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

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

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

The invention relates to the technical field of medical data analysis, in particular to a brain tumor curative effect analysis system which comprises a tumor data sensing layer for collecting multi-department diagnosis and treatment data, tumor image data and patient pathology monitoring data; the tumor feature processing center extracts data features, correlates data and core features before and after treatment through an attention mechanism, and generates a tumor complete-cycle unified feature map; the therapeutic effect dynamic analysis unit evaluates the therapeutic effect in stages, and outputs a therapeutic effect index and a recurrence risk value through a self-supervised model; the dynamic adaptation decision module is used for generating personalized treatment adjustment suggestions based on the dynamic change of the blood brain barrier in combination with the curative effect index, the recurrence risk value and the multi-omics characteristics of the patient; and the AI multi-department consultation unit automatically matches similar cases with field expert suggestions, and formulates a target diagnosis and treatment scheme based on a visual platform and multi-department doctor collaborative consultation in combination with personalized treatment adjustment suggestions. Therefore, the problems of lagging effect evaluation, insufficient diagnosis and treatment suggestions and the like in the prior art are solved.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly improved.

The invention discloses a multi-scale brain tumor segmentation method and system based on adaptive KAN, and a storage medium. The method comprises the following steps: preprocessing three-dimensional brain magnetic resonance imaging data; the preprocessed data are input into an encoder, the encoder comprises a plurality of levels, and each level executes convolution operation to extract local features, executes spatial KAN processing to extract spatial features and downsamples a feature map; the output of the encoder is input into a bottleneck layer, and the bottleneck layer captures a multi-scale global context by using a plurality of parallel expansion convolution branches; the output of the bottleneck layer is input into a decoder, the decoder comprises a plurality of stages, and each stage executes transposing a convolution up-sampling feature map, executes cross-scale gating processing to fuse encoder jump connection features and decoder features, and executes spatial KAN processing to optimize features; and the output of the decoder is input into the output module. According to the method, the problems of low calculation efficiency, poor tumor heterogeneity adaptation, insufficient multi-scale context capture and the like in the existing brain tumor segmentation can be effectively solved.

The invention provides a missing-modal brain tumor image segmentation method, device, equipment, medium and product, and belongs to the technical field of image segmentation, a knowledge distillation model architecture is constructed, the knowledge distillation model architecture comprises a teacher network and a student network, the student network comprises at least one feature reconstruction module, and the teacher network comprises at least one feature reconstruction module; the feature reconstruction module is used for reconstructing brain tumor image features of a missing mode based on semantic relevance between brain tumor images of different modes; the student network is trained based on the trained teacher network, an image segmentation model is obtained according to the trained student network, and the image segmentation model is used for performing image segmentation on the to-be-segmented missing modal brain tumor image. The student network can reconstruct the brain tumor image features of the missing mode based on the semantic relevance between the brain tumor images of different modes, can better represent the brain tumor image features of the missing mode, and effectively improves the segmentation accuracy of the brain tumor images of the missing mode.

The invention discloses a brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, and relates to the field of medical image analysis. The fusion output of the multi-modal magnetic resonance image is input into an image segmentation model to obtain image embedding, the boundary embedding of the brain tumor and the real boundary of the tumor are obtained through a boundary mining network, the boundary features are enhanced through an adaptive boundary enhancement model, the image embedding and the boundary embedding are output as fusion embedding by applying dynamic fusion, and the real boundary of the tumor is obtained. Inputting the fused embedded and adaptive enhanced boundary features and the features of the prompt encoder into a mask decoder of a segmentation model to obtain a segmentation result, and obtaining a total loss function in combination with a boundary mining network and a loss function based on region prediction; according to the brain tumor region segmentation method based on adaptive boundary guided aggregation SAM, accurate segmentation of the brain tumor region is realized.

The invention relates to the technical field of deep learning, and discloses a brain tumor feature extraction method and system of a multi-scale dynamic calibration detection network based on comparative learning, and the method comprises the steps: inputting a brain tumor image into a self-supervised comparative learning network, extracting key features through self-supervised comparative learning, and outputting a multi-scale feature map; performing down-sampling and up-sampling combined operation on the multi-scale feature map, inputting the multi-scale feature map into a multi-scale adaptive network for dynamic feature calibration, and optimizing multi-scale boundary features through dynamic sampling and shape self-calibration; and the output of the multi-scale adaptive network for dynamic feature calibration is input into the multi-domain attention network for dynamic deformable convolution, and key feature focusing is enhanced through residual connection and dynamic deformable convolution. And the accuracy and reliability of brain tumor feature extraction are improved.

