282 results about "Blood vessel structure" patented technology

The invention relates to the technical field of orthopedic diagnosis and treatment, in particular to a monitoring method and system for dynamically monitoring the lower limb vein blood flow state and a storage medium. The method comprises the following steps: acquiring a lower limb vein ultrasound image sequence of a target patient, and establishing a blood vessel space probability distribution map based on anatomical priori knowledge; encoding the blood vessel space probability distribution map into conditional prior information, and constructing a blood vessel ontology knowledge base; constructing a multi-level diffusion probability model according to the blood vessel ontology knowledge base; and performing vascular structure reconstruction and blood flow feature extraction on the lower limb vein ultrasound image sequence by using the multi-level diffusion probability model to obtain blood vessel fluid feature data. Vascular positioning, branch recognition and morphological analysis are completed step by step from macroscopic to microcosmic through the three-level diffusion model, blood flow dynamic information is extracted in combination with time sequence features, and comprehensive representation of a vascular system is achieved.

The invention discloses a heart mitral valve medical image segmentation model, a training method, a segmentation method and equipment. The segmentation model is improved on the basis of a Double U-Net architecture, and two independent U-Net networks which are connected in series respectively comprise an encoder and a decoder; an OfficientNetB7 network is used as a feature extraction encoder of the front end U-Net network; after features are extracted from the two U-Net networks, a multi-kernel residual error convolution (MKRC) module, a cavity space convolution pyramid pooling (ASPP) module and a mixed triple attention (TAG) module are sequentially introduced into the two U-Net networks; a triple attention gate (TAM) module is introduced into the input end of the decoder. Segmentation model training adopts a mixed loss function, binary cross entropy loss and Dice loss are combined, the class imbalance problem is balanced, and segmentation precision is improved. The heart and related blood vessel structures can be automatically and accurately segmented, so that the technical problem that heart mitral valve image segmentation is difficult to accurately and efficiently perform in the prior art is solved.

The invention relates to the technical field of medical image analysis, and discloses an HER2 positive breast cancer auxiliary prediction method based on multi-modal data fusion. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

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 relates to the field of cardiovascular medicine, and discloses a coronary artery vulnerable plaque detection and analysis system which comprises a data collection module used for collecting multi-mode imaging data of a coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery, and a plaque area identification module used for positioning the branch opening position of the coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery. Multi-class plaques of coronary arteries are separated out; the plaque classification detection module is used for extracting structural features of multiple categories of plaques, identifying vulnerable plaques and stable plaques in the coronary artery and setting a plaque classification detection system of the coronary artery; the vulnerable plaque evaluation module is used for constructing a coronary artery vulnerable plaque scoring mechanism according to the plaque vulnerability index, the plaque component and the radial wall strain value; and the result feedback module is used for generating a vulnerable plaque feedback result of the coronary artery in combination with the vulnerable plaque scoring mechanism and the plaque classification detection system. According to the invention, the accuracy of coronary artery vulnerable plaque detection can be improved.

The invention discloses a blood vessel continuous segmentation method based on a graph network, and relates to the technical field of blood vessel continuous segmentation, multi-scale texture features based on coronary artery influence and topological structure features of blood vessels are fused, and correlation among different features is enhanced through an attention mechanism; segmenting the fused multi-scale texture features influenced by the coronary artery and topological structure features of the blood vessel, extracting multi-scale features, and reducing the resolution; gradually recovering the resolution by using a decoder to obtain a segmentation result, and optimizing the network weight by using an error between the segmentation result and a real label; and applying the trained segmentation network to test data to obtain a three-dimensional segmentation result of the coronary artery, and evaluating the accuracy of the segmentation result by comparing the difference between a predicted value and a true value to obtain connection constraint loss. The problem that a three-dimensional blood vessel structure is difficult to extract by a general medical segmentation model is solved, so that a blood vessel segmentation result is more continuous and more accurate.

