37 results about "Mri brain" patented technology

The invention discloses a multi-axis RWKV-UNet + + multi-mode MRI (Magnetic Resonance Imaging) brain tumor segmentation method, and belongs to the technical field of medical image processing. According to the invention, multi-modal MRI three-dimensional body data is input and preprocessed, and fusion features are output through a modal fusion module; the fusion features are input into an encoder containing multi-axis RWKV sequence modeling, and long-range dependence is extracted; after the output of the encoder is processed by the bottleneck layer, the global Token aggregator converges the global context and reinjects the global context; the enhanced features are input into a UNet + + nested topology decoder, the jump features are fused with the up-sampling features after being subjected to jump RWKV semantic alignment, and finally a three-dimensional segmentation probability graph is generated through mapping. The method is mainly used for accurate three-dimensional segmentation of the multi-mode MRI brain tumor, and provides support for clinical brain tumor diagnosis and treatment.

The invention discloses an improved multi-modal MRI (Magnetic Resonance Imaging) brain tumor segmentation method, which comprises the following steps of: firstly, acquiring a multi-modal MRI image containing a brain tumor, and preprocessing the multi-modal MRI image; secondly, based on the preprocessed multi-modal MRI image, enhanced modal correlation modeling and feature fusion are carried out, and smooth edge features are obtained; and finally, based on the smooth edge features, performing multi-task decoding and result output to obtain a complete brain tumor segmentation image. According to the method, the key problems of insufficient modal feature alignment, inter-task information segmentation, fuzzy segmentation boundary and the like in the existing multi-modal brain tumor segmentation are effectively solved.

The invention provides a fetal magnetic resonance image brain region segmentation method and system, computer equipment and a medium, and belongs to the technical field of automatic segmentation of fetal brain magnetic resonance imaging. The method comprises the steps that firstly, 2D low-resolution fetal magnetic resonance images collected in multiple directions are processed, and 3D high-resolution fetal brain images are generated; secondly, performing selective interlayer labeling on the 3D image, and generating a complete segmentation label through a contour interpolation algorithm; and finally, performing mutual supervision learning by using a double-independent initialized segmentation network, including the steps of data enhancement consistency constraint, cross pseudo label supervision, feature comparison learning and the like. According to the fetal magnetic resonance image brain region segmentation method and system, the computer equipment and the medium, a high-performance divider can be trained only through a very few labels, the labeling cost is remarkably reduced, the segmentation efficiency is improved, the method and system are suitable for actual clinical fetal brain magnetic resonance research, and efficient technical support is provided for fetal brain development evaluation.

The invention provides a scalp-to-brain magnetic resonance imaging generative model training method, a scalp-to-brain magnetic resonance imaging generative model generating method and scalp-to-brain magnetic resonance imaging generative model generating equipment, and relates to the technical field of image processing. The network comprises an encoder, a generator, a feature discriminator and an image discriminator, the encoder and / or the generator is integrated with a neural network model based on a frequency domain attention mechanism, and the encoder and the generator jointly form a target generation model after being trained; and the model parameters are jointly optimized by using a composite loss function comprising a frequency domain loss item. According to the method, the problems that an existing brain image generation technology neglects scalp structure information and is difficult to model global dependence and high-frequency details can be solved, brain magnetic resonance imaging data can be highly generated from scalp magnetic resonance imaging data, the structural integrity and topological fidelity of the generated data can be improved, and the application reliability of auxiliary diagnosis can be improved.

The invention discloses an automatic segmentation system and method based on a multi-mode MRI brain tumor image, and the system comprises an MCFE-Net network composed of an encoder and a decoder, and achieves the automatic segmentation of lesion regions WT, ET and TC of the MRI brain tumor image through the MCFE-Net network. A multi-convolution module is arranged in the middle layer of the encoder and the decoder, a cavity space receptive field module is arranged in the deepest layer of the encoder, and a modal feature attention module is arranged at the jump joint of the encoder and the decoder; wherein the multi-convolution module is used for improving the coding capability of the network while improving the width of the network; the cavity space receptive field module is used for fully utilizing the deep enriched semantic features and providing multi-scale context information so as to enhance the extraction of the network and utilize the deep semantic features; and the modal feature attention module is used for eliminating the influence of noise and irrelevant backgrounds and enhancing local expression. According to the method, the WT, ET and TC regions in the MRI brain tumor image can be efficiently segmented.

