223 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 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 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 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 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 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 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 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.

The invention discloses an incomplete modal brain tumor MR image segmentation method, system and device based on a local-global modeling structure and a medium, and the method comprises the steps: preprocessing a brain tumor MR image, including registration, normalization, data amplification and simulation modal deletion; an SD4M-Net network is constructed and trained, the SD4M-Net network comprises a segmentation coding module, a four-cascade Mama module (M4M), a space deformable convolution module (SDM) and a segmentation decoding module, the M4M captures three-dimensional long-range dependence, and the SDM enhances local features; evaluating the model by using a Dice coefficient; outputting a segmentation result; the system comprises a data preprocessing module, a feature extraction and fusion module and a segmentation output module. The equipment comprises an image collector, a program processor and a display, and incomplete modal brain tumor MR image segmentation is realized based on the method. The method can cope with modal deficiency, is high in segmentation precision, is superior to a mainstream method in the BraTS 2020 data set, and provides reliable support for clinic.

The invention discloses an intelligent monitoring system for tumor nursing. The intelligent monitoring system comprises a wearable sensing terminal group, a patient terminal, a cloud platform intelligent analysis center and a medical care terminal. The wearable terminal is used for collecting multi-modal physiological and behavior data such as electroencephalogram, near-infrared brain blood oxygen, heart rate variability, body movement, electrodermal response and gaits; the patient terminal is used for recording subjective symptoms and completing cognition and exercise task evaluation. The cloud platform intelligent analysis center constructs a multi-parameter individualized baseline through data fusion, noise suppression and time alignment, comprehensively analyzes real-time deviation, electroencephalogram abnormality, cognitive deviation and gait change by using a short-term acute early warning model and a long-term trend early warning model, and generates a risk level. The digital twinborn visualization module is used for dynamically displaying the state of the patient, and the medical care terminal performs clinical response based on the graded alarm board. According to the invention, extra-hospital multi-mode continuous monitoring, individualized trend identification and intelligent early warning can be realized, and the real-time performance of follow-up management of brain tumor patients is improved.

A diagnostic platform for detecting and analyzing brain tumors includes a portable fiber Surface Enhanced Raman spectroscopy (SERS) module, a removable fiber probe for intraoperative use, disposable nanoplasmonic cartridges for individual sample loading, and an updatable spectral library collected from healthy and diseased samples. The platform is designed to analyze blood and tissue signatures and employ artificial intelligence (Al) models to classify SERS spectra based on an updatable proteomics and SERS spectral library encompassing distinct brain tumor types. The platform is further configured to utilize artificial intelligence (Al) algorithms for the analysis and classification of the recorded SERS spectra.

The invention discloses a brain tumor nuclear magnetic resonance image segmentation method based on deep learning, and belongs to the technical field of medical image processing. According to the method, a multi-scale feature extraction module is introduced into a coding path and a decoding path of the U-Net network to enhance the extraction and fusion capability of the network on multi-scale information in a brain tumor nuclear magnetic resonance image; meanwhile, a coordinate attention module is introduced into the jump connection to enhance the modeling capability of the network on the dependency relationship between the tumor space position information and the channels, so that the common problems of detail loss, inaccurate small target segmentation and the like in the medical image segmentation task are solved. Besides, aiming at the serious category imbalance problem existing in the brain tumor data set, the method utilizes a mixed loss function to guide the training of the model, so that the model can better learn the characteristics of the multi-modal brain tumor in the training process.

The present invention is related to a C—X—C motif chemokine 12 (CXCL12) antagonist for use in a method for treating a tumor in a subject, wherein the method comprises administering to the subject—the C—X—C motif chemokine 12 (CXCL12) antagonist, —a radiotherapy, and—an anti-angiogenic compound, wherein the tumor is a brain tumor.

The application discloses a drug-loaded micelle capable of effectively crossing the blood-brain barrier, which is an amphiphilic conjugate composed of a reduction-sensitive paclitaxel prodrug and a nucleic acid complex, wherein the reduction-sensitive paclitaxel prodrug is used as a hydrophobic part, and the nucleic acid complex is used as a hydrophilic part, and the drug-loaded micelle is self-assembled in an aqueous environment; the reduction-sensitive paclitaxel prodrug is formed by the reaction of paclitaxel and a disulfide bond-containing linker; and the nucleic acid complex is formed by connecting an antisense oligonucleotide and an interfering RNA through a DNA bridge. The drug-loaded micelle exhibits superior blood-brain barrier penetration, effectively realizes enrichment in brain tumors, solves the problem of low blood-brain barrier penetration efficiency of existing nano-carriers, and provides an effective basis for brain drug delivery and brain diseases such as brain tumor imaging and treatment.

