64 results about "Vascular morphology" patented technology

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of image processing, in particular to a cerebrovascular three-dimensional model construction method and system based on cerebrovascular image segmentation. The method comprises the following steps: acquiring an initial cerebrovascular image set; registering the initial cerebrovascular image set to obtain a cerebrovascular image set; performing weight mapping feature level fusion on the cerebrovascular image set to obtain an enhanced cerebrovascular feature map; performing multi-scale large vessel segmentation on the enhanced cerebrovascular feature map to obtain a large vessel segmentation feature map; performing spatial attention capillary segmentation on the enhanced cerebrovascular feature map to obtain a capillary segmentation feature map; performing vascular morphological constraint fusion on the large vessel segmentation feature map and the capillary segmentation feature map to obtain a complete cerebrovascular mask map; and performing geodesic distance field calculation on the complete cerebrovascular mask image to obtain a cerebrovascular center line candidate point set. According to the invention, the accuracy and efficiency of cerebrovascular image segmentation are improved.

The invention discloses a capillary image evaluation method, device and equipment and a medium, and the method comprises the steps: obtaining IQ data of a to-be-detected region, and carrying out the beam forming of the IQ data; performing clutter filtering processing on the IQ data of each continuous data frame after beam forming, and extracting a blood flow signal; removing background noise from the blood flow signal to obtain a blood vessel structure image, and segmenting the blood vessel structure image to obtain a distribution position of a blood vessel region; screening a peak region based on the distribution position of the blood vessel region, and determining a peak center position; trajectory tracking is carried out based on the peak value center position, and all trajectories are superposed and displayed to generate a capillary image; and analyzing the generated capillary image, and extracting related parameters of the capillary. Therefore, the method is not limited by image resolution, and can be applied to microvascular images with limited resolution and super-resolution microvascular images, and vascular morphology and hemodynamic parameters can be extracted from the microvascular images with limited resolution and super-resolution.

The invention discloses a blood vessel plaque intelligent identification method and system based on optical coherence tomography, and relates to the technical field of medical image processing. Firstly, a three-dimensional blood vessel grid model is constructed through OCT volume data, vertex curvature parameters are quantified to generate a dynamic threshold template, the segmentation process is made to adapt to the local form of a blood vessel, and the problem of wrong segmentation of a traditional algorithm in a complex anatomical region is solved. Secondly, introducing a fractal dimension verification mechanism, quantitatively evaluating an initial segmentation result based on edge complexity, and when fractal features deviate from a preset threshold value, dynamically adjusting curvature weight parameters and iteratively optimizing segmentation, forming'segmentation-verification-correction 'closed-loop control, and remarkably suppressing artifact interference; and the finally output three-dimensional identification result comprises plaque space coordinates and morphological characteristics, and a visual decision basis is provided for clinic.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention discloses a two-stage segmentation method for a vascular structure in a medical CT image and a related device. The method comprises the following steps: acquiring the medical CT image and a coarse segmentation image thereof; performing blood vessel shape interpolation processing on the coarse segmentation image and performing HU value similarity judgment to generate first annotation data and HU value similarity mapping data; performing vessel connectivity judgment on the HU value similarity mapping data, extracting a condition label and generating second annotation data; and extracting a target region in the repair compensation data by using a blood vessel extraction method based on boundary corrosion, and finally obtaining a target segmentation result. By combining HU value judgment, vessel connectivity analysis and boundary corrosion technologies, the accuracy and connectivity of vessel segmentation are improved, and the method is suitable for fine extraction of complex vessel structures.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

