234 results about "Tumor region" patented technology

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

The invention discloses a tumor image segmentation method and system of a multi-scale feature fusion network based on boundary enhancement, and relates to the technical field of image segmentation, and the method comprises the steps: obtaining a to-be-segmented tumor image; constructing a tumor image segmentation model based on a pyramid visual converter PVTv2 backbone network; training the tumor image segmentation model through the to-be-segmented tumor image and the known tumor image to obtain an optimal tumor image segmentation model; and acquiring a real-time to-be-segmented tumor image and inputting the real-time to-be-segmented tumor image into the optimal tumor image segmentation model to obtain a tumor image segmentation result. Aiming at the endoscope image segmentation of the kidney tumor, the kidney tumor in the endoscope image is efficiently, stably and automatically segmented through the boundary-enhanced multi-scale feature fusion network, clinical doctors are helped to provide accurate tumor area positioning in endoscopy and surgical operations, and compared with the most advanced method, the method has the advantages that the accuracy is high, and the efficiency is high. According to the method, better segmentation capability and stronger generalization capability are obtained.

The invention discloses a brain multi-modal multi-sequence data registration method and device based on deep learning. The method comprises the steps of performing first iteration processing on a target image and a first moving image to obtain a first deformation field, wherein the first iteration processing comprises image alignment constraint processing of the target image, smooth constraint processing of the first deformation field, and area alignment constraint processing of a tumor area in the image; registering the first moving image to the target image based on the first deformation field to obtain a second moving image; performing second iteration processing on the target image and the second moving image to obtain a second deformation field; and registering the second moving image to the target image based on the second deformation field to obtain a registered moving image. In the process of performing unsupervised training on the deep learning model, after multiple times of iterative processing, model parameters are updated based on image alignment constraint loss with a target image, deformation field smooth constraint loss and region alignment constraint loss of a tumor region in the image.

The invention relates to the technical field of medical image processing, in particular to a tumor image classification method and system based on deep learning, and the method comprises the following steps: obtaining a tumor image gray-scale map, positioning an edge, recognizing a closed structure region, dividing tumor region image blocks, calling a standard image block, analyzing the contour and texture offset degree, and carrying out the layering comparison of labels. And reversely marking image blocks, analyzing edges and consistency of a detail layer, a texture layer and a structure layer, constructing a collaborative matrix to carry out multi-task mapping, and outputting a tumor classification set. According to the method, a closed structure area is positioned and extracted through gray boundary change, the initial recognition precision is enhanced, non-structural interference is reduced, a standard image block is mapped to realize morphological comparison, the classification distinguishing force is improved, label collection prediction errors are compared in a layered manner, error saliency image blocks are dynamically labeled, and a more sensitive feedback mechanism is realized; the multi-layer division enhances the hierarchical expression of the features, enhances the multi-scale aggregation capability, and improves the classification accuracy and the structure recognition generalization capability.

The invention relates to the technical field of minimally invasive ablation, in particular to a liver tumor microwave ablation postoperative contraction deformation prediction method, which comprises the following steps: performing unified standardization processing on different periods of liver MRI images; accurately marking a preoperative liver tumor area, a postoperative ablation area and a postoperative liver tumor ghost area of the liver MRI image to obtain masks of the corresponding areas; designing a distance perception function as an attention parameter of different areas of the liver MRI image; constructing a multi-sequence distance guide complementary network model; selecting a loss function; selecting an elastic registration method to perform pre-operation and post-operation registration; and calculating an ablation safety boundary through the ablation area mask and the adjusted tumor mask. The liver tumor microwave ablation postoperative curative effect evaluation accuracy can be effectively improved, and a foundation is laid for formulating a subsequent treatment plan of a patient.

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 provides a liver tumor early diagnosis method and system based on artificial intelligence, and relates to the technical field of biomedical engineering, and the method comprises the steps: obtaining original information metadata related to the liver, the original information metadata comprises image data original information metadata and biomarker original information metadata, the image data comprises liver CT (computed tomography) and MRI (magnetic resonance imaging) image data, the biomarkers comprise serum tumor marker levels, and the physiological and environmental factors of the patients comprise age, gender, dietary habits and genetic backgrounds of the patients. According to the method, the problems that a tumor in a small and complex area is easily missed or misdiagnosed in the prior art are solved, the path density is predicted through denoising, segmentation and feature extraction of image data preprocessing in combination with a path simulation model based on a graph theory and a Monte Carlo simulation method, and a potential tumor area can be accurately judged.

The invention relates to a planning method, device and equipment for treating brain glioma through multi-fiber laser ablation. The method comprises the following steps: according to pre-acquired image information of a target patient and a segmentation result of a tumor region, sequentially performing segmentation sampling processing on the image information to obtain a candidate optical fiber path set; screening the candidate optical fiber path set according to an optical fiber planning objective function and an optical fiber planning constraint condition to obtain an intermediate optical fiber path set, and determining the number of optical fibers required by tumor ablation according to the intermediate optical fiber path set; determining a target optical fiber path set from the intermediate optical fiber path set according to the number of optical fibers required by tumor ablation and a predetermined path evaluation score; the target optical fiber path set at least comprises position information corresponding to each target optical fiber path. The method can improve the tumor ablation rate and reduce the use of optical fibers.

The invention relates to the technical field of image recognition, in particular to an osteosarcoma image intelligent recognition and classification method and system based on an image recognition model. The method comprises the following steps: collecting a tumor image and carrying out multi-sequence disturbance equilibrium enhancement to generate an enhanced block tumor image, then obtaining a color standardized image through color space transformation remapping, then extracting a tumor region image and carrying out contrast inversion processing, reconstructing a tumor feature image, and finally, carrying out multi-sequence disturbance equilibrium enhancement on the tumor feature image. Based on a preset image recognition model, tumor edge features are recognized, the edge rule degree is determined, preliminary classification is carried out, a benign tumor image is obtained, surrounding metabolite signal features of the benign tumor image are analyzed, a benign reference group is formed through combination, metabolism state comparison is carried out on tumor feature images, and metabolism difference data are generated. And performing category correction according to the metabolic state mapping data so as to identify the osteosarcoma category. According to the invention, intelligent analysis of tumor features is realized, the image processing flow is optimized, and the processing efficiency 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 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 edge segmentation, in particular to a tumor interventional therapy preoperative planning system based on artificial intelligence image recognition, and the method comprises the steps: preliminarily screening out an infiltration edge region based on the gray distribution asymmetry condition of a sliding window in a CT image and the texture disorder condition in an MRI image; further fusing DSA blood vessel data, and screening a concerned infiltration region by analyzing the position distribution condition of the blood vessel region of the infiltration edge region; analyzing curvature mutation conditions of the tumor associated blood vessel segments on the basis of the concerned infiltration region to generate a blood vessel anomaly index; and finally, fusing the infiltration index and the blood vessel characteristics to construct a blood vessel-infiltration coupling index, and correcting the edge strength map according to the blood vessel-infiltration coupling index, so that the boundary optimization based on risk classification is realized, the accuracy of the obtained tumor region edge structure curve is higher, and the accuracy of tumor region segmentation according to the tumor region edge structure curve is improved.

The invention discloses a longitudinal CT image-assisted rectal cancer neoadjuvant radiotherapy and chemotherapