646 results about "Brain section" patented technology

The invention discloses an MRI medical image correction method and system based on a convolutional neural network and a computer readable storage medium, and the method comprises the steps: obtaining a brain MRI medical image, and carrying out the preprocessing; extracting lesion features of the brain lacuna infarction from the preprocessed MRI medical image through a convolutional neural network, and positioning a lesion area of the brain lacuna infarction; performing detail enhancement on the lesion area of the cerebral lacuna infarction, segmenting the lesion area and optimizing a segmentation result; and carrying out artifact restoration on the segmented image, and generating a three-dimensional focus visualization model based on the restored image. According to the method, through processing in multiple aspects such as quality evaluation, lesion recognition, segmentation, artifact repair and three-dimensional visualization of the MRI image, the lesion area can be recognized more efficiently and accurately, the image quality is improved, and an efficient and accurate brain lacuna infarction diagnosis auxiliary tool is provided.

The invention relates to the technical field of biomedical engineering, in particular to a multi-mode brain anomaly detection method and system based on machine learning. The method comprises the following steps: acquiring brain medical image data of different modalities, and realizing spatial registration and alignment through a multi-modal registration algorithm based on mutual information; a multi-branch feature extraction model including a convolutional neural network, a converter and a state space model is utilized to perform feature embedding on the original image of each modal; performing frequency decoupling on the features of each mode through adaptive approximate wavelet transform, and decomposing the features into high-frequency detail information and low-frequency global information; a frequency band fusion strategy based on an attention mechanism is implemented on high and low frequency features of different modal images, and fused frequency sub-band features are input into a space-frequency Mama module. Through the adaptive frequency domain decomposition and cross-modal fusion mechanism, the multi-modal brain image information is effectively integrated, and the accuracy and robustness of brain anomaly detection are remarkably improved.

Systems and methods for cooling an organ to an individualized temperature based on organ parameters. A transport container can store the organ at a target temperature determined based on characteristics of the organ and the donor. A processor can determine the target temperature for preservation of the organ. The determination may be based on preservation time, the type of the organ, the age of the donor, the circulatory condition of the donor, the brain condition of the donor, and the metabolic rate of the donor.

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

The embodiment of the invention discloses an image annotation method and system applied to brain MRI image segmentation, and the method comprises the steps: obtaining a brain MRI image data set of a target object, and the brain MRI image data set comprises original image sequences of a plurality of scanning levels; performing multi-modal feature fusion processing on the original image sequence to generate an enhanced image feature set; calling a multi-layer cascade segmentation network to perform hierarchical feature extraction on the enhanced image feature set to obtain a multi-scale anatomical structure feature map; and performing region boundary optimization processing based on the multi-scale anatomical structure feature map, and generating a marked brain structure segmentation image. Therefore, the boundary of each structure of the brain can be accurately defined, the segmented image is more accurate and clearer, and the image segmentation and marking of the brain MRI image can be accurately and clearer realized.

The present application pertains to the technical field of neural stimulation. Disclosed are a temporal interference-based closed-loop multimodal neural stimulation system and method. The system comprises a temporal interference stimulation system, an electroencephalography-functional near-infrared spectroscopy sampling system, and an upper-level control system. The temporal interference stimulation system utilizes a beat-frequency electric field generated by two sets of electrodes to precisely stimulate a specified brain region. The electroencephalography-functional near-infrared spectroscopy sampling system is a bimodal collector coupling electroencephalography and functional near-infrared spectroscopy, including two parts: signal extraction and correlation analysis, and analyzes stimulation effects and adjusts stimulation schemes by integrating unified brain signal data that combines the temporal precision of EEG and the spatial precision of fNIRS. The upper-level control system includes bimodal fusion model computation, graph convolutional neural network prediction, and stimulation scheme formulation. The present application addresses the problems that traditional stimulation methods lack a closed-loop regulation system, have no means for calibration and optimization, and require a long adaptation period between the stimulation scheme and the user, thus being disadvantageous for applications.

The invention discloses an artificial intelligence multi-modal medical image processing diagnosis and treatment system, and relates to the technical field of medical image processing and diagnosis and treatment, and the system collects multi-angle multi-modal brain image data, extracts brain region features based on multi-modal brain images, formulates a brain normal model, carries out the set fusion of the obtained brain region features, and carries out the diagnosis and treatment of the brain normal model. Generating a three-dimensional structure model of the brain area, compensating the three-dimensional structure model by using the treatment data of the patient to obtain patient features, and analyzing the patient features to obtain a diagnosis report; according to the method, the brain three-dimensional structure model is constructed by fusing the multi-modal image features and the patient treatment data, and the pathophysiological process of the brain diseases is simulated, so that the diagnosis accuracy and reliability are improved, and technical support is provided for early diagnosis and personalized treatment of the brain diseases.

