27 results about "Brain atlas" patented technology

The invention relates to the technical field of neurotrauma prognosis image analysis, and discloses a craniocerebral trauma prognosis prediction analysis system based on a three-dimensional model. According to the system, multi-scale segmentation and topology construction are carried out on a craniocerebral three-dimensional image of a patient, the morphological evolution rate of a trauma area is tracked, and key signal events in the trauma evolution process are accurately recognized in combination with an edema signal change curve. The system further quantitatively analyzes dynamic deviations associated with the integrity of normal brain tissue fiber bundles when a signal event occurs, thereby generating a lesion propagation path and mapping it to functional network nodes of a standard brain map, ultimately identifying a prognostic key brain network. According to the technical scheme, key event capture and path foresight prediction in the dynamic propagation process of the secondary injury after the craniocerebral trauma are realized, and the accuracy of prognosis evaluation is improved.

This invention relates to the field of biomedical image processing technology, and particularly to a cross-modal brain region image registration method based on differential homeomorphism. The method includes: registering the original T1-w image with an NMT-averaged standard template; registering the acquired cell architecture imaging and fluorescence imaging with the registered T1-w image; downsampling the fluorescence imaging and cell architecture imaging respectively; performing intensity correction; smoothing the cell architecture imaging / fluorescence imaging; re-downsampling the smoothed cell architecture imaging / fluorescence imaging; registering the T1-w image to the intensity-corrected cell architecture imaging / fluorescence imaging through affine transformation to obtain a deformation field; registering the T1-w D99 brain atlas to the cell architecture imaging / fluorescence imaging using the deformation field; and sampling the D99 brain atlas from the cell architecture imaging / fluorescence imaging to complete the registration. The advantages are: stronger robustness, higher accuracy, and no reliance on subsequent manual correction.

The embodiment of the invention provides an electroencephalogram emotion recognition method based on a graph neural network and federal learning. The method is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring a resting state functional magnetic resonance imaging time sequence and non-image personalized data; preprocessing the resting state functional magnetic resonance imaging time sequence, and constructing a dynamic graph sequence for the preprocessed resting state functional magnetic resonance imaging time sequence based on a plurality of preset brain maps by adopting a sliding window technology; inputting the dynamic graph sequence into a shared feature layer for feature extraction to obtain a space-time shared feature vector; inputting the non-image personalized data into an independent personalized layer for feature extraction to obtain a personalized feature vector; performing feature fusion processing on the space-time sharing feature vector and the personalized feature vector to obtain a fused feature; and the fused features are mapped into the electroencephalogram emotion category probability through the classifier, an electroencephalogram emotion recognition result is obtained, and the electroencephalogram emotion recognition accuracy and the generalization ability of the model are improved.

The invention discloses a method for detecting mouse brain nucleus activation based on manganese-enhanced magnetic resonance imaging, and belongs to the field of image processing, and the method comprises the steps: converting an acquired mouse head manganese-enhanced magnetic resonance image into an NIFTI format, and enabling the direction and voxel size of the image to be consistent with a standard mouse brain map template; performing offset field correction on the image; loading a PLKA-nnUNet model, carrying out image segmentation on the image, outputting a binary brain mask, and extracting an individual mouse brain image from the image after bias field correction; carrying out image registration and intensity normalization; for each registered individual mouse brain image, calculating relaxation rate mean values of four hippocampal subregions of the mouse brain R1 image, wherein the relaxation rate mean values are used for quantitatively comparing mouse brain activation conditions; and carrying out voxel-level statistical test on the registered and normalized individual mouse brain images to identify brain regions with intensity differences among different experimental conditions. According to the method, the dependence on professional operation is reduced.

The invention provides a mental disease classification method based on a two-stage multi-atlas neural network, and belongs to the technical field of medical information intelligent diagnosis. The technical problems that early-stage features are excessively mixed due to multi-atlas information interaction in the prior art, unique disease-related specific characterization of each atlas can be diluted, and potential noise in cross-atlas connection can be possibly amplified are solved. The method comprises the following steps: firstly, on the basis of fMRI data, constructing a functional connection matrix by using various brain maps, and constructing cross-map edges through spatial proximity to obtain a joint map; then, adopting a two-stage graph neural network alternate propagation mechanism: only starting a graph inner edge to extract stable features in an odd number layer, starting a cross-graph edge in an even number layer, and realizing multi-graph information fusion in combination with an action mechanism; and finally, realizing mental disease prediction through graph-level pooling and a classifier. According to the method, premature aliasing of map features can be effectively avoided, and the rationality and robustness of cross-map information interaction are enhanced.