The invention relates to the technical field of lung perfusion, in particular to an AI-based lung perfusion evaluation system, which comprises an image quality screening module, a blood vessel positioning analysis module, a blood flow velocity measurement module, a blood vessel anomaly analysis module and an anomaly response module, and is characterized in that the image quality screening module monitors the pixel density of an image and the variation amplitude of color gradient. According to the method, through real-time image definition evaluation and screening, the image selection process is optimized, it is ensured that all the images used for analysis reach the high-quality standard, the gray level change and the edge contour of the blood vessel are automatically calculated, the potential lesion area is accurately positioned and marked, more detailed blood vessel structure analysis is provided, and the accuracy of blood vessel analysis is improved. In addition, the system can rapidly analyze the speed deviation of the blood vessel segment, timely identify the abnormal blood flow, effectively improve the judgment speed of diseases such as pulmonary embolism or pulmonary hypertension, enhance the efficiency of coping with emergency medical conditions through automatic abnormal detection and recording, improve the accuracy and speed of judgment, and support more effective clinical decisions.

The invention relates to the technical field of medical image processing, in particular to an automatic blood vessel extraction method and system based on a CT image, and the method comprises the steps: 1, constructing a blood vessel local coordinate system, and carrying out the double-domain separation; 2, dynamic double-domain fusion, wherein contrast self-adaptive weighted fusion is carried out on the spatial domain enhanced image and the fundamental frequency blood vessel image; 3, direction constraint morphological reconstruction: constructing a structural element with a long axis parallel to the direction of the blood vessel according to the main direction vector v of the blood vessel; sequentially executing directional opening operation and directional closing operation along the direction of the main direction vector v of the blood vessel; and 4, anatomically-driven blood vessel repair and correction: tracking contours of adjacent slices along the direction of the main direction vector v of the blood vessel and fitting a repair path on the basis of a generated calcified region mask Mcal marked region, and correcting a topological structure at a bifurcation point according to a branching angle and hemodynamic constraint. The vascular structure can be accurately and stably extracted from the CT image by comprehensively utilizing restoration technologies such as spatial domain enhancement and frequency domain separation.

The invention relates to the technical field of medical image processing and tumor diagnosis, in particular to a tumor recognition method based on multi-modal information fusion. The method comprises the following steps: acquiring tumor multi-modal information of a target object, wherein the multi-modal information comprises tumor image data, tumor genomics data and individual tumor related information; the tumor-related information comprises a vascular structure, a blood flow velocity, fluid viscosity and fluid pressure of the target object; determining a tumor cell density, a diffusion coefficient and a cell proliferation rate according to the tumor image data and the tumor genomics data; predicting a diffusion path of the tumor in the local tissue according to the tumor cell density, the diffusion coefficient and the cell proliferation rate; predicting a metastasis path of tumor cells through a vascular system according to the individual tumor related information; and according to the diffusion path of the tumor in the local tissue and the metastasis path of the tumor cells through the vascular system, identifying the potential metastasis site of the tumor.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method combining a convolutional neural network and a graph network, and the method comprises the following steps: forming a data set CoronarySet by using coronary artery CT angiography three-dimensional image data collected by a computed tomography (CT) device, and randomly dividing the data set CoronarySet into a training set CoronarySet 1 and a test set CoronarySet 2; the method comprises the following steps of: segmenting data of a training set CoronarySet1 into a plurality of patches; inputting the patchings into a double-branch parallel encoder to obtain a multi-layer coronary artery texture feature map and a multi-layer coronary artery topological feature map; constructing a feature fusion module, and fusing the texture feature maps and the topological feature maps of different layers based on an attention mechanism module to obtain fused blood vessel features; shallow layer features of a three-dimensional convolution module and fused blood vessel features are sequentially input into each layer of a segmentation decoder, a three-dimensional blood vessel structure can be extracted, and a blood vessel segmentation result is more continuous and more accurate.

