29 results about "Brain imag" patented technology

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The present invention relates to a method for segmenting a brain image by using virtual data and, more specifically, to a method for constructing a general-purpose modality-agnostic artificial intelligence model by using virtual data and a method for segmenting a brain image by using the constructed artificial intelligence model. To this end, provided is a method for constructing a general-purpose modality-agnostic artificial intelligence model by using virtual data comprising: a step (S100) for inputting a label map (100) of a medical image including one or more regions related to a disease of a human body; a step (S120) in which an image generation model (120) generates a plurality of deformed images by deforming the label map (100); a step (S140) for training the general-purpose modality-agnostic artificial intelligence model (200) on the basis of at least one of the medical image, the label map (100), or the plurality of deformed images; and a step (S160) for determining that the general-purpose modality-agnostic artificial intelligence model (200) has been constructed when an error between an expected label map (220) output by the general-purpose modality-agnostic artificial intelligence model (200) and the label map (100) is within a prescribed range.

The application relates to the technical field of brain image analysis, and discloses a fine-grained brain age prediction method, a system, a terminal and a storage medium.The method comprises the following steps: a plurality of brain tissue structures are segmented from a brain structure nuclear magnetic resonance image of a target object after preprocessing; a plurality of feature maps are generated according to the brain tissue structures and are mapped to a plurality of positive faces; a four-stage network model is used to process the mapped samples; and dynamic adaptive lateral attention is added after each stage to predict the brain age of each brain region.The application predicts the brain age by using the fine-grained brain cortex region level, and introduces a dynamic adaptive lateral attention mechanism to simulate the real brain structure lateral relationship, so that the topological structure and local features of the cortex can be effectively expressed, and the error caused by the brain morphology difference is reduced.

The application provides a child neuropsychological development monitoring and management system. The system uses brain physiological parameters, brain MRI images, development scale characteristics and corresponding child neuropsychological development suffering grades to construct a child neuropsychological development evaluation model. The constructed child neuropsychological development evaluation model generates a current child neuropsychological development suffering grade of a user according to real-time brain physiological parameters collected by a system terminal, real-time brain MRI images monitored by a hospital and corresponding real-time development scale characteristics, provides a basis for whether the corresponding child needs further monitoring according to the current child neuropsychological development suffering grade, and improves the probability of finding potential abnormal development children. The application uses three kinds of fusion parameters related to child brain nerves, such as brain physiological parameters, brain image characteristics and subjective measurement table characteristics, to construct a high-precision real-time processing model, realizes intelligent monitoring of the morbidity of child neuropsychological development disorders by doctors, and reduces the burden on families.

An apparatus for diagnosing an autism spectrum disorder (ASD) based on a graph neural network includes a preprocessor configured to acquire brain image data, designate a region of interest (ROI) of the brain, and generate preprocessed data for neural network input, a spatial feature extractor configured to extract spatial features from the preprocessed data, a temporal feature extractor configured to analyze changes in brain activity over time and extract attention-based temporal features, a spatial and temporal convergence feature unit configured to analyze spatiotemporal correlations by combining the spatial features and the temporal features, a graph generator configured to convert connectivity between regions of interest into a graph structure and implement the same as nodes and edges, and a graph classifier configured to analyze a spatiotemporal pattern of the connectivity through the graph structure and classify whether or not there is an ASD.

A method for deriving dementia-related information using volume predicted from brain CT includes: a step of an analysis apparatus receiving a brain CT (Computed Tomography) image of a subject; a step of the analysis apparatus inputting the brain CT image into a pre-trained segmentation model to extract regions of interest; a step of the analysis apparatus inputting pixel information of the regions of interest into a pre-trained first learning model to predict the volume of at least one region among the regions of interest; and a step of the analysis apparatus inputting the volume of the at least one region into a pre-trained second learning model to derive dementia-related information of the subject.

