114 results about "Cerebral Tumor" patented technology

The invention discloses a brain tumor image diagnosis large model pre-training method, diagnosis method and system, and the method comprises the steps: obtaining the image data of a brain tumor patient and a corresponding diagnosis text, and the image data comprises a plurality of sequences; constructing a visual unified model, carrying out complete sequence standard training and missing sequence distillation training on the visual unified model by utilizing the image data, learning unified visual representation of the image data of any sequence combination, and taking characteristics of the complete sequence image data as teacher characteristics in the missing sequence distillation training; the features of the missing sequence image data serve as student features, and distribution alignment of the student features and the teacher features in the feature space is restrained; constructing a visual language model, taking unified visual representation output by the trained visual unified model as input based on a multi-task target, and training the visual language model in combination with the diagnosis text; the trained model can effectively process the sequence missing condition, and the accuracy and robustness of diagnosis are improved.

The invention discloses a brain tumor area automatic segmentation method and system based on deep learning, and belongs to the technical field of image segmentation, and the method comprises the following steps: obtaining a disclosed brain tumor segmentation data set, and constructing a multi-modal brain tumor medical image database; performing data preprocessing on the multi-mode brain tumor medical image database; dividing the preprocessed multi-modal brain tumor medical image database into a training set, a verification set and a test set in proportion; a deep learning model LG-FPN-TransBTS based on the improved TransBTS is designed, and the deep learning model LG-FPN-TransBTS is designed; and training, evaluating and applying the model. The system comprises a data preprocessing module, a data set division and secondary processing module, a deep learning model construction module, a model training and optimization module and a model evaluation and application module. According to the method, the precision and robustness of brain tumor image segmentation are remarkably improved.

The invention belongs to the technical field of medical image processing and analysis, and relates to an MRI (Magnetic Resonance Imaging) brain tumor segmentation method based on multi-modal feature fusion, which comprises the following steps of: constructing an image segmentation model comprising an encoder and a decoder; according to the encoder part, firstly, preliminary feature extraction is carried out on input MRI images of four modes of T1, T1c, T2 and Flair through a standard convolution normalization activation module and a cavity convolution encoding enhancement module, and then importance weighting and selection are carried out on extracted features through a mode-level feature fusion module to obtain fused features; according to the decoder part, feature representation is further extracted and optimized through a space and channel-level feature fusion module, and finally a prediction segmentation image is output through an up-sampling channel and space feature fusion convolution module. According to the method, the MRI brain tumor segmentation precision is improved, and powerful technical support is provided for clinical accurate diagnosis, treatment plan making and patient monitoring.

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention belongs to the technical field of image analysis, and particularly relates to a brain tumor classification method based on an improved OfficientNet network model. According to the method, an OfficientNet network model is improved, and a local attention enhancement module for dynamically adjusting channel importance of different regions in a brain tumor medical image by using a local region importance weighting mechanism is added at an input position of the OfficientNet network model; a to-be-classified brain tumor medical image is sequentially processed by the ECA self-attention layer, the first feature extraction layer, the soft pooling layer, the second feature extraction layer, the first activation layer and the feature size adjustment layer to obtain a first feature having the same size as the to-be-classified brain tumor medical image; and the feature optimization layer performs feature optimization based on the first feature and the brain tumor medical image to be classified, and inputs an optimization result into the OfficientNet network model to obtain a brain tumor classification result, so that the classification precision of the model is enhanced while the calculation complexity is reduced.

The invention discloses a lightweight brain tumor segmentation network based on CNN-Mama parallel coding, and belongs to the technical field of neural networks and Mama architectures. The lightweight network adopts a U-shaped network architecture, and a network main body consists of four layers of encoders and decoders, a bottleneck layer and a jump connection part; wherein each layer of encoder and decoder is composed of a PMamba module and a corresponding sampling module, and the jump connection part is composed of an MDA module. According to the lightweight network, local features are efficiently extracted by using a PMamba module, the modeling capability of the network on a remote dependency relationship between brain tumor images is enhanced, and the utilization efficiency of global features is effectively improved. Meanwhile, redundant background information is filtered by focusing associated feature information of the same layer of the encoder and the decoder from three view directions of the 3D brain tumor image through an MDA module. According to the network, the segmentation precision of each region of the brain tumor is effectively improved while relatively low calculation cost is maintained.