The invention discloses a brain tumor real-time identification system and method based on cortical electroencephalogram signals, and relates to the technical field of medical artificial intelligence, and the method comprises the following steps: obtaining multi-channel ECoG signals in real time; the multi-channel ECoG signals are updated in real time, the multi-channel ECoG signals updated in real time are preprocessed, and the preprocessed multi-channel ECoG signals are obtained; performing channel feature extraction on the ECoG signal of each channel to obtain various ECoG signal features under each channel as channel features; calculating segmentation thresholds of different channel characteristics under each channel based on an automatic threshold segmentation algorithm; carrying out confidence evaluation on the segmentation threshold values of different channel characteristics under each channel; and marking the channel of which the confidence coefficient is smaller than a set tumor marking threshold value as a tumor area. Accurate positioning of a tumor area can be achieved, functional state changes of the cerebral cortex can be reflected in real time, and doctors can conveniently master real-time changes of cortex functions in time.

This invention belongs to the field of medical image analysis technology and relates to a brain tumor segmentation method based on a boundary-aware mechanism. The method involves inputting T1, T1c, T2, and FLAIR images of the brain tumor into a trained image segmentation model, which outputs a predicted segmented image. The predicted segmented image is a brain tumor MRI image with a complete tumor region, a tumor core region, and an enhanced tumor region, obtained through prediction. The proposed brain tumor segmentation method based on a boundary-aware mechanism incorporates boundary information into the image segmentation model, improving the model's ability to distinguish features and achieving accurate segmentation of tumor subregions. A multimodal fusion method is used to integrate complementary information from different MRI sequences, providing a comprehensive understanding of tumor characteristics. Furthermore, uncertainty quantification and an uncertainty-based loss function are combined to provide confidence measurements for the segmentation results, enhancing the accuracy and reliability of the segmentation and assisting clinicians in evaluating the prediction results.

GD2 specific chimeric antigen receptor effector cells for the treatment of solid tumors, possibly in combination with an enhancer of a Zeste homolog 2 (EZH2) inhibitor. The invention relates to a GD2-specific chimeric antigen receptor (GD2. CAR) effector cell for use in the treatment of solid tumors, in particular in the treatment of extracranial GD2 + tumors, such as soft tissue sarcoma and osteosarcoma, neuroblastoma, melanoma, lung cancer, bladder cancer and retinoblastoma, and brain tumors. In addition, the present invention also relates to the use of the GD2. CAR genetically modified effector cell in combination with an enhancer of a Zeste homolog 2 (EZH2) inhibitor for the treatment of solid tumors.

The invention belongs to the technical field of medical image processing and artificial intelligence, and particularly relates to a weakly supervised brain tumor segmentation method which comprises the following steps: acquiring a low-level glioma segmentation data set of a cancer genome map, and randomly dividing the data set into a training set and a test set according to patient division; based on the image data in the data set, carrying out transfer learning by using a pre-trained ResNet50 as a backbone network; a global optimization mechanism of an SZIO algorithm is utilized to perform adaptive optimization and intelligent screening on features extracted based on a traditional class activation mapping technology, and fine fusion and semantic enhancement of multi-level features are realized; and performing fine adjustment on the ResNet50 backbone network at a preset initial learning rate. According to the method, the performance breakthrough of weakly supervised brain tumor segmentation can be realized, the practicability, robustness and clinical transformation potential are relatively high, and an efficient and reliable intelligent segmentation solution is provided for the field of medical image analysis.

The invention provides application of an arsenene nano preparation and / or beta-elemene in preparation of a product for treating brain tumors, and belongs to the technical field of biology. The arsenene nano preparation and / or beta-elemene are / is applied to brain glioma and lung cancer brain metastatic tumor model mice, so that tumor cells can be effectively killed, tumor metastasis can be inhibited, in-vivo immune response can be activated, maturation of dendritic cells can be promoted, NK cells and CD8 + T cells can be effectively activated, and the secretion amount of tumor suppression cell factors can be increased. Meanwhile, the beta-elemene effectively reduces cardiotoxicity caused by accumulation of the arsenene nano preparation in vivo.