The invention provides a real-time processing method for endoscopic image blood vessel enhancement and microcirculation evaluation, which comprises the following steps of: processing an original endoscopic image, and locally enhancing a brightness channel through a contrast-limited adaptive histogram equalization technology; self-adaptive frequency domain-space domain decomposition is realized based on local texture complexity analysis; performing multi-scale Hessian matrix blood vessel detection on the low-frequency component; applying a directional Gabor filter bank to the high-frequency component; spatial-temporal feature fusion is realized through multi-resolution pyramid optical flow calculation; synchronously completing blood vessel probability prediction, blood vessel diameter estimation and blood flow direction prediction by using a lightweight multi-task deep learning network; the blood flow velocity is analyzed and calculated based on a speckle mode, the perfusion density is subjected to accelerated statistics through an integrogram, and vascular morphological parameters are extracted by adopting an improved skeleton algorithm. According to the invention, an enhanced blood vessel visualization effect and a real-time microcirculation quantitative evaluation function can be provided, and the overall improvement of the endoscope image processing quality and efficiency is realized.

The invention discloses a spatial registration and image fusion method for deep learning based on SS-OCT and TOF data, relates to the technical field of multi-modal medical image processing, and aims to improve alignment and fusion effects based on SS-OCT and TOF blood vessel data. According to the method, firstly, blood vessel features are enhanced through preprocessing, precise registration and fusion of cross-modal data are achieved by combining high-resolution two-dimensional tomography of SS-OCT and three-dimensional blood vessel form data of TOF and utilizing an improved SIFT algorithm and a cross attention Transformer model, and finally a high-precision three-dimensional blood vessel model is generated and can be used for treating cerebrovascular diseases (such as cerebrovascular diseases). Image support with details and a global structure is provided for preoperative planning and intraoperative navigation of a patient (such as aneurysm and stenosis), and the accuracy of diagnosis and treatment is remarkably improved.

The invention relates to the technical field of medical image processing, in particular to a gynecological endoscope image intelligent analysis and cervical lesion precise diagnosis system. Comprising an image enhancement module, a differential geometric feature extraction module, a topology preserving manifold learning module, a curvature-blood vessel correlation analysis module, a biopsy point accurate positioning module, a real-time intelligent auxiliary diagnosis module, a clinical feedback module and a self-adaptive optimization module. Micro morphological change features are extracted through multi-scale curvature analysis; a topology sensitive encoder and a manifold alignment decoder are adopted, and a key topology structure is kept; establishing a correlation model of the tissue surface curvature change and the blood vessel morphology, and analyzing differential morphological characteristics; determining an optimal biopsy position based on the comprehensive characteristics; according to the method, the diagnosis result and basis are generated, visual display is provided, the detection rate of cervical lesions, especially early minimal lesions, is remarkably increased, the missed diagnosis rate is reduced, and multi-point biopsy is reduced.

The invention relates to the technical field of image processing, in particular to a retinal vessel extraction method based on a strong feature dynamic fusion gating network, which adopts strong feature dynamic fusion gating U-Net, designs a core module as a center node by introducing a centrality thought in a graph theory, dynamically aggregates key information of a multi-layer encoder, and improves the extraction efficiency of the retinal vessel. Therefore, effective fusion of global and local features is realized. The feature fusion hub module balances the relationship between global context information and local detail features by using the long-range dependency relationship of multilayer features, and the adaptive gating residual block screens key features and inhibits redundant information through a dynamic gating mechanism, so that the segmentation robustness in complex vascular morphology and lesion regions is enhanced, and the segmentation efficiency is improved. The parallel information focusing module combines a channel and a space attention mechanism, global position information and long-range dependence are effectively modeled, fine-grained and complex features of blood vessels are captured, and the segmentation performance is further improved.

The invention belongs to the technical field of medical image processing, and provides a multi-modal medical image analysis system, medium and equipment for gastrointestinal disease recognition, and the technical scheme is as follows: extracting multi-scale texture features and structural features of a gastrointestinal endoscopy color image and a gastrointestinal endoscopy OCT depth image respectively, and fusing to obtain a cross-modal feature sequence; based on the feature sequence added with the position code, learning a spatial corresponding relation of different modal data on blood vessel morphology and gastrointestinal tract wall interlayer structure; obtaining a multi-scale global context feature sequence and a local detail feature sequence based on the aligned cross-modal blood vessel and gastrointestinal tract wall interlayer structure features, and fusing the multi-scale global context feature sequence and the local detail feature sequence to obtain fused multi-modal features; obtaining an adjusted dense deformation field based on the fused multi-modal features; and performing coarse registration and fine registration based on the adjusted dense deformation field to obtain a registered gastrointestinal endoscopy color image and a registered gastrointestinal endoscopy OCT depth image. The anti-interference capability is strong, and the data stability is high.

