320 results about "Brain section" patented technology

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 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 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 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 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.

The invention discloses a multi-modal medical image processing method and device, a storage medium and computer equipment. Comprising the following steps: performing three-dimensional discrete wavelet transform on a brain MRI image of a target patient to generate an MRI wavelet coefficient; inputting the MRI wavelet coefficient into a diffusion model to obtain a reference PET wavelet coefficient of the brain of the target patient in a healthy state; performing inverse wavelet transform on the reference PET wavelet coefficient to generate a reference PET image; and comparing the brain PET image of the target patient with the reference PET image, and determining the metabolic deviation index of the brain of the target patient. Therefore, each patient can take the condition without the neurodegenerative change as a contrast, space standardization does not need to be carried out on a group template, anatomical structure distortion caused by the space standardization is greatly reduced, voxel-level accurate analysis of the neurodegenerative disease is realized, tiny pathological change aiming at the patient can be identified, and the accuracy of voxel-level accurate analysis of the neurodegenerative disease is improved. And clinical doctors are assisted in early diagnosis.

The invention provides a multi-frequency time-domain interference stimulation simulation optimization method and device. The method comprises the following steps: constructing a corresponding Gaussian function according to time-domain interference stimulation parameters for a target clinical user, and obtaining Fourier series of each channel of a transcranial electrical stimulator; generating a brain model based on the brain T1 weighted imaging data, and setting corresponding target spot position information to optimize the target spot electric field direction in any direction; determining an effective electrode arrangement area of each axial section of the craniocerebral model and arranging electrodes; and optimizing the electrodes arranged on each axial section so as to determine each grid of the craniocerebral model and the intensity amplitude of the time domain interference stimulation electric field, and optimizing the input current of each channel. According to the invention, multi-frequency stimulation simulation optimization for each channel of the transcranial electrical stimulator can be realized, the focusing performance of time domain interference stimulation can be improved, the calculation complexity of time domain interference stimulation parameter configuration can be reduced, and personalized time domain interference stimulation simulation can be effectively and quickly realized in combination with clinical data.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for selecting automatically a machine learning model that locates region(s) of the brain of a subject that is / are associated with a clinically relevant outcome. One of the methods includes: receiving a brain image dataset of a subject; receiving, from a user, an indication of a patient outcome of interest; selecting, based on the indication of a patient outcome of interest, a model from a plurality of models to produce a selected model; determining brain data of interest for the patient outcome of interest; determining, using the selected model, subject specific brain data of interest based on the brain image dataset of the subject and on the brain data of interest; and taking an action based on the subject specific brain data of interest.

The invention discloses a multi-modal interactive brain training system, method, device and medium. The system comprises a multi-modal signal acquisition layer, a brain dynamics modeling layer, a cross-modal cooperative training layer, a three-dimensional evaluation layer and a brain function adaptation scene generation layer. The multi-mode signal acquisition layer synchronously acquires surface physiological, behavior and deep brain image signals; the brain dynamics modeling layer preprocesses the signal, constructs a brain region interaction model and positions a core brain region pair and an interaction strength threshold value; the cross-modal cooperative training layer generates and adjusts a training task according to the training task; the three-dimensional evaluation layer fuses related features to generate a comprehensive rehabilitation score; the brain function adaptation scene generation layer adjusts training scene elements based on brain function bias features. According to the invention, through synchronous acquisition of multi-modal signals and dynamic modeling of brain functions, accurate adaptation of training tasks, scenes and brain region functions is realized; three-dimensional evaluation guarantees effect quantification and mechanism explanation, and rehabilitation pertinence, scientificity and user compliance are improved.

