54 results about "Brain imag" patented technology

The invention relates to the technical field of biomedical engineering, in particular to an atlas making and registering method and system for a mouse brain in a special development period, and the method comprises the following steps: carrying out the data preprocessing of a mouse brain image, and obtaining a reconstructed image; averaging the plurality of reconstructed mouse brains by adopting an iterative registration mode, making an average template, then labeling the average template to make a brain region template, and then generating a structure information file of a mouse brain map; and performing registration on the individual mouse brain image and the mouse brain map to realize accurate alignment of the brain region. According to the method provided by the invention, the appropriate atlas can be specially drawn for the mouse brain in the special development period and registration is carried out under the condition that no existing atlas exists, the characteristics of the VISoR imaging technology are fully considered, good adaptation with the imaging technology is realized, the accuracy of image registration of the mouse brain in the special development period is improved, and the accuracy of image registration of the mouse brain in the special development period is improved. A more accurate tool is provided for neuroscience research, and deep research on the brain structure and function of the mouse in the special development period is promoted.

Methods for treating a neuropsychiatric disorder by administering an iboga alkaloid and a cardioprotective agent in conjunction with analysis of brain image data is described. Also described are methods to improve brain health and to slow or reverse brain aging by disorder by administering an iboga alkaloid and a cardioprotective agent, where analysis of brain image data is used to monitor and / or evaluate treatment effectiveness.

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 discloses a brain injury identification method and an identification system. The identification system identifies the injury of a patient according to the identification method. The identification method comprises the following steps: firstly, the electrical impedance tomography (EIT) of the brain of the patient is performed to obtain the EIT image of the brain of the patient; then, the region with abnormal resistivity distribution in the EIT image of the brain is taken as a reference for scanning of a near-infrared spectrometer, and a scanning path of the near-infrared spectrometer is planned; finally, the near-infrared spectrometer is scanned along the scanning path, and the lesion range and the lesion type are determined according to the obtained near-infrared spectral data. The electrodes need not be arranged on the whole head of the patient, and the automatic and rapid identification of the lesion range and the nature of the brain injury is realized.

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.

A time sequence brain image calibration method based on multi-task adversarial learning belongs to the technical field of medical images, and comprises the following steps: pre-processing a pre-acquired time sequence brain image to remove irrelevant information; a brain image phenotype calibration network is divided into two sub-tasks (local detail reconstruction and global structure transformation), learning is carried out in different modes, and a discriminator in an adversarial learning strategy is adopted to restrain and generate a better calibration brain image. The method is very valuable for improving the brain image and gene association performance, and the state of the human brain can be effectively observed and understood.

The application relates to the technical field of image processing, in particular to a brain image classification method, a brain image classification device, equipment and a storage medium. A self-attention network is applied to a plurality of features of a detected brain image to calculate the correlation degree between any two features; the plurality of features are corrected according to the correlation degree to obtain a plurality of features after correction; a graph convolution network is applied to the plurality of features after correction to obtain a classification result of the detected brain image output by the graph convolution network. The application inputs the plurality of features into the self-attention network, the self-attention network outputs the correlation degree between the plurality of features, then the plurality of features are corrected according to the correlation degree between the plurality of features, finally the plurality of features after correction are input into the graph convolution network, and the graph convolution network outputs the classification result. The application fully considers the correlation degree between the features, so that the classification result of the application for the brain image is relatively accurate.

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.

We describe a computer-implemented method for determining a patient's brain age and optionally stratifying patients into dementia risk groups based on the determined brain age. The methods comprise extracting at least one volumetric feature from an image of a brain by: obtaining at least one volume value for at least part of the patient's brain, and normalising the at least one obtained volume value to obtain the at least one volumetric feature. Brain age is predicted by inputting the at least one extracted volumetric feature into a pre-trained brain age model, wherein the brain age model is a linear regression model. Bias of the linear regression model may also be corrected. A classification model, such as a logistic regression binary classifier, may be used to stratify patients into dementia risk groups.

