551 results about "Brain section" patented technology

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 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 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 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 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 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 method for determining brain PET standardized reconstruction parameters based on clinical brain MRI and PET data, and relates to the field of medical image processing. The method comprises the following steps: firstly, collecting clinically paired MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) images for preprocessing, carrying out virtual reconstruction by combining a partial volume correction algorithm and system parameters of equipment to estimate'real 'brain activity distribution so as to obtain a first simulated PET image, and calculating difference or similarity between the first simulated PET image and a clinical PET image; through multiple iterations, obtaining an optimal'real 'brain activity distribution diagram, and processing the optimal'real' brain activity distribution diagram into a'standardized PET image 'according to standardized Gaussian filtering; then, constructing different candidate combinations of reconstruction parameters, inputting an optimal'real 'brain activity distribution diagram, traversing a second simulated PET image generated by simulation reconstruction under each combination, and respectively performing difference or similarity calculation with the'standardized PET image', so as to obtain an optimal'real 'brain activity distribution diagram; and selecting the group of candidate reconstruction parameters with the minimum difference or the highest similarity as final standardized reconstruction parameters. The method gets rid of dependence on a physical motif.

The invention discloses an optical and neural feedback multi-modal data analysis method for a nerve regulation target, and belongs to the technical field of cranial nerve treatment, and the method specifically comprises the steps: receiving a head movement instruction of a patient through an interactive interface, pausing transcranial magnetic stimulation after receiving the instruction, and switching to a head movement monitoring state; an infrared optical navigation device is combined with a magnetic resonance image to construct an individualized brain three-dimensional model, head position changes are tracked in real time, and autonomic nerve feedback signals are collected at the same time; synchronously aligning the head coordinates with the neural feedback signals to generate a multi-modal data set, and analyzing relevance to obtain real-time regulation and control parameters; the positioning and stimulation intensity of the transcranial magnetic stimulation coil are adaptively adjusted according to the parameters, and related information is displayed on a treatment interface; after the head of the patient finishes moving and is stable, a rapid re-calibration process is automatically triggered, high-precision monitoring is recovered, and magnetic stimulation output is reactivated; according to the invention, the treatment stability and adaptability are improved.

The invention relates to a brain glioma image classification method and apparatus, an electronic device and a storage medium. The method comprises the steps of obtaining a brain glioma image; the brain glioma image comprises a plurality of layers of magnetic resonance slice images; segmenting each layer of magnetic resonance slice image into a plurality of image blocks through an image block embedding layer, and mapping each image block into a corresponding embedding vector; setting a first position coding vector for each image block; adding a preset first classification mark vector, each embedding vector and each first position coding vector to obtain a first vector; performing feature extraction on the first vector through a feature extraction network layer to obtain a first feature vector; obtaining a second feature vector according to the first feature vector corresponding to each layer of magnetic resonance slice image and a deep learning neural network model; and obtaining a classification result of the brain glioma image according to the second feature vector and the classification network model, thereby improving the accuracy of the classification result of the brain glioma image.

Provided herein are methods to treat various neurodegenerative disorders comprising delivering a viral particles to the whole brain, including both superficial and deep structures. In some aspects, the viral particles are administered at low dose levels to the CSF, in conjunction with microbubbles followed by application of focused ultrasound (FUS) to a region of interest of the brain, thereby causing entry of the viral particles to the brain.

The embodiment of the invention discloses program control equipment and a medical system. The program control equipment is in communication connection with a stimulator implanted into a target object, and comprises a display interface and a processor, the display interface comprises a display unit for at least displaying a postoperative brain model of a target object, an operation unit for an operation object to execute parameter selection, and a program control unit for delivering a stimulation instruction to the stimulator; wherein the processor is configured to select and generate a target electric field model displayed on the postoperative brain model displayed on the display unit in an overlapping manner based on target stimulation parameters of an operation object on the operation unit, so that the operation object adjusts the target stimulation parameters on the operation unit based on the target electric field model displayed on the display unit; and under the condition that the operation object triggers the program control unit, the target stimulation parameter is sent to the stimulator in a soft output mode. According to the technical scheme, the accuracy of the adopted stimulation parameters can be improved.

The invention relates to the technical field of medical instruments, in particular to a woven type electroencephalogram signal collecting support which comprises a support body. The electrodes are distributed on the surface of the support body, the surfaces of the electrodes are coated with insulating layers, and signal acquisition ends are exposed; one end of the wire is connected with the electrode, and the other end converges to form a physical interface. The brain-computer interface braided stent implanted into the brain in a vascular intervention mode is used for solving the problems that in the prior art, a brain-computer interface electrode device is large in implantation trauma, the contact stability of an electrode and brain tissue is low, and the signal collection coverage range is limited.

The invention discloses a cerebral apoplexy patient postoperative neural function real-time monitoring method. The method comprises the steps that brain MRI image data and clinical information of a to-be-monitored patient and post-operation electroencephalogram data collected in real time are collected and preprocessed; respectively extracting neurophysiological features and radiomics features of the patient to be monitored based on the preprocessed data; performing significant feature screening on the clinical information, the neurophysiological features and the radiomics features, performing preprocessing on the screened data, and combining minimum absolute contraction and selection operator regression analysis to obtain quantitative electroencephalogram data feature indexes and radiomics scores; inputting the screened clinical information, the quantitative electroencephalogram data characteristic index and the radiomics score of the to-be-monitored patient into a trained prediction model, and predicting a risk index of early neurological deterioration of the to-be-monitored patient; the problem that the evaluation result is inaccurate due to the fact that a single clinical feature is adopted to evaluate the neural function in a traditional method is solved.