The invention relates to the technical field, in particular to a cerebral hemorrhage intelligent decision support system based on deep adaptive feature fusion, which comprises a multi-modal data standardization module, a cross-modal feature fusion module, a focus segmentation calculation module, a hemorrhage type identification module and an illness state grading output module. According to the method, deep association between fusion features and bleeding types is mined through an attention mechanism, related feature indexes are converted into standardized scores by referring to clinical common scoring standards, model attention weight distribution is optimized by combining actual prognosis result deviation, the suitability of illness state grading and the clinical scoring standards is improved, and the probability of illness state grading is lowered. An accurate grading result and prognosis prediction reference are provided for clinical treatment decision and rehabilitation intervention; effective data are screened from multiple types of brain images according to image quality related indexes, clinical text key information extraction and association labeling are combined, data integrity and consistency verification is carried out at the same time, and the standardization degree of multi-source data is improved.

A computer-implemented method for decoding speech, language and related semantic neural activity includes: collecting neural signals from an array of electrodes implanted in or on a brain; extracting features from the neural signals to detect distributed signatures of linguistic encoding using non-contiguous coverage of the electrode array; and decoding linguistic units, including phonemes and semantic embeddings from the extracted features. The decoding can utilize a custom neural language model for a limited or impaired brain adapted from a generalized neural language model trained on other human brains with intact speech, linguistic and cognitive regions.

The invention discloses a multi-modal feature fusion prediction method and system for early screening of Alzheimer's disease, and belongs to the technical field of brain disease prediction. The method comprises the following steps: acquiring multi-dimensional data containing cognitive test scores, brain image scanning results and biomarker concentration levels from a patient record database, and performing standardization processing to obtain a multi-dimensional data set in a unified format; key feature vectors are extracted through a dimensionality reduction analysis method to capture the covariant relation between cognitive test scores and brain image changes; when the reduction range of the cognitive test score exceeds a preset threshold value and the brain image displays an atrophy sign, a classification model is constructed through an integrated learning method to preliminarily classify abnormal signals; and fusing the biomarker concentration level to obtain an abnormal signal vector. According to the method, the accuracy and efficiency of early screening of brain diseases such as Alzheimer's disease are remarkably improved through accurate evaluation of key parts of the human brain, such as hippocampus.

The invention discloses a depression detection device, which is applied to the depression diagnosis field, and comprises an MEG signal acquisition module used for acquiring brain MEG signals; the memristor DWT calculation module is used for extracting a signal time-frequency characteristic of the brain MEG signal through the memristor array written with the DWT wavelet coefficient; the memristor CNN classification module is used for performing depression diagnosis on the time-frequency characteristics of the signals through the memristor array in which CNN model parameters are written, and outputting a diagnosis result; the diagnosis output module is used for visually displaying the diagnosis result; and the conductivity detection and calibration module is used for reading the conductivity values of the memristor arrays in the memristor DWT calculation module and the memristor CNN classification module, and calibrating the conductivity values based on a standard conductivity value. A DWT + CNN detection architecture is constructed, low-power-consumption parallel calculation is realized through the memristor, and conductivity deviation of the memristor caused by time drift and temperature fluctuation is avoided through conductivity detection calibration.

The invention relates to a brain anomaly network positioning method and system based on functional connection network mapping in the technical field of neural image data processing. The brain abnormal network positioning method comprises the following steps: calculating a whole brain function network diagram connected with each abnormal site by using resting state function connection data of large-scale health subjects based on a plurality of dispersed abnormal sites reported in previous literatures; then superposing the function network diagrams to obtain a network probability graph; and finally, filtering the probability graph through a threshold value to obtain a final core anomaly network graph. According to the method, the inherent functional connection architecture of the brain is used as a reference system, abnormal sites which seem to be uncorrelated in different researches are successfully traced and unified to a common and stable functional network, and compared with a single brain region marker, the generated network-level biomarker integrates more source evidences, so that the network-level biomarker has the advantages that the network-level biomarker can be widely applied to the field of biomarkers of the brain region. Therefore, the problems of result heterogeneity and inconsistency in brain abnormality discovery are solved.

