51 results about "Cranial aneurysm" patented technology

The invention relates to the technical field of medical treatment, in particular to an intracranial aneurysm rupture risk assessment and management system based on an internet hospital, which comprises a patient end module, a patient end module, a management end module and a management end module, and the patient end module uploads medical history data and performs automatic filing and labeling processing on basic health data through a system standardization questionnaire and a multi-modal data acquisition mechanism; according to the method, the consistency and comparability of the health data are realized through vectorization coding and standardization processing; a blind deconvolution technology is adopted to improve the definition of a cerebrovascular image, and risk assessment is optimized in combination with the image and clinical features; privacy is protected through a self-adaptive watermarking technology, and data security and compliance are ensured in combination with double encryption and de-identification processing; the system timely identifies risks and pushes personalized health intervention through dynamic monitoring and intelligent early warning, and the accuracy and initiative of chronic disease management are improved. In addition, the block chain technology ensures data traceability and tampering prevention, and the doctor-patient trust is enhanced.

The invention discloses an intracranial aneurysm rupture risk assessment method, device and equipment and a storage medium, and the method comprises the steps: firstly, generating a time sequence image containing flow field information based on the maximum density projection and center line extraction processing of a two-dimensional angiography image sequence in combination with contrast agent concentration change curve analysis; performing three-dimensional reconstruction, region-of-interest extraction and opening extension processing by using the three-dimensional image sequence to obtain detailed morphological parameters and a region-of-interest model after extension processing; secondly, displaying a time sequence image with flow field information and a region-of-interest model of which an opening is not prolonged in an overlapping manner, further optimizing setting of boundary conditions and performing hydrodynamic simulation by calculating a flow field vector in an observation ball and determining an actual blood flow direction, and calculating key hemodynamic parameters; and finally, in combination with the morphological parameters, the hemodynamic parameters and clinical information of the patient, obtaining an evaluation result of the aneurysm rupture risk.

The invention relates to a multi-modal medical image intracranial aneurysm segmentation method based on geometric deep learning, and the method comprises the steps: 1, designing a medical diffusion network based on a deep neural network of frequency domain convolution; 2, preprocessing the three-dimensional grid data to realize the manifold of the model and calculate the characteristics of the grid model; and step 3, using a multiple loss function to reduce the influence of the unbalanced data set, and the like. According to the method, the tedious process of manual feature design and multi-stage processing in a traditional segmentation method is avoided; the MedDiffusion Net designed by the method solves the problem of intracranial aneurysm segmentation of medical images of various modalities, and experimental results on MRA, CTA and DSA data show that the method can obtain accurate intracranial aneurysm segmentation without training data of different modalities respectively; and adaptively fusing the features in the neighborhood by learning a diffusion parameter t.

The invention discloses an information processing method and device for intracranial aneurysm hemodynamic prediction, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a three-dimensional medical image containing an intracranial aneurysm, inputting the three-dimensional medical image into a target hemodynamic prediction model, and capturing the comprehensive morphological characteristics of the intracranial aneurysm from the three-dimensional medical image; inputting the blood flow boundary condition into the target hemodynamics prediction model, and performing low-dimensional mapping on the blood flow boundary condition to obtain a first low-dimensional feature vector; receiving a spatial point coordinate and a directional distance corresponding to each sampling point in the CFD model formed in the intracranial aneurysm area, and performing low-dimensional mapping on the spatial point coordinate and the directional distance to obtain a second low-dimensional feature vector; and performing feature fusion on the comprehensive morphological features, the first low-dimensional feature vectors and the second low-dimensional feature vectors through a feature fusion module to predict hemodynamic parameters. And end-to-end hemodynamic parameter prediction is realized.

An aneurysm rupture risk prediction device based on stacking and CTGAN enhanced data comprises a processor configured to obtain case data of an intracranial aneurysm patient and process the case data to obtain a training set; inputting the training set into a CTGAN framework, enabling the generator to learn a potential data distribution mode through adversarial training between the generator and a discriminator, generating a synthetic sample simulating the training set by using the stabilized generator, and combining the synthetic sample with the training set in proportion to form an enhanced data set; constructing a heterogeneous model pool containing a random forest, a LightGBM, logistic regression and a support vector machine, taking the logistic regression as a meta-model, training four basic classifiers by adopting a training set, inputting a verification set into the trained basic classifiers, inputting a prediction result as a meta-feature into the meta-model, and obtaining a prediction result of the random forest, the logistic regression and the support vector machine; and carrying out weighted summation on the prediction result to obtain a final aneurysm rupture risk prediction result. The device provided by the invention can improve the prediction accuracy.

