form of the bionic blood vessel network channel by pumping a fluid medium into and out of the blood vessel network channel or adjusting the internal pressure; the light-transmitting bionic epidermal layer covers the outer surface of the dynamic bionic layer and is fixedly connected with the dynamic bionic layer, and bionic skin textures are arranged on the outer surface of the light-transmitting bionic epidermal layer. The invention further discloses a robot and a skin control method. The method is high in simulation degree, and physiological phenomena such as blood vessel filling contraction and artery rhythm can be achieved.

The invention discloses a culture medium for adipose-derived stem cells, and particularly relates to the technical field of stem cell culture. The culture medium comprises a basic culture medium and nerve-vascular niche components, wherein the basic culture medium comprises DMEM / F12, 10% of human-derived platelet lysate and 1% of antibiotic solution; the nerve-vascular niche comprises the following components: a Slit3 recombinant protein, a nerve growth factor-beta, a glial cell-derived neurotrophic factor, a lysyl oxidase inhibitor, dexamethasone, 3-isobutyl-1-methylxanthine and insulin. The traditional fetal calf serum is replaced by the human-derived platelet lysate, so that the risks of immunological rejection and pathogen pollution are avoided; through the synergistic effect of nerve-vessel niche factors, blood vessel maturation and innervation are promoted, programmed mechanical stretching culture is combined, the adipogenic differentiation efficiency of adipose-derived stem cells is remarkably improved, and engineered adipose tissue which is huge in lipid droplet, compact in structure and provided with a nerve-vessel network is formed.

The present invention provides a proxy model and construction method for the outlet boundary conditions of a vascular network, which are applied to the field of data processing technology. The present application performs data compression and spatiotemporal normalization processing on multi-scale vascular feature data to generate compressed and normalized data, which is composed of the dimensionality reduction results of an autoencoder, spatiotemporal normalization relationships, and data mixing and enhancement results; processes the compressed and normalized data to generate target proxy model parameters based on DeepONet; processes the target proxy model parameters to generate proxy model output data; processes the proxy model output data and high-dimensional and variable data of the pipeline network to generate an outlet boundary condition proxy model based on symbolic regression, wherein the outlet boundary condition proxy model is used to characterize the outlet boundary conditions of the vascular network; and processes the outlet boundary condition proxy model, fitness evaluation, and genetic operation strategy based on a genetic algorithm to generate a target outlet boundary condition proxy model and verification results.

The embodiments of the present invention relate to the field of cryoablation and disclose a cryoablation temperature prediction method, device, electronic device and storage medium, including: constructing a vascular network diagram based on the vascular centerline in the three-dimensional imaging data before the cryoablation operation, the nodes in the vascular network diagram represent the vascular bifurcation points, and the blood edges represent the vascular connection relationship; during the cryoablation process, using a pre-trained temperature prediction model to predict the temperature field of the target sampling point, the temperature prediction model includes a physical information neural network and a graph neural network; the physical information neural network uses the biological heat conduction equation as a constraint condition to predict the temperature field of the target sampling point, and the graph neural network is used to predict the blood perfusion coefficient of each node in the vascular network diagram to determine the blood perfusion coefficient contained in the biological heat conduction equation, which solves the problem of real-time prediction of cryoablation temperature in accordance with physical laws, so that the results conform to physical laws and meet the real-time requirements during the operation.

The invention provides a three-dimensional vascular structure recovery device and method fusing prior information, and relates to the technical field of biomedical image processing. Comprising a data acquisition module, a data extraction module and a complex blood vessel missing structure recovery module. The method comprises the following steps: firstly, acquiring SR-CT data of a blood vessel network, and then carrying out image segmentation on the blood vessel network to obtain a binary mask; decomposing the complex blood vessel into a series of single blood vessel images according to an image segmentation result of the blood vessel network; according to a series of single blood vessel images obtained through decomposition, selecting a single blood vessel image containing a missing structure through a connected domain analysis method; a spatial transformation network based on shape prior is constructed, and a missing structure of a single blood vessel is recovered; and finally, recombining the single blood vessel image, namely the blood vessel image without the missing structure and the blood vessel image with the restored missing structure to generate a complete blood vessel network, thereby realizing missing structure restoration of the complex blood vessel network.

The invention relates to the field of biomedical ultrasonic imaging, and discloses a pulse wave detection method and system based on ultrafast Doppler imaging, and the method comprises the steps: firstly obtaining a dynamic power Doppler image of the blood flow of a biological tissue; performing spectral analysis on the Doppler signal of each pixel point in the image to obtain a function that the frequency spectrum changes along with time; and on the basis, calculating a blood flow speed time sequence, an impedance index and a pulsation index of each pixel point, and finally obtaining an impedance index distribution diagram and a pulsation index distribution diagram of the whole blood vessel network. According to the method, ultrafast imaging and spectral analysis technologies are ingeniously combined, the structure and the function of the vascular network can be comprehensively evaluated, a new technical means is provided for hemodynamic research and disease diagnosis of organs such as spinal cords, hearts and brains, and the method has a wide application prospect.