The invention discloses a multi-parameter MRI brain age prediction method and device based on uncertainty perception, equipment and a medium, and the method comprises the steps: determining a first feature sequence of an sMRI image, a second feature sequence of a DTI image, and a third feature sequence of an fMRI image through a feature extraction module; and determining a GM sampling vector, a DTI sampling vector and an fMRI sampling vector based on the first feature sequence, the second feature sequence and the third feature sequence by using a probability distribution encoder, and performing brain age prediction by using the GM sampling vector, the DTI sampling vector and the fMRI sampling vector. According to the method and the device, the fusion feature vector determined by fusing the sMRI, DTI and fMRI information is utilized to perform brain age prediction, so that multi-dimensional information of the brain structure and function can be more comprehensively acquired, and the accuracy of brain age prediction is improved. Meanwhile, a probability distribution encoder is introduced in the process of fusing the multi-parameter MRI images, multi-parameter MRI feature mapping is converted into Gaussian distribution through the probability distribution encoder to model feature uncertainty, richer potential information in the multi-parameter MRI images is captured, and the accuracy of brain age prediction is further improved.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The present application relates to a deformation registration system of MRI brain nerve images based on a double-flow cross network. The system comprises an acquisition module, a training module and an output module. The acquisition module is configured to acquire an MRI brain nerve image dataset. The training module is configured to input the dataset into a deformation image registration model based on a double-flow cross network, obtain a forward registration deformation field and a reverse registration deformation field, transform the obtained forward registration deformation field and reverse registration deformation field through a spatial transformation network, and generate a forward registration image and a reverse registration image. The output module is configured to output the forward registration image and the reverse registration image of two MRI brain nerve images to be deformation registered using the deformation image registration model learned in the training module. The system can automatically complete the deformation registration of any two 3D MR brain nerve images. When the images contain labels, brain atlas label migration can also be achieved, so that another image is automatically labeled.

The application relates to a brain tumor segmentation method and device based on TriSAM-UNet, which comprises the following steps: S1, collecting a multi-modal MRI brain tumor image dataset, and performing pretreatment and data enhancement; S2, constructing a hybrid segmentation network TriSAM-UNet, designing a hybrid module SAHM, capturing three-dimensional dependence and cross-region jump correlation, and realizing all-around global context perception; S3, constructing an FUE module, inhibiting fuzzy and noise response to ensure that high-confidence features are preferentially transmitted; S4, constructing a WFAB module, and restoring the boundary and texture details lost in up-sampling; and S5, training and optimizing the network model and performing full-automatic segmentation on the brain tumor image. The application aims to solve the problems of insufficient global context capture, indiscriminate forwarding of noise features in jump connection and loss of boundary details in up-sampling in brain tumor segmentation, and is suitable for the field of brain tumor image analysis.

The application discloses a 2.5D MRI brain tumor segmentation method based on Mamba state space modeling and belongs to the technical field of medical image segmentation. The application aims at the problems of the existing brain tumor segmentation method, such as long-range dependence modeling deficiency, high calculation cost, limited boundary segmentation precision and poor adaptability of the Mamba architecture to the 2.5D segmentation scene, and a cross-slice priority traversal module suitable for the 2.5D slice space structure is designed, local details and global semantic features are balanced through a feature calibration and controlled fusion module, and a global perception and edge enhancement dual-decoder structure is constructed to decouple tumor global modeling and boundary modeling tasks. The application reduces the calculation complexity while ensuring the segmentation precision, solves the problems of tumor segmentation discontinuity and boundary blur and is suitable for clinical conventional image workstations and has a good application prospect.