The invention discloses a collaborative optimization multi-task non-enhanced CT blood vessel segmentation device. The collaborative optimization multi-task non-enhanced CT blood vessel segmentation device comprises an image acquisition module, an image preprocessing module, a network construction module, a multi-task multi-scale joint loss function construction module, a branch loss construction module, a joint training module and a branch training module. According to the method, collaborative optimization of double tasks can be promoted while the parameter quantity is greatly reduced in an encoder sharing mode, branch parameters of the tasks are independently and finely adjusted, so that the branch parameters of the tasks are optimal, and the blood vessel form mask and the blood vessel structure mask can be effectively segmented from the non-enhanced CT image.

The invention discloses a vein blood vessel image processing method and device, equipment and a storage medium. The vein blood vessel image processing method comprises the steps of collecting a vein blood vessel image; the collected vein blood vessel image is preprocessed; an improved U-Net segmentation network and an improved Zhang-Suen skeletonization algorithm are adopted to process the preprocessed image, and multi-modal results are fused to obtain a blood vessel segmentation result; and based on a blood vessel segmentation result, performing blood vessel three-dimensional modeling by adopting a multi-stage B-spline mapping method, and quantitatively analyzing blood vessel spatial geometric features and hemodynamic parameters to obtain a blood vessel form-function joint evaluation index. The vein blood vessel image is processed and analyzed, three-dimensional modeling is carried out on the blood vessel, the radiation path of radiotherapy is planned, and a foundation is laid for follow-up treatment of varicosity through in-vitro radiation.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

The invention provides an eye red grade analysis method and device combined with blood vessel morphological analysis and a medium, and relates to the technical field of image processing, the method comprises the following steps: inputting a target eye image into a pre-trained segmentation model to obtain a conjunctiva region and a cornea region, determining a corneal limbus region based on the conjunctiva region and the cornea region, and determining the corneal limbus region based on the cornea limbus region; extracting a red pixel region in the conjunctiva region and the corneal limbus region, performing skeletonization processing to obtain a skeletonized mask image, expanding the skeletonized mask image, and performing XOR operation on the skeletonized mask image and the red pixel region to obtain a bleeding region; determining a blood vessel region in the red pixel region based on the hemorrhage region and measuring a blood vessel shape value, determining an eye red level based on an area ratio of the red pixel region in the conjunctival region and the limbal region, and further correcting the eye red level through the blood vessel shape value. The method can accurately separate the blood vessel and determine the eye red grade in combination with the blood vessel form, and eye red analysis is more comprehensive and scientific.

The invention discloses a coronary artery lesion detection method based on retinal vessel marker concept perception and multi-modal fusion, and relates to the problem that in the coronary artery lesion detection technology, the existing method is difficult to effectively utilize retinal vessel markers and realize multi-modal feature fusion detection. Coronary artery lesion is a main cardiovascular disease threatening human health, and early diagnosis is of great significance. Traditional detection depends on contrast examination and has the limitations of trauma, high cost and the like. The retinal blood vessel reflects the microcirculation state of the whole body, and a new way is provided for non-invasive detection by combining clinical indexes. The invention provides a coronary artery lesion detection method based on fusion of retinal vascular marker concept perception and multi-mode. The method has the advantages that (1) markers such as blood vessel density and curvature are calculated, and retinal blood vessel morphology is quantified; and (2) image features and clinical features are fused through a cross-modal attention aggregation mechanism, so that coronary artery disease diagnosis performance is improved. The method can be used for auxiliary detection of coronary artery lesions.