This disclosure reveals a method, system, and computer-readable recording medium for the diagnosis of intracranial aneurysms. The method includes: acquiring a first image, the first image being a three-dimensional image having a intracranial aneurysm; generating a mask, and performing intracranial artery segmentation on the first image to obtain a intracranial artery region; using a segmentation model to perform intracranial aneurysm segmentation on the intracranial artery region in the first image to generate a segmentation result; and using a classification model to classify the segmentation result into intracranial aneurysm and non-intracranial aneurysm, and screening out a true positive result from the classification result. This disclosure proposes an artificial intelligence platform-based intracranial aneurysm assisted diagnosis model, optimizing the intracranial aneurysms recognition process and significantly improving the accuracy of intracranial aneurysms recognition.

The invention belongs to the technical field of medical image segmentation, and relates to an intracranial aneurysm image detection method, system and device and a storage medium. The invention designs a new network architecture, namely a multi-scale axial residual projection network, which is used for improving the accuracy of intracranial aneurysm detection and segmentation. In the aspect of model input, preprocessing steps including matched filtering, Gaussian filtering and Laplacian filtering are adopted for enhancing blood vessel structure and edge information and reducing computing resource requirements. In the aspect of a model structure, multi-scale feature extraction is realized by using a DWR module, and the segmentation capability of the model on a small target is enhanced; besides, the ACRE module is used for enhancing the foreground and inhibiting the background through the axial attention module and the context relation encoder module, and the processing capacity of the network for the edge information of the foreground is improved. According to the invention, the accuracy of intracranial aneurysm detection and segmentation can be improved.

The invention discloses an intracranial aneurysm detection method and device based on a bottom double-branch network and confidence coefficient calibration, and the method comprises the steps: carrying out the low-dimensional feature extraction of an image in the bottom double-branch network, and obtaining a feature map, dividing the feature map into a first sub-feature map and a second sub-feature map, and performing feature extraction and fusion through different convolution operation branches to obtain a final feature map; performing prediction and matching of a real bounding box through an optimal transmission algorithm, and training a bottom double-branch network according to a matching result; in the bottom double-branch network training process, confidence coefficient calibration is carried out at the output end of the model by constructing a loss function based on confidence coefficient scores, and finally the trained bottom double-branch network is obtained to serve as an intracranial aneurysm detection model; and inputting the new to-be-detected picture into the intracranial aneurysm detection model, and detecting to obtain a high-confidence-coefficient intracranial aneurysm prediction bounding box so as to accurately position the intracranial aneurysm. According to the invention, the accuracy and reliability of intracranial aneurysm detection can be obviously improved.

An intracranial stent includes a proximal end, a distal end, and a tubular sidewall extending there between and a patch covering at least a portion of the sidewall; wherein the patch is capable of diverting blood flow past the neck of an intracranial aneurysm. The patch may be made of a photon-activatable material or a tightly woven metal material with a density greater than a density of the sidewall itself.

The present invention relates to a novel intracranial aneurysm occlusion device and a manufacturing method thereof. The occlusion device includes an occlusion device body, a radiopaque marker, a delivery guide wire, a separation point, a delivery tube, and a separator. The radiopaque markers are respectively fixed to the distal end and the proximal end of the occlusion device body. The occlusion device is woven into a net shape by 24 - 72 strands of nitinol shape memory alloy wire. After the occlusion device is fully expanded, radiopaque markers are provided at both the distal end and the proximal end, and the radiopaque markers respectively bundle and fix the nitinol shape memory alloy wires at the distal end and the proximal end of the occlusion device. The delivery guide wire is located inside the delivery tube. A separation point is provided between the radiopaque marker at the proximal end of the occlusion device and the distal end of the delivery guide wire. The proximal end of the delivery guide wire is connected to the separator. The manufacturing method of the occlusion device is shaped by an occlusion device mold. Without craniotomy, for intracranial aneurysms with a narrow upper part and a wide lower part, giant aneurysms, and bifurcation aneurysms, by implanting the occlusion device body, the occlusion device has a stable structure, can quickly and densely fill the aneurysm cavity after deployment, thereby slowing down the blood flow in the aneurysm cavity, reducing the impact pressure of the blood on the aneurysm cavity, enhancing the repair ability, and achieving the purpose of treating aneurysms.