The present disclosure discloses a multi-modal brain network calculation method, apparatus, device, and storage medium. The method is configured to train a brain disease prediction model. After the brain region structural feature and the brain region functional feature are separately extracted from magnetic resonance diffusion tensor imaging data and brain functional magnetic resonance data, a graph representation diffusion learning network is used to separate the universal feature and the unique feature in the brain region structural feature and the brain region functional feature. And then, multi-modal universal and unique feature fusion is implemented based on an alignment algorithm and adaptive weighting technology. Thus, complementary information between the multi-modal data is fully mining. The model can learn an effective feature of a related disease in a training process, and a finally obtained brain region disease prediction model has higher precision and better prediction effect.

The invention discloses a brain tumor segmentation method and system based on a diffusion model, and relates to the field of medical images and deep learning. The method comprises the following steps: acquiring a disclosed three-dimensional brain medical image data set, and making a two-dimensional brain medical image through data processing; performing data preprocessing and data enhancement operation on the two-dimensional image, and dividing the two-dimensional image into a training set, a verification set and a test set according to a certain proportion; a DiffIRseg network model is built, a two-stage training strategy is adopted for training, and optimal model weight parameters are stored; and inputting a to-be-segmented brain image to the trained DiffIRseg network, outputting a predicted health image, obtaining a brain tumor segmentation result through difference analysis, and comparing the brain tumor segmentation result with the fine annotation for verification. According to the method, the prior knowledge of the health image and the denoising characteristic of the diffusion model are introduced, so that the labeling cost and complexity are reduced, the accuracy and efficiency of brain tumor segmentation are improved, and reliable technical support is provided for clinical diagnosis and treatment planning.

The invention relates to the field of brain tumor analysis, and discloses a brain tumor image analysis system based on artificial intelligence, comprising: a spatial alignment unit for acquiring original image data of the brain of a subject; performing spatial alignment on the original image data according to a cross-modal registration algorithm to obtain standardized image data; the feature extraction unit is used for performing tumor region initial segmentation on the standardized image data according to a three-dimensional convolutional neural network so as to obtain a coarse segmentation probability graph; and extracting three-dimensional geometric feature parameters of the tumor candidate region according to the coarse segmentation probability graph. According to the method, the original image data is spatially aligned through the cross-modal registration algorithm, and the spatial consistency between different image sources is ensured, so that the image data under different modals can be accurately compared and analyzed, and an accurate spatial reference is provided for subsequent tumor region identification and processing.

The invention discloses a brain MRI image segmentation method based on an RWKV model, and belongs to the technical field of medical image processing and artificial intelligence crossing. Firstly, an RWKV linear self-attention module is introduced into a U-Net network framework, and association between remote pixels is established with relatively low calculation overhead, so that the recognition precision of a brain tumor area is improved, the reasoning time is effectively controlled, and the practicability of a model is enhanced. Secondly, according to the method, multi-scale coding and a feature fusion mechanism are combined, local and global information is extracted in a combined manner, features are mined from different resolution levels, and adaptive fusion is realized, so that the fine-grained segmentation effect is improved. And finally, in order to enhance the generalization ability and lightweight deployment performance of the model, the network structure is further optimized, and the overall computing resource demand is reduced, so that the method can better adapt to cross-patient MRI data in a complex clinical environment, and has good practical value and popularization potential.

The invention discloses a brain data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal image data of the brain of a target object, the multi-modal image data at least comprising resting state functional magnetic resonance imaging data and diffusion tensor imaging data at a plurality of collection moments; for each brain region of the brain, a brain region dynamic model of the brain region is constructed according to the diffusion tensor imaging data and the resting state functional magnetic resonance imaging data at the multiple acquisition moments, and the brain region dynamic model comprises disturbance parameters; by adjusting disturbance parameters of a brain region kinetic model of the brain region, simulation time sequences of the brain region under the multiple disturbance parameters are obtained, critical indexes of the brain region are determined according to the multiple simulation time sequences, and a critical toughness coefficient of the brain region is determined according to the critical indexes under the multiple disturbance parameters; constructing a critical toughness map of the brain according to the critical toughness coefficients of the plurality of brain regions, and displaying the critical toughness map; therefore, the brain health state is quantitatively evaluated.