The embodiment of the invention provides a craniocerebral image cutting method, a craniocerebral image segmentation model training method and related equipment. The craniocerebral image cutting method comprises the following steps: acquiring a to-be-processed craniocerebral image; a to-be-processed craniocerebral image is input into the craniocerebral image segmentation model, a segmentation result is obtained, and the segmentation result comprises a craniocerebral mask; cutting the craniocerebral image based on the segmentation result to obtain a cutting result; wherein the craniocerebral image segmentation model comprises an encoder and a decoder; inputting the to-be-processed craniocerebral image into the craniocerebral image segmentation model to obtain a segmentation result, specifically comprising: inputting the to-be-processed craniocerebral image into an encoder to carry out predetermined times of downsampling and feature extraction to obtain a target feature image; and inputting the target feature image into a decoder, performing up-sampling for a predetermined number of times, and then performing feature fusion to obtain a segmentation result. According to the technical scheme provided by the embodiment of the invention, the cutting duration can be shortened from several minutes to a second level, and the segmentation accuracy of data in different scanning ranges is ensured.

A method and a system are provided for determining a location of a brain target for neuromodulation therapy without requiring MRI imaging of the head. The system includes a sensor or a plurality of sensors configured to acquire data describing a geometry of a head of a subject. A processor is coupled to the sensor and is configured to receive the data describing a geometry of the head of the subject gathered by the sensor, determine 3D coordinates of head-surface contour points from the acquired data, apply a predictive model to the determined 3D coordinates of head-surface contour points, receive 3D coordinates of a located brain target as output from the model, and output 3D coordinates of the located brain target for subsequent delivery of neuromodulatory brain therapy. The model is pre-trained on a dataset of head and brain images and configured to associate head-surface coordinates with 3D coordinates of at least one deep brain target.

The application discloses a fast high-compression-ratio data compression method for mesoscopic three-dimensional brain images, and comprises the following steps: acquiring an original mesoscopic three-dimensional brain image; extracting a multi-scale three-dimensional feature of the original mesoscopic three-dimensional brain image, encoding the multi-scale three-dimensional feature into a latent representation, and constructing a learnable three-dimensional dictionary; adopting a three-dimensional cross-attention model to predict a conditional probability distribution of each voxel in the latent representation; performing lossless compression and restoration on the latent representation according to the predicted probability distribution to obtain the conditional probability distribution of each voxel; and reconstructing a three-dimensional brain image with the resolution of the original mesoscopic three-dimensional brain image by using the conditional probability distribution of each voxel. The method simultaneously realizes high compression ratio, high processing speed and detail fidelity, is especially suitable for processing of large-scale three-dimensional data blocks, and can meet the demand of high-throughput real-time processing.

This invention provides a method and system for improving the segmentation accuracy of smaller categories of brain structures in whole-brain structure segmentation. The method involves inputting acquired 3D brain images into a trained FCN brain classification network and a trained MAD brain classification network for segmentation, respectively. The segmentation results from the two networks are then fused to improve the segmentation accuracy of smaller categories of brain structures in whole-brain structure segmentation based on the FCN brain classification network. This invention considers the balance between whole-brain data input segmentation and GPU memory usage during FCN network training for whole-brain segmentation. FCN networks typically perform convolutional downsampling on the input images, leading to information loss and affecting the recognition of smaller categories of brain structures. Therefore, this invention proposes a method for reclassifying smaller categories of brain structures. Finally, the results are merged with the original FCN network results to achieve high-precision segmentation of each brain structure.

Systems and methods for reliably returning a neuromodulation system to a predetermined position relative to a user's head is disclosed. The system includes a neuromodulation device and a stimulation control computing environment. The stimulation control computing environment can be configured with data processing functions to focus ultrasound emission to a target brain region. The system identifies an initial position of one or more ultrasound-emitting elements with respect to the head of a user, uses brain images to identify the target brain region, and performs acoustic simulations to focus ultrasound emissions from the initial position to the target brain region. The distance between an inner surface of the device and a user's head can be measured, and one or more spacing elements can be placed on the inner surfaces based on the measured distances, which enables the reliable return of the neuromodulation device to the initial position with minimal displacement.