The application provides a method for imaging-based diagnosis of neuropsychiatric lupus based on machine learning. The method comprises: obtaining a brain MRI image of a patient; segmenting the intracerebral region of interest of the preprocessed brain MRI image; extracting radiomics features in the region of interest; screening features related to the diagnosis and prediction of neuropsychiatric lupus; dividing the screened features into a first training set and a first test set; building N machine learning-based radiomics prediction models; saving the trained N machine learning-based radiomics prediction models; using multiple linear regression to save the radiomics prediction model corresponding to the optimal AUC brain region; obtaining the serological indicators of the NPSLE patient; dividing the serological indicators into a second training set and a second test set; building a joint prediction model combining radiomics features and serological indicators; and inputting the second test set into the joint prediction model to obtain the prediction classification result of the patient's neuropsychiatric lupus. The application fills the gap of magnetic resonance imaging in the diagnosis of NPSLE, and provides practical guidance for clinicians in assisting the diagnosis of NPSLE.

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.

The invention relates to a new biomarker and combination of biomarkers and to their use in screening of traumatic brain injury in paediatric patients and selecting those patients in need of a brain image technique and / or a subsequent treatment intervention. The method allows to rule out the performance of a brain imaging technique if it is determined that the patient does not have any intracranial lesion which would be detectable by means of said brain imaging technique. Particular kits and means for carrying out the methods are also disclosed.

The invention discloses a child brain metabolism template establishing method, a processing device, a system, equipment and a medium. The method comprises the following steps: dividing a PET image of an original child brain by body weight and injection dosage to obtain a standardized uptake value image; performing spatial standardization processing on the standardized shooting value image; based on the age interval of the brain of the child, extracting a region of interest from the standardized target image, and calculating a standardized average shooting value of the region of interest; and establishing a brain metabolism template containing each brain region of each age group. When the children brain metabolism template is established, brain metabolism templates of different ages are established according to age groups, and the standardized uptake mean value of each brain region is extracted. The finally obtained children brain metabolism template can be more accurately used for analyzing children brain PET images of corresponding age groups, change trends of different age groups and brain regions are obtained, and a metabolism rule of children brain development is obtained.

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.

The invention belongs to the technical field of image recognition, and discloses a brain MRI image recognition system and method fusing tensor and superpixel image features. According to the method, a lightweight residual block fused with three-dimensional tensor attention is constructed through BTNet-TS, and a three-dimensional tensor is adopted to capture multi-dimensional lesion semantic features of brain MRI; secondly, providing a multi-scale feature grouping dense connection fusion strategy, and fusing bottom detail features and depth semantic information; and finally, designing an image convolution network based on superpixel segmentation, and deeply mining local correlation features of the focus. Therefore, efficient brain MRI image recognition is realized.

Embodiments of the present application disclose a brain image registration method and device, electronic equipment, storage medium and program product. The method comprises: obtaining an individual brain image and a template brain image, and a pre-trained registration model; inputting the individual brain image and the template brain image into the registration model, to register the template brain image onto the individual brain image based on the following steps by the registration model: using an affine registration network to perform affine registration associated with the individual brain image and the template brain image, to obtain an affine transformation matrix, and using a first spatial transformation layer to perform spatial transformation of the template brain image based on the affine transformation matrix, to obtain an intermediate brain image; using a deformation registration network to perform deformation registration on the intermediate brain image and the individual brain image, to obtain a deformation field, and using a second spatial transformation layer to perform spatial transformation of the intermediate brain image based on the deformation field, to obtain a target brain image. The problem that the brain image registration process is relatively cumbersome is solved.

The present invention provides a method for predicting the risk of cerebral palsy in children with BIPI. The method comprises: obtaining a data set; the data set is a brain MRI image of a child with BIPI; building an automatic lesion segmentation model for the child with BIPI; obtaining a lesion segmentation result; constructing a KD grading prediction model; obtaining a Kidokoro score result; constructing a lesion feature extraction network model; automatically extracting the image features of the brain MRI image of the child with BIPI corresponding to the data set; constructing a cerebral palsy risk prediction model for the child with BIPI, and using the quantitatively converted GMs evaluation result as the output of the prediction model; integrating the image features of the brain MRI image corresponding to the data set, the quantitatively converted Kidokoro score result, and the perinatal clinical index data into a combination, and using the combination as the input of the prediction model; obtaining a cerebral palsy risk prediction result for the child with BIPI. The cerebral palsy risk prediction model for the child with BIPI of the present invention incorporates multidimensional factors to comprehensively evaluate the risk of cerebral palsy in the child with BIPI, innovatively improving the accuracy and practicality of early prediction.

