1213 results about "Mri image" patented technology

The invention discloses a brain tumor multi-mode large model construction method, device and equipment and a storage medium, and is applied to the technical field of brain tumor imagines.The method comprises the steps that pixel-concept level alignment is conducted on a multi-mode MRI image and a pathological text; constructing a multi-modal feature fusion network for fusing image features and text features by adopting an attention mechanism of pathology perception and combining medical semantic information; training the multi-modal feature fusion network to generate an analysis report and a segmentation result; according to the technical scheme of multi-task cooperation, cross-modal pathological semantic accurate alignment, pathological knowledge graph injection and lightweight and continuous optimization parallelization, full-process coverage of brain tumor accurate segmentation, analysis report generation and prognosis prediction is achieved, the problems that a traditional model lacks pathological semantic support and is insufficient in clinical adaptability are solved, and the clinical adaptability of the traditional model is improved. And the deployment feasibility and the dynamic optimization capability are also considered.

The invention provides a multi-modal large language model for generating a hepatocellular carcinoma key pathological diagnosis report, a framework main body is a visual coding module, and a multi-modal feature alignment module, a multi-head low-rank attention mechanism, an enhanced medical MoE mechanism and a structured output decoding layer are also introduced. The visual coding module is constructed on the basis of a Swin Transform architecture, visual pre-training is completed on hepatocellular carcinoma MRI data, and after a task specific classification head is stripped, a trunk feature extraction network is reserved to serve as an image modal representation encoder. The multi-modal feature alignment module guides the model to learn a cross-modal semantic mapping relation between a hepatocellular carcinoma MRI image and a key pathological diagnosis report language, image modal input is a visual feature sequence, and text modal output is a structured description text; and the structured output decoding layer generates six types of liver cancer focus attributes. According to the method, the pre-operative multi-parameter and multi-stage enhanced MRI image is utilized, and the open-source large model is finely adjusted to generate a matched liver cancer postoperative pathology report.

A magnetic resonance image reconstruction device according to an embodiment is a magnetic resonance image reconstruction device that reconstructs magnetic resonance image data in which an artifact due to undersampling is removed or reduced based on undersampled k-space data, and includes a reconstruction unit reconstructing the magnetic resonance image data using a reconstruction network having a correction module. The correction module includes a regularization block generating second image data by performing a regularization process on first image data using a first neural network, and a data consistency block generating third image data by performing a data consistency process so that k-space data corresponding to the second image data approaches the undersampled k-space data. The correction module further includes at least one of a data consistency adjustment block adjusting the data consistency process and a regularization adjustment block adjusting the regularization process.

The invention relates to an uncertainty perception passive multi-target field adaptive image classification method, which is used for image recognition of autism spectrum disorder patients. According to the method, firstly, source domain model parameters are obtained and used for initializing a target model, then resting state functional magnetic resonance images of a plurality of imaging centers are preprocessed, and a plurality of target domains are constructed. On this basis, a current most representative target domain is selected through a minimum inter-domain difference strategy, an uncertainty modeling method based on evidence deep learning is adopted to train a target model, and class feature consistency is improved through domain contrast learning based on a class prototype in combination with a dynamically expanded auxiliary data set; and generating a pseudo tag to relieve the influence caused by tag noise. And finally, a trained target model is obtained through fine tuning optimization, and accurate classification of unknown images is realized. The method does not need to access source domain data, has the advantages of high robustness, high generalization ability and the like, and is suitable for actual cross-center medical image analysis scenes.

The invention relates to a multi-contrast magnetic resonance image super-resolution reconstruction method and system. The method comprises the following steps: collecting an image data set, preprocessing the image data set, and dividing the image data set into a training set and a test data set; combining a Hilbert curve, a state space model and a frequency domain enhancement mechanism to construct a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba; and training a multi-contrast magnetic resonance super-resolution reconstruction model based on Hilbert double-domain fusion Mamba by using the training data set, and then completing the test of the test data set to obtain a super-resolution reconstruction image of the target contrast magnetic resonance image. Frequency domain features are scanned through a Hilbert curve, a cross-modal global frequency dependency relationship is captured, and high-frequency texture details are effectively recovered. And multi-modal local texture information is dynamically fused through a channel attention mechanism, so that neglect of local details by linear scanning is avoided, and the texture reconstruction precision is improved.