The invention discloses an automatic retinal vessel segmentation method fusing large-kernel multi-scale cavity convolution and an attention mechanism, which is suitable for accurate extraction of a fine-grained vessel structure in a fundus image. According to the method, an improved deep neural network (LKD-UNet) is constructed, a collaborative receptive field module is embedded in each layer of an encoder, and large kernel convolution and multi-scale cavity convolution are combined to enlarge the receptive field and enhance the texture analysis capability; an efficient Transform module is introduced into a bottleneck layer, a cross-regional semantic relationship is modeled through a multi-head self-attention mechanism, and the topological consistency of microvessels is improved; the decoder adopts a shallow symmetric structure and a kernel scale scheduling strategy, and realizes high-resolution reduction in combination with jump connection. In addition, a four-category pixel-level error visualization mechanism is introduced and is used for assisting segmentation error diagnosis and result interpretation. Experimental results show that the method is superior to an existing mainstream model in multiple public data sets, has remarkable advantages in the aspects of segmentation precision, structural coherence and deployment efficiency, and is suitable for remote fundus screening and blood vessel extraction tasks in embedded medical equipment.

The invention relates to the technical field of operation planning, in particular to an adaptive drainage basin segmentation and operation planning system based on deep learning, which comprises a chest imaging data modal layer used for acquiring chest imaging data modal information including lung lobes, lung segment boundaries and lung blood vessels; the end-to-end multi-task deep learning model layer is in communication connection with the chest imaging data modal layer and comprises a blood vessel segmentation module used for performing blood vessel segmentation on the chest imaging data modal; the hierarchical drainage basin segmentation module is used for carrying out hierarchical drainage basin segmentation based on the blood vessel segmentation result; the topological basin network module is used for analyzing the topological relation of the vascular structure; the drainage basin visualization module is used for generating visualization results of lung lobes and lung segments; the path planning module is used for generating an operation scheme; the evaluation and decision-making module is used for carrying out evaluation and decision-making analysis on the operation scheme; according to the invention, full-process automatic processing from medical image input to surgical scheme output is realized.

The invention relates to the technical field of medical image analysis, and discloses a cardiovascular disease detection method based on image analysis. The method comprises the steps of obtaining a medical image sequence of a target patient, and extracting a blood vessel region contour to generate an initial blood vessel topological graph; the initial topological graph is registered with a standard cardiovascular model, the curvature deviation degree and the pipe diameter variation coefficient of each blood vessel branch are calculated, and a blood vessel elastic characteristic matrix is generated in combination with abnormal displacement nodes; and performing multi-scale fusion on the curvature deviation degree, the pipe diameter variation coefficient and the elastic characteristic matrix, outputting a vascular structure anomaly index, and generating a hemodynamic parameter set. Dividing risk areas according to gradient distribution of the parameter set on the three-dimensional model, performing texture co-occurrence matrix analysis on pixel clusters in the high-risk areas, and extracting texture fingerprints of calcified plaques and lipid deposition. And determining the type and severity level of the cardiovascular disease according to the peak value distribution. According to the invention, accurate and objective detection and risk assessment of cardiovascular diseases are realized.

The invention relates to the field of medical image processing, in particular to a medical image segmentation method, system and device based on fuzzy logic modeling and a dynamic graph convolutional network, and a medium, and the method comprises the following steps: S1, constructing a BT-Net 3D U-shaped deep network integrated with a boundary integrity enhancement module and a trajectory continuity enhancement module; s2, extracting input features; s3, a boundary integrity enhancement module obtains enhanced boundary features based on uncertain estimation and a fuzzy neural network; the track continuity enhancement module obtains enhanced continuity imaging features based on the dynamic graph convolutional network; and S4, performing feature reconstruction to obtain a final segmentation result. According to the method, uncertainty is reduced based on fuzzy logic modeling, the integrity of the blood vessel boundary can be iteratively enhanced, so that the ability of the network to determine the blood vessel boundary under complex conditions is improved, the continuity of the blood vessel trajectory is iteratively enhanced based on the dynamic graph convolutional network, and the ability of the network to process cross-scale complex micro blood vessel structures is improved.