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.

One embodiment of the present disclosure provides a dementia diagnosis method, comprising the steps of: generating genetic feature data by comparing and analyzing genetic information of normal individuals and genetic information of dementia patients; encoding the brain image data of the normal individuals and the brain image data of the dementia patients, respectively, and generating brain feature data corresponding to the encoded brain image data of the normal individuals and the dementia patients, respectively; and using data obtained by combining the genetic feature data and the brain feature data in a predetermined manner as training data, generating a dementia diagnosis model trained to determine whether or not a specific individual has dementia based on the genetic information and genetic feature data of the specific individual.

The present invention discloses a method for segmenting and extracting fetal brain images from twin MR images. The method includes training a target detector using a twin brain dataset with bounding box labels. The trained target detector is then tested on the twin brain dataset to obtain a twin brain dataset after removing maternal tissue. The single fetal brain dataset is then input into a segmentation network to encode the fetal brain region and extract initial low-level features. Low-resolution features are upsampled to obtain deep high-level features. Low-level features and high-level features of the same resolution are concatenated using skip connections. The output feature map is calculated to obtain a probability output, which is then input into a cross-entropy loss function along with the corresponding labels to calculate the loss value. The network parameters are then updated to obtain a pre-trained model. The pre-trained model is then loaded, and the twin brain dataset, after having been subjected to the target detector and removed maternal tissue, is input into a segmentation network and trained to obtain the final segmentation model. The present invention can quickly and efficiently segment and extract twin brain images.

The invention discloses a gene and brain image feature representation method based on depth self-encoding collaborative representation, which comprises the following steps: firstly, expressing gene and brain image data by using a self-encoder, and developing a depth self-encoding collaborative representation model; and secondly, an optimization scheme is also provided to solve the constructed collaborative representation perception layer model, so that a data-driven gene and brain image correlation analysis method is established, and brain image data related to the gene is obtained. And finally, brain image features are selected from the brain image feature data related to the genes by adopting a support vector machine. According to the method, strong correlation can be established, and key regions of interest in different brain images related to genes can be identified, so that guidance is provided for brain state interpretation and prediction.

Exemplary systems, methods, and computer-accessible medium are provided that can generating functional magnetic resonance imaging data (MRI) for an entire brain in a fraction of the time of traditional MRI. Thus, exemplary systems, methods, and computer-accessible medium can be provided that can obtain a single average of an echoplanar image (EPI) sequence to generate functional MRI data, and acquire or generate a fully-formed image of the entire brain based on the functional MRI data using the single average of EPI sequence. This fully formed brain image can be obtained in, e.g., approximately 1-2 seconds (of course other timing of acquisition is possible). Further, the single average of the EPI sequence can acquire a fully formed brain image by acquiring all of k-space with Cartesian sampling.

The present invention relates to a multi-branch segmentation system for cerebral hemorrhage and surrounding edema based on automatically generated labels, which includes the following modules: a preprocessing module, which preprocesses the collected 3D cranial CT images; a label image generation module, which uses computer vision and machine learning algorithms to automatically generate cerebral hemorrhage label images and surrounding edema label images for the preprocessed 3D brain parenchyma CT images; a training module, which uses the automatically generated label images to train a multi-branch segmentation model for cerebral hemorrhage and surrounding edema based on deep learning, and then segments cerebral hemorrhage or surrounding edema or both at the same time; a measurement module, which uses the deep learning segmentation results to perform morphological measurements and present the results. The present invention combines computer vision and machine learning algorithms to automatically generate label images of cerebral hemorrhage and surrounding edema, and trains a multi-branch segmentation network based on the automatically labeled images, thereby obtaining accurate three-dimensional segmentation results of cerebral hemorrhage and surrounding edema.

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.