The invention provides a method for predicting occlusion after intracranial unruptured aneurysm blood flow guiding treatment. The method comprises the following steps: acquiring clinical data and DSA imaging data of a patient receiving treatment of a blood flow guiding device; the DSA iconography data comprises DSA data of preoperative, postoperative immediate and follow-up visit; based on DSA data immediately before and after an operation, aneurysm morphological parameters and hemodynamic parameters immediately before and after the operation are measured through three-dimensional reconstruction, finite element simulation and computational fluid mechanics methods; on the basis of follow-up DSA data, clinical data, aneurysm morphological parameters and hemodynamic parameters, a statistical analysis prediction model is adopted, and the occlusion condition after intracranial unruptured aneurysm blood flow guiding treatment is predicted. According to the scheme, intracranial aneurysm occlusion after FD treatment can be accurately predicted.

The invention relates to the technical field of medical image processing, in particular to an intracranial aneurysm image segmentation method based on a 3D U-Net optimization model, and the method comprises the following steps: obtaining intracranial aneurysm CTA image data; an aneurysm area is recognized and positioned in the intracranial aneurysm CTA image data through a MedLAM algorithm, and a multi-scale probability graph containing aneurysm position information is obtained; and inputting the multi-scale probability graph into a 3D U-Net network to segment the aneurysm region to obtain a segmentation result of the anatomical structure of the aneurysm. According to the method, firstly, the MedLAM is used for preliminarily identifying and positioning the intracranial aneurysm, then the 3D U-Net is used for precise segmentation, and the pre-positioning step of the MedLAM is introduced before the intracranial aneurysm is segmented, so that the intracranial aneurysm can occupy a significant part in the image, the defect that the long-distance correlation is not enough to be captured in the 3D U-Net can be effectively made up, and the accuracy and the accuracy of the segmentation of the intracranial aneurysm can be improved. The pre-positioning is matched with the 3D U-Net, so that the segmentation precision is remarkably improved, and the overall efficiency can also be improved.

The utility model relates to a controllable intracranial aneurysm plugging device. The controllable intracranial aneurysm plugging device comprises a conveying mechanism and a controllable expansion body, a far-end developing point and a near-end developing point are respectively arranged at two ends of the controllable expansion body; the conveying mechanism comprises a thin tube and an inner core wire penetrating through the thin tube, and the far end of the inner core wire penetrates into the controllable expansion body from the near-end developing point to be connected to the far-end developing point; the inner core wire can move relative to the thin tube in the length direction of the thin tube. Due to the fact that the releasing state and the releasing size of the expansion body can be controlled, the expansion body can be better matched with the shape and the size of the aneurysm, the expansion body can be better attached to the aneurysm, redundant operation in the operation process is reduced, operation time is saved, operation safety is guaranteed, and operation is convenient.

The invention belongs to the field of biomedicine, and provides application of bile acid and derivatives thereof in prevention or treatment of intracranial aneurysm. The invention particularly relates to application of ursodesoxycholic acid and bile acid combined with ursodesoxycholic acid in preparation of drugs for preventing or treating intracranial aneurysm. According to the method, metabonomics analysis is carried out on a human blood sample, and the early warning effect of ursodesoxycholic acid and bile acid combined with ursodesoxycholic acid on intracranial aneurysm rupture is found; the verification of the influence effect of ursodesoxycholic acid and bile acid combined with ursodesoxycholic acid on the rupture probability of intracranial aneurysm in a mouse intracranial aneurysm model proves that the substance has a protective effect on intracranial aneurysm, namely the risk of intracranial aneurysm rupture is reduced; and an experimental basis is provided for the drug treatment of intracranial aneurysm and the reduction of clinical rupture risk.