The invention relates to the technical field of image processing, in particular to a clinical focus auxiliary segmentation system based on a nuclear magnetic resonance image. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, a corresponding clinical nuclear magnetic resonance image set of a patient can be obtained, image position alignment and gray level adjustment processing can be carried out, and meanwhile a corresponding clinical image lesion segmentation model is constructed to carry out multi-scale fusion auxiliary segmentation. Generating a clinical focus region segmentation fusion image; obtaining a focus confidence probability corresponding to each pixel point in the segmentation image through the clinical focus region segmentation fusion image, and carrying out probability segmentation boundary correction on the clinical focus region segmentation fusion image to obtain a clinical focus region segmentation correction result image; and performing focus edge shape smoothing processing on the clinical focus region segmentation correction result map to generate a clinical focus edge shape segmentation optimization result. According to the invention, high-precision segmentation of the focus in the MRI image can be realized.

The invention relates to the field of biomedicine, and particularly discloses an epileptic focus artificial intelligence positioning system and method based on magnetic resonance and electroencephalography, and the system comprises the following contents: a data collection module is used for synchronously collecting T1 weighted magnetic resonance images and electroencephalogram signals of scalp; the data preprocessing module is used for performing brain tissue segmentation on the magnetic resonance image, obtaining structural features of each brain region, constructing vectors including whole brain structural features, and obtaining magnetic resonance structural feature vectors; artifacts of the electroencephalogram signals are removed, electroencephalogram features of all brain areas are extracted through time-frequency analysis, a matrix containing whole electroencephalogram physiological features is constructed, and the electrophysiological features are obtained; the cross-modal confidence coefficient dynamic evaluation module is used for establishing a bidirectional constraint rule to perform confidence coefficient calibration on the magnetic resonance structure feature vector and the electrophysiological feature; a positioning model construction and training module; a positioning result output module; according to the technical scheme, noise can be reduced during magnetic resonance and electroencephalogram fusion, and the epileptic focus positioning precision is high.

The invention discloses a swallowing function evaluation method and system based on tongue motion nuclear magnetic image registration, and belongs to the technical field of medical image processing. The system comprises a time sequence nuclear magnetic image reading module, a tongue body dynamic mask generation module, a tongue body dynamic image extraction module, a dynamic tongue body sequence registration module and a patient swallowing recovery evaluation module. Through an automatic segmentation and image registration technology, the system can accurately extract tongue motion displacement and calculate a local displacement vector, and evaluate the swallowing function recovery condition of a tongue cancer postoperative patient. The method comprises the steps of image reading, image screening, data set making, model training and tongue swallowing recovery evaluation, and the system can identify a tongue motion abnormal mode and provide an objective and quantitative evaluation result. According to the method, the accuracy and efficiency of tongue motion feature analysis are remarkably improved, and the method has a wide clinical application prospect.

The invention discloses an orbit MRI image analysis method and system based on deep learning, and belongs to the field of image analys.The method comprises the steps that MRI image data of an orbit area of a patient are obtained, and the MRI image data comprise a conventional T1WI sequence, a T2WI plain scanning sequence, a conventional enhancement sequence and an extraocular muscle fibrillation enhancement sequence; performing preprocessing on the MRI image to obtain a standardized image; inputting the standardized image into a deep learning segmentation model, and outputting segmentation masks of extraocular muscle, lacrimal gland and intraorbital fat; calculating quantitative indexes of a target structure based on the segmentation mask, wherein the target structure comprises extraocular muscle, lacrimal gland and intraorbital fat; and generating a diagnosis report containing the quantitative index. Human experience dependence is avoided, and objective and uniform anatomical structure segmentation results are ensured.

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 multi-organ medical image segmentation method based on a multi-feature fusion Swindow-Unet architecture, and belongs to the field of medical image processing. The core of the method is that CT and MRI images are input into a pre-trained CMFSA-UNet model for segmentation, and the model comprises an encoder, an MAFR module, an MFDF module, a decoder and a jump connection layer. CNN-Swin Transform double branches are adopted by the encoder, local details and long-range semantics are extracted, and Attention Gate reinforcement is carried out; the MAFR module widens a receptive field through double branches, combines an attention mechanism with residual connection, reduces the calculated amount and gives consideration to local and global features; and the MFDF module fuses multi-scale dense connection and frequency domain processing, so that feature loss is reduced. The decoder extracts features through Swin Transform Block, resolutions are recovered through 4 times of up-sampling, and the segmentation precision is optimized in combination with depth supervision and a mixed loss function. According to the method, local and long-range feature modeling is efficiently cooperated, precision and efficiency are balanced, segmentation global consistency, boundary accuracy and training stability are improved, the method is suitable for multi-modal multi-organ segmentation, and reliable support is provided for clinical diagnosis and the like.