The present invention provides a method and device for obtaining the pressure difference of blood vessels. The method for obtaining the pressure difference of a blood vessel includes: receiving anatomical data of a part of a blood vessel segment, obtaining a geometric model of a target blood vessel according to the anatomical data; obtaining a blood flow model of the target blood vessel and the target blood vessel according to the anatomical data and combining individual data The blood flow velocity V; the geometric model is preprocessed, the cross-sectional morphological model is established, and the shape difference function f(x) of the target blood vessel lumen is calculated, based on the shape difference function f(x) of the target blood vessel lumen And the blood flow velocity V, the pressure difference value ΔP at any two positions of the target blood vessel is calculated. The method for obtaining vascular pressure difference provided by the present invention introduces the concept of morphology to clarify the influence of vascular morphology on the calculation of vascular pressure difference and improve the accuracy of calculation of vascular pressure difference.

The invention discloses a vascular morphological parameter calculation method and system for a single angiography image, electronic equipment and a computer readable storage medium. The method comprises the following steps: acquiring an angiography image, preprocessing the angiography image, and segmenting the angiography image into a plurality of target vascular segments; correcting the two-dimensional diameter of the target blood vessel segment to obtain the three-dimensional diameter of the target blood vessel segment; correcting the two-dimensional length of the target blood vessel segment to obtain a spatial three-dimensional length; a diameter-length curve is constructed based on the three-dimensional diameter and the spatial three-dimensional length of the target blood vessel segment, and the morphological parameters of the target blood vessel are calculated by using a moving average method. The three-dimensional diameter and the three-dimensional length of the target blood vessel segment are obtained through correction, morphological parameter calculation is carried out, accurate blood vessel morphological parameter information can be obtained through a single angiography image, overall calculation is simpler, and clinical usability is higher.

The invention provides a puncture method and system based on ultrasonic guidance, and relates to a data processing technology, and the method comprises the steps: constructing a part model corresponding to a puncture part, determining a partition scanning line of the part model, controlling ultrasonic equipment to carry out the segmented scanning of the puncture part based on the partition scanning line, and obtaining a segmented scanning graph; the method comprises the following steps: acquiring segmented scanning graphs, determining the segmented scanning graphs meeting puncture conditions as sub-adaptive graphs, splicing adjacent sub-adaptive graphs to obtain a spliced puncture graph, judging the blood vessel form of veins in the spliced puncture graph to obtain candidate puncture graphs, and selecting the candidate puncture graphs to obtain a puncture guide graph. The puncture point location of the puncture vein in the puncture guide map is determined based on the proximal direction, and accurate positioning during vein puncture can be achieved under ultrasonic guidance.

Embodiments of the present application provide a blood vessel detection system, an electronic device, a storage medium and a computer program product. The system comprises a catheter and a host. The catheter is provided with a first signal collector and a second signal collector. The first signal collector is used to collect intravascular ultrasound echo signals, and the second signal collector is used to collect blood flow source signals. The host is used to generate an intravascular ultrasound image according to the intravascular ultrasound echo signals of a blood vessel to be detected, determine blood flow information of the blood vessel to be detected according to the blood flow source signals of the blood vessel to be detected, register the intravascular ultrasound image and the blood flow information in the collection time, determine first blood flow information matched by a first intravascular ultrasound image when a blood vessel lumen reaches a maximum expansion state and second blood flow information matched by a second intravascular ultrasound image when the blood vessel lumen is in a resting state according to a registration result, and calculate a blood flow reserve characteristic value according to the determined blood flow information. In this way, comprehensive evaluation of blood vessel morphology and function can be achieved.