The present application relates to a semi-automatic measurement method for morphological parameters of intracranial aneurysms, including: A1, based on a first mask combination of a single aneurysm, obtaining a first aneurysm diameter plane and a first blood flow direction vector; A2, based on the first aneurysm diameter plane, the first blood flow direction vector and the aneurysm mask in the first mask combination, according to the calculation rules of each parameter value in the parameter information, obtaining the parameter information of the aneurysm; A3, based on the predefined aneurysm circumscribed rectangular frame condition, according to the first aneurysm diameter plane, the first blood flow direction vector and the aneurysm mask in the first mask combination, obtaining the aneurysm circumscribed rectangular frame; A4, based on the first mask combination, the aneurysm circumscribed rectangular frame and the aneurysm parameter information, outputting a visualization result for doctors to view. The present method assists the segmentation of aneurysms and tumor-bearing vessels by manually adjusting tools, thereby ensuring the accuracy of parameter calculation.

A novel shape memory polymer (SMP)-based device for surgical treatment of an intracorporeal defect (e.g., a void or anomaly) such as an intracranial aneurysm or fistula. In at least one non-limiting embodiment, the SMP device is a 3D-printed SMP material sized to specifically fit and thus occlude an intracranial aneurysm (ICA). The SMP device may be delivered to the intracorporeal defect via a catheter having a heating mechanism wherein the SMP device is raised above its glass transition temperature as it is deployed, causing the SMP device to return to its permanent shape after it is deployed into the intracorporeal defect. SMP device delivery systems that include the SMP devices, as well as methods of making and using the devices and systems, are also disclosed.

The present invention belongs to the technical field of medical image segmentation, and relates to a method, system, device and storage medium for detecting intracranial aneurysm images. The present invention designs a new network architecture, namely a multi-scale axial residual projection network, to improve the accuracy of intracranial aneurysm detection and segmentation. In terms of model input, the present invention adopts preprocessing steps including matched filtering, Gaussian filtering and Laplacian filtering to enhance vascular structures and edge information and reduce the demand for computing resources. In terms of model structure, the present invention uses the DWR module to achieve multi-scale feature extraction and enhance the model's segmentation ability for small targets; in addition, the ACRE module is used to enhance the foreground and suppress the background through the axial attention module and the context relationship encoder module, improving the network's processing ability for the edge information of the foreground. The present invention can significantly improve the accuracy of intracranial aneurysm detection and segmentation.

The present invention discloses a detection device for the risk of intracranial aneurysm rupture based on multidimensional feature fusion, comprising: an automatic segmentation module, used to obtain a segmented aneurysm image by introducing a preset segmentation model of a lesion-aware attention mechanism and a multi-level semantic supervision mechanism; a three-dimensional reconstruction module, used to establish a three-dimensional model of the aneurysm using the segmented aneurysm image as input to obtain a three-dimensional model of the aneurysm; a multidimensional feature extraction module, based on angiography images, segmented images and a three-dimensional model of an aneurysm of an object to be detected as input, extracts multidimensional features related to the risk of aneurysm rupture; a risk detection module, used to eliminate irrelevant features in a multidimensional feature set according to a preset optimal feature set, and then input the remaining features of the object to be detected into a preset detection model, and finally obtain the rupture risk of the aneurysm of the object to be detected. The present invention combines the aneurysm segmentation and recognition task with the rupture risk detection task to reduce the interference of irrelevant information.

The utility model discloses a model teaching aid for simulating intracranial aneurysm, which comprises a Willis ring, a traffic artery body, two side cerebral anterior artery initial segments, two side internal carotid artery tail segments, two side posterior traffic artery bodies and two side cerebral posterior artery initial segments, a subarachnoid cavity is arranged on the outer side of a brain tissue body, and the subarachnoid cavity is communicated with the subarachnoid cavity. A brain shell is arranged on the outer side of the subarachnoid cavity, a second demonstration lamp bead is arranged at the subarachnoid cavity, a set of arc supports are arranged at the Willis ring, each arc support is provided with a set of third demonstration lamp beads, a pair of outlet supports are arranged on the outermost arc support, and a pair of third demonstration lamp beads are arranged on the outlet supports. The outer ends of the two outlet supports and between the two outlet supports are connected with a group of connecting supports which are guided to the lower cavity of the arachnoid, and the two outlet supports and the connecting supports are respectively provided with a group of fourth demonstration lamp beads. The device can provide a detailed simulation process from intracranial aneurysm formation to rupture to subarachnoid hemorrhage, and is worthy of popularization and application.