The invention discloses a weight-sharing double-flow attention registration method and system based on large kernel convolution LKA, and particularly relates to the technical field of medical image registration, an input brain fixed image and a moving image enter a double-flow structure of DELCA-Net, the DELCA-Net combines the advantages of a convolutional neural network CNN and Transformer, and the weight-sharing double-flow attention registration method and system based on the large kernel convolution LKA are obtained. Semantic information of an image is deeply mined through a self-attention mechanism, more general and rich feature representation can be learned by means of a weight-sharing encoder design and a network, on the basis, accurate feature matching of common features is realized by using a cross self-attention mechanism and DELCA-Net, and the accuracy of registration is remarkably improved; in order to reduce the calculation complexity, the DELCA-Net decomposes a large convolution kernel into a deep convolution module and a deep cavity convolution module which are cascaded. In addition, through a multi-scale attention optimization mechanism, the DELCA-Net effectively fuses spatial correspondence and anatomical semantic association under different scales.

The invention discloses a brain nuclear magnetic imaging focus reasoning method based on a multi-hop memory network. The method comprises the following steps: S1, acquiring brain nuclear magnetic resonance imaging data and corresponding clinical annotation data; s2, generating preprocessed brain nuclear magnetic resonance image data; s3, constructing a medical priori knowledge graph; s4, constructing a multi-hop memory network model; s5, generating an image feature vector; s6, generating an image feature vector fused with the prior knowledge; s7, storing the updated features in a memory unit; s8, performing hop-by-hop calibration on the judgment of the focus position, the focus range and the focus property, and outputting a focus reasoning result; and S9, performing fusion display with the original brain nuclear magnetic resonance image data. According to the method, remarkable innovation is achieved on the algorithm level, and meanwhile the accuracy and clinical applicability of the focus reasoning result are remarkably improved.

The invention discloses a multi-modal feature combined depression auxiliary diagnosis system. The system comprises a sampling unit which is used for constructing a multi-modal depression data set by acquiring a depression screening scale, an electroencephalogram, a magnetoencephalogram and functional magnetic resonance imaging based on acquisition equipment; the feature extraction unit is used for extracting multi-modal brain features based on the depression data set, and the multi-modal brain features comprise power spectral density obtained by electroencephalogram signals, event-related potential, micro-state, prefrontal lobe gamma frequency band power spectral density obtained by magnetoencephalogram and event-related magnetic field; gray matter volume and resting state functional connection density are obtained through functional magnetic resonance imaging; a data preprocessing unit; the diagnosis model unit is used for constructing a multi-modal depression diagnosis model and training the model on the basis of the multi-modal brain features in combination with a fusion strategy; and an analysis and prediction unit. The extracted features are comprehensive and reasonable, the defect of each mode is overcome by the feature fusion method, and the fused features are advanced.

The invention discloses a collaborative optimization method and device for three-dimensional tissue segmentation and registration of a brain nerve image, and the method comprises the steps: S01, constructing a segmentation and registration collaborative model which comprises a shared feature encoder, a segmentation path and a registration path, the segmentation path is used for generating a segmentation probability distribution diagram, and the registration path is used for generating a deformation field; s02, acquiring a training set of the brain three-dimensional magnetic resonance image pair; s03, performing cooperative training on the segmentation and registration cooperative model according to a multi-task cooperative loss function, the loss function including segmentation loss, registration loss and a cooperative regularization term, and the cooperative regularization term modulating a deformation field gradient penalty term by using a multi-scale boundary weight map and a tissue-specific mechanical weight; and S04, receiving an image pair to be registered in real time, and inputting the image pair to be registered into the trained segmentation registration collaborative model to obtain a registration result. According to the method, the calculation efficiency can be remarkably improved while the segmentation and registration precision is ensured.