This application relates to a brain image processing method, computer device, and storage medium. The method includes: inputting a brain image to be analyzed into a preset first segmentation model to obtain a segmentation result of a region of interest (ROI) in the brain image; the ROI is a brain region within a preset range surrounding the trigeminal nerve in the brain image; the first segmentation model is obtained by training a preset first initial segmentation model based on a sample brain image and a gold standard for brain region segmentation of the sample brain image; inputting the segmentation result of the ROI and the brain image into a preset second segmentation model to obtain a segmentation result of the trigeminal nerve; the second segmentation model is obtained by training a preset second initial segmentation model based on a sample brain image, a gold standard for brain region segmentation of the sample brain image, and a gold standard for trigeminal nerve segmentation of the sample brain image. This method can automatically classify the pain level of trigeminal neuralgia based on medical images.

A brain magnetic resonance imaging (MRI) analysis device and method using a neural network are disclosed. The brain MRI analysis device using a neural network according to one embodiment comprises: a memory for storing a neural network model; and a processor, which is connected to the memory so as to control an analysis device, wherein the processor receives one or more brain MRI images so as to generate input data, and inputs the input data into the neural network model so as to acquire output data, and the neural network model is trained to determine neurological prognosis of a cardiac arrest patient if the input data is input into the neural network model.

The present invention discloses a brain image classification method based on discretized data, includes: dividing an original brain image dataset into an original training set, an original validation set, and an original test set; constructing a multi-objective function including an information loss before and after dataset discretization, a classification error rate, and a discrete data complexity, and obtaining a discretization scheme; discretizing the original training set, the original validation set and the original test set according to the discretization scheme; performing feature selection on a discrete training set and a discrete validation set, and performing feature reduction on the discrete training set, and a discrete test set using the feature selection result to obtain a reduced discrete training set and a reduced discrete test set; and training a classifier using the reduced discrete training set to classify the reduced discrete test set, to obtain a brain image data classification result.

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 utility model discloses a microwave antenna and a cerebral hemorrhage detection system, and relates to the technical field of biomedical engineering. Wherein the microwave antenna comprises a first curved surface dielectric layer (1a) and a second curved surface dielectric layer (1b); the first curved surface dielectric layer (1a) and the second curved surface dielectric layer (1b) are respectively provided with a first excitation port (36) and a second excitation port (55); wherein the first curved surface dielectric layer (1a) and the second curved surface dielectric layer (1b) are respectively provided with a plurality of groups of first radiation patches connected with the first excitation port (36) and a plurality of groups of second radiation patches connected with the second excitation port (55). The objective of the utility model is to solve at least one of the technical problems of ionizing radiation, high cost, high probability of missing gold first-aid time due to overlong time, unsuitability for bedside detection and pre-hospital first aid and the like in current CT and MRI brain image examination.

The application discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting, comprising the following steps: calculating the p percentile of a brain image and performing image cropping, contrast stretching, and Gaussian filter smoothing denoising; performing Canny edge detection on the filtered image to obtain a binary edge image; performing boundary enhancement and connection through inflation and closing operation, extracting all connected regions, and selecting the largest area contour as the contour of the brain region boundary; determining a reference point based on the contour of the brain region boundary, calculating the polar angle and distance of each point except the reference point and performing sorting, performing convex hull fitting through Graham Scan scanning based on the reference point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the light field reconstruction brain image through a mask function; the application can accurately adapt to the special boundary form of the light field reconstruction image, does not need deep learning and training data, and the generated mask has high geometric consistency.

The invention discloses a brain myelin sheath-function coupling calculation device based on myelin sheath development double covariant. The device comprises image collection, wherein brain image data are collected through an MRI scanner; data preprocessing: performing minimum preprocessing on the brain image data; quantifying the myelin sheath content: calculating T1w / T2w to represent the myelin sheath content and distribution thereof of brain tissues, and sampling the voxel level T1w / T2w to the medium-thickness surface based on cortex reconstruction to reflect the cortical myelin sheath content; constructing a multi-modal brain connection group, wherein the multi-modal brain connection group comprises a crowd level myelin development covariant brain network gMC, an individual myelin development covariant brain network sMC and a grey matter function brain network FC; calculating myelin sheath-function coupling: associating a single node myelin sheath double covariant connection mode with a function communication connection mode based on a multivariate linear model; and detecting significance of myelin sheath-function coupling: analyzing a spatial distribution mode of the MFC, detecting whether the MFC is specific to a real connection mode by using spin test, and analyzing and quantifying age-related development characteristics of the MFC, relevance between the MFC and a functional main gradient, and linear correlation between the spatial mode of the MFC and gene expression information.