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

Embodiments of the present application relate to the field of computer model-based image processing, and particularly relate to a system and method for processing brain images. The system for processing brain images provided by the embodiments of the present application encodes the electromagnetic signals of the brain obtained by a signal acquisition unit into tokens by using an encoder and a language large model, and performs in-depth semantic processing on the tokens, so that an image decoder processes the processed tokens and obtains an image. The encoder, the language large model and the image decoder work together to convert the electromagnetic signals into accurate brain images. The method for processing brain images provided by the embodiments of the present application can ensure the authenticity of the signals and reduce the amount of data by compressing the electromagnetic signals and extracting the effective information therein to obtain tokens; and the brain image obtained by decoding the processed tokens after inputting the tokens into the language large model for processing is more accurate.

The invention provides a depression detection method, system and equipment based on a multi-modal brain image and a medium. The method comprises the following steps: acquiring a functional magnetic resonance image and a structural magnetic resonance image of the brain of a subject; performing collaborative analysis on the time sequence change information of the functional magnetic resonance image and the spatial relationship of the brain region, and extracting brain function characteristics representing brain function activity characteristics from the functional magnetic resonance image; carrying out collaborative analysis on voxel distribution and regional hierarchical relationship in the structural magnetic resonance image, and extracting brain structure features representing brain tissue morphology from the structural magnetic resonance image; inputting the brain function features and the brain structure features into a cross-modal interaction module, and performing cross-modal feature fusion on the brain function features and the brain structure features to generate cross-modal brain features; and inputting the cross-modal brain features into a classification module to obtain a depression detection result of the subject. According to the invention, the depression identification precision can be greatly improved.

The invention provides a neural feedback training scheme automatic screening and effect suggestion method. The method comprises the following steps: acquiring biological data of a testee by using a brain wave collection device; wherein the brain wave collecting device comprises a household brain wave collecting device; transmitting the biological data to a brainwave database of a remote cloud system through a network; the brain wave database converts the biological data into a corresponding training parameter suggestion; and remotely feeding back the training parameter suggestions to a neural feedback training module for the testee to perform heart and brain training. Therefore, suggested training parameters are provided through a software interface, and superior and inferior brain area networks, digital therapy suggested training schemes and related health education are arranged, so that the effects of long-distance feedback and improvement of cognitive competence by brain training at home are achieved.

The invention relates to a two-dimensional multi-view brain tumor medical image segmentation method and a two-dimensional multi-view brain tumor medical image segmentation system based on a Vision Mama time sequence model, which utilize a novel visual representation model to complete focus segmentation of a brain tumor two-dimensional medical image. The method is used for solving the problems that when an existing two-dimensional segmentation method is used for processing two-dimensional brain medical images, space depth information cannot be fully utilized, and three-dimensional structure features are difficult to accurately capture. The method comprises the following steps: 1, acquiring and preprocessing data; 2, constructing a multi-view brain tumor segmentation network based on edge feature fusion and a spatial state model; 3, constructing a combined loss function of weighted cross entropy and weighted Dess loss, and meanwhile, storing an optimal model weight in training for prediction; and 4, predicting a brain tumor medical image by using the trained optimal model, calculating evaluation indexes and performing result comparison. Through the combination of the methods, the boundary feature extraction quality of the fuzzy edge of the complex focus and the two-dimensional segmentation precision of the model on the brain tumor are effectively improved.

The invention discloses a method, a device and a system for planning an SEEG electrode implantation path. The method comprises the following steps: processing medical image data of the brain of an epileptic to obtain a target brain anatomical image; matching and searching a target experience module in an experience database according to the clinical description data of the epileptic; the experience database is established according to prior experience, a plurality of experience modules are arranged in the experience database, and each experience module comprises a plurality of combinations of a first target range and a second target range of a fixed electrode; and determining a target path of each electrode based on the target brain anatomy image and the target experience module. According to the method, the experience database containing a plurality of experience modules is established, the target experience module is screened in combination with the clinical description data of the patient, the target path of each electrode is further screened in combination with the target brain anatomy image of the patient, the proper path of the SEEG electrode is accurately and efficiently planned for the individual patient, and the accuracy of the SEEG electrode is improved. The method provided by the invention can reduce the learning and use threshold of doctors, and has high popularization value.