The application discloses a cerebral vascular recognition model construction method and a network construction method, and is suitable for magnetic resonance image processing and analysis in the field of neuroscience and clinical treatment. The method comprises the following steps: acquiring magnetic resonance image training data of cerebral vessels, inputting the magnetic resonance image training data into an initial recognition model constructed based on a deep learning segmentation framework to perform training, and obtaining a target recognition model capable of outputting prediction results of each anatomical region. The application realizes accurate extraction, integration and recognition of cerebral vascular information in different anatomical regions in the magnetic resonance image, can effectively solve the inclusion and exclusion relationship problems between corresponding structures of each anatomical region, and accurately segments various structural regions of the cerebral vessels in the image. After processing the prediction results output by the segmentation model, the nodes and edges of the cerebral vascular network are further analyzed, and information such as vascular morphological characteristics, anatomical regions, network hierarchical structure and blood flow direction is embedded as graph attributes to form a cerebral vascular information graph.

The application provides an animal eyeball fixation recognition system and method, and relates to the technical field of ophthalmic detection and treatment equipment.The method comprises the following steps: acquiring a time interval of continuous imaging of an animal eyeball; controlling at least two light sources of different wavelengths to alternately irradiate the eyeball in the interval of continuous imaging of the animal eyeball, and collecting a multispectral image sequence comprising a scleral vascular network; extracting at least one set feature point of the scleral blood vessels from the multispectral image sequence, and establishing a feature set; acquiring feature point position information, and obtaining micro-motion vector information of the eyeball; and compensating the acquired continuous frame images according to the micro-motion vector information of the eyeball.The animal eyeball fixation recognition method provided in the application embodiment can reduce the constraint performance of a traditional rigid fixation device, and in turn reduce physical damage that may be caused to the animal eyeball.On the other hand, for high-frequency or periodic physiological micro-motions such as heartbeat and respiration, more accurate and real-time compensation can be achieved.

The present invention relates to systems, compositions and methods for multicellular bioprinting vascularized tissue constructs comprising a hierarchical vascular network. The methods employ a dual bio-ink strategy involving fibrous microgel matrix bio-inks with adjustable hardness, self-healing rheology, and microporosity to support capillary self-assembly, and sacrificial bio-inks that define a large pourable channel after removal. The system integrates top-down printing and bottom-up cell self-organization within a programmable bio-printing platform. The key aspect of the invention is to engineer the physical topography of the matrix to actively guide vascular morphogenesis beyond conventional permissive stents. The integrated platform enables the manufacture of complex bionic vascular networks with enhanced mass delivery, cellular viability, and in vivo integration potential. The invention represents an important progress in the aspect of extendable production of functional human tissues, and has application in regenerative medicine, disease modeling and drug discovery.

The invention relates to the technical field of medical image processing, in particular to a digital ophthalmology virtual intelligent doctor system based on artificial intelligence, and the system comprises an image obtaining module which is used for obtaining and preprocessing a fundus image to form a standardized image; the blood vessel segmentation module is used for extracting a blood vessel network, constructing a blood vessel topological map and recording blood vessel branch and path information; the micro-hemorrhage candidate generation module is used for screening red spots and performing path matching in combination with vascular topology to form candidate spots; the staged reasoning module is used for roughly scanning and finely classifying the candidate spots to generate micro bleeding points; the historical image management module is used for comparing the candidate points with historical images of the patient, marking new or changing spots and forming priority information; and the output and feedback module is used for generating a detection result according to the candidate micro bleeding points and the priority information. Automatic identification and sorting of the micro bleeding points are realized, and the screening efficiency and reliability are improved.

The application belongs to the field of intelligent medical treatment, and particularly relates to a method for constructing a refractory mycoplasma pneumoniae pneumonia prediction model and equipment. The method comprises the following steps: acquiring a baseline time data set and a follow-up time label of a mycoplasma pneumoniae patient; inputting the image data set into a lung blood vessel segmentation model to obtain a lung blood vessel network, obtaining a total lung blood vessel volume based on the lung blood vessel network, and obtaining a blood vessel volume of different cross-sectional areas based on a cross-sectional area of the lung blood vessel network; inputting a ratio of the blood vessel volume of different cross-sectional areas to the total lung blood vessel volume into a machine learning model to obtain a prediction label, and obtaining a refractory mycoplasma pneumoniae pneumonia prediction model after iterative optimization of the machine learning model based on a difference between the prediction label and the follow-up time label. The application segments a lung CT image to obtain a blood vessel network diagram, quantitatively obtains a percentage of a blood vessel volume of different thicknesses in a total lung blood vessel volume, and constructs a refractory mycoplasma pneumoniae pneumonia prediction model based on an innovative feature extraction method.

The present teaching provides device and method of use thereof, for example, for the placement of a medical implant inside the cerebrovasculature. Various embodiments of the present teachings include a medical implant, a delivery system, and an Optical Coherence Tomography (OCT) imaging system. The OCT imaging system is used to reveal the three dimensional (3D) volumetric structure of the vascular network, as well as vessel density and geometric features, blood flow velocity, flow rate, and other data useful for selecting suitable medical implant as well as treatment location. This OCT imaging system is also used to guide the medical implant deployment and post implantation assessment.