The invention relates to an intracranial aneurysm in-vitro amplification model blood circulation experiment system and method, and the system comprises an intracranial aneurysm in-vitro amplification model, a peristaltic pump, a liquid storage tank, a flow detection part and a pressure detection part, one end of the pump pipe is connected with an outlet of the liquid storage tank, the other end of the pump pipe is connected with an inlet of the intracranial aneurysm in-vitro amplification model, and an outlet of the intracranial aneurysm in-vitro amplification model is connected with an inlet of the liquid storage tank to form an extracorporeal circulation path. The experimental method comprises the steps of model and boundary condition zooming, blood flow simulation system construction, in-vitro patient specificity model and circulation path establishment, real-time monitoring and data acquisition. On the premise of meeting a flow similarity criterion, amplification construction of a patient specific aneurysm model, accurate simulation of physiological pulsating blood flow and systematic monitoring of key hemodynamic parameters can be realized; therefore, an in-vitro experiment platform which is more reliable and closer to the physiological state is provided for hemodynamic research of intracranial aneurysm.

The application relates to a method for generating an intracranial aneurysm blood vessel model and a computer readable storage medium, the method comprising: acquiring a three-dimensional medical image containing an intracranial aneurysm; segmenting the three-dimensional medical image to obtain a first level set based on an intracranial artery vessel surface; screening an interested region containing an intracranial aneurysm from an intracranial aneurysm blood vessel model generated based on the first level set; extracting a center line and a radius along the line for the interested region, the extraction of the center line comprising extraction of an intracranial artery center line and extraction of an intracranial aneurysm center line; generating an initial pipe model according to each center line, and then obtaining a second level set based on a surface of the initial pipe model, and generating an intracranial aneurysm blood vessel model after segmentation. The application optimizes the cumbersome preprocessing operation in the traditional method, improves the generation speed of the model on the basis of ensuring the accuracy of the model.

The invention discloses an intracranial aneurysm automatic segmentation method and system, and the method comprises the steps: constructing a cerebral vessel segmentation pre-training network, obtaining the weight of a pre-training model, and constructing an intracranial aneurysm segmentation network; the aneurysm segmentation network is composed of a double-branch artery-aneurysm encoder and an aneurysm decoder, a grouping self-adaptive inquiry attention mechanism is constructed, aneurysm feature expression is enhanced by using artery features of the encoder, and interference of an artery structure on aneurysm segmentation is reduced; a depth separable cavity convolution module is constructed in a depth supervision framework of an aneurysm decoder, and multi-scale features are captured through three parallel depth separable cavity convolution branches. Modeling is carried out on the context relationship between the intracranial artery structure and the aneurysm, pre-trained blood vessel features are migrated to guide aneurysm segmentation, instability possibly caused by dependence on pre-segmentation of the artery structure is reduced, and accurate segmentation of aneurysms of various sizes is achieved.

The invention provides a robust intracranial aneurysm segmentation method for unlabeled CTA scanning, and the method comprises the following steps: designing a triple learning framework comprising pseudo-supervised learning, comparative learning and confidence learning, and learning a robust intracranial aneurysm segmentation model from brain CTA scanning with unreliable labels. The brain CTA scan is pre-processed to remove redundant information and generate various enhanced views of the initial input. And the preprocessed CTA image is sent to a pseudo-supervised learning module, and an enhanced average teacher model is adopted to carry out pseudo-supervised learning on CTA scanning with unreliable labels. A conversion network is added to an enhanced teacher network and is used for voxel-level contrast learning. According to the method and the diagnosis system, the accuracy of intracranial aneurysm segmentation is improved, the diagnosis time of a doctor can be effectively shortened, and the efficiency is improved. Meanwhile, missed diagnosis or misdiagnosis caused by different diagnosis levels or fatigue of doctors is reduced.