The invention discloses a vascular respiratory movement map construction method, device and equipment and a medium, and relates to the technical field of medical treatment, the method establishes a vascular digraph based on a topological connection relationship in a vascular structure, and quantifies the overall displacement of a blood vessel and the posture of each branch through the extraction of structure control points and the calculation of branch bending energy. The propagation direction of motion between branch points is defined, and consistency constraint is provided for vascular motion modeling. Vascular deformation in motion modeling is quantified by adopting adaptive deformation compensation, and motion deformation of each section of a branch is represented by using rotation measurement and stretching measurement under a spherical coordinate system of a manifold space. Meanwhile, the bending energy weight is adaptively distributed for each branch based on the vascular graph and the SRVF constraint, and the learning efficiency of the model for local attitude deformation is improved.

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.

The invention discloses a medical blood vessel segmentation dynamic optimization method and system based on anatomy prior perception, and belongs to the field of artificial intelligence and the technical field of medical image processing. The method comprises the following steps: acquiring medical image data and corresponding blood vessel labeling data, constructing a data set, and preprocessing and post-processing data in a training set; constructing a segmentation model, wherein the segmentation model comprises a multi-scale feature fusion module and a dynamic snakelike convolution module; constructing a loss function of the segmentation model, and training the segmentation model by using the training set and the blood vessel labeling data; and performing blood vessel segmentation on the medical image data in the test set by using the trained segmentation model. According to the invention, through the dynamically configured multi-scale feature fusion module and fusion mechanism and the dynamic optimization strategy of topology perception, the problem that it is difficult to consider fine segmentation of the blood vessel structure and overall topology integrity in the prior art is effectively solved, and the robustness and operation efficiency of the medical blood vessel segmentation model are significantly improved.

The invention relates to the technical field of medical image analysis, and discloses a breast cancer treatment response prediction system based on multi-time-sequence images. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

An implantable system for monitoring a vascular structure can include a sensor configured to be positioned within the vascular structure and further configured to detect a property of a flow of blood in the vascular structure, a communication circuitry configured to be positioned within the vascular structure, the communication circuitry connected to the sensor and configured to transmit data related to the property of the flow of blood detected by the electronic sensor, and an antenna configured to be positioned within the vascular structure, the antenna connected to the communication circuitry and further configured to facilitate transmission of the data related to the property of the flow of blood detected by the sensor.

The invention discloses a human body peripheral blood vessel photoacoustic three-dimensional imaging device and an imaging method thereof, and belongs to the field of blood vessel imaging. Through high resolution and functional monitoring capability of photoacoustic imaging, detailed blood vessel structure and blood flow dynamic information are provided, and early recognition and treatment effect evaluation of common lesions such as diabetic foot and peripheral artery diseases are promoted; meanwhile, a mechanical system is adopted, so that the detection front end with relatively high quality can be borne, and stable operation is realized, and a relatively good photoacoustic imaging result is obtained; the system has a highly stable accurate scanning imaging mode, and is convenient for multiple imaging modes of clinical application; through the arrangement of the imaging water tank and the lifting structure of the imaging water tank, switching of imaging modes in a limited space can be ensured, so that the device can be suitable for the layout of a portal type three-dimensional moving device. The system is expected to promote the portability of equipment, reduce the research and development cost of the equipment, be widely applied to clinical practice and community medical treatment, and improve the disease management level.

The invention relates to the technical field of image modeling, in particular to a three-dimensional modeling method and system for medical image data, and the method comprises the steps: carrying out the preprocessing of the obtained two-dimensional or three-dimensional medical image data, extracting a blood vessel region based on the image texture and gray features, and generating a preliminary blood vessel structure segmentation image; in combination with a blood flow simulation or real acquisition means, obtaining dynamic feature data from the vascular structure segmentation map, and mapping the dynamic feature data to a corresponding vascular structure position to form a vascular distribution map with blood flow constraint; and inputting the blood vessel distribution diagram into a three-dimensional modeling module, expanding and complementing small blood vessel branches by utilizing blood flow constraint, dynamically correcting the form of a main blood vessel, and generating a three-dimensional blood vessel structure model with real fluid physiological characteristics. The method can be widely applied to medical research and clinical aid decision-making scenes such as preoperative simulation, individualized intervention path planning and blood flow reconstruction analysis, and has good popularization and application prospects and engineering practical value.