A wearable brain multi-stimulation pain control device is provided, comprising a main support unit, an auxiliary support unit, an audio stimulation unit, and an optical frequency-flashed stimulation unit. The main support unit includes two ear portions corresponding to a user's ears and a front side portion, and the auxiliary support unit includes a mounting portion. Two opposite ends of the mounting portion are pivoted to the main support unit, and the audio stimulation unit includes two speakers that are respectively arranged on the ear portions and can broadcast a binaural beats with frequency following response. The optical frequency-flashed stimulation unit is arranged on the front side portion and can stimulate at least one eye of the user with flickering light, so that the user can obtain multiple stimulations at the same time in a single course of treatment to achieve the effect of improving pain.

The application discloses a brain network analysis method based on a hypergraph and a gravity model, relates to the technical field of electroencephalogram signal analysis and complex network science, and comprises the following steps: S1, acquiring multi-channel stereoelectroencephalogram signals, and calculating the phase locking values between each pair of channels; and S2, based on the phase locking values, taking the stereoelectroencephalogram channels as nodes, and constructing a 3-consistent weighted hypergraph, wherein each hyperedge in the 3-consistent weighted hypergraph comprises three nodes, and the nodes in the 3-consistent weighted hypergraph are divided into multiple groups; the driving force between groups is obtained through layer-by-layer deduction, the high-order correlation characteristics between brain groups can be reflected in multiple dimensions, the interaction of different brain groups can be quantified from the aspect of the driving direction, the multiple quantitative indexes derived can enrich the analysis dimension of the driving relationship between brain groups, and the method is suitable for various brain signal research scenes, so as to meet the actual research and use requirements of the fine analysis of brain interaction mechanisms.

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

Some embodiments are directed to a brain scanning system including one or more noise generators configured to generate an electromagnetic noise field, the noise generator being arranged for positioning in proximity to the subject's head, wherein during operation of the brain scanning system the generated electromagnetic noise field interacts with the brain's electromagnetic field, resulting in an interfered noise signal.

The application discloses a model training method, an image processing method, a device, equipment and a storage medium. The method comprises the following steps: acquiring a training task set; wherein the training task set comprises a plurality of training tasks, each training task comprises a brain segmentation task or a brain classification task; inputting an N+1th training task into an Nth to-be-trained model, updating an Nth model parameter of the Nth to-be-trained model based on the N+1th training task, and obtaining an N+1th updated parameter; wherein N is a positive integer; obtaining a target model parameter based on the N+1th updated parameter and the Nth model parameter; and obtaining a target model based on the target model parameter. The target model obtained based on the training task set can simultaneously process the brain segmentation task and the brain classification task, and has a wide application range.

The application discloses a brain function state analysis method and device, which comprises the following steps: collecting a brain image sequence combination of a target object, preprocessing, then performing feature extraction on the brain image to obtain image features, and selecting key image features; performing quantitative analysis on the key image features to generate a feature parameter set; inputting the feature parameter set into a brain function state prediction model to determine the brain function state of the target object; the brain function state prediction model is obtained by integrating and then training a semantic retrieval model and a fine-tuning generation model, the semantic retrieval model is trained based on a brain knowledge document library, and the fine-tuning generation model is trained based on reinforced learning parameters, brain information segments retrieved by the semantic retrieval model, and a labeled data set of a mapping relationship; statistical characteristic values corresponding to each brain function state are determined, and brain function physiological data of the target object is analyzed. The application can quantitatively and standardize overall analysis of the brain function state.

1. The name of the design product: electrode cap. 2. The use of the design product: used for auxiliary collection of brain electrical signals, realizing the rapid positioning of brain electrode points. 3. The design points of the design product: the shape of the product; specifically: the electrode cap composed of detachable connection of electrode seat and electrode. 4. The picture or photo that best indicates the design points: front view.