The invention provides a method and a system for exporting brain atlas target data, which are used for determining a brain data subset of a patient and comprise the following steps: acquiring original brain data, preprocessing the original brain data, selecting target data, acquiring associated brain atlas data, determining one or more brain partitions associated with the selected target data, obtaining brain map data in the partition; obtaining target data: determining a subset of the brain atlas data associated with the determined partition, and outputting the subset to a user side so as to be presented to a user; according to the method, the required brain atlas subset clinically related to the user is quickly determined, and the subset is displayed to the user, so that the user does not need to search and analyze in a large amount of clinically irrelevant data; the time required for a user to discover brain atlas portions useful for themselves is greatly reduced, thereby bringing improved results to patients, users, and / or clinicians.

The application discloses a CT-guided old brain PET image spatial standardization system, method and device, the system comprises a PET / CT image acquisition and format conversion module, a PET / CT preprocessing module, a PET / CT image coarse standardization module, a PET / CT image fine standardization module and an automatic SUV extraction module, the old brain PET molecular image spatial standardization is carried out by using a low-dose CT brain structure image as an auxiliary, and the SUV of a region of interest is automatically extracted by using a standard space brain atlas partitioning, the application adopts a two-step strategy from coarse to fine, and by means of an optimized process, an accurate, stable and user-friendly spatial standardization system is provided for the old brain PET image, and the application has important clinical application value and scientific research significance.

This invention relates to the field of biomedical image processing technology, and discloses a method and system for quantitative analysis of hypothalamic immunofluorescence images. The method includes: performing spectral unmixing on multispectral fluorescence microscopy images based on a nonnegative matrix factorization algorithm to obtain a clean signal distribution map; using Gaussian Laplace filtering and watershed transform to achieve cell detection and segmentation; performing affine and B-spline registration between slice images and standard brain atlases to generate regions of interest masks for neural nuclei; using a local background adaptive correction strategy to perform fluorescence quantification and positive determination; and calculating Pearson correlation coefficient and Manders overlap coefficient to achieve colocalization analysis. The system includes a spectral unmixing module, a cell detection and segmentation module, an atlas registration and region recognition module, a fluorescence intensity quantification module, and a colocalization analysis and statistical output module.

The application provides a three-dimensional brain atlas template construction method, which comprises the following steps: step one, deformation field acquisition: a sequence of two-dimensional images is selected, and for any two adjacent two-dimensional images, the former is registered to the latter, and a registration process generates a deformation field; step two, intermediate deformation field generation: for the deformation field generated by the registration of any two adjacent two-dimensional images, an intermediate deformation field is calculated by integrating the deformation path according to time; and step three, interpolation image generation: for any two adjacent two-dimensional images, a to-be-interpolated image is calculated according to the intermediate deformation field. The three-dimensional brain atlas template construction method, storage medium and electronic equipment provided by the application avoid problems such as fragment holes, gap overlaps and the like that may be caused by a traditional three-dimensional reconstruction idea.

The invention relates to a method for realizing AFD phase inversion risk prediction based on function connection and a graph neural network, and the method comprises the following steps: preprocessing an image, extracting a time sequence of each brain region based on a predefined brain map, and calculating a whole brain FC matrix; constructing the FC matrix into graph structure data, and inputting the graph structure data into a graph neural network; the Euclidean distance between the feature representation of the AFD patient to be evaluated and the average feature representation of the BD patient population is calculated. The method, the system, the device, the processor and the medium for realizing AFD phase inversion risk prediction based on the function connection and the graph neural network are high in prediction precision, combine GNN with an edge weight attention mechanism, can capture high-order topological characteristics of a brain network, have early warning capability, and can predict and output FC and brain regions which are most critical to decision. The technology fusion innovativeness is high, the brain connection omics, the graph neural network and representation learning are seamlessly fused, and the clinical transformation potential is large.

The application relates to the technical field of neural image processing, and particularly discloses a brain atlas division system based on a multi-modal multi-dimensional lateralization index similarity network. The method first performs spatial uniform random sampling on the left hemisphere cortical surface of an individual, and extracts the 5-layer neighborhood of the sampling points by using the grid topological connection relationship; then, according to the cross-hemisphere vertex correspondence, the symmetric neighborhood is positioned in the right hemisphere, and the lateralization index (LI) distribution of the cortical features is calculated; by kernel density estimation modeling and morphological counter divergence algorithm, the LI-MIND correlation matrix representing the whole brain symmetry is constructed; finally, the spectral clustering algorithm is used for feature decomposition and dimension reduction of the matrix, the optimal clustering number is determined according to the contour coefficient, and the smooth individualized brain region division atlas is generated. By introducing the topological neighborhood and the lateralization distribution characteristics, the problem that the traditional brain atlas cannot effectively capture the individual organization left-right hemisphere difference is solved, and the brain region division scheme depending on the lateralization information is provided.