The invention discloses a multi-scale lightweight brain tumor segmentation method based on improved YOLOv8n-Seg, belongs to the technical field of medical image processor artificial intelligence, and designs a novel down-sampling module FLRDown fusing low-rank convolution and Fourier transform to replace standard convolution operation in a YOLOv8n-Seg backbone network. Further improvement is carried out on the basis of a SimAM attention mechanism, and a novel attention module-AdaSimAM is provided; on the basis of an original YOLOv8n-Seg segmentation header, an LCSDSH segmentation header network is designed, the structure of the header network is optimized, and redundant parameters are reduced. According to the method, a structure optimization and feature enhancement mechanism is introduced, the calculation complexity of the model is effectively reduced, meanwhile, the perception ability for a small target area is enhanced, the robustness and generalization ability of the model in multi-scale tumor recognition are improved, and therefore the method is more suitable for the actual requirement for automatic segmentation of the brain tumor in clinical practice.

The invention discloses a brain tumor detection method based on attention mechanism and MRI (Magnetic Resonance Imaging) multi-modal fusion, and relates to the technical field of brain tumor image analysis. Calculating a consistency measurement score of the two heat maps, and if the score does not meet a preset threshold value, starting feedback iteration: merging the images to generate an enhanced training set, performing weighted training on a loss item corresponding to the multi-modal image by taking the normalized measurement score as a quality weight, and iteratively optimizing the model until a condition is met; and finally, outputting a brain tumor detection result with high confidence. According to the invention, by introducing an internal closed-loop verification and adaptive optimization mechanism, the core problem that the confidence of the detection result cannot be self-verified and guaranteed in the prior art is effectively solved. And finally, the accuracy of brain tumor detection confidence closed-loop verification optimization based on attention mechanism and MRI multi-mode fusion is improved.

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

The 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 provides an MRI (Magnetic Resonance Imaging) missing mode generation method based on a learnable frequency domain module, which is used for reconstructing and synthesizing any missing mode of a brain glioma medical multi-mode image. The method comprises the steps that a training set and a test set of brain tumor multi-mode MRI images are acquired, the training set and the test set comprise a plurality of samples, each sample comprises four original mode images, and each original mode image is an available mode or a missing mode; a missing modal brain tumor generation network model is constructed, and the missing modal brain tumor generation network model comprises a learnable frequency domain module and a condition diffusion module based on modal attention; inputting the training set into the model for training, setting a joint loss function, and optimizing parameters of the model through the joint loss function of each sample to obtain an optimized model; and inputting the multi-mode MRI image in the test set into the optimized model to obtain a generated image corresponding to the missing mode.

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 discloses a multi-modal MRI (Magnetic Resonance Imaging) brain tumor image segmentation method. The method comprises the following steps: data preprocessing and feature extraction: collecting and processing image data from different MRI sequences; adaptive feature selection and alignment: dynamically selecting the most representative modal features for alignment according to the information of the brain tumor; multi-scale feature fusion enhancement: introducing an extra network layer to perform feature fusion, the network layer being responsible for learning a weight distribution strategy, dynamically adjusting a fusion weight and strategy, inputting a fused feature map into a segmentation network, performing preliminary segmentation, and on the basis of the preliminary segmentation, performing multi-scale feature fusion enhancement; introducing an attention mechanism to guide the segmentation network to pay attention to the tumor region; reinforcement learning optimization: adopting a reinforcement learning algorithm to optimize the parameters and the structure of the segmentation network; uncertain region optimization: further optimizing the uncertain region in the segmentation result by adopting a post-processing method; and obtaining a three-dimensional tumor segmentation result: outputting the three-dimensional tumor segmentation result.

The invention discloses a preparation method of a polymer vesicle capable of crossing a blood brain barrier, and belongs to the field of high polymer materials and medical engineering. The preparation method of the polymer vesicle capable of crossing the blood brain barrier comprises the following steps: synthesizing an amphiphilic block copolymer by utilizing ring-opening polymerization reaction of polycaprolactone and anhydride in an amino acid ring, then assembling the polymer into the polymer vesicle, and modifying a monoclonal antibody of a transferrin receptor on a polypeptide chain. The polymer vesicle prepared by the invention is simple and convenient in preparation process, short in period, good in stability, good in biocompatibility and good in biodegradability, can be used as an excellent carrier of chemotherapeutic drugs, and can effectively realize drug delivery of targeting brain tumor cells across a blood brain barrier. Compared with a commercial drug delivery carrier, the polymer vesicle has the advantage that the permeability of the drug in a bionic blood brain barrier and the targeted uptake rate of brain tumor cells are remarkably improved.