The invention discloses a medical image optimization method and system based on vascular branch selective blurring. The method comprises the following steps: carrying out preprocessing and blood vessel enhancement on a three-dimensional angiography DICOM image; performing precise blood vessel segmentation by using an encoder-decoder network comprising a wide activation and residual cavity space pyramid module; identifying an interference branch which shields the target blood vessel structure at a specific working angle through blood vessel topology analysis; selective blurring repair is performed on the interference branches based on a Poisson equation and a bidirectional convolution LSTM to generate an optimized image which is visually unobstructed and keeps topological continuity. And a safety mechanism of virtual-real combined display and multi-angle plan planning is introduced, so that the reliability of surgical navigation is ensured. And finally, integrating the optimized 3D blood vessel model to a radiography system supporting real-time synchronization, and using the 3D blood vessel model as a road map. The clinical problem that the optimal working angle cannot be used due to blood vessel shielding can be effectively solved, and the precision and safety of an endovascular interventional operation are remarkably improved.

The invention relates to the technical field of blood vessel structure recognition, and discloses an automatic recognition method and system for an OCT image blood vessel structure, and the method comprises the steps: 1, blood vessel layer boundary feature extraction: extracting blood vessel layer boundary features, including geometric features and physical features, from an OCT image based on multi-scale Hessian matrix response, and forming a feature response graph; step 2, three-layer topological structure identification: constructing a topological relation of a blood vessel structure according to the feature response diagram, and extracting a nested annular structure; identifying and outputting a three-layer initial boundary coordinate point set and a structure score of an inner membrane, a middle membrane and an outer membrane of a blood vessel; and step 3, optimizing a blood vessel structure: accurately optimizing the initial boundary coordinate point set, detecting special cells in an optimized region, and generating a result report. According to the method, full-automatic recognition of the OCT blood vessel structure is achieved, reliability, accuracy and interpretability of a recognition result are ensured, and standardization and unification of vascular disease diagnosis are ensured.

The invention relates to a neural puncture dynamic obstacle avoidance navigation method and system based on multi-modal image fusion, and belongs to the technical field of medical image navigation and surgical operations. A unified space-time reference model is constructed by fusing preoperative high-resolution static images and intraoperative real-time dynamic ultrasonic data; a three-dimensional model of a key nerve and blood vessel structure is automatically segmented and reconstructed by using an artificial intelligence technology, an optimal puncture path can be dynamically calculated based on real-time image data, a collision risk in a needle inserting process can be monitored in real time, and visual navigation guidance of multi-sensory fusion is provided for an operator through an augmented reality technology; by recording and analyzing data of the whole operation process, a big data platform is used for carrying out continuous iterative optimization on a segmentation and obstacle avoidance algorithm, and a self-perfect intelligent closed loop is formed. The problems that in a traditional nerve puncture operation, experience of an operator is relied on, real-time dynamic obstacle avoidance cannot be achieved, and the system lacks learning ability are effectively solved, and the accuracy, safety and intelligent level of the operation are remarkably improved.

A vascular image processing method performed by a processor includes extracting, from a vascular image, a first vascular region corresponding to an entire blood vessel included in the vascular image, a second vascular region corresponding to a target vessel and one or more branch vessels connected to the target vessel, and a third vascular region corresponding to the target vessel, and predicting a vascular structure in the vascular image, based on the first vascular region, the second vascular region, and the third vascular region, which are extracted from the vascular image.

The invention relates to the technical field of face injection, in particular to a face precise injection system based on real-time B-ultrasonic imaging and a using method, and the method comprises a high-frequency ultrasonic imaging module which is provided with a deformable curved surface probe array; the image processing unit integrates a blood vessel segmentation algorithm, a three-dimensional reconstruction module and a dangerous area dynamic calibration function; the navigation positioning module comprises an electromagnetic positioner and an optical tracker; the injection executing mechanism is composed of a six-degree-of-freedom mechanical arm, a pressure feedback type injector and a needle tip microspur sensor; the main control unit is used for coordinating data interaction of all the modules and generating injection safety early warning; by adopting high-frequency ultrasonic imaging, the skin layer, fat layer, muscle and blood vessel structures can be clearly displayed, deviation caused by traditional touch typing injection is avoided, tiny blood vessels can be recognized through an AI blood vessel automatic segmentation algorithm, and therefore the risk of wrong injection in the blood vessels is remarkably reduced.