The invention relates to the technical field of medical devices, and specifically discloses a high-precision automatic venous blood sampling system and method, wherein the system comprises: an optimal puncture vessel decision module determines an optimal puncture vessel data set; a dynamic ultrasound data acquisition module guides an ultrasound probe on a mechanical arm to perform skin-fitting ultrasound imaging based on the optimal puncture vessel coordinates in a camera coordinate system to obtain dynamic ultrasound data of an optimal puncture area; an ultrasound vessel data acquisition module enhances the vessel signal in the dynamic ultrasound data and extracts ROI; an ultrasound vessel data analysis module extracts all potential vessel morphology distribution data sets from a time-series vessel ROI candidate set, and performs puncture preference and credibility analysis on all potential vessel morphology distribution data sets to determine a final needle insertion point and a final puncture angle; and a venous blood sampling puncture control module is used to control the puncture needle to perform puncture and blood sampling at a final puncture position at a final puncture angle; and is used to realize high-precision automatic control of venous blood sampling.

The invention discloses a PCI (Peripheral Component Interconnect) drug balloon postoperative effect prediction method, device and equipment based on a multi-modal image and a storage medium. The method comprises the following steps: firstly, collecting historical preoperative digital subtraction angiography (DSA) images of a plurality of patients, corresponding intracavitary image data and postoperative effect label data; further extracting vascular morphological parameters of a target lesion segment from the DSA image, extracting plaque microstructure features from the intracavity image, combining the vascular morphological parameters, the plaque microstructure features, clinical data of a patient and parameters of a drug balloon instrument into an input feature vector, and taking postoperative effect label data as a regression target; training an ensemble learning regression model; and finally, for a to-be-evaluated patient, acquiring a preoperative DSA image and an intracavity image, extracting features in the same way, inputting the features into the trained model, and outputting an immediate and / or long-term vasodilation gain prediction value of the patient after the PCI drug balloon operation so as to improve objectivity and reliability of a DCB preoperative decision.

This invention discloses a method and system for predicting severe ischemic events in the nervous system caused by large vessel arteritis based on multimodal deep learning. Belonging to the field of intelligent medical image analysis and clinical risk prediction technology, the method includes: acquiring the patient's magnetic resonance angiography data and clinical information; preprocessing the image data; extracting three-dimensional morphological feature vectors from the point cloud of the blood vessel surface based on a point cloud neural network; extracting blood vessel wall feature vectors from the images based on a three-dimensional convolutional neural network; encoding clinical information to generate clinical feature vectors based on a pre-trained basic model; fusing the above three feature vectors to generate a multimodal fusion feature representation; and outputting an individualized risk score based on this fusion feature through a discrete-time survival analysis model. This invention, through multimodal deep collaborative modeling, achieves automated deep fusion analysis of three-dimensional vascular morphology, blood vessel wall structure, and clinical information, significantly improving prediction accuracy, objectivity, and clinical applicability.

The application discloses a blood vessel feature extraction and analysis device and method based on morphology and time sequence prediction, and the device comprises a single-frame image processing module, a time sequence prediction module and an intelligent diagnosis module. The single-frame image processing module can divide blood vessel images into single frames for processing and classify blood vessel feature points. The time sequence prediction module performs time sequence analysis and prediction analysis on the classified blood vessel feature points. The intelligent diagnosis module compares the prediction analysis result with a stored knowledge graph and gives a result suggestion. In summary, the application provides a blood vessel feature extraction device and method based on blood vessel morphology and time sequence prediction, which have higher accuracy and stability.

The invention discloses a three-dimensional watertight blood vessel geometric reconstruction method based on a scanning ball method, and relates to the technical field of graphics three-dimensional geometric reconstruction, and the method comprises the following steps: collecting three-dimensional watertight blood vessel geometric reconstruction data including medical image data, scanning ball image data and physiological data, and carrying out the preprocessing of the collected data; extracting blood vessel center line point set data from the preprocessed medical image data; in combination with the preprocessed physiological data and a multiple linear regression algorithm, a radius correction coefficient is obtained, and then blood vessel center line point set data is updated; according to the method, the multiple linear regression technology, the implicit function form fitting technology and the scanning ball modeling technology in the method are closely combined with the modern information technology, and the three-dimensional watertight blood vessel geometric model is obtained. The intelligent degree in the three-dimensional watertight blood vessel geometric reconstruction process based on the scanning ball method is obviously enhanced.