The invention provides a brain glioma segmentation method and system based on a region of interest combined with artificial intelligence, and relates to the technical field of image segmentation, and the method comprises the steps: carrying out the preprocessing of a brain magnetic resonance image; extracting image features by using a multi-scale convolutional neural network; calculating an attention weight to generate a thermodynamic diagram, and binarizing to obtain a candidate region; adaptively adjusting convolution kernel parameters according to the size of the candidate region, extracting multi-scale features by adopting spatial pyramid pooling, and fusing the multi-scale features; semantic segmentation is carried out through a segmentation module; and marking a brain glioma area. According to the method, the accuracy and efficiency of brain glioma segmentation are improved, and the consumption of computing resources is reduced.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The present disclosure relates to a brain image processing method and apparatus. One specific implementation of the method comprises: using a spatiotemporal feature extraction unit to perform feature extraction on a brain magnetic resonance image of a first modality to obtain spatiotemporal features of a brain region; using a specific convolutional layer to perform feature extraction on a brain magnetic resonance image of a second modality to obtain structural features of the brain region, the specific convolutional layer having a convolution kernel of a set size and a convolution kernel stride; and performing feature fusion on the spatiotemporal features of the brain region and the structural features of the brain region to obtain fused features, and performing feature classification on the basis of the fused features to obtain an image processing result.

An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging dMRI image of a patient's brain. The processing unit is configured to: 1) determine an estimate of an orientation of neurons at each voxel in the dMRI image; 2) determine a plurality of fiber tracts in the at least one dMRI image; 3) select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts; and 4) determine a neurological disease classification.

The invention is applicable to the technical field of brain discipline, and provides a brain function connection modeling and coding method, device and equipment and a storage medium, the method comprises the following steps: constructing a first brain function connection graph according to rs-fMRI data, performing graph enhancement processing on the first brain function connection graph by adopting a preset adaptive graph enhancement strategy to obtain a second brain function connection graph, performing feature coding on the second brain function connection graph by adopting a preset topological attention coding strategy to obtain corresponding graph features, and performing feature projection on the graph features by adopting a preset feature projection strategy to obtain brain function discrimination features for brain function connection classification, therefore, the generated brain function discrimination features can accurately identify subtle differences of different types of brain function connections, and high-accuracy discrimination can be realized whether normal and abnormal brain function states or brain connection modes corresponding to different diseases, so that powerful support is provided for clinical aid decision making, and the accuracy of brain function discrimination is improved. And the accuracy and scientificity of brain disease diagnosis and treatment can be improved.

The invention discloses a multi-mode brain tumor robust segmentation method based on graph-guided adaptive distillation, and the method comprises the steps: firstly constructing a brain tumor segmentation model which comprises a graph-guided adaptive refining module GARM module, a double-bottleneck distillation module BBDM module and a lesion perception reliability module LGRM module; secondly, acquiring a multi-modal brain medical image, executing standardization preprocessing, and then dividing a training set and a test set according to a proportion; and finally, inputting the training set into the brain tumor segmentation model to obtain a segmentation result graph for training, and performing evaluation through the test set. According to the method, the student model can still have higher adaptability and robustness under the condition of lack of modals, the effectiveness and generalization ability of knowledge distillation are greatly improved, and accurate and efficient brain image segmentation is realized.

The embodiment of the invention discloses an electrical stimulation parameter determination method, device and equipment, a medium and a medical system. The method comprises the following steps: acquiring a brain three-dimensional model corresponding to a target user of which the brain is implanted with at least one stimulation electrode; determining at least one electrode contact combination corresponding to the target nucleus simulation model according to the first spatial position information of the target nucleus simulation model and the second spatial position information of the electrode simulation model; determining at least one group of candidate electrical stimulation parameters of the electrode contact combination according to the electrode contact combination and a target nucleus simulation model; determining the total power consumption of the stimulator of the candidate electrical stimulation parameters according to a preset total power consumption quantitative model of the stimulator and the candidate electrical stimulation parameters; and determining at least one group of target recommended electrical stimulation parameters from the plurality of groups of candidate electrical stimulation parameters according to the total power consumption of the plurality of stimulators. According to the technical scheme, the effect of accurately screening the recommended electrical stimulation parameters with low energy consumption and efficient stimulation effect according to the electrical stimulation power consumption is achieved.

The invention provides a brain image denoising method based on a U-shaped dynamic convolution multi-scale multi-branch network. The brain image denoising method comprises the steps that brain noise images to be denoised are input into three branch networks formed by U-netAM, DSHFN and MSDSRN in parallel; in the U-netAM branch, multi-level features are extracted through an encoder in sequence, after weighting is conducted through a channel and a space attention mechanism, the spatial resolution is recovered through a decoder, and a first feature map is obtained; in the DSHFN branch, the dynamic convolution kernel generates a corresponding convolution kernel in real time according to input image features, the image is decomposed into a low-frequency part and a high-frequency part, and the low-frequency part and the high-frequency part are subjected to weighted fusion after being processed by a low-pass filter and a high-pass filter respectively to obtain a second feature map; in the MSDSRN branch, adopting multi-scale depth separable convolution to extract multi-scale features in parallel, and obtaining a third feature map through residual connection and fusion; inputting the three feature maps into an FPB block for fusion to obtain a noise feature map; and performing pixel-by-pixel subtraction on the original brain noise image and the noise feature map, and outputting a denoised brain image.