The invention relates to a multi-modal MRI brain tumor segmentation method based on convolution attention, and the method comprises the steps: carrying out the parallel coding of multi-modal medical image data, and extracting the specific low-layer space features of each modal; performing feature fusion and high-level coding on the low-level spatial features to obtain shared features; carrying out convolution attention processing on the shared features to obtain enhanced features; and carrying out decoding processing on the enhanced features to obtain a segmentation result. According to the method, more accurate tumor boundary segmentation and tissue differentiation can be realized in medical image analysis such as brain MRI (Magnetic Resonance Imaging), meanwhile, the parameter efficiency, the calculation speed and the generalization ability under limited data of the model are remarkably improved, and the clinical practicability is enhanced.

The invention discloses a model, a method, an electronic device and a medium for MRI brain glioma segmentation, and the model comprises a learnable bias field correction module which is used for adaptively estimating and correcting a low-frequency bias field in an original brain glioma MRI image; the multi-resolution feature extraction module is used for carrying out multi-resolution feature extraction and outputting a multi-resolution feature graph group containing different resolution features and shallow layer features; the multi-resolution feature fusion module is used for carrying out feature fusion on the multi-resolution feature map group and recovering relatively high resolution to obtain deep features; and the statistical space gating fusion module is used for receiving the deep layer features and the shallow layer features, performing feature fusion through channel statistical screening and a space attention gating mechanism, and outputting an MRI brain glioma segmented image. The technical problems that an existing brain glioma MRI image segmentation method is generally poor in adaptability, insufficient in boundary continuity, low in fusion efficiency and poor in global and local feature balance are solved.

The invention relates to a multi-modal brain tumor segmentation method and device based on hierarchical attention enhancement, and the method comprises the following steps: S1, collecting a multi-modal MRI brain tumor image, constructing a data set, and carrying out the preprocessing; s2, constructing a multi-view region sensing feature aggregation module, performing feature extraction and attention enhancement on the brain tumor MRI, and fusing local features and global features; s3, constructing an adaptive correlation collaborative enhancement fusion module, focusing the key region, and carrying out adaptive fusion; s4, network parameters are updated through a category importance balance region perception composite loss function, and a network model is trained and adjusted to optimize segmentation performance; and S5, performing MRI brain tumor segmentation according to the trained segmentation model. According to the method, the multi-modal information can be effectively processed from the MRI image, the relation between different regions of the brain tumor is extracted and fused, and the brain tumor is accurately positioned and accurately segmented by combining the local features and the global features.

This invention provides a method, system, computer equipment, and medium for fetal magnetic resonance imaging (MRI) brain region segmentation, belonging to the field of automated segmentation technology for fetal brain MRI. The method includes: first, processing multi-planar acquired 2D low-resolution fetal MRI images to generate 3D high-resolution fetal brain images; second, performing selective inter-layer annotation on the 3D images and generating complete segmentation labels using a contour interpolation algorithm; and finally, using a dual-independent initialization segmentation network for mutual supervision learning, including steps such as data augmentation consistency constraints, cross-pseudo-label supervision, and feature comparison learning. This invention, employing the aforementioned fetal MRI brain region segmentation method, system, computer equipment, and medium, requires only a very small number of labels to train a high-performance segmenter, significantly reducing annotation costs and improving segmentation efficiency. It is suitable for practical clinical fetal brain MRI research, providing efficient technical support for fetal brain development assessment.

The invention provides a method for constructing a multi-sequence MRI (Magnetic Resonance Imaging) tumor diagnosis model, which comprises the following steps of: performing iterative training on a constructed MRI brain tumor diagnosis model: performing first-stage training: performing iterative training on the constructed MRI brain tumor diagnosis model by adopting a Focal loss function based on a first original sample set; after the first-stage training is completed, a difficult case list and an optimal MRI brain tumor diagnosis model are obtained; when the number of iterations of the first-stage training and / or the AUC index reach a corresponding preset threshold value, entering second-stage training; second-stage training: adding the obtained difficult case list to a second original sample set as a second-stage training sample set; based on the second-stage training sample set, performing iterative training on the optimal MRI brain tumor diagnosis model by adopting a joint loss function formed by Focal loss and Dice Loss until the model is converged; structural defects of single-round training and fixed matching are systematically solved, and more balanced optimization of the model among minority class recall, overall distinction degree and deployment threshold performance is achieved.