The invention provides a cervical cancer MRI image automatic segmentation method based on multi-modal fusion, and the method comprises the following steps: S1, obtaining tumor segmentation mask images of three modals T1c, T2 and DWI of a cervical cancer MRI image through an automatic prompt segmentation method, and carrying out the cross-modal attention interaction of a task token and an output token, dynamic alignment of medical image features and segmentation tasks is achieved, and segmentation mask images of all modes are generated in combination with sparse and dense prompts; and S2, performing registration and feature extraction on the segmentation mask images of the three modes to obtain a structured medical description of the segmentation mask image of each mode, and performing fusion processing on the structured medical descriptions by using a large language model to obtain a tumor segmentation mask image after multi-mode fusion. According to the method, the segmentation mask image of the MRI multi-mode image is fused by introducing the large language model, and the fused tumor segmentation mask image is finally obtained by combining the segmentation characteristics of different modes, so that the segmentation precision of the MRI image is greatly improved.

The invention discloses a head and neck MRI image classification method, system and terminal, and the method comprises the steps: constructing an image classification model which comprises an improved U-Net segmentation module and an edge perception learning module; inputting the head and neck MRI images into an improved U-Net segmentation module to obtain multi-scale features, performing aggregation to obtain associated features, performing up-sampling on the associated features to obtain up-sampling features, performing adaptive fusion perception on the up-sampling features and the multi-scale features to obtain fusion features, and generating fine masks according to the fusion features; inputting the associated features into an edge perception learning module for semantic decoupling and up-sampling to obtain three regional masks; calculating edge loss according to the three regional masks; obtaining a target model according to the loss optimization model; and obtaining a target head and neck MRI image, inputting the head and neck MRI image into the target model, and outputting a classification result of the head and neck MRI image. According to the method, the accuracy of a head and neck MRI image classification result is effectively improved.

The invention discloses a brain tumor MRI image semantic segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. According to the invention, the problem of low segmentation precision obtained based on the existing brain tumor MRI image semantic segmentation technology is solved. The invention provides a semantic segmentation model which combines a U-Net framework with an LWD module, an MFDF module and various filters, the LWD module can keep information as much as possible in a down-sampling process, the MFDF module extracts operators by constructing new directional gradient features, and combines the operators in different directions by using dual-channel filtering, so that the semantic segmentation of the U-Net framework is realized. And the constructed multi-directional filter can respectively extract low-frequency and high-frequency characteristic direction information. The MFDF module transmits detail features from the coding module to the corresponding decoding module, so that spatial information including feature boundaries and textures is recovered, the precision of model segmentation is improved, and the method has good adaptability to the randomness of brain tumor shapes, sizes and boundaries. The method can be applied to brain tumor MRI image segmentation.

The invention relates to the technical field of medical image auxiliary diagnosis, in particular to a nuclear magnetic resonance image auxiliary diagnosis method and system based on multi-modal fusion. The method comprises the following steps: step 1, synchronously acquiring a three-dimensional T1 weighted structure image, a T2 weighted fluid attenuation inversion recovery image and diffusion weighted imaging data of a subject, carrying out spatial registration by taking the T1 weighted image as a reference, and executing skull stripping and gray scale standardization; 2, individualized brain region segmentation is carried out based on a brain anatomical map, the lesion sensitivity weight of each modal is calculated for each segmented brain region, and the weight is obtained by quantifying the following parameters; step 3, extracting multi-modal image features in each brain region; and 4, inputting the fusion features of the whole brain region into a multi-task classifier. The standardization and alignment of the multi-mode MRI image in the space and gray level are realized, and the problems of space mismatch and feature interference among different modes are effectively solved.

The invention relates to the technical field of medical equipment, and provides a multi-modal lymphedema assessment and surgical navigation system based on image processing, which comprises a 3D scanning modeling module used for acquiring three-dimensional point cloud data of the body surface of a patient through structured light 3D scanning equipment, an iconography examination analysis module used for performing U-Net image segmentation on CT / MRI image data, and an image processing module used for processing the CT / MRI image data. The ultrasonic result calculation module is used for carrying out Canny edge detection on an ultrasonic image to determine the boundary of a surgical site, and the data integration and navigation generation module is used for realizing multi-modal data fusion through mutual information maximization, generating a surgical path based on FMM and carrying out real-time navigation in combination with an AR technology. According to the method, objective evaluation of lymphedema and accurate navigation of the operation are realized through accurate integration of multi-modal data and an image processing technology, the diagnosis and treatment accuracy is effectively improved, the operation risk is reduced, complications are reduced, and a scientific basis is provided for personalized treatment.

The invention belongs to the technical field of medical image processing, and particularly relates to a glioma segmentation method based on a multimodal fusion network guided by anatomical symmetry, which comprises the following steps of: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1c image of the same glioma into a trained image segmentation model, and outputting a predicted segmentation image by the trained image segmentation model, the prediction segmentation image is a glioma MRI image with three segmentation areas obtained through prediction, and the three segmentation areas are an edema area, an enhanced tumor area and a necrosis area respectively; the image segmentation model comprises an encoder, a jump connection part and a decoder; the encoder comprises an ASG module, the jump connection part comprises an IMP module, and the decoder comprises a CMF module. Through a three-module cooperation mechanism, the performance of tumor localization, cross-modal fusion, subregion segmentation and the like is improved, and a reliable image basis is provided for glioma operation plan formulation, prognosis evaluation and personalized treatment decision.

The invention provides a multi-mode nuclear magnetic resonance image glioma segmentation method and application thereof, and belongs to the field of medical image processing. The invention provides an M2ES-UNet network aiming at the problems of insufficient utilization of spatial features, single multi-modal fusion mechanism and cross-level semantic loss of an existing segmentation method. According to the method, multi-view anatomical information is extracted through a multi-plane feature collaboration module; utilizing an orthogonal dimension fusion convolution and modal introspection-collaboration module to respectively realize differential fusion of shallow and deep features; and the progressive jump transmission of the features is realized through a coding information smooth transmission module. According to the method, multi-plane and multi-mode complementary information can be effectively mined, the boundary precision and robustness of brain glioma segmentation are remarkably improved, and clinical diagnosis is assisted.

The invention relates to the technical field of image enhancement, and discloses an intestinal dynamic MRI image enhancement method and system based on deep learning, through the image enhancement method based on deep learning, the quality of a small intestine cine-MRI image is improved, the problems of low resolution and poor contrast of a traditional image are improved, and the identifiability of a small intestine intestinal wall structure is enhanced. On the basis, pixel-level registration processing of continuous frame images is combined, the intestinal segment movement track is accurately extracted, and movement parameters such as contraction frequency, lumen diameter variation amplitude, pixel displacement mean value and variance are calculated. Furthermore, a pre-training evaluation model is utilized to intelligently output a small intestine dynamic state judgment result based on motion parameters, and standardized auxiliary recognition of small intestine dynamic disorder diseases and severity of the small intestine dynamic disorder diseases is achieved. According to the method, the image processing quality, the motion feature extraction accuracy and the evaluation intelligence level of small intestine dynamic evaluation are integrally improved, and the method has important clinical application value and popularization significance.

The invention discloses a time-of-flight magnetic resonance blood vessel image cerebral vessel segmentation method, system and device and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining a to-be-processed time-of-flight magnetic resonance blood vessel image; reinforcing blood vessel features in the to-be-processed time-of-flight magnetic resonance blood vessel image to obtain a preprocessed image; according to the preprocessed image, performing cerebrovascular segmentation by adopting a few-sample segmentation model to obtain a blood vessel probability graph; the few-sample segmentation model is obtained by migrating knowledge of a pre-training video word segmentation device to train 3D U-Net; and performing post-processing on the blood vessel probability graph and the to-be-processed flight time magnetic resonance blood vessel image based on a human-computer interaction interface and a conditional random field to obtain a final segmented image. According to the method, a high-precision and high-robustness segmentation effect can be realized only by a small number of samples, and result optimization can be carried out through an efficient man-machine interaction mode.