The invention discloses a baseline brain CT image processing method and platform and a storage medium. The platform comprises an image segmentation module used for obtaining a baseline brain CT image of a user and inputting the baseline brain CT image into a pre-trained segmentation model to obtain a brain parenchyma region bleeding image and a bleeding volume; the image vector construction module is used for inputting the brain parenchyma area bleeding image into a pre-trained radiomics model to obtain a radiomics score; constructing an image vector based on the radiomics score and the bleeding volume; the text vector construction module is used for extracting blood biochemical marker information and medical record information of the user and constructing a text vector; and the output module is used for inputting the image vector and the text vector into a pre-trained risk judgment model and outputting a risk score of the baseline brain CT image. According to the method, the baseline brain CT image can be automatically subjected to quantitative evaluation based on the image processing platform, and the cerebral hemorrhage expansion risk score is output for reference of the user.

A temporal brain image calibration method based on multi-task adversarial learning, belonging to the field of medical imaging technology, includes the following steps: preprocessing pre-acquired temporal brain images to remove irrelevant information; dividing the brain image phenotypic calibration network into two sub-tasks (local detail reconstruction and global structural transformation), learning them in different ways, and employing a discriminator in an adversarial learning strategy to constrain the generation of better calibrated brain images. This invention is highly valuable for improving the correlation performance between brain images and genes, enabling effective observation and understanding of the human brain state.

This invention discloses an interactive hematoma segmentation and analysis method and system based on brain CT images. The method includes: acquiring brain medical images and setting window width and window level for the medical images; determining a first hematoma region based on the human-computer interaction selection area; performing three-dimensional median filtering; performing three-dimensional Otsu threshold segmentation; performing three-dimensional opening operation; selecting the largest three-dimensional connected component of the binary segmented image to obtain the segmentation result of the second hematoma region; obtaining the hematoma centroid and hematoma volume; and calculating the long axis direction of the hematoma using PCA. This method has a small memory footprint, is easy to integrate into software and deploy on various devices, and provides relatively complete hematoma information, effectively improving the calculation speed and hematoma segmentation accuracy. It can assist doctors or surgical robots in brain hematoma localization analysis and puncture path planning, and has the characteristics of lightweight, easy integration, ease of operation, information diversity, accuracy, and speed.

A device of integrating an external coordinate system and a brain model to generate a biopsy path and a method thereof are disclosed. In the device, through registration of brain images of a target person, a registration integration image is generated, binary mask images of different cerebral tissues are extracted from the registration integration image, 3D models are reconstructed based on the binary mask images of the cerebral tissues, an external coordinate system on a skin surface model in the 3D models is established; after a target area in the 3D models is selected and a biopsy point is set, a biopsy path in the 3D models is calculated based on position information of the target area and a coordinate of the biopsy point in the external coordinate system, and the biopsy path in the 3D models is marked.

A method for simultaneous imaging and image processing of neuromelanin and nigrosome 1 using 3D multi-echo GRE performed by a computer includes: a step (S120) of acquiring a neuromelanin (NM) image (130) and a multi-echo gradient recalled echo (GRE) brain image with a more enhanced contrast ratio than an MRI image by applying two spatial saturation pulses to a 3D multi-echo GRE; a step (S150) of generating a susceptibility map weighted imaging (SMWI) image (140) for nigrosome 1 (N1) using the 3D multi-echo GRE image acquired in the acquisition step (S120); and an inference step (S170) of analyzing, by a neural network (NN) model, the NM image (130) and the SMWI image (140) to quantify volumes of the neuromelanin (NM) and the nigrosome 1 (N1).