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 belongs to the technical field of image processing, and particularly relates to a mixed U-Net brain tumor segmentation method and system based on quantum attention, and the method comprises a data preprocessing module which is used for obtaining and preprocessing a medical image; the image segmentation module is used for segmenting an image through a trained QHAU-Net segmentation model, and the QHAU-Net segmentation model comprises an encoder used for extracting multi-level features of the input image; the bottleneck layer is used for receiving the semantic features of the highest dimension output by the encoder and generating enhanced features modulated by quantum through a quantum multi-head attention module; the decoder is used for recovering the spatial resolution of the feature map and fusing the features through jump connection; the quantum boundary enhancer is used for performing quantum enhancement optimization on the output features of the decoder and outputting the output features; and the result output module is used for outputting and visualizing the segmentation result graph. The problem of low boundary medical image segmentation precision in medical image processing is solved.

The invention requests to protect a brain tumor segmentation network method in a missing mode based on mode perception, aims to solve the problem of missing modes in multi-mode MRI data, and belongs to the technical field of computer vision and medical image processing. The method comprises the following steps: step 1, providing a dynamic sampling encoder module which dynamically extracts available modal features and explicitly fills missing modal information through cross-modal feature mapping to enhance the robustness of a model; 2, a modal perception attention fusion module is designed, the module combines local and global attention mechanism adaptive fusion features, and the long-tail tumor region segmentation capability is further improved. And step 3, a two-stage training strategy is designed and comprises self-supervised modal reconstruction pre-training and supervised segmentation fine tuning, and the self-supervised modal reconstruction pre-training and the supervised segmentation fine tuning are supervised by a double-task loss function so as to optimize the network to adapt to various missing modal scenes.

The invention belongs to the technical field of multi-modal magnetic resonance image segmentation processing, and particularly relates to a brain tumor magnetic resonance image segmentation method based on a dynamic multi-modal interaction network, which comprises the following steps: step 1, in the dynamic multi-modal interaction network, designing a two-stage segmentation architecture to complete modal feature extraction and dynamic fusion; step 2, adding a dynamic mask weighting module MASK-AOE to a jump connection part of the encoder and the decoder, wherein the dynamic mask weighting module MASK-AOE comprises a dynamic mask mechanism and a lightweight proxy network; mask and dynamic fusion is carried out on input modal features, and the adaptive capacity of the model to the modal missing condition is enhanced; a double-path fusion interaction module is added to the connection part of the encoder and the decoder; the dual-path fusion interaction module comprises modal grouping fusion, a bidirectional interaction mechanism and a visual Mangbar layer; and the dual-path fusion interaction module is used for realizing collaborative relationship modeling and feature optimization between modes. According to the method, a high-quality segmentation result can still be generated even under the condition of mode missing.

The invention discloses an incomplete multi-modal brain tumor segmentation method based on key modal guided contrast learning, which comprises the following steps: 1, inputting four different modal MRI images, extracting features through two groups of encoders respectively, and simulating a missing modal through modal codes to obtain FLAIR modal guided group fusion features and T1c modal guided group fusion features; 2, constructing a mode-oriented comparison branch, and shortening the distance between other modes and the key mode through comparison learning; 3, constructing a multi-modal segmentation branch, alternately training the multi-modal segmentation branch and a modal-oriented comparison branch, and optimizing a segmentation result; and 4, segmenting the input multi-modal MRI image data by using the trained incomplete multi-modal brain tumor segmentation model and discarding the modal-oriented contrast branch. According to the method, the distance between other modals and the key modals can be reduced in the scene of medical MRI image modality deficiency, and key source information is supervised and reserved through the segmentation result, so that the medical image segmentation performance can be improved.

The invention discloses a brain tumor MRI image segmentation method based on a soft clustering KAN network, and belongs to the technical field of medical information processing, and the method comprises the steps: carrying out the voxel alignment and intensity normalization of a multi-modal three-dimensional brain tumor MRI image, and constructing a unified input sample; layer-by-layer downsampling and multi-scale feature extraction are realized through a KAN residual encoder formed by alternately cascading double-layer KAN attention subnets and three-dimensional maximum pooling layers; three-dimensional position coding is added to the highest-layer features, and bottleneck features containing global structure priori are obtained through soft K-means clustering bottleneck layer modeling; fusing the jump connection feature and the up-sampling feature by using an attention gating mechanism to complete decoding; and through soft clustering regularization and a multi-scale depth supervision constraint optimization model, finally through fusion of a main segmentation branch and a refined branch, outputting a multi-subarea three-dimensional segmentation result of the whole tumor, the tumor core and the enhanced tumor.