The application discloses a cognitive impairment development trajectory structural magnetic resonance classification method based on multiple space scales, and comprises the following steps: selecting a first-level brain atlas, splitting and merging the first-level brain atlas into second and third-level atlases according to spatial structure relationships of sub-brain regions; pre-processing a structural magnetic resonance image; extracting seven structural magnetic resonance features of each sub-brain region in the three levels; using the three-level atlases, constructing a three-space-scale intracerebral layer connection network through multiple linear regression, combining the three-space-scale features, and then considering the relationship among the three levels to construct an interlayer connection network with a space scale of three; selecting a connection with a difference from the obtained connection matrix, recursively eliminating the features to obtain prediction features, taking single-space-scale features, three-space-scale fused features and interlayer features of multiple space scales as inputs, respectively, and using a classifier to evaluate the feature performance; and finally, the optimal classification features obtained have a further understanding of the widely recognized brain region connection.

The application discloses a brain mapping method based on point-constrained optimal transmission and related equipment, the method obtains a functional signal image by processing functional magnetic resonance imaging data, extracts average time sequences by using different brain atlases, calculates Montreal Neurological Institute coordinates, and then combines optimal transmission algorithm to calculate a constructed cost matrix and a mask matrix, obtains an optimal transmission transfer matrix between atlases, inputs the average time sequences, obtains new average time sequences, obtains a functional connection body of the brain, and finally realizes mapping between two different brain atlases; the optimal transmission model of point-line relationship adopted by the method introduces a point-line distance, so that the model increases an index, describes local structure constraints of a data domain, effectively realizes extraction of structure information of the data domain, and makes the converted atlas also well reflect individual information of the original atlas; and the accuracy of conversion between atlases is improved, and time cost is saved.

The present application belongs to the field of brain science and visualization technology, and specifically relates to a human brain digital twin 3D visualization method. The method comprises the following steps: S1: constructing a cerebral cortex outer surface model and an independent brain region model; S2: registering a voxelized brain atlas to a standard brain template space to obtain a correspondence relationship between the vertexes of the cerebral cortex outer surface model and the brain atlas region labels as a projection rule; S3: according to the projection rule, displaying BOLD signals and EEG backtracking signals to the cerebral cortex outer surface model; S4: constructing connection pathways between each brain region in the independent brain region model, and performing deterministic fiber bundle tracking, cleaning and integration on individual diffusion weighted imaging data to obtain biologically authentic and representative fiber bundles between any two brain regions as the connection pathways; and S5: integrating the connection pathways into the independent brain region model to visualize brain network and brain region function effects. The present application realizes dynamic and interactive visualization of multi-modal brain data in three-dimensional space.

The invention discloses a method for diagnosing and treating AD (Alzheimer's disease) by recognizing olfactory disorder through PET (Polyethylene Terephthalate). The method comprises the following steps: (1) observing olfactory and cognitive behavioral changes of AD mice, and evaluating olfactory and cognitive function conditions of the 3 * Tg mice by carrying out behavioral experiments on the 12-month-old 3 * Tg mice; and (2) quantifying A beta in the intracranial olfactory related area of the AD mouse through PET imaging, performing A beta-PET imaging on a 12-month-old 3xTg mouse, reconstructing a PET image through a PET / CT scanner, performing SUV quantification on the intracranial olfactory related area, and observing the deposition condition of the A beta in the olfactory related area. And (3) observing A beta and tau phosphorylation expression and distribution conditions of AD mice in pathology, observing A beta expression and distribution conditions in brains of 12-month-old 3xTg mice through immunofluorescence, dividing olfaction-related regions by referring to an Allen mouse brain map, observing expression conditions of A beta in the olfaction-related regions, and calculating average fluorescence intensity of each region for semi-quantitative analysis.