Particles are provided that include a liposome having a negatively charged outer surface and a lipid-drug conjugate, a first layer of cationic polymer such as poly-L-arginine (PLR), that is non-covalently associated with the negatively charged outer surface of the liposome, and a second layer having a mixture of an anionic polymer and polyethylene glycol modified anionic polymer that is non-covalently associated with the first layer. The particles can be formulated as pharmaceutical compositions that are useful in methods that target neurological disorders such as brain tumors and other neurological diseases, and that can deliver and / or transport therapeutic drugs across the blood brain barrier.

The application discloses a brain tumor detection method based on an attention mechanism and MRI multi-modal fusion, relates to the technical field of brain tumor image analysis, and comprises the following steps: acquiring a first tumor heat map and a second tumor heat map; calculating a consistency measurement score of the two heat maps; if the score does not satisfy a preset threshold, starting feedback iteration: merging images to generate an enhanced training set, and performing weighted training on a loss term corresponding to a multi-modal image pair by taking the normalized measurement score as a quality weight, and iteratively optimizing a model until a condition is satisfied; and finally outputting a brain tumor detection result with high confidence. The application introduces an internal closed-loop verification and self-adaptive optimization mechanism, effectively solves the core problem that the confidence of a detection result cannot be verified and guaranteed in the prior art, and finally realizes the accuracy of closed-loop verification and optimization of the confidence of brain tumor detection based on the attention mechanism and the MRI multi-modal fusion.

The application discloses a kind of multi-modal brain tumor image segmentation methods based on self-supervised learning, including obtaining multi-modal MRI image set, MRI image set includes multiple brain tumor case images, each case image corresponds a segmentation label, and includes four kinds of modal images;Build self-supervised learning multi-modal brain tumor image segmentation network, including feature extraction unit, global feature modeling unit, decoding unit, global feature modeling unit uses cross-pixel Transformer model, when training, first pre-train global feature modeling unit then train the whole self-supervised learning multi-modal brain tumor image segmentation network, finally tumor image is segmented using the trained model.The application can fully capture local features and global features in medical images, and the computational complexity is only 1 / 4 of the original structure, with high accuracy.

The application finds that SDF4 in a body fluid sample can become a marker for detecting cancer patients with good sensitivity and specificity. By using a reagent for specifically detecting SDF4, gastric cancer, breast cancer, colorectal cancer, pancreatic cancer, esophageal cancer, liver cancer, liposarcoma, bladder cancer, brain tumor, head and neck cancer, gallbladder cancer, ovarian cancer, tongue cancer, or uterine cancer can be detected. The measurement of SDF4 in a body fluid sample is a detection method capable of detecting various cancers by a single test, and can be used for large-scale screening of cancers. In addition, early-stage cancers can be detected with good sensitivity and specificity, so that cancers can be found and treated in an early stage.

The invention discloses a multi-mode nuclear magnetic image brain tumor segmentation system based on optical flow method pixel correlation, relates to the field of image processing, and solves the problems that complex physiological correlation among multiple modes cannot be captured and specific characteristics of a tumor area are difficult to reproduce in an existing missing mode completion method. The segmentation system comprises a data preprocessing module, a cross-modal optical flow estimation module, a pixel association weight calculation module, a missing modal complementation module and a fusion segmentation module. The system is simple in structure and reasonable in design, breaks through the limitation that a traditional optical flow model depends on a gray level consistency hypothesis, and adapts to the characteristic that the gray level difference of the multi-mode MRI is remarkable. By capturing the consistency of the gray gradient direction of the same anatomical structure in different modals, cross-modal pixel correlation mapping is accurately established, optical flow estimation deviation caused by gray mismatching is avoided, a reliable correlation basis is provided for subsequent deletion completion, and the accuracy of cross-modal information transmission is guaranteed.

The application discloses a brain tumor infiltration degree prediction method, device, storage medium and equipment, and belongs to the technical field of medical image processing. The application innovatively takes tumor segmentation probability and fiber density features of a tumor edge region as infiltration analysis features, constructs a brain tumor infiltration degree prediction model, device, storage medium and equipment, and is used for predicting the infiltration degree of a brain tumor. Experiments show that the brain tumor infiltration degree predicted by the model has a relationship (P<0.05) with survival, and the survival time of the high-infiltration degree is obviously lower than that of the low-infiltration degree, which has important clinical significance.

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