The present invention belongs to the technical field of medical image analysis, and relates to a brain tumor segmentation method based on uncertainty estimation. The method is as follows: input the T1, T1c, T2, and Flair images of the brain tumor into a trained image segmentation model, which includes 4 encoder modules, 1 decoder module, 1 Monte Carlo simulation module, and 1 image processing module; the Monte Carlo module repeatedly calls the encoder module and the decoder module for sampling; the image processing module processes the output to obtain a mean image, a random uncertainty image, an epistemic uncertainty image, an entropy uncertainty image, and a mutual information uncertainty image, so as to achieve accurate segmentation and analysis of the brain tumor. The present invention improves the accuracy of segmentation and the reliability of diagnosis, has strong scalability, and is applicable to a variety of medical image analysis tasks.

The invention relates to the technical field of medical auxiliary evaluation, in particular to a perioperative pain evaluation system for a brain tumor surgery patient, which comprises a surgery pain data acquisition module for acquiring an HRV signal, an intracranial pressure waveform, a subjective VAS score and pain part data of the patient; the feature extraction module processes the data through abnormal value detection and sliding window filtering, extracts HRV and ICP features in combination with time domain analysis, and outputs standardized data after integrating the HRV and ICP features with subjective feedback features; the AI pain quantification module is used for generating a pain risk probability value based on an AI model constructed by an XGBoost algorithm, triggering three-level early warning and associating pain properties and causes of a patient; the intervention pushing module triggers a multi-terminal prompt and pushes an intervention scheme, and a patient performs graphic interaction and feedback through a mobile phone terminal; and the management and tracing module is used for associating single-time whole-process data, recording an intervention effect and automatically upgrading an unexpected scheme. Therefore, the problems that in the prior art, intracranial pressure and pain coupling is not quantified, intervention lags and the like are solved.

Handheld surgical systems having an adjustable, ergonomic handheld controller and a series of interchangeable surgical instruments with dexterous, end-effectors for performing a surgical procedure, e.g., removing brain tumor tissue from confined spaces, and methods of use thereof are disclosed. The end-effector may be actuated in one or more degrees of freedom via a tendon routing system comprising a plurality of antagonistic pairs of tendons extending from the end-effector to a plurality of independently rotatable capstan shafts disposed within a housing of the interchangeable instrument and configured to be releasably and operatively coupled to one or more motors disposed within the handheld controller.

The invention discloses a hierarchical optimization multi-instance learning method for digital pathological section brain tumor classification. The hierarchical optimization multi-instance learning method comprises the following steps: extracting and aggregating multi-instance features through alternate training of a feature encoder and an aggregator; a region of interest is automatically searched in a low-magnification image, and then feature extraction and classification are performed on a high-magnification region of interest. According to the method, the region-of-interest is detected by using low magnification, and then the tumor category is comprehensively judged in multiple magnification near the region-of-interest in stages. More importantly, in a model training link, a feature extractor is trained by utilizing a pseudo tag of patch in a region of interest, so that features are better extracted for a specific task and a data set. In order to reduce the noise of the false label, the patent provides a label correction mechanism to ensure the purity of the false label. In general, the HOMIL combines low-magnification tissue structure characteristics and high-magnification cell characteristics, comprehensively predicts brain tumors, and greatly reduces the model reasoning time.

The invention relates to a data treatment method, system and device for assisting a special brain tumor disease and a medium, and the method comprises the steps: extracting a special brain tumor disease model structured field from input multi-mode special brain tumor disease data through a structured extraction model; the structured extraction model is evaluated by using a judgment model, and the first parameter quantity of the structured extraction model is smaller than the second parameter quantity of the judgment model; and judging whether the judgment result is higher than a preset evaluation score threshold, if not, adjusting the structured extraction model through the fine tuning instruction data set until the accuracy of the judgment result is higher than the preset evaluation score threshold, and updating the adjusted structured extraction model. According to the method, the data identification precision can be improved, a large amount of manual annotation is not needed, and automatic evaluation can be completed.

The invention relates to a missing modal brain tumor segmentation method based on feature decoupling and multilayer attention fusion, and belongs to the technical field of medical image processing. Aiming at the problems of modal missing of multi-modal MRI data caused by equipment failure or scanning limitation and feature redundancy and sudden drop of segmentation performance caused by improper processing of a traditional method, the method comprises the following steps: 1) a multi-scale feature decoupling module is provided for separating high-frequency local details, an intermediate-frequency region structure and a low-frequency global contour through frequency domain filtering and a hierarchical attention mechanism; 2) a dynamic weight distribution and multi-layer attention module is used for realizing missing compensation based on pixel-level Softmax weighting and cross-modal iteration interaction; and 3) the U-Net decoder combines with a random modal shielding training strategy. According to the method, the segmentation precision and robustness in a modal missing scene are effectively improved, noise introduction in a traditional completion method is avoided, and the real-time performance and accuracy of clinical diagnosis are guaranteed.