Embodiments of the present application provide an image processing method, device and storage medium. In the method, the obtained brain image is input to a feature extraction layer in a brain image processing model, the brain image is subjected to multi-dimensional down-sampling processing to obtain low-level feature maps of multiple dimensions of the brain image, and the low-level feature maps represent low-level semantic information. The low-level feature maps of multiple dimensions are input to a brain region segmentation layer, multi-dimensional up-sampling processing is performed based on the low-level feature maps of the maximum dimension, in the up-sampling process, the low-level feature maps of multiple dimensions obtained by up-sampling are fused with the low-level feature maps based on a skip connection to obtain a global feature map; and the brain image is segmented and labeled according to the global feature map. In this way, the brain region segmentation and labeling can be performed based on the global feature of the entire brain image, the brain image does not need to be segmented into image blocks, and thus the loss of local information between the image blocks can be avoided, and the accuracy of the segmentation and labeling is improved.

This invention provides an automatic segmentation and scoring method and system for lesions in ischemic stroke of the brainstem, including preprocessing brain CT images; automatic segmentation of the brainstem; and acquisition of mirror brainstem images and a brainstem atlas containing the midbrain, pons, and medulla oblongata regions. stem The method involves region segmentation of brainstem and mirror brainstem images; construction of a brainstem infarction lesion detection and segmentation network model; the brainstem infarction lesion detection and segmentation network model contains three encoders, one decoder, and six difference calculation modules. Each encoder consists of three convolutional layers, each decoder consists of three deconvolutional layers, and the difference calculation module includes multi-scale pyramid convolutions and feature fusion modules at each scale; prediction scoring is performed based on the segmented lesions. The lesion detection and segmentation method of this invention can quickly, accurately, and objectively detect and segment lesions.

The present application relates to the technical field of image processing, in particular to a brain image processing method, device and storage medium. The present application first divides an original brain image into multiple sub-images of multiple scales based on a residual network structure, and extracts initial feature maps of each sub-image. Then, a convolution algorithm is used to further extract local features of each initial feature map. After that, the individual feature maps of multiple scales are fused to obtain a common feature map. Since the initial feature maps of multiple scales obtained by using the residual network structure cover the global features of the original brain image, and the individual feature maps obtained by using the convolution cover the local features, the common feature map after fusion has both the global features and the local features of the original brain image, so the fusion feature map based on the common feature map can more accurately reflect the features of the original brain image.

A method of predicting an effect of ischemic stroke on an individual's cognitive status comprises (1) acquiring at least one medical brain image of an individual's brain after the ischemic stroke of the individual; (2) processing the medical brain image to obtain at least one feature of the image; (3) generating a gray matter brain age (GMBA) value of the individual based on the at least one feature of the image; and (4) predicting an effect of the ischemic stroke on the individual's post-stroke cognitive status (PSCI) using the GMBA value.

A method for mapping functionally related brain regions of a subject. The method includes receiving a structural brain image and a dataset of resting-state functional MRI (rs-fMRI) three-dimensional (3D) image frames of a brain of the subject includes 3D image frames of the subject's brain over time. A functional connectivity map identifying groupings of functionally connected voxels is overlaid on the structural brain image. Spontaneous brain activations associated with each voxel in a grouping of functionally connected voxels are time-correlated with spontaneous brain activations in one or more other voxels in the grouping of functionally connected voxels. A reference location map for a pre-defined resting state network is generated, including reference information output. The reference location map includes brain reference locations that are informative of the one or more resting-state networks to assist a user in identifying the resting state network of the functional connectivity map.

The present invention provides a brain information display device and a brain information display method that can generate a hologram as a three-dimensional image showing brain information that forms a three-dimensional curved surface from brain image information, etc. [Solution] The brain information display device 1 comprises a blade, an LED device arranged on the blade, a rotation drive unit, and a control unit 20. The control unit 20 includes an information acquisition unit 41 that acquires brain image information, video information, and / or functional magnetic resonance imaging information obtained by functional magnetic resonance imaging; a coordinate information generation unit 42 that generates three-dimensional coordinate information of the brain information displayed in the image information etc. acquired by the information acquisition unit; a polar coordinate conversion unit 43 that converts the three-dimensional coordinate information generated by the coordinate information generation unit into polar coordinates for a blade-type hologram display; and a hologram generation unit 44 that controls the blade and LED device to generate a hologram as a three-dimensional image showing brain information.