The invention provides a hepatocellular carcinoma vascular network three-dimensional reconstruction method and system, electronic equipment and a medium, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a plurality of original pathological section images, wherein the original pathological section images are obtained by slicing a hepatocellular carcinoma sample; performing spatial position alignment on the plurality of original pathological section images to obtain a plurality of corresponding first pathological section images; performing blood vessel enhancement, segmentation and filling on the plurality of first pathological section images to obtain a plurality of corresponding second pathological section images; and performing three-dimensional reconstruction according to the plurality of second pathological section images to obtain a three-dimensional model of a hepatocellular carcinoma vascular network formed in the hepatocellular carcinoma sample, and performing visualization. According to the method, the three-dimensional model of the hepatocellular carcinoma vascular network can be efficiently, accurately and reliably constructed through a computer vision technology.

This invention relates to the field of organoid culture technology, specifically to a vascularized tumor organoid chip. It redesigns the traditional micropillar-separated culture medium and cell culture channels into a dynamically separated structure using acupuncture needles. This avoids the physical limitations of traditional micropillars on blood vessel growth, significantly increases the contact area between vascular cells, and promotes the growth of the vascular network. Simultaneously, the needle insertion channel and reservoir allow for free switching between micro-infusion pump and gravitational potential energy delivery modes. Furthermore, it enables dynamic detection during the co-culture of blood vessels and tumor organoids.

The invention provides a capillary network hemodynamic modeling method with a multilayer structure, and aims to solve the problem that a traditional model is difficult to truly reflect a capillary multilayer structure and blood flow topological characteristics, and combines the hierarchical structure characteristics of a capillary network disclosed by a high-resolution imaging image. And constructing a multi-layer blood vessel network model consisting of a microarteriovenous layer, a connecting layer and a capillary network layer. Wherein the micro artery and vein layer is a tree-shaped blood vessel network formed by micro arteries and micro veins; the capillary network layer is of a regular network blood vessel structure constructed based on a hexagonal grid; and the connecting layer is a connecting blood vessel for connecting the microarteriovenous layer and the capillary network layer. Through standardized numbering rules, node insertion and blood vessel splitting operation, embedding, reconstruction and overall structure integration of blood vessel segments are completed, and therefore a capillary network system which is complete in structure and is in topological communication is constructed. On the basis of structural modeling, a linear equation solving system is established for hemodynamic simulation. The method has the advantages in the aspects of structural definition, simulation precision and expansibility, and has important scientific research and engineering application value.

The invention relates to the technical field of medical image processing, in particular to a cross-modal fusion positioning method and system for accurate navigation of a coronary guide wire, and the method comprises the steps: firstly, obtaining a coronary X-ray angiography image and guide wire head end electromagnetic positioning data, constructing a vascular topological space expression, and carrying out the fusion positioning of the coronary X-ray angiography image and the guide wire head end electromagnetic positioning data; mapping the vascular network from the Euclidean space to a non-Euclidean topological space; secondly, processing a blood vessel connected graph and guide wire position data through two-channel feature extraction, and extracting features by adopting a topological sensitive convolution kernel; fusing vascular topological features and guide wire position features based on a manifold constraint cross attention mechanism to realize bidirectional information flow; further executing self-calibration state estimation, establishing a probability distribution model to quantify uncertainty, and realizing multi-source data fusion; according to the method, deep fusion of X-ray angiography and electromagnetic positioning data is achieved, and compared with a traditional method, the navigation precision is improved by about 40%.

Computational methods are used to create cardiovascular simulations having desired hemodynamic features. Cardiovascular modeling methods produce descriptions of blood flow and pressure in the heart and vascular networks. Numerical methods optimize and solve nonlinear equations to find parameter values that result in desired hemodynamic characteristics including related flow and pressure at various locations in the cardiovascular system, movements of soft tissues, and changes for different physiological states. The modeling methods employ simplified models to approximate the behavior of more complex models with the goal of to reducing computational expense. The user describes the desired features of the final cardiovascular simulation and provides minimal input, and the system automates the search for the final patient-specific cardiovascular model.

This application provides a medical image processing method and system. The method includes: acquiring vascular image volume data from photon-counting computed tomography scans of a target region; based on the vascular image volume data, performing skeletonization processing on the vascular network, extracting multiple skeleton points in the vascular network, and forming a vascular centerline network according to the connection relationships between the skeleton points; identifying endpoints and bifurcation points from the vascular centerline network, thereby dividing the vascular centerline network into several continuous and unbifuzzy vascular segments, and constructing a vascular centerline diagram structure based on each vascular segment; establishing and solving a blood flow analysis model based on the vascular centerline diagram structure to obtain the blood flow analysis results of the vascular network; wherein, the blood flow analysis results include the blood flow rate and / or pressure difference between the two ends of each vascular segment. This application improves the accuracy of the blood flow analysis results.