The invention provides an interventional operation ultrasonic imaging method and system, and aims to solve the problems that the manual switching efficiency of an ultrasonic probe is low, and a single probe is difficult to adapt to different blood vessel forms and dynamic environments, so that guide wire positioning lags behind, and the operation risk is high. The system comprises an ultrasonic imaging device used for collecting, processing and displaying ultrasonic images in real time; a probe assembly is installed on an end effector of the multi-degree-of-freedom mechanical arm, and the probe assembly comprises a convex array probe and a linear array probe which can be switched; the control module automatically switches probe types according to the diameter of the blood vessel identified in the ultrasonic image: when the diameter of the blood vessel is detected to be greater than a preset threshold value, the control module controls the mechanical arm to rotate the end effector so as to switch to the convex array probe; when it is detected that the diameter of the blood vessel is smaller than or equal to the preset threshold value, the mechanical arm is controlled to rotate the end effector to be switched to the linear array probe. By recognizing environmental parameters such as the diameter of the blood vessel in real time, automatic and rapid switching of the probe is achieved.

This invention belongs to the field of medical image processing technology and discloses an automatic annotation method and system for cervical lesion areas in colposcopy. The method includes: converting the cervical RGB image to the CIELAB color space to construct an acetic acid sensitivity index map to enhance color difference contrast; inputting the sensitivity index map and the original image into a dual-stream encoding and decoding segmentation network to accurately segment the conversion area; extracting three complementary features within the conversion area: acetic acid reaction features, vascular morphology features, and iodine staining spatial features; outputting four probabilities (normal, CIN1, CIN2, and CIN3) by a multi-branch hierarchical classifier and adjusting the segmentation threshold accordingly; and marking each level of area with different colored contours and indicating the biopsy location with arrows.

The invention provides a task joint optimization method and device for blood vessel morphology and blood vessel blood flow characteristics, and solves the technical problem that blood vessel segmentation cannot utilize the physical consistency of blood flow functions. The method comprises the following steps: in an encoding process, carrying out image feature extraction by utilizing a hybrid encoder at least suitable for two task types; according to the task type, configuring a decoder to decode the image features, and forming a decoding branch of at least one blood vessel morphology task type and a decoding branch of at least one blood flow functional task type; in a pair of decoding branches of different task types, a feature bidirectional correction process for correcting the output feature of one decoder by using the output feature of the other decoder is formed between the decoders of the two task types. And collaborative improvement of blood vessel anatomical reconstruction and hemodynamic precision is realized. The problem of scarcity of hemodynamic labeling is relieved, and the generalization ability and segmentation precision of the model are improved.

The invention discloses a PCI drug balloon postoperative effect prediction method, device and equipment based on coronary DSA and plaque property judgment and a storage medium. The PCI drug balloon postoperative effect prediction method comprises the steps that firstly, a PCI preoperative DSA image sequence of a patient to be evaluated is obtained; reconstructing a three-dimensional blood vessel center line of the target lesion segment based on the DSA image sequence, calculating lumen geometric features, inputting the lumen geometric features into a pre-trained plaque property judgment model, and outputting a plaque property judgment result of the target lesion; further combining a plaque property judgment result, DSA-derived vascular morphological parameters, clinical information of a patient and drug balloon instrument parameters to construct a comprehensive feature set; and finally, inputting the comprehensive feature set into a pre-trained regression prediction model, and outputting a PCI drug balloon postoperative prediction effect. According to the method, the plaque type is inversed through the geometrical characteristics of the lumen, the defect that conventional DSA evaluation lacks plaque histological information is overcome, and the reliability of drug balloon postoperative effect prediction is improved.