176 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 belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

The invention provides a brain aneurysm intelligent detection and positioning method and system based on multi-feature fusion, and relates to the technical field of image processing, and the method comprises the steps: receiving and preprocessing a head angiography image; utilizing a multi-scale feature extraction network to obtain feature maps and fusing the feature maps; constructing anatomical candidate regions by using density clustering; extracting a tumor contour through a graph cut energy function; constructing a vascular network topological graph based on probability feature mapping, and calculating a position feature descriptor; and finally carrying out classification discrimination and marking and displaying a detection result. According to the method, various characteristics are fused, the accuracy and sensitivity of cerebral aneurysm detection are improved, and the misdiagnosis rate is reduced.

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 belongs to the technical field of organoid culture, and particularly relates to a co-differentiated vascularized liver organoid and a construction method thereof. Aiming at the problems that the existing liver organ vascularization mostly adopts a co-culture mode, the operation process is complicated, the cell source heterogeneity is relatively high, the randomness is strong, the uniformity is poor and the like, the invention provides the construction method of the co-differentiated vascularized liver organ. The preparation method comprises the following steps: sequentially inducing and culturing directional endoderm cells, posterior preintestinal cells and hepatic endoderm cells, adding an S1 culture medium containing VEGF to induce and culture for 1-3 days, then adding an S2 culture medium containing VEGF, BMP4 and FGF2 to induce and culture for 3-5 days, and then adding an S3 culture medium containing VEGF to induce and culture for 14-20 days to obtain the co-differentiated vascularized liver organ. According to the method disclosed by the invention, the operation process is simplified, the experiment efficiency is improved, the liver organ which is rich in blood vessel network, high in maturity and of a blood vessel-bile duct structure is obtained, and a foundation is laid for large-scale production and clinical application.

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.

The invention discloses a brain arteriovenous malformation vascular structure lumped model simulation method and system, and the method comprises the steps: obtaining the three-dimensional image data of a preset region of the head of a human body, and recognizing a brain arteriovenous malformation vascular mass region in the three-dimensional image data; constructing a blood vessel three-dimensional structure model, and acquiring three-dimensional space coordinate data of each voxel in the cerebral arteriovenous malformation vascular cluster region in the model; calibrating an inlet and an outlet of the blood vessel three-dimensional structure model and corresponding pressure set values and / or flow velocity set values; extracting a plurality of skeleton points of the blood vessel based on the blood vessel three-dimensional structure model to obtain a microscopic blood vessel network; based on the three-dimensional space coordinate data, the length and the average diameter of each blood vessel in the microscopic blood vessel network are obtained, and the flow resistance of the blood vessels is calculated; based on the pressure set value and the flow velocity set value of the blood vessel three-dimensional structure model and the flow resistance of each blood vessel, the blood flow velocity and pressure distribution data of each blood vessel are calculated through a lumped parameter model, and the hemodynamic characteristics of the cerebral arteriovenous malformation blood vessel are obtained.

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.

The invention relates to the technical field of medical image analysis methods, in particular to a lung drainage basin analysis method based on hemodynamics, which comprises the following steps: acquiring lung CT image data; acquiring lung anatomy data and clinical medical knowledge; generating a pulmonary artery blood vessel tree model based on the lung CT image data; establishing a hemodynamic model according to the pulmonary artery blood vessel tree model; calculating a blood flow velocity field and a pressure gradient field based on the hemodynamic model; according to the pressure gradient field, executing a watershed watershed segmentation algorithm to obtain a lung watershed division result; according to the method, an analysis report containing a lung drainage basin division result is generated, the segmentation precision of a lung blood vessel network is greatly improved by adopting an advanced image processing technology and a blood vessel tracking algorithm, and the method can more accurately reflect individual differences and blood flow characteristics in a pathological state by dynamically adjusting boundary conditions and blood parameters. And meanwhile, an efficient numerical solution algorithm is adopted, so that the calculation complexity is remarkably reduced.

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 present invention relates to a composition for maturation of adrenal organoids, a method for maturation of adrenal organoids using same, and a method for transplantation of organoids prepared by the method. In the present invention, the structurally and functionally mature adrenal organoids prepared under optimized culture conditions using the composition for maturation of adrenal organoids containing adrenocortical hormone and cyclic adenosine monophosphate have been structurally and functionally improved, and it was confirmed that the adrenal organoid chip having a three-dimensional vascular network structure manufactured using the same actually replaces the function of the adrenal gland without side effects after transplantation into a mouse. Accordingly, the present invention can be advantageously used for transplantation or regenerative therapy, drug screening, drug responsiveness evaluation, and the like.

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 belongs to the technical field of organoid culture, and particularly relates to a pulmonary alveolar organoid with an endogenous vascular network and a construction method of the pulmonary alveolar organoid. Aiming at the problems that mesoderm and endoderm are difficult to induce at the same time, the operation process is long, the difficulty is high and the like when a vascularized alveolar organ is constructed by an existing method, the invention provides the construction method of the alveolar organ with the endogenous vascular network. The method comprises the following steps: simultaneously and directionally inducing and differentiating human induced pluripotent stem cells (hiPSC) into fixed endoderm and mesoderm, anterior intestine endoderm, abdominal side anterior intestine endoderm and lung progenitor cells by adopting a one-step method, and promoting endothelial cell proliferation in an endogenous induced differentiation process through a specific growth factor combination. A functional vascular network is formed; and vascularized alveolar organs are induced to be generated. According to the invention, a qi-blood barrier structure in which vascular endothelial cells and pulmonary epithelial cells are tightly connected is successfully reproduced, the gas exchange function of lung tissues can be simulated, and a new way is provided for construction of vascularized alveolar organs.

The invention provides an agent model of a vascular network exit boundary condition and a construction method, and is applied to the technical field of data processing. According to the method, data compression and space-time normalization processing are carried out on multi-scale blood vessel feature data, compressed and normalized data are generated, and the compressed and normalized data are composed of an automatic encoder dimension reduction result, a space-time normalization relation and a data mixing and enhancing result; processing the compressed and normalized data, and generating target agent model parameters based on DeepONet; processing the target proxy model parameters to generate proxy model output data; the proxy model output data and the pipe network high-dimensional changeable data are processed, an exit boundary condition proxy model is generated based on symbolic regression, and the exit boundary condition proxy model is used for representing a vascular network exit boundary condition; and processing the exit boundary condition proxy model, the fitness evaluation and the genetic manipulation strategy based on a genetic algorithm to generate a target exit boundary condition proxy model and a verification result.

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 invention relates to the technical field of disease analysis, and provides an acute-stage cerebral infarction detection system and method.The method comprises the steps that basic information, physical sign data and blood sample data of a brain patient are collected, and data integration is conducted to obtain integrated data; a brain scanning image is obtained, and a high-definition brain image is obtained after artifacts are removed; recognizing image semantics, performing image segmentation, evaluating a segmentation level, and obtaining a target image when a preset level is met; identifying brain lesion features by using the target image; an angiogram is collected, the brain blood vessel caliber is calculated, and the brain blood perfusion condition is analyzed; constructing a blood vessel network by using the angiogram, and analyzing a blood vessel variation degree; based on the blood vessel caliber, the blood perfusion condition and the blood vessel variation degree, analyzing the brain blood vessel lesion degree; collecting an electroencephalogram, and analyzing brain activity characteristics; and performing acute-stage cerebral infarction analysis based on the integrated data, the brain lesion features, the brain vascular lesion degree and the brain activity features. The invention aims to improve the rationality of acute cerebral infarction detection.

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.