The invention discloses an improved brain tumor image segmentation algorithm based on a generative adversarial network model. The problem of inaccurate segmentation of tumor edges and detail regions is solved. According to the method, a generative adversarial network is improved, and cross-scale jump links are introduced for a generator part of a model so as to better capture context information and extract more complete edge detail features; in a decoder stage, position information and a channel relation are captured in combination with a coordinate attention mechanism, so that the network is focused on tumor features, and irrelevant features are inhibited at the same time; and the objective function combining L1 loss and Dice loss is used to solve the problem of sample category imbalance, and the accuracy and robustness of the brain tumor MR image are significantly improved. Experimental results show that the improved network has a better segmentation effect than other typical brain tumor segmentation methods.

The invention discloses a novel EGFR (epidermal growth factor receptor) degradation agent as well as a preparation method and application thereof. The EGFR degradation agent is PROTAC with GROs (such as AS1411) as an MDM2 recruitment element, target protein EGFR and E3 ubiquitin ligase MDM2 can be pulled close in vivo, so that the EGFR is labeled with ubiquitin, and then the EGFR is degraded through a ubiquitin-proteasome pathway. Experiments show that the EGFR degradation agent can effectively degrade EGFR and generate corresponding curative effects (such as inhibition of tumor cell proliferation and promotion of tumor cell apoptosis), and has very good application prospects and research values. In addition, the inventor also finds that AS1411 can specifically target and penetrate a hematoma barrier, and can be used for in-vivo imaging, diagnosis and detection of brain tumors.

The invention belongs to the technical field of medical image processing, and relates to a brain tumor segmentation method based on three-axis context coding, which comprises the following steps: jointly inputting an FLAIR image, a T2 image, a T1 image and a T1ce image of the same glioma into a trained image segmentation model, and outputting a prediction segmentation image with a non-enhanced necrosis region, an edema region and an enhanced tumor region by the trained image segmentation model; the image segmentation model comprises an encoder and a decoder; the encoder comprises a TCE module, and the TCE module adaptively learns relative importance among an axial plane branch, a coronal plane branch and a sagittal plane branch to realize dynamic weighted fusion of multi-plane features. By pertinently solving the problems of insufficient anisotropic feature capture and poor multi-modal fusion adaptability, high-precision segmentation of a non-enhanced necrosis region, an edema region and an enhanced tumor region is realized, and reliable technical support is provided for clinical diagnosis and treatment planning of brain tumors.

This invention belongs to the field of multimodal magnetic resonance imaging (MRI) image segmentation technology, specifically relating to a brain tumor MRI image segmentation method based on a dynamic multimodal interactive network. The method includes the following steps: Step 1: In the dynamic multimodal interactive network, a two-stage segmentation architecture is designed to complete modality feature extraction and dynamic fusion; Step 2: A dynamic masking weighting module (MASK-AOE) is added to the skip connection part of the encoder and decoder, including a dynamic masking mechanism and a lightweight proxy network; masking and dynamic fusion of input modality features enhance the model's adaptability to modality loss; a dual-path fusion interaction module is added to the connection part of the encoder and decoder; the dual-path fusion interaction module includes modality grouping fusion, a bidirectional interaction mechanism, and a visual Mamba layer; the dual-path fusion interaction module realizes the modeling of collaborative relationships between modalities and feature optimization. This invention can still generate high-quality segmentation results even under modality loss conditions.

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

The application discloses a brain tumor MRI segmentation method based on a cross-stage local network structure, and steps are as follows: step 1, 3D image data set is acquired; step 2, 2D image set of effective pixels in the 3D image is obtained; step 3, an optimized U-Net model is constructed, and the optimization process is specifically as follows: the first convolutional layer of each image compression path block in the image compression path in the U-Net model is a cross-stage local network structure, and / or the second convolutional layer of each image expansion path block in the image expansion path is a cross-stage local network structure; a loss function of the U-Net model is obtained by weighted summation of a generalized dice loss function and a weighted cross-entropy loss function; step 4, the U-Net model is trained through the effective pixels of the image; and step 5, real-time image segmentation is carried out by using the trained U-Net model. The application can effectively reduce the possibility of replication in the information integration process, reduce the information loss in the up-sampling, and can be used for small-scale tumor segmentation.