The invention relates to the technical field of medical image processing, and discloses an adversarial generative network model for three-dimensional reconstruction of a brain cell-level vascular network. The model comprises a three-dimensional blood vessel feature extraction module, a dynamic attention generator, a multi-scale discriminator and the like. Characteristics are obtained through multi-modal medical image data (magnetic resonance angiography and confocal microscope scanning data), and high-precision vascular network three-dimensional reconstruction is realized by utilizing cooperative work of all the modules. The dynamic attention generator calculates the topological connection probability of the vascular branches, and the multi-scale discriminator globally and locally evaluates the reconstruction result. Meanwhile, model training is optimized through an adversarial training controller, and the blood vessel network is perfected through a capillary network completion module. According to the method, the advantages of multi-modal data are effectively fused, the reconstruction precision is improved, and powerful support is provided for research and diagnosis of brain vascular diseases.

The invention relates to the technical field of health risk assessment, in particular to a neurosurgery risk prediction method and system based on big data analysis, and the method comprises the steps: obtaining the neural signal intensity of a brain region in preoperative functional image data of a patient, analyzing the balance degree of neural signal transmission, and analyzing the balance of oxygen metabolism distribution according to the nerve conduction rate. And preoperative nerve state information is formed. According to the method, by accurately extracting the preoperative neural activity signals and analyzing the signal transmission delay, the fluctuation amplitude and the oxygen metabolism balance, the accuracy of individual neural function state evaluation is improved. In the postoperative recovery stage, the abnormal region is dynamically recognized in combination with the neural signal change trend, and the metabolism matching capacity is evaluated, so that the recovery mode is more refined. Neural network connection adjustment capability analysis enhances signal transmission stability evaluation, and abnormal region identification accuracy is improved by combining time deviation and oxygen metabolism recovery consistency.

The invention discloses a nerve regulation and control method, device and equipment based on electrical stimulation and a storage medium. The method comprises the steps that metabolism image data, functional image data and anatomical image data of a detected person are acquired; based on the metabolism image data and the functional image data, respectively calculating the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions; on the basis of the standardized uptake value ratio of each brain region of the detected person and the functional connection strength between each brain region and all other brain regions, determining the abnormal characteristics of the brain of the detected person and the position data of a target spot; and time domain interference electrical stimulation parameters are set based on the position data of the target spot, the brain abnormal characteristics of the detected person and the anatomical image data. According to the method, the PET image data and the rs-fMRI image data can be combined and analyzed, the metabolism and function state of the brain of the detected person can be evaluated more comprehensively, and corresponding nerve regulation and control can be carried out based on the time domain interference electrical stimulation parameters.

The invention discloses a polarization-based multichannel fNIRS auditory stimulation detection system, which comprises a signal generator module, a light source and detector module, an amplifier module, a voltage-current module, a power supply module, a data acquisition module and an intelligent terminal, the light source and detector module generates two emergent light sources with different wavelengths to a to-be-detected area according to the current signal with the carrier wave, and receives second polarized light and third polarized light reflected by the to-be-detected area; the amplifier module amplifies and converts the second polarized light and the third polarized light into voltage signals; the intelligent terminal is used for calculating the blood oxygen concentration change through the electric signal. According to the invention, the interference of reflected light and noise of surface tissues on blood oxygen concentration signals of a brain area is reduced.

The invention discloses a person training scheme evaluation method and device based on a virtual reality environment, a storage medium and electronic equipment, and the method comprises the steps: obtaining the brain data, physiological data and behavior data of a to-be-evaluated person in a training process based on a training scheme; performing feature fusion on the brain data, the physiological data and the behavior data to obtain fusion features of the to-be-evaluated person; obtaining a plurality of state features of the to-be-evaluated person based on the fusion features; and determining an evaluation result of the training scheme according to the scores of the plurality of state features. According to the evaluation method, the training effect can be comprehensively and accurately evaluated, the monitoring accuracy and robustness are improved, and powerful support is provided for skill training and task preparation of the to-be-evaluated personnel.