231 results about "Blood vessel structure" patented technology

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 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 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 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 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 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.

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.

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 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.

The invention relates to a method for operating an X-ray device, comprising a carrier, in particular a C-arm, and an adjusting device at least for moving the carrier for setting a projection geometry. The method comprises: providing a vascular model of a vascular structure of an examination subject, in which a target position of an instrument movable in the vascular structure is marked in the vascular model, providing an initial position of the instrument in the vascular structure of the vascular model, and providing a path from the initial position to the target position; specifying a support point along the path; in the optimization process of the target function by using the blood vessel model, the optimization trend of the projection geometric structure is determined aiming at the supporting points; wherein the objective function comprises at least one first term relating to optimization of the image content of the observer at the respective support point and at least one second term that minimizes the number of changes in the projection geometry along the path by movement of the carrier; and determining, for each projection geometry, an adjustment parameter for controlling the adjustment device.

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 utility model discloses a high-simulation individualized liver intervention model. The high-simulation individualized liver intervention model comprises a model body, a puncture needle and a computer system, a liver model and a tumor model are arranged on the model body, a first simulation pipeline for simulating an intrahepatic bile duct and a plurality of second simulation pipelines for simulating intrahepatic blood vessels are arranged in the liver model, the first simulation pipeline is connected with a first water bag through a first external pipeline, and the first external pipeline is connected with a first water pump; the second simulation pipeline is connected with a second water bag through a second external pipeline, the second water bag is connected with a second water pump, and the color of liquid carried in the first water bag is different from that of liquid carried in the second water bag; a first sensing element is arranged in the tumor model, and second sensing elements are arranged on the surface of the first simulation pipeline and the surface of the second simulation pipeline. The device can simulate structures such as intrahepatic tumors, intrahepatic bile ducts, intrahepatic blood vessels and the like, can simulate puncture targets with different puncture difficulties, and can pre-warn puncture risks and record the puncture success rate.

In an X-ray imaging method, at least two X-ray images are obtained of a region to be depicted in an object, wherein the at least two X-ray images correspond to different acquisition timeframes and represent a vascular structure of the object in different contrast-agent filling states. A shared vessel mask, which represents the vascular structure, is generated based on the at least two X-ray images. At least two live images of the region to be depicted are obtained, which correspond to different second acquisition timeframes. For each image of the at least two live images, depending on the shared vessel mask, a corresponding overlay image for displaying on a display device is generated.

The invention provides a trauma bleeding detection method and system based on infrared thermal imaging and ultrasonic multi-modality, and relates to the technical field of medical image processing, and the method comprises the steps: collecting and registering infrared thermal imaging and ultrasonic images of a trauma part, executing adaptive shear wave transformation to extract a feature coefficient, constructing a thermoacoustic coupling feature matrix, and obtaining a thermoacoustic coupling feature matrix; and reconstructing a vascular structure to generate a blood flow velocity field distribution diagram, establishing a time sequence mapping relation between local blood flow volume and accumulated bleeding volume, and calculating bleeding dynamic parameters by adopting a variational particle filtering method. The method can accurately evaluate the duration of traumatic hemorrhage and the tissue injury degree, and provides an accurate diagnosis basis for clinical first aid.

The invention relates to the technical field of blood vessel image segmentation, and provides a fundus blood vessel image accurate segmentation method based on a U-Net model, aiming at solving the problem that the existing blood vessel segmentation method based on deep learning is difficult to effectively balance global context and local details. In order to solve the problem of inaccurate blood vessel structure segmentation caused by factors such as poor image quality and low contrast ratio, a method of combining a wavelet and a visual state space model and an adaptive dynamic fusion module is provided to improve the integrity of a slender blood vessel and the structural segmentation accuracy of a complex blood vessel network in a fundus blood vessel image. By integrating multiple efficient feature extraction mechanisms, accurate recognition of small blood vessels and the whole blood vessel structure is achieved, the accuracy and robustness of blood vessel segmentation in complex medical images are remarkably improved, and the method can be widely applied to analysis of eye blood vessels such as retinopathy and hypertensive fundus lesion and research of biomarkers related to the blood vessels.

The invention discloses an application of 4-octyl itaconic acid (4-OI) in preparation of a medicine for improving pulmonary arterial hypertension (PAH). In-vivo experiments show that 4-OI can significantly reduce pulmonary vascular wall thickening and collagen deposition of a PAH rat model, and effectively improve PAH and vascular remodeling. In-vitro experiments further reveal that 4-OI reduces endoplasmic reticulum stress (ERS) by inhibiting a PERK-eIF2alpha pathway, further blocks SM22 dependent smooth muscle cell phenotypic transformation and endothelial-mesenchymal transformation (EndMT) processes, finally inhibits vascular remodeling, and plays a role in treating PAH. The three key pathological processes of ERS, EndMT and vascular remodeling are closely linked, a new perspective is provided for deeply understanding the correlation among oxidative stress, endothelial dysfunction and vascular structure change in PAH, meanwhile, a theoretical foundation is laid for developing PAH and vascular remodeling accurate treatment drugs, and clinical transformation value is achieved.