The present application relates to a method, computer device, readable storage medium and program product for obtaining the segmented position of an intracranial aneurysm in a blood vessel, comprising: obtaining the three-dimensional position coordinates of the aneurysm; obtaining a segmented intracranial arterial tree; combining the three-dimensional position coordinates of the aneurysm and the segmented intracranial arterial tree to obtain a three-dimensional intracranial vascular model including the intracranial arterial tree and aneurysm position information; establishing a standard intracranial vascular model and performing segmented annotation; spatially registering the three-dimensional intracranial vascular model with the standard intracranial vascular model to obtain a registered three-dimensional intracranial vascular model; on the registered three-dimensional intracranial vascular model, selecting pixel points on the aneurysm whose distance from the intracranial arterial tree is within a preset threshold range, obtaining mapping points corresponding to the pixel points on the standard intracranial vascular model, and obtaining the segmented position of the aneurysm on the intracranial arterial tree based on the positions of the mapping points.

The application belongs to the field of biological medicine and medical imaging technology, and relates to a rapid full-automatic intracranial aneurysm auxiliary detection post-processing system and method. The system comprises an image preprocessing module, an image adaptive threshold classification module, an automatic blood vessel seed point searching module, a blood vessel segmentation module, a blood vessel center line extraction, segmentation and classification module, an aneurysm enhancement module, an aneurysm screening module and an aneurysm display module. The application can be used for full-automatic detection of three common medical image data (MRI, CT and 3D-DSA) intracranial aneurysms, and has high processing speed.

The invention belongs to the technical field of clinical image processing, and particularly relates to an intracranial aneurysm multi-center clinical image data standardized labeling and sharing platform which comprises a multi-center operation end and a sharing platform management end, the multi-center operation end receives and preprocesses data, labeling is carried out in an artificial dominant and AI auxiliary mode, and the sharing platform management end is connected with the multi-center operation end. The model is optimized based on local manual correction data increment; and the sharing platform management end synchronously and uniformly marks specifications, generates a standardized data set through automatic checking and expert rechecking, and performs grading sharing according to roles. According to the method, data cross-center unified multiplexing is realized, the labeling efficiency is improved, sharing and privacy security are balanced, and multi-center research and clinical diagnosis and treatment are assisted.

The present application relates to the medical technical field, specifically to the intracranial aneurysm rupture risk assessment and management system based on the internet hospital, including: patient end module, the patient passes through uploading the medical history data through the system standardization questionnaire and multi-modal data acquisition mechanism to the automatic filing and label processing of basic health data, the present application realizes the consistency and comparability of health data through vectorization coding and standardization processing; the blind deconvolution technology is used to improve the brain blood vessel image definition, and the risk assessment is optimized in combination with the image and clinical characteristics; the privacy is protected through the adaptive watermark technology, the data safety and compliance are ensured in combination with the double encryption and de-identification processing; the system identifies the risk and pushes the personalized health intervention in time through the dynamic monitoring and intelligent early warning, improves the accuracy and initiative of chronic disease management. In addition, the block chain technology ensures the data traceability and anti-tampering, enhances the doctor-patient trust.

The application relates to the technical field of medical image processing, and discloses an intracranial aneurysm recognition method and system based on a brain CT. The method obtains a brain CT image sequence, adopts an anisotropic diffusion filtering algorithm for pretreatment to suppress noise and retain blood vessel edge details. Subsequently, a region growing algorithm is used to segment an intracranial blood vessel region, and a skeletonization algorithm is used to extract a blood vessel center line to calculate curvature and diameter changes. Finally, based on curvature anomaly and diameter ratio analysis, combined with a multi-scale sliding window and a blood vessel topological structure verification, automatic recognition of intracranial aneurysms is realized, and the accuracy and reliability of diagnosis are improved.

The invention relates to the technical field of aneurysm treatment equipment, in particular to an intracranial aneurysm plugging device and a use method thereof. The plugging device comprises a support rod body, the support rod body is of a cylindrical structure, and at least one of the two ends of the support rod body is of a flaring structure; the local film covering part is located in the middle area of the stent rod body, and a covering film is arranged on the portion, where the local film covering part is located, of the stent rod body. In the plugging device, the local film covering part is only arranged in the middle area of the stent rod body, so that the local film covering part can accurately plug the aneurysm, hemodynamics can be changed, the aneurysm and blood circulation can be isolated to prevent rupture, and the space occupying effect caused by filling materials in the aneurysm sac can be thoroughly avoided; and the risk of pressing surrounding brain tissues is avoided.