The application discloses a brain glioma segmentation method based on CrossMAE, aiming at extracting visual language representation by combining MRI and diagnostic report to enhance segmentation performance. The method learns cross-modal deep correlation by randomly masking images and text information and reconstructing the masked content, thereby realizing accurate segmentation of MRI brain glioma. The method comprises the following steps: S1: collecting multi-modal MR brain tumor images and diagnostic reports, and constructing a data set; S2: designing a graphic-text mask encoder to selectively mask the data; S3: constructing a CrossMAE-based MRI brain glioma segmentation model; S4: designing a multi-task cascaded loss function, training and optimizing the segmentation model using the data set, and obtaining the trained MRI brain glioma segmentation model; S5: performing MRI brain glioma segmentation according to the trained MRI brain glioma segmentation model. The application effectively extracts and utilizes cross-modal complementary information from MRI, thereby realizing accurate segmentation of MRI brain glioma.

This invention discloses a method and system for neural fiber tract clustering and segmentation based on diffuse hypervoxels, relating to the fields of medical image analysis and artificial intelligence. The method includes: receiving and preprocessing three-dimensional brain magnetic resonance imaging (MRI) data; establishing a population-matched personalized MRI brain template using an iterative registration method; constructing a diffusion-weighted undirected graph and segmenting diffuse hypervoxels based on diffusion geodesic distance; clustering fiber tracts based on the maximum clusters of the neural fiber connectivity graph of the diffuse hypervoxels; registering the diffuse hypervoxels to an individual space and using the maximum clusters to achieve personalized fiber tract segmentation; constructing a center curve for the fiber tract clusters and mapping diffusion features to this curve; establishing a statistical analysis method for the center curve based on permutation tests and building a machine learning classification model.

The application discloses a kind of multi-modal MRI brain tumor segmentation methods of cross-modal super-resolution guide, it is related to image segmentation technical field.The application is first by a multi-modal shared encoder to the four modal MRI of input is characterized learning, for T1 and T2 two low-resolution mode, independently designed super-resolution reconstruction branch is inputed with shared encoder output, using light-weight convolution-attention module is carried out global 2 times reconstruction, and 4 times fine enhancement is applied in candidate lesion area, super-reconstruction result is shared gradient with segmentation branch in optimization process, ensure that the high-frequency information generated meets segmentation semantics, segmentation branch uses decoder structure to predict tumor area, network is according to BraTS annotation standard and output three sub-regions: whole tumor (WT), tumor core (TC) and enhanced tumor (ET), introduce uncertainty estimator on the basis of segmentation output.This module is based on entropy estimation through shared encoder, ensure that super-resolution and segmentation task are consistent on bottom feature.

MRI BRAIN TUMOR IMAGE DETECTOR

The invention disclosed herein presents a method and system for individualized target localization for transcranial magnetic stimulation (TMS) in treating depression based on group-level differential statistical maps. The system acquires resting-state functional MRI (R-fMRI) brain imaging data from subjects in both a major depressive disorder (MDD) group and a matching normal control group, followed by data preprocessing. Taking the spherical subgenual anterior cingulate cortex (sgACC) as the seed point, functional connectivity calculations are performed for each subject, and sgACC functional connectivity maps within the mask of the dorsolateral prefrontal cortex (DLPFC) region are extracted. A two-sample t-test is conducted on the sgACC functional connectivity maps of the MDD and normal control groups to identify clusters within the DLPFC mask that show significant differences between the two groups, which are used as group-level localization targets. By integrating the obtained group-level localization targets with preprocessed individual MRI brain imaging data, individualized TMS targets are derived using a dual regression algorithm. This invention thoroughly considers the overall abnormal brain activities and individual functional variations of patients with depression, achieving precise individualized localization for TMS treatment in MDD patients.