The invention discloses a blood fat monitoring system based on a vascular ultrasound image, and belongs to the technical field of medical image recognition. The problems that an existing system cannot automatically recognize the blood vessel structure and evaluate the blood fat level and cannot ensure the stability and real-time performance of data transmission are solved, the intima-media membrane and lumen contours in the blood vessel ultrasonic image are automatically recognized in a short time through the convolutional neural network model, multi-dimensional features are extracted from the intima-media membrane and lumen contours, and the accuracy of the blood vessel ultrasonic image is improved. The identification precision and the diagnosis accuracy are improved; a multi-node gateway communication channel is arranged, and an optimal gateway is selected for data feedback in combination with a network distance threshold value, so that a blood fat prediction result is fed back to a medical interaction terminal in real time, it is ensured that early warning is triggered in time when the blood fat level exceeds a normal range, and the situation that a certain gateway breaks down or the communication distance exceeds the threshold value is prevented. And the system can be automatically switched to other available gateways, so that the stability and the reliability of the data in the transmission process are ensured.

The embodiment of the invention discloses an acute ischemic stroke collateral circulation noninvasive assessment method and device. The method comprises the steps of obtaining a CTP data set and a DSA data set of different collateral circulation levels, constructing an image segmentation model based on a space-modal attention network and an intracranial artery blood supply partition model based on an atlas, subdividing and extracting different areas of the ischemic side from the angles of perfusion function and vascular structure, and taking a DSA score as a gold standard, combining perfusion function quantitative information and vascular structure quantitative information in each region after ischemic side subdivision to construct a collateral circulation scoring model; and taking perfusion function quantitative information and vascular structure quantitative information of each artery blood supply region, the lateral split region, the infarction core region and the penumbra on the ischemic side as input and model training, and predicting to obtain an individual collateral circulation scoring result. According to the method, the non-invasive evaluation accuracy of collateral circulation can be improved by jointly analyzing the perfusion function and the blood vessel structure information provided by the CTP.

The invention provides an artificial intelligence-based lumen lesion feature recognition method and system, and the method comprises the steps: obtaining an original image frame sequence collected by an electronic endoscope and a collection timestamp of each frame, and forming a pseudo-time image sequence set; inputting the pseudo time image sequence set into a pre-constructed candidate region extraction model, and outputting a focus candidate region set; performing vascular structure enhancement on the lesion candidate region set, constructing a graph neural network, extracting structural features through the graph neural network, outputting a lesion confidence score in combination with the region features, and finally generating an enhanced structured candidate region set in combination with the lesion candidate region set, the structural features and the lesion confidence score; performing trajectory aggregation on the structured candidate region set based on structural feature similarity and time continuity to form a cross-frame focus trajectory set; and calculating a space center position and a time range of a focus track according to the cross-frame focus track set, and generating a structured report.

The invention discloses a transcranial Doppler flexible ultrasonic probe detection data management system and method, and relates to the technical field of ultrasonic probe design, and the method comprises the steps: obtaining and preprocessing head image data of a patient, and extracting structural features of a temporal window part from the preprocessed data; constructing a three-dimensional grid model containing a blood vessel structure according to the extracted structural features, and analyzing an angle relation between an ultrasonic probe and a target blood vessel in the model and signal quality performance of the ultrasonic probe; collecting head image data, blood flow measurement data and probe angle data of a plurality of patients to construct a piezoelectric ceramic angle prediction model, and predicting the optimal angle of each piezoelectric ceramic in the ultrasonic probe under different patient head structures and blood flow conditions; the surface of the ultrasonic probe is covered with a flexible material, a closed-loop control system is introduced into the probe, the voltage of piezoelectric ceramics is adjusted according to a model prediction result, and angle control over the probe is achieved.