24 results about "Cranial aneurysm" patented technology

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.

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.

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 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 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.

This invention relates to the field of image processing technology, specifically to an automated intracranial aneurysm detection system based on CT images. The system includes: analyzing all local regions of each vascular segment based on vascular CT images to obtain the probability of local vascular abnormalities; identifying potential abnormal bulging regions based on these probabilities; further obtaining candidate regions for the vascular segment and candidate regions for bifurcation points; obtaining a first aneurysm probability based on the degree of protrusion and perpendicularity of the growth direction of the candidate regions, and distinguishing between physiological curvature and aneurysm regions based on the first aneurysm probability; calculating a second aneurysm probability based on the positional difference value and sphericity matching degree of the candidate regions for bifurcation points, and distinguishing between natural bulges at bifurcation points and aneurysm regions based on the second aneurysm probability. By analyzing different morphologies and structures of intracranial vessels, the system distinguishes between normal vascular structures and aneurysm regions, thereby achieving accurate identification of intracranial aneurysms.

The invention discloses an aneurysm wall enhanced local quantitative analysis method and system based on ray projection, and the method comprises the steps: collecting an MRI sequence of a patient, calculating the contrast corresponding to each voxel, and determining an optimal contrast threshold; constructing a preliminary aneurysm sac model and a preliminary enhanced area model, establishing a spatial position corresponding relation between the two models, and performing spatial alignment; calculating the local geometric wall thickness G corresponding to each triangular patch; determining each sampling point and acquiring the contrast ratio of each sampling point; calculating the reinforcement wall thickness E and the average reinforcement strength M corresponding to each surface patch; and integrating the enhanced wall thickness E and the average enhanced strength M corresponding to each patch into a patch-level data set, mapping the patch-level data set into color attributes of the surface of the three-dimensional model, and obtaining and outputting a three-dimensional color heat map. According to the method, local quantification of enhanced wall thickness and strength is realized by establishing a surface patch level analysis framework, and the core technical problem of enhanced quantitative analysis of the intracranial aneurysm wall is solved.

The application provides an intracranial aneurysm occlusion device, belonging to the technical field of interventional medical instruments; the intracranial aneurysm occlusion device comprises an intratumor part, an extratumor occlusion part, a connecting part, a delivery guide wire and a delivery catheter; the intratumor part and the extratumor occlusion part both have a contracted state and an expanded state; the intratumor part is arranged in the inner cavity of the aneurysm; the extratumor occlusion part is used for pressing the outer side wall of the aneurysm entrance; the connecting part is arranged between the intratumor part and the extratumor occlusion part and is used for connecting the intratumor part and the extratumor occlusion part; the extratumor occlusion part can move on the connecting part to adjust the distance from the intratumor part; the distal end of the delivery guide wire is detachably fixedly connected with the proximal end of the connecting part; the delivery catheter is used for containing the intratumor part and the extratumor occlusion part in the compressed state; and the technical problem that the existing intracranial aneurysm treatment device cannot effectively prevent blood flow from further flowing into the aneurysm and cause damage to the aneurysm is solved.

The application relates to an intracranial aneurysm microfluidic blood vessel chip, system, preparation method and application, and belongs to the medical technical field.The intracranial aneurysm microfluidic blood vessel chip comprises a Y-shaped microfluidic channel, a negative pressure suction cavity and an elastic film, the negative pressure suction cavity is used for being connected with a negative pressure suction device to vacuumize the negative pressure suction cavity, the Y-shaped microfluidic channel meets at a crossing, the crossing is provided with an opening, the elastic film is arranged at the opening and separates the negative pressure suction cavity from the Y-shaped microfluidic channel, the elastic film is a treated polyisoprene film, and the treatment comprises plasma treatment; when the negative pressure suction cavity generates negative pressure, the elastic film can bulge towards the negative pressure suction cavity, and has different bulging degrees under different sizes of negative pressure. The application has good biocompatibility, can simulate the continuous change of a tumor, and has consistency with the intracranial aneurysm in the body in terms of hemodynamic characteristics.

Aspects of the present invention relate to a method for assessing cerebral aneurysms comprising the steps of collecting imaging and blood flow data of an aneurysm and the attached arteries in a subject, creating a 3D model of the aneurysm and the attached arteries from the imaging data, generating blood flow simulations with the 3D model and the blood flow data, extracting one or more parameters from the simulations, processing the one or more parameters to generate one or more stress indices, calculating a risk assessment score for the subject based on a statistical analysis of the one or more parameters and the one or more stress indices.

The application discloses an information processing method and device for intracranial aneurysm hemodynamics prediction, equipment and medium, relates to the field of artificial intelligence technology, and includes the following steps: receiving a three-dimensional medical image containing an intracranial aneurysm, and inputting the three-dimensional medical image into a target hemodynamics prediction model to capture comprehensive morphological features of the intracranial aneurysm from the three-dimensional medical image; inputting blood flow boundary conditions into the target hemodynamics prediction model, performing low-dimensional mapping on the blood flow boundary conditions, and obtaining a first low-dimensional feature vector; receiving spatial point coordinates and directional distances corresponding to each sampling point in a CFD model formed by the intracranial aneurysm region, performing low-dimensional mapping on the spatial point coordinates and the directional distances, and obtaining a second low-dimensional feature vector; and performing feature fusion on the comprehensive morphological features, the first low-dimensional feature vector and the second low-dimensional feature vector through a feature fusion module to predict hemodynamics parameters. The application realizes end-to-end hemodynamics parameter prediction.