The invention provides a hierarchical decoupling autonomous catheter navigation control method and system, and the method comprises the steps: extracting the position of a catheter and the structure information of a blood vessel based on an obtained intraoperative real-time DSA image, and constructing a topological structure diagram for describing the connectivity of a blood vessel network; performing global path planning and navigation sub-target selection according to the vascular topological graph and the final target; receiving a current navigation sub-target specified by the high-level semantic planning step, and generating a continuous motion control instruction for driving a catheter to the sub-target according to real-time local visual feedback and a pre-trained reinforcement learning strategy; and converting the control instruction into a robot driving signal to drive the surgical robot to move. According to the method, the interpretability and transparency of decisions are improved, doctors can understand and trust the decisions conveniently, the modularization degree and robustness of the system are enhanced, and development, maintenance and clinical popularization of the system are facilitated.

The application discloses a percutaneous lung puncture path planning method and device based on lung segment level blood vessel structure constraint, and the method comprises the following steps: performing isotropic resampling, lung lobe segmentation and bronchial tree extraction on CT image data to generate a three-dimensional lung dissection model; inputting the three-dimensional lung dissection model into a lung segment semantic segmentation module to output a lung segment boundary; reconstructing the topological connection relationship between lung segment level blood supply arteries and drainage veins from the lung segment boundary to obtain a structured lung segment level blood vessel atlas; constructing a dynamic puncture risk field voxel map according to the structured lung segment level blood vessel atlas; sampling a plurality of candidate needle entry points in a surface feasible needle entry region to generate an initial path set in the three-dimensional lung dissection model; and weighting the path length, cumulative risk value, puncture angle penalty and high-density lung tissue volume according to a multi-objective optimization function to output a final puncture path. The application significantly improves the safety boundary and clinical applicability of percutaneous lung puncture.

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 application discloses a kind of heart mitral valve medical image segmentation model, training method, segmentation method and equipment.The segmentation model is improved based on DoubleU-Net architecture, and the two independent and serial U-Net networks contain encoder and decoder;Use EfficientNetB7 network as the feature extraction encoder of front-end U-Net network;Two U-Net networks are sequentially introduced into multi-core residual convolution (MKRC) module, hollow space convolution pyramid pooling (ASPP) module and mixed triple attention (TAG) module after extracting features;The input end of the decoder introduces triple attention gate (TAM) module.The segmentation model training uses hybrid loss function, combines binary cross-entropy loss and Dice loss, balances the class imbalance problem and improves the segmentation precision.The application can automatically and accurately segment heart and related vascular structures, thereby solving the technical problems that accurate and efficient heart mitral valve image segmentation is difficult in the prior art.

The invention discloses a coronary artery segmentation method and system based on boundary perception and a dynamic multi-scale attention network. An image segmentation model is designed based on a U-shaped network, a dual attention enhancement module and a dynamic multi-scale feature fusion module are introduced into a network bottleneck layer, and a boundary optimization module is introduced into a decoder stage. The dual attention enhancement module focuses the network on a key vascular structure in combination with channel and space attention so as to suppress background interference; the dynamic multi-scale feature fusion module fuses different receptive field features through a self-adaptive gating mechanism to adapt to the blood vessel scale change; and the boundary optimization module explicitly enhances blood vessel boundary information by using superficial layer characteristics. In addition, a composite loss function containing boundary weighted loss is adopted for training, and the sensitivity of the model to the blood vessel fuzzy boundary is enhanced. Experiments show that compared with an existing method, the method can effectively improve the segmentation performance of the blood vessel in the coronary artery angiography image.

The present application relates to the technical field of medical navigation, in particular to an augmented reality blood vessel fusion navigation system for transcatheter occlusion, comprising a real-time image acquisition module, a blood vessel three-dimensional model construction module, an image enhancement module, a navigation module and a fusion module, the image acquisition module is connected with an ultrasound catheter to acquire blood vessel three-dimensional images, the model construction module creates a blood vessel three-dimensional model based on the images, the image enhancement module enhances the blood vessel images through spatial adaptive transformation, key point recognition, region adaptive segmentation and edge feature extraction, the navigation module determines the catheter tip position by using the blood vessel three-dimensional model and the enhanced images, the fusion module fuses the enhanced images with the blood vessel model to generate fusion display data, and an AR device overlays and displays the blood vessel three-dimensional model at the catheter tip position based on the catheter tip position and the fusion data, which significantly improves the visualization effect of complex blood vessel structures and provides an accurate spatial reference for catheter navigation.

The invention provides an angiography image segmentation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining an initial angiography image which comprises a to-be-segmented blood vessel structure region; based on a preset blood vessel response function, performing enhancement processing on a blood vessel structure region contained in the initial angiography image to obtain an enhanced angiography image; segmenting the enhanced angiography image through the target segmentation model to obtain an initial blood vessel structure region corresponding to the enhanced angiography image, the initial blood vessel structure region including a blood vessel center line; and performing expansion processing on the blood vessel center line based on the initial blood vessel structure region to obtain a blood vessel segmentation result. According to the embodiment provided by the scheme, the accuracy and reliability of angiography image segmentation can be improved.