The application relates to a brain region segmentation method based on deep learning, and relates to brain region segmentation. The steps are as follows: 1) obtaining the result segmented by a Destrieux brain atlas as a training label for model training and evaluation, and obtaining a training set of 122 brain region labels after merging and reducing the labels of part of left and right brain regions; 2) designing a deep learning network model with a multi-scale and multi-branch attention module based on an Unet network; 3) constructing a loss function of the network; 4) solving the optimal parameters of the deep learning network by using the training set obtained in step 1); and 5) inputting brain data to be segmented into the trained network for segmentation. By introducing a multi-scale segmentation attention module and a multi-branch cross attention module, the receptive field of different scales is increased, so that deep features are obtained, accurate segmentation of more brain regions of different scales is realized, the network structure can segment the whole brain into 161 brain regions, and the network structure has the characteristics of fast segmentation speed and high segmentation precision.

The invention belongs to the technical field of brain science and visualization, and particularly relates to a human brain digital twin 3D visualization method. Comprising the following steps: S1, constructing a cerebral cortex outer surface model and an independent brain region model; s2, registering the voxelized brain atlas to a standard brain template space, and obtaining a corresponding relation between the vertex of the cerebral cortex outer surface model and a brain atlas brain region label as a projection rule; s3, displaying the BOLD signal and the EEG traceability signal on the cerebral cortex outer surface model according to a projection rule; s4, constructing a connection path of each brain region in the independent brain region model, performing deterministic fiber bundle tracking on the individual diffusion weighted imaging data, and cleaning and integrating fiber bundles with biological authenticity and representativeness in any two brain regions as the connection path; and S5, integrating the connection path into the independent brain region model for visualizing the brain network and the brain region function effect. According to the method, the dynamic and interactive visualization of the multi-modal brain data in the three-dimensional space is realized.

The application discloses a method for detecting mouse brain nucleus activation based on manganese-enhanced magnetic resonance imaging, and belongs to the field of image processing, which comprises the following steps: converting the manganese-enhanced magnetic resonance image of the mouse head collected into NIFTI format, keeping the direction and voxel size of the image consistent with the standard mouse brain atlas template; performing bias field correction on the image; loading the PLKA- nnUNet model, performing image segmentation on the image, outputting a binary brain mask, and extracting individual mouse brain images from the image after bias field correction; performing image registration and intensity normalization; calculating the mean relaxation rate of the four hippocampal subregions of the mouse brain R1 map for each individual mouse brain image after registration, which is used for quantitatively comparing the activation of the mouse brain; and performing statistical testing at the voxel level on the registered and normalized individual mouse brain images to identify the brain regions with intensity differences between different experimental conditions. The method reduces the dependence on professional operation.

The application discloses a subcortical functional atlas-based magnetic drainage regulation device and method, comprising: an acquisition module for acquiring an individualized brain atlas model based on a subcortical functional atlas; a regulation module for determining the position of a subcortical deep nucleus stimulation target according to the individualized brain atlas model and assisting transcranial direct current stimulation with a drainage magnetic field to perform deep nucleus stimulation regulation; wherein the position of the stimulation target is taken as an electromagnetic focusing center, and the drainage magnetic field is formed through a magnetic source coil; and a feedback module for dynamically adjusting the magnetic source coil according to a stimulation result to realize safe and accurate regulation of the target. The technical scheme of the application can solve the problem that transcranial direct current stimulation is difficult to balance stimulation safety and stimulation depth.

The invention discloses a fNIRS whole-brain detector special for rodents and a manufacturing method of the fNIRS whole-brain detector, relates to the field of brain function imaging and is used for supporting functional near-infrared brain function imaging of the conscious rodents. The detector comprises a head cap support, the inner surface of the head cap support is of a rodent skull bionic curved surface structure, first insertion openings and second insertion openings are vertically formed in the head cap support, the second insertion openings are formed in the periphery of any first insertion opening in a surrounding mode, and the positions of the second insertion openings cover a rodent whole-brain functional area and correspond to the positions of a rodent brain atlas partition; the head cap fixing belt is connected with a head cap bracket; the light source arrays are installed on the first insertion openings in the head cap support in a one-to-one correspondence mode, and the detector arrays are installed on the second insertion openings in the head cap support in a one-to-one correspondence mode. The near-infrared brain function imaging detection of the rodent in the waking state can be realized, and the blank that the fNIRS technology cannot be applied to the waking rodent is effectively filled.

The invention discloses a depression classification method based on a hierarchical multi-map fusion brain function network, and the method comprises the steps: firstly carrying out the preprocessing of resting state functional magnetic resonance imaging data, and extracting a brain region time sequence signal; then calculating a functional connection relationship between brain intervals, generating a sparse binary adjacency matrix and a node feature matrix, thereby constructing an individual brain function diagram, extracting high-order brain network topology and functional features, and obtaining global individual brain function representation through a learnable weighted fusion mode; feature enhancement and soft allocation aggregation based on sparse attention are executed from the node level to generate fusion node features capable of representing cross-brain region function interaction; performing node-level splicing on fusion node features from different brain maps, and realizing cross-map semantic alignment and information interaction; depression classification is completed through a multi-layer sensor, and joint optimization is carried out by combining cross entropy loss and InfoNCE-based fusion node comparison loss. According to the method, the depression classification accuracy and robustness can be remarkably improved.