The invention discloses a liver tumor image real-time segmentation method and system based on a YOLO algorithm, and the method comprises the steps: obtaining CT images and MRI images, and calculating the contrast indexes and signal-to-noise ratio indexes of a plurality of CT images; preprocessing the CT image, and dynamically adjusting an image enhancement strategy; a residual attention module is added on the basis of the YOLOv8 network, a multi-scale mask branch is introduced, and an optimized YOLO-Med segmentation network is constructed; inputting the enhanced CT image into a segmentation network, and training the segmentation network in combination with a loss function; and when an MRI image is input, through a multi-modal feature fusion mechanism, features of the MRI image and the enhanced CT image are aligned and fused and then are input into the segmentation network, and the segmentation network outputs a pixel-level segmentation mask for real-time segmentation of the liver and the tumor. According to the invention, by fusing the density characteristic of CT and the soft tissue resolution capability of MRI, the small tumor (diameter lt; 5 mm).

The invention relates to the technical field of liver surgery, and discloses a liver surgery planning system based on data fusion. The system comprises an image data fusion module, a three-dimensional model construction module, an operation path planning module, a risk assessment module and a scheme optimization module. The image data fusion module integrates CT and MRI images to generate a standardized image fusion model; a three-dimensional model building module generates a liver three-dimensional visual model according to the three-dimensional model; the operation path planning module plans an optimal resection path to form a preliminary operation path scheme; the risk assessment module assesses the surgical risk and generates a report; the scheme optimization module generates a final surgical planning scheme based on the report adjustment parameters. According to the system, through the multi-source data fusion and three-dimensional visualization technology, the liver structure and the tumor position are accurately presented, the operation path is scientifically planned, the operation risk is evaluated and avoided, the operation scheme is optimized, and accurate implementation of the liver operation is assisted.

The invention relates to the technical field of image recognition, in particular to a medical image intelligent evaluation system based on image recognition. The system comprises an image registration module, an image segmentation module, a preliminary fusion module, an image evaluation module, an optimization feedback module and an image output module. According to the method, the CT image and the MRI image are subjected to image registration, spatial alignment is ensured, then the region of interest is segmented and fused, namely, the skeleton contour in the CT image is superposed on the MRI image, and due to the fact that motion artifacts generated by movement of a patient in the scanning process possibly exist in the original CT image, the skeleton contour in the CT image is fused with the motion artifacts in the MRI image. If the skeleton contour does not exist in the MRI image, the overlapping degree and the blank degree of the skeleton contour and the anatomical structure edge of the MRI image are analyzed, and an optimized registration parameter or segmentation parameter is fed back, so that when the segmentation network is trained, the segmentation precision under the conditions of artifacts and low contrast is improved, spectrum and texture information of the two images is reserved to the maximum extent, and the fusion effect is guaranteed.

Systems and methods for fast reconstruction of motion-resolved magnetic resonance images using space-time-coil convolutional networks are disclosed. The system can receive a plurality of k-space data sets. The system can detect a motion signal therefrom. The system can classify the k-space data sets according to states of the motion signals. The system can resolve the k-space data set to Euclidean space images. The system can resolve the Euclidean space images to a combined Euclidian space image. For example, the system can use a convolutional network that exploits spatial, temporal and coil correlations without k-space data consistency to minimize computation time.

The invention relates to a colorectal cancer MRI image segmentation method and system based on multi-dimensional feature fusion, and relates to the technical field of medical image processing. The method comprises the following steps: firstly, collecting multi-source data of a colorectal cancer patient, preprocessing the data, and constructing a multi-scale feature extraction module to obtain feature data of an image; through a multi-dimensional feature fusion module, features of seven dimensions including texture, shape, gray scale, modal association, life habits, functional metabolism and tissue specificity are integrated, and feature screening and weight distribution are realized in combination with a space-channel attention mechanism; and finally, carrying out feature decoding through a U-Net network, and optimizing a segmentation boundary by adopting a full-connection conditional random field (CRF) of an adaptive potential function. According to the method, multi-modal data integration, multi-scale feature extraction, multi-dimensional feature fusion, attention mechanism enabling, U-Net network decoding and CRF post-processing are combined, so that the accuracy and robustness of colorectal cancer MRI image segmentation are improved.