The invention relates to a brain tumor image segmentation method based on multi-modal fusion and cascade segmentation, and belongs to the field of digital image processing. The method comprises the following steps: S1, acquiring brain tumor images of different modalities, unifying the sizes of the images, preprocessing the images, and dividing the images into a training set and a test set; s2, constructing a brain tumor image segmentation model, and inputting the training set into the model for training; s3, performing multi-modal feature extraction, multi-modal fusion and cascade segmentation on the input image data by the model in each round of training, and by taking the weighted Dice Loss sum of segmented regions as a loss function, comparing with a label, calculating loss and performing back propagation on training model parameters until the model parameters converge; and S4, segmenting the brain tumor image by using the trained brain tumor image segmentation model to obtain a brain tumor segmentation result. According to the invention, complementary information between multi-modal images can be fully utilized, and a network is helped to pay attention to boundaries between regions.

The invention provides a brain tumor classification method and device based on a magnetic resonance image, and a medium. The method comprises the following steps: obtaining a magnetic resonance image to be processed; preprocessing the magnetic resonance image to obtain a preprocessed first image; the first image is input into a trained implicit high-dimensional state space hybrid network, a classification result corresponding to the first image is obtained, the implicit high-dimensional state space hybrid network comprises a convolutional embedding layer, a backbone network and a classification module which are cascaded, the backbone network is composed of a plurality of stages, and the classification module is used for classifying the first image; each stage includes a number of stacked state space multiplicative interaction blocks. The backbone network adopts a multi-layer state space multiplicative interaction block stacking structure, and through selective long-range aggregation and implicit high-order feature interaction, the computational complexity is reduced, and meanwhile, the perceptual ability to a tumor region and a boundary thereof is enhanced; on the premise of ensuring the light weight of the model, the accuracy and generalization ability of the brain tumor multi-classification task are remarkably improved.

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 brain tumor segmentation method based on Dual-SwinTransBTS, and mainly relates to the technical field of medical image segmentation. Comprising the following steps of: 1, constructing a multi-modal cross attention module (MCA) based on a Swin-Transform and a Swin-Transform interactive fusion module (STFusion), and constructing the multi-modal cross attention module (MCA) based on the Swin-Transform and the STFusion module (STFusion) based on the Swin-Transform; 2, constructing a brain tumor segmentation model Dual-SwinTransBTS in combination with the MCA module and the STFusion module; 3, data preprocessing, data division and data enhancement; fourthly, the training set obtained after preprocessing is input into a Dual-SwinTransBTS segmentation model to be trained; 5, inputting the multi-mode nuclear magnetic resonance imaging data to be segmented into the Dual-SwinTransBTS brain tumor segmentation model, and carrying out segmentation on the multi-mode nuclear magnetic resonance imaging data to be segmented; according to the invention, the problem of poor automatic segmentation effect of the existing multi-modal nuclear magnetic resonance imaging data can be solved.

The invention discloses an intelligent detection method for a brain tumor focus area, and relates to the technical field of medical image processing. The method comprises the following steps: acquiring a multi-modal image; the multi-modal image is an MRI (Magnetic Resonance Imaging) image generated according to T1 weighting, T2 weighting and an FLAIR sequence; the multi-modal image is preprocessed, and a normalized image is obtained; constructing a focus detection model; the focus detection model adopts a double-branch network structure and comprises a global branch, a local branch and a weighted fusion module; the global branch and the local branch are both connected with the weighted fusion module; the weight fusion module adopts a dynamic weight fusion strategy; inputting the normalized image into the focus detection model to generate a preliminary focus segmentation result; and performing post-processing optimization on the preliminary focus segmentation result to generate a final focus positioning result. According to the invention, the detection precision of the brain tumor lesion area can be improved.

The invention discloses a brain tumor disease treatment effect evaluation method, and belongs to the technical field of radiotherapy plan evaluation, and the method specifically comprises the steps: obtaining the brain multi-modal image data of a patient, and extracting a three-dimensional tumor region and a corresponding perfusion parameter; constructing a biological target region hierarchical structure containing a tumor overall region and an anoxic subregion; a relative hypoxia degree parameter is obtained by calculating the cerebral blood flow ratio of the hypoxia subregion to other regions of the tumor; historical treatment case data are deconstructed, and a dose-curative effect associated parameter set is established; training and outputting a dose adjustment strategy model for the hypoxia subregion; and generating a final radiotherapy dose planning scheme according to a dose adjustment value output by the model in combination with a clinical guide basic dose. Through a data-driven dose decision-making mechanism, the limitation of traditional uniform dose irradiation is overcome, so that an objective and reliable evaluation basis is provided for clinical selection and optimization of personalized treatment schemes.