This invention relates to a method for multi-level information fusion of brain networks, comprising: extracting time series of each Region of Interest (ROI) based on a brain atlas and constructing a multi-granularity brain network; extracting multi-channel features from the constructed multi-granularity brain network to obtain a feature matrix; and fusing multi-channel features based on the obtained feature matrix. This invention also relates to a multi-level information fusion system for brain networks. This invention can fuse local-global multi-level information of the brain, deeply mine the features of multi-granularity brain networks, and improve the expressiveness of brain spatial characteristic modeling.

This invention discloses a severe depression assessment model based on cloud-edge collaboration and knowledge-guided cross-comparative learning, belonging to the field of intelligent medical technology. The model employs a cloud-edge collaborative architecture, acquiring fMRI data and demographic information at the edge, combining this with AAL and Harvard brain atlases to extract ROI time series, modeling using Pearson correlation coefficients, conducting inter-group t-tests for significance analysis, and constructing a functional connectivity graph through feature fusion before uploading it to the cloud. On the cloud, based on multi-view brain map hierarchical analysis, it integrates graph attention mechanisms, default mode network medical prior knowledge, and cross-comparative learning to complete the training and optimization of graph attention, subgraph generation, and the prediction network. Then, feature extraction, knowledge-guided pruning, and cross-view fusion are performed on the functional connectivity graph to be tested, and the depression assessment result is obtained through inference by the prediction network. This invention shortens transmission time, reduces bandwidth pressure, avoids privacy leakage risks, and improves assessment accuracy.

This invention relates to the field of neurotrauma prognostic image analysis technology, and discloses a three-dimensional model-based system for predicting and analyzing the prognosis of craniocerebral trauma. The system involves multi-scale segmentation and topology construction of three-dimensional images of the patient's brain, tracking the morphological evolution rate of the traumatic region, and combining this with edema signal change curves to accurately identify key signal events in the trauma progression process. Furthermore, when these signal events occur, the system quantifies and analyzes the dynamic deviation of the integrity of fiber bundles associated with normal brain tissue, thereby generating damage propagation paths and mapping them to functional network nodes in a standard brain atlas, ultimately identifying key prognostic brain networks. This technical solution achieves the capture of key events and prospective prediction of pathways in the dynamic propagation process of secondary injuries after craniocerebral trauma, improving the accuracy of prognostic assessment.

This invention belongs to the technical field of animal intracranial surgical positioning tools, specifically relating to an arc-shaped operating instrument for deep brain surgery in animals. It includes an arc arm assembly and a calibration table, and is compatible with both rectangular and polar coordinate systems. In practical use, through simple coordinate transformation, the animal standard brain atlas based on the rectangular coordinate system can be applied to the more complex spatial positioning polar coordinate system. It can be directly installed on commercially available stereotaxic instruments, resulting in relatively low operating costs. Through the cooperation of numerous adjustable joints, this device can easily perform "zeroing" operations on commercially available standard stereotaxic instruments, thereby aligning the zero point of the rectangular coordinate system in the standard stereotaxic instrument with the zero point of the polar coordinate system in this device. This enables the conversion between two-dimensional and three-dimensional coordinates, offering advantages such as more convenient and accurate positioning of complex brain regions, high experimental efficiency, strong flexibility, and diverse application scenarios.

The embodiment of the invention relates to a method and device for predicting an early Alzheimer's disease based on SFC features, and the method comprises the steps: setting a first brain region set and a first brain map corresponding to the first brain region set, and designing a first prediction model; big data acquisition is carried out on brain multi-modal MRI images of an early Alzheimer patient group and a healthy crowd to obtain an original sample set; generating a first data set according to the first brain map and the original sample set; training a first prediction model based on the first data set; and after training is finished, receiving a brain multi-modal MRI image of any testee, calculating according to the first brain atlas and the current multi-modal image to obtain a first SFC feature vector, and inputting the first SFC feature vector into a first prediction model for prediction to obtain a current prediction result. According to the method, the SFC features can be mined, and binary classification prediction can be performed on the early Alzheimer's disease based on the SFC features.