A brain magnetic resonance imaging (MRI) analysis device and method using a neural network are disclosed. The brain MRI analysis device using a neural network according to one embodiment comprises: a memory for storing a neural network model; and a processor, which is connected to the memory so as to control an analysis device, wherein the processor receives one or more brain MRI images so as to generate input data, and inputs the input data into the neural network model so as to acquire output data, and the neural network model is trained to determine neurological prognosis of a cardiac arrest patient if the input data is input into the neural network model.

The utility model discloses a microwave antenna and a cerebral hemorrhage detection system, and relates to the technical field of biomedical engineering. Wherein the microwave antenna comprises a first curved surface dielectric layer (1a) and a second curved surface dielectric layer (1b); the first curved surface dielectric layer (1a) and the second curved surface dielectric layer (1b) are respectively provided with a first excitation port (36) and a second excitation port (55); wherein the first curved surface dielectric layer (1a) and the second curved surface dielectric layer (1b) are respectively provided with a plurality of groups of first radiation patches connected with the first excitation port (36) and a plurality of groups of second radiation patches connected with the second excitation port (55). The objective of the utility model is to solve at least one of the technical problems of ionizing radiation, high cost, high probability of missing gold first-aid time due to overlong time, unsuitability for bedside detection and pre-hospital first aid and the like in current CT and MRI brain image examination.

The present invention discloses a method for establishing a clinically interpretable PD diagnostic model based on multi-sequence magnetic resonance brain features, which aims to promote a more accurate diagnosis of PD by extracting multi-level and multi-dimensional brain imaging information from multimodal brain magnetic resonance imaging data and constructing a diagnostic model with the help of machine learning. The method includes: brain imaging data preprocessing, feature standardization and screening, model construction, independent validation set and cross-disease set validation, and obtains a set of brain function and metabolic characteristics mainly based on PD abnormal perfusion patterns and substantia nigra iron distribution. The constructed model has high diagnostic efficacy in both the training set and the independent validation set. Secondly, the clinical interpretability of the model is promoted by calibration curve, DCA, CIC analysis, and correlation analysis between the predicted individual scores and clinical symptoms obtained based on the model. The diagnostic efficacy of the PD diagnostic model constructed by the present invention in different disease sets shows that the model has good diagnostic efficacy for PSP and MSA diseases.

This application discloses an automatic hippocampal segmentation method based on HAU-Net and gated spatial attention. The method includes: acquiring a three-dimensional MRI brain image of the target object to be segmented; sampling at least one target slice from the three-dimensional MRI brain image, and constructing a multi-channel two-dimensional input feature map for each target slice, wherein the multi-channel two-dimensional input feature map is formed by stacking multiple consecutive slices containing the target slice along the channel dimension; inputting the multi-channel two-dimensional input feature map into a pre-trained hippocampal segmentation network to output a hippocampal segmentation probability map; and performing thresholding processing on the hippocampal segmentation probability map to obtain the final binarized hippocampal segmentation result. This achieves an optimized balance between segmentation accuracy and computational efficiency, significantly improving the accuracy and continuity of hippocampal segmentation while maintaining the computational efficiency of the two-dimensional network.

This application discloses an MRI brain tumor segmentation method, which relates to the field of computer vision technology. The method includes: acquiring brain MRI image data; preprocessing the brain MRI image data to obtain preprocessed brain MRI image data; inputting the preprocessed brain MRI image data into a brain tumor region segmentation model to obtain a prediction result of the brain tumor segmentation region; wherein the SwinGhostU-Net model includes an input layer, an encoder, a FUSA feature fusion module, a decoder, and an output layer connected in sequence, and the decoder is also jump-connected to the encoder; the encoder includes multiple GDG downsampling convolutional encoding modules connected in sequence, and the FUSA feature fusion module includes an enhanced spatial attention mechanism module and an adaptive channel attention mechanism module. This application improves the accuracy of brain tumor region segmentation.