The invention relates to a self-supervised cerebral apoplexy focus segmentation method based on sparse stripe sampling and a lightweight encoder. The invention relates to the technical field of cerebral apoplexy image segmentation, and the method comprises the steps: carrying out the preprocessing of input MRI image data, and carrying out the sparse fringe patch sampling of the preprocessed data; establishing a lightweight encoder, and carrying out self-supervision pre-training; based on the trained lightweight encoder, performance optimization and task adaptation are realized through a lightweight fine tuning mode, and optimization of the encoder is completed; and according to the optimized lightweight encoder, encoding and decoding the area after sparse fringe patch sampling, and outputting a segmentation result. According to the method, local convolution and global attention modeling are comprehensively considered, and the structural recognition capability of the focus with the complex form and the fuzzy boundary is effectively improved.

The invention discloses a scanning control method and system for a magnetic resonance spectrometer, and the method comprises the following steps: an upper computer analyzes and loads sequence file parameters meeting the Pulseq standard, and integrates the sequence file parameters and pre-configured system parameters into a scanning parameter set; the upper computer generates an executable spectrometer hardware control instruction based on the scanning parameter set, and writes the spectrometer hardware control instruction into a register of a PCIE board card through a PCIE driving module; the PCIE board card generates a hardware control signal according to the content written in the register, controls the spectrometer hardware module to execute corresponding operation, and generates magnetic resonance original data; and the upper computer reads the magnetic resonance original data and reconstructs the magnetic resonance original data into magnetic resonance image data based on analysis of the magnetic resonance original data. According to the invention, the compatibility and flexibility of the system are significantly improved, different upper computers can communicate and cooperate with the spectrometer hardware module, sequence sharing and use among different devices are facilitated, and the integration and maintenance costs of the system are reduced.

The invention relates to the technical field of computer image processing and medical image analysis, in particular to a cervical cancer MRI (Magnetic Resonance Imaging) image segmentation method for improving a U-Net structure based on a feed-forward channel double-attention mechanism. In order to solve the problems that a traditional U-Net structure is weak in multi-scale information extraction capability, inaccurate in boundary fuzzy region recognition and the like in processing female abdominal cervical cancer MRI images, the method proposes that a feedforward connection mechanism and a double-attention mechanism are embedded into an encoder and a bottleneck module to form a novel U-Net segmentation model; the recognition and segmentation precision of the cervical cancer focus area is improved, and a high-quality image basis is provided for subsequent clinical diagnosis and treatment. A double-attention mechanism is integrated into a plurality of key nodes of the model, a joint channel-space attention module is added to the tail of each convolution module in an encoder, a cavity space pyramid pooling structure is introduced to a bottleneck position, a channel attention mechanism is embedded, and joint modeling of cross-scale, multi-channel and space context information is achieved.

The invention relates to the technical field of brain tumor image segmentation, in particular to a brain tumor segmentation method and system based on anatomical perception symmetric comparison and cross-modal migration. The method comprises the following steps: carrying out data preprocessing on acquired multi-modal MRI image data; constructing a brain tumor segmentation model based on anatomical perception symmetric comparison and cross-modal migration; performing model training based on a two-stage decoupling training strategy; and performing model reasoning by using the trained model, and outputting a brain tumor segmentation result. Through a self-supervised learning framework, pre-training is carried out by using unmarked MRI data, dependence on a large-scale marked data set is greatly reduced, the problems of time consumption and high cost of medical image marking are solved, and the applicability of a model in a limited data scene is improved.

The invention belongs to the field of medical image processing, and relates to a three-dimensional reconstruction method and system based on multi-modal medical image fusion, and the method comprises the steps: obtaining an original multi-modal image sequence; the original multi-modal image sequence comprises a CT image and an MRI image; performing spatial alignment on the original multi-modal image sequence to obtain a spatial alignment image pair; the space alignment image pair comprises an aligned CT image and a corresponding aligned MRI image after alignment; performing multi-scale layered cognitive feature extraction on the spatial alignment image pair to obtain a corrected feature map; performing time-space frequency domain collaborative attention fusion on the corrected characteristic spectrum to obtain fusion characteristics; performing topological constraint three-dimensional diffusion reconstruction on the fusion features to obtain a medical image three-dimensional model; the three-dimensional reconstruction precision of multi-modal fusion is improved, and more reliable three-dimensional model support is provided for clinical diagnosis and treatment.