The invention provides a brain tumor segmentation method and device based on an MSCG model and a storage medium. The method comprises the following steps: establishing a three-dimensional image Imri; an MSCG model is constructed; encoding the three-dimensional image Imri by using an SAM image encoder in the MSCG model to generate an SAM encoding feature Ai; a CLIP image encoder in the MSCG model is utilized to carry out encoding processing on the three-dimensional image Imri, and a CLIP feature Bclip is generated; aggregating the CLIP feature Bclip into the SAM coding feature Ai by using an SC-aggregation module in the MSCG model, and generating an output feature Ft; a fusion decoder in the MSCG model is used to carry out fusion prediction on the output feature Ft, and a brain tumor segmentation result St is generated; and performing deep supervision on the brain tumor segmentation result St, and outputting a final brain tumor segmentation result Sout. According to the invention, the robustness and accuracy of brain tumor segmentation can be improved.

The present invention discloses a medical imaging brain tumor detection method and system based on an improved Faster R-CNN. The method comprises: obtaining a magnetic resonance image dataset from a database; preprocessing the images of the dataset using an optical flow method and a generative adversarial network to perform data enhancement; constructing an improved Faster R-CNN deep network model; using the obtained dataset to train the constructed improved Faster R-CNN deep network model; inputting the magnetic resonance image to be predicted into the trained network model, and outputting the brain tumor target detection result. The present invention adopts a data enhancement method based on an optical flow method and a generative adversarial network, and designs an improved Faster R-CNN target detection network model, adds an image feature pyramid structure and a convolutional gated recurrent unit (ConvGRU) module, modifies the data reading method during network training, enhances the continuity of tumor detection results, and improves the retrieval accuracy and recall rate of brain tumors.

The invention discloses a brain tumor segmentation algorithm based on section anisotropy CASVim and uncertainty gating UCG, and belongs to the technical field of medical image analysis. Comprising the following steps: acquiring and preprocessing an original medical image, and performing feature extraction by using a visual Mama feature extraction module CASVimm fusing multiple sections; an uncertainty gating module UCG is matched with a lightweight residual module in jump connection to decode different level features, feature uncertainty is distinguished by calculating channel dimension standard deviation, and the parameter quantity is reduced. The low-uncertainty features are input into a multi-view fusion module (MPFB), and the high-uncertainty features are fused through multi-level decoding. And a mode of combining a cross entropy fusion Dice loss function and an uncertainty loss function is adopted, so that the consistency of segmentation results is ensured, and the model prediction uncertainty is concerned. The method has especially obvious advantages in the aspect of processing focus boundary blur and complex background.

The application discloses a brain glioma segmentation and three-dimensional visualization model training method and system based on multi-modal fusion, and comprises the following steps: inputting multi-modal medical image data into a Laplacian pyramid multi-modal fusion learning model to generate a fusion graph, then inputting the fusion graph into a U-net segmentation model comprising an encoder and a decoder to obtain a segmentation mask under the multi-modal fusion graph; and finally, based on the multi-modal fusion graph and the segmentation mask, adopting a Marching cubes (MC) algorithm to perform three-dimensional reconstruction visualization of brain regions and brain tumor region labeling. The brain glioma segmentation and three-dimensional visualization model training method and system based on multi-modal fusion provided by the application optimizes the disadvantages of insufficient information and incomplete modeling under single modal, fully utilizes the advantages of the completeness of multi-modal data, performs multi-modal fusion, segmentation and three-dimensional visualization, can more comprehensively assist surgeons in treatment, and improves the accuracy in the operation process.

The application provides a brain tumor image classification method based on an improved EfficientNetV2 network and an attention mechanism, adopts an improved brain tumor image classification method based on an EfficientNetV2 network model, adds a residual structure after the first layer 3x3 convolution layer of the basic EfficientNetV2 network structure, adds three layers of 3x3 convolution layers, one layer of ECA attention mechanism and one layer of 1x1 convolution layers in the residual structure to extract feature information. Meanwhile, the CMAB attention mechanism is added in the shortcut connection of the Fused-MBConv module of the first three layers, so that the network pays more attention to the key information in the shallow network, and finally, the output result of the residual structure and the output result of the MBConv module of the last layer of the main network are added, so that the feature information of the shallow layer and the deep layer is better extracted while the network depth is ensured, feature fusion is performed, and the classification network performance is improved.

The invention discloses a brain tumor image three-dimensional segmentation method based on deep learning. The method comprises the following steps: carrying out three-dimensional segmentation data preprocessing on a brain tumor MRI data set; dividing the obtained preprocessed data set to obtain a training set, a verification set and a test set; a new network segmentation model SMSF-Mmba UNet of a multi-scale feature fusion module SMSF based on the selectable weight and a Mmba module is constructed; and inputting the training set and the verification set into an SMSF-Mamba UNet network model for training and verification to obtain a brain tumor segmentation model. According to the invention, an accurate brain tumor segmentation image can be rapidly obtained.