The invention discloses an incomplete multi-mode brain tumor segmentation method based on a double-level uncertainty guidance network, and the method achieves the precise positioning of a brain tumor. The method comprises the following steps: S1, carrying out preprocessing and data enhancement on a brain tumor image, and constructing a data set by constructing a random modal missing condition and setting different missing proportions; s2, constructing an uncertainty guidance cross-domain enhancement module, explicitly quantifying uncertainty in a modal inner layer, and cooperating with a spatial domain and a frequency domain to suppress local noise to obtain enhanced single-modal features; s3, constructing an uncertainty guidance confidence coefficient calibration fusion module, and dynamically adjusting a fusion weight on an inter-modal level by using uncertainty difference to obtain a fusion feature; s4, performing MRI brain tumor segmentation according to the trained MRI brain tumor segmentation model; according to the method, the robustness and segmentation precision of the model in a clinical complex and missing scene are remarkably improved by modeling from the intra-modal level and the inter-modal level and suppressing uncertainty.

The invention provides a children MRI brain tumor grading method. The children MRI brain tumor grading method comprises the following steps: preprocessing brain MRI image sequences of T1, T1c, T2, Flair and ADC modals to obtain tensors corresponding to the brain MRI image sequences; inputting the tensor of the T1 mode into an SE feature extraction channel for feature extraction, wherein the SE feature extraction channel performs feature extraction based on an SE attention mechanism; tensors of the T1c, the T2, the Flair and the ADC mode are input into an MSFE multi-scale feature extraction module for feature extraction; features extracted by the SE feature extraction channel and the MSFE multi-scale feature extraction module are subjected to feature fusion and then are input into a classification module for classification prediction to obtain a classification prediction result; according to the method, undetectable subtle changes in early tumors can be recognized, and the calculation load is effectively reduced while high precision is maintained.

The application relates to the field of digital medical technology, in particular to an image segmentation method and device based on artificial intelligence, equipment and medium. The method uses a first MRI brain image set to train a preset first model, uses model parameters of the first model to update a second model, uses the updated second model to cut a target MRI brain image to be segmented to obtain M sub-images, classifies and predicts each sub-image to obtain a target classification category, clusters sub-images with adjacent pixel positions and the same target classification category in the target MRI brain image, obtains clustering results of all the sub-images, segments the target MRI brain image based on the clustering results, realizes training of a model using less labeled images, updates the model for image segmentation in a parameter optimization mode, and further realizes training of the model and segmentation of the image, reduces the influence of problems such as high cost and low efficiency caused by manual labeling, and helps to improve the application effect of digital medical treatment in auxiliary diagnosis and treatment.

The present invention discloses a brain tumor segmentation method based on spatial attention and edge recognition enhancement, comprising: inputting an MRI brain tumor image into a trained brain tumor segmentation model and outputting a corresponding predicted brain tumor segmentation mask; the brain tumor segmentation model is constructed based on a U-net network, comprising an encoder composed of several down-convolution modules, a decoder composed of several up-convolution modules corresponding one-to-one to the down-convolution modules of the encoder, and an edge attention module arranged at the bottom layer of the U-net network; the output of each down-convolution module in the encoder is jump-connected to the input of the corresponding up-convolution module in the decoder, and a spatial attention module is arranged in each jump connection. The present invention improves the network's ability to capture long-range dependencies and contextual information of the entire image through the spatial attention module, and provides complete edge feature information for the feature map through the edge attention module, thereby improving the accuracy of brain tumor segmentation.

The invention provides an MRI (Magnetic Resonance Imaging) brain tumor small focus high-precision detection system based on HMA-DETR (Hidden Markov Amplification-DETR), which is characterized in that an HMA-DETR model is an improved RT-DETR model and specifically comprises a backbone network, a hybrid encoder and a converter encoder; wherein a plurality of hybrid gating convolution modules are arranged on the backbone network; a multi-scale expansion interactive attention module is arranged on the hybrid encoder to replace the AIFI module; the hybrid encoder is also provided with a plurality of spatial attenuation enhancement modules to replace a RepC3 module in the cross-scale feature fusion CCFM. And processing the output characteristics of the hybrid encoder through a converter encoder to obtain a brain tumor small focus detection result. According to the MRI brain tumor small focus high-precision detection system based on the HMA-DETR, the small target detection precision and the cross-modal robustness are remarkably improved while the reasoning speed is kept.