The application provides an application of a peripheral blood biomarker combination in construction of a prediction model for intracranial aneurysm rupture, solves the problems of the existing intracranial unruptured aneurysm prediction method and the lack of related early warning biomarkers in the prediction model, and the technical scheme is as follows: an application of a biomarker combination in construction of a prediction model for intracranial aneurysm rupture, the biomarker combination is factor: GPC1, ECM2 and PLEC, a prediction model for predicting intracranial aneurysm rupture is constructed based on the biomarker, and the application and application method in detection reagents and kits have the advantages and positive effects that the rupture of an aneurysm is predicted early, the pain of a patient is reduced, and a reference basis is provided for individualized diagnosis and treatment; the prediction model constructed by the biomarkers has the characteristics of high sensitivity and good specificity, and is significantly better than the PHASES scoring method and the unruptured intracranial aneurysm treatment scoring method.

The invention discloses a device for segmenting an intracranial aneurysm from 3D-MRA based on hierarchical clustering transformer, and the device comprises the steps: receiving a structure MRI image of a brain and a corresponding 3D-MRA image, carrying out the spatial alignment, and carrying out the 3D block sampling, thereby obtaining an MRI-3D block and an MRA-3D block; the intracranial aneurysm segmentation is carried out based on an MRI-3D block and an MRA-3D block by adopting a segmentation model, and the method comprises the following steps: carrying out feature extraction on the input MRI-3D block based on a first transformer encoder to obtain a structural feature, and carrying out hierarchical feature extraction and stage clustering on the MRA-3D block based on a hierarchical clustering second transformer encoder to obtain two types of contrast features, and fusing the structural features and the two types of contrast features and then predicting an aneurysm segmentation result, so that accurate segmentation of the intracranial aneurysm can be realized.

The application relates to the technical field of medical image processing, in particular to a method for automatically identifying and segmenting intracranial aneurysms in 3.0T high-resolution MRI T1 sequences, which extracts high-dimensional features of intracranial aneurysms in the T1 sequence through convolution operation in the encoding path in a full convolutional neural network; further extracts higher-dimensional features and reconstructs the features through decoding path convolution and deconvolution operation in the network, removes irrelevant background voxels in the T1 image, retains voxels related to the intracranial aneurysm, and realizes accurate identification and segmentation of the intracranial aneurysm.

This invention belongs to the field of biomedical engineering technology, specifically a system for predicting the inflammation level of the intracranial aneurysm wall based on a multi-scale deformable convolutional network. The system includes a data acquisition module and an inflammation level prediction module. The data acquisition module acquires high-resolution magnetic resonance imaging (MRI) data of the intracranial aneurysm wall and inflammation level labels at different locations. The inflammation level prediction module trains a model based on the high-resolution MRI data to predict the inflammation level of the aneurysm wall. The prediction model employs a multi-scale deformable convolutional U-shaped network, specifically a four-level encoder-decoder structure, including an encoder, a bottleneck layer, a decoder, and an output head. This invention achieves non-invasive prediction of the inflammation level of the intracranial aneurysm wall from MRI images, providing a better technical means for risk assessment of intracranial aneurysms.

The invention discloses an intracranial aneurysm CTA image segmentation method and device and a storage medium. The method comprises the following steps: introducing a multi-path convolution module and a multi-view pooling Transform module in a specific downsampling stage in an encoder path to construct a basic image segmentation model; training the basic image segmentation model by using the preprocessed CTA image data of the intracranial aneurysm patient to obtain a CTA image segmentation model; and based on the model, inputting preprocessed 3D CTA volume data, and outputting a corresponding 3D intracranial aneurysm segmentation binary mask. According to the method, the MPConv module and the MVPFormer module can be efficiently integrated, so that the MPConv module and the MVPFormer module work cooperatively, the segmentation precision of the intracranial aneurysm is remarkably improved on the premise that the calculation burden is not excessively increased, the problems that an existing method is not sensitive to segmentation of the small-size intracranial aneurysm and missing detection is prone to occurring are solved, and the goodness of fit between the segmentation boundary and the real situation is improved.

The application discloses a kit for assisting in diagnosing or detecting a cerebral aneurysm CHPT1 The kit is characterized in that the kit comprises reagents for detecting CHPT1 a target sequence in a gene promoter region, CHPT1 the target sequence in the gene promoter region is shown as SEQ ID NO. 2: CGGCAGCCGGGCAGGCCGGCCTGACCTCGACCTCCGCCGTGCGGGCCCGACCGGTGAGTCCAGCCCGGCAGTCGCAGGACCCGGCCG, and the reagents comprise CHPT1 a methylation-specific amplification upstream primer, a methylation-specific amplification downstream primer and CHPT1 a methylation-specific sequencing primer, and further comprise CHPT1 RT-qPCR quantitative amplification primers for a transcriptome, so that the intracranial aneurysm can be detected at a molecular level in a convenient and fast manner, and the detection efficiency is high and the detection is targeted.

The application provides an intracranial aneurysm detection method and device based on MRA images, which comprises the following steps: pre-processing MRA image samples; extracting target image blocks based on the pre-processed MRA image samples; wherein the target image blocks are image blocks containing arteries; generating MIP images based on the target image blocks; generating a training data set based on the MIP images and the target image blocks corresponding to the MIP images; obtaining a trained aneurysm detection model based on the constructed aneurysm detection model and the training data set; and predicting whether the to-be-detected MRA image contains an aneurysm based on the trained aneurysm detection model and the to-be-detected MRA image. The MRA images are fully decomposed, so that the aneurysm detection model can more accurately identify and extract the features of the aneurysm. The deep learning algorithm is combined with the logic of manual reading to detect the aneurysm in the MRA images, and the sensitivity and accuracy of the detection are high, and the occurrence of false positive results is reduced.