The invention relates to the technical field of medical data processing, and particularly provides a brain tumor distinguishing model construction method, which is characterized in that a radiomics model is constructed based on a brain-tumor junction region (BTI), and the model can effectively distinguish glioblastoma (GBM) and isolated brain metastases (SBM); the GBM and the SBM have significant overlap in subjective image features, the brain tumor distinguishing model based on radiomics constructed by the invention effectively makes up for uncertainty possibly caused by subjective judgment, and the BTI region-based logic regression model provided by the invention has high prediction capability and clinical practicability. An important auxiliary tool is provided for clinical diagnosis, and the diagnosis accuracy is remarkably improved.

The invention relates to the field of medical artificial intelligence, in particular to a mucus-like brain tumor multi-source data auxiliary differential diagnosis system based on deep learning, and the system collects pathological images, medical images, immunohistochemical data and molecular biology data through a multi-source heterogeneous data collection and standardization module, and carries out standardization processing; a multi-scale feature extraction module extracts key features from the data to generate multi-modal feature data; a cross-modal correlation analysis module analyzes correlation among the feature data and generates attention mapping data and feature correlation data; the diagnosis reasoning and decision-making module identifies the tumor type by using the data, carries out differential diagnosis and calculates the uncertainty of a diagnosis result; the interpretability display module is used for generating a structured diagnosis report which comprises multi-level attention visualization data and positioning a key diagnosis area; and through multi-source data fusion and correlation analysis, the diagnosis accuracy is remarkably improved.

The invention relates to a self-adaptive tumor segmentation method and system, electronic equipment and a storage medium, and belongs to the technical field of image processing. According to the method, local features are extracted from multiple scales through a multi-scale convolution layer of an adaptive Mama module, global features are captured from different perspectives through a dual-granularity Mama layer, and the global features, namely dual-granularity features, are re-weighted through an adaptive feature fusion layer; a frequency domain is introduced into a feature enhancement module, a high frequency spectrum pays attention to edge and texture changes, a low frequency spectrum represents a global structure and a continuous region, and features with rich information are selected from the low frequency spectrum and the high frequency spectrum to supplement spatial features; an auxiliary brain tumor classification loss is introduced into a double-constrained mixed loss function module to improve the attention of the model to a small-proportion tumor area. According to the invention, the brain tumor segmentation precision can be improved.

The invention discloses a multi-modal brain tumor segmentation method and system based on channel adaptive structure feedback, and the method comprises the steps: obtaining multi-modal brain tumor image data, carrying out the three-dimensional data preprocessing, and obtaining the preprocessed multi-modal brain tumor image data; introducing a multi-modal pyramid feature coding module, a spatial shift attention mechanism module and a channel adaptive fusion module, and constructing a multi-modal brain tumor segmentation model; and based on the multi-modal brain tumor segmentation model, carrying out image segmentation processing on the preprocessed multi-modal brain tumor image data to obtain a segmented brain tumor image. According to the method, the multi-modal brain tumor image with weak tumor boundary information can be effectively segmented, and the segmentation precision of the multi-modal brain tumor image is improved. The multi-modal brain tumor segmentation method and system based on channel adaptive structure feedback can be widely applied to the technical field of medical image processing.

The invention relates to the technical field of nursing data management, and particularly discloses a nursing data management optimization method and system for a brain tumor patient, which is used for performing structured conversion and FHIR format standardization processing based on a multi-dimensional rule on vital sign data and electronic medical record data of the brain tumor patient. Therefore, the consistency of data formats is ensured. For medical image data of a patient, a large model technology is particularly introduced to carry out automatic structured description on a medical image, through image feature extraction and semantic segmentation based on deep learning, morphological features and a spatial distribution mode of a focus are identified, a structured image description text is generated, and FHIR format conversion is further completed. Finally, unified access, storage, query and analysis of vital sign data, electronic medical record records and medical image reports are achieved by deploying an integrated server meeting the FHIR standard. According to the method, the integration level of nursing data can be remarkably improved, information islands are broken, and data interoperability is improved.

The invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on a boundary perception mechanism, and the method comprises the steps: inputting T1, T1c, T2 and Flair images of a brain tumor into a trained image segmentation model, and outputting a prediction segmentation image through the trained image segmentation model, the prediction segmentation image is a brain tumor MRI image which is obtained through prediction and has a complete tumor area, a tumor core area and an enhanced tumor area; according to the brain tumor segmentation method based on the boundary perception mechanism provided by the invention, the boundary perception mechanism is introduced, and the boundary information is fused into the image segmentation model, so that the discriminability of the model to features is improved, and accurate segmentation of tumor subregions is realized; a multi-modal fusion method is adopted, different MRI sequence complementary information is integrated, and tumor features are comprehensively understood; in combination with uncertainty quantification and a loss function based on uncertainty, confidence measurement is provided for a segmentation result, the accuracy and reliability of segmentation are enhanced, and a clinician is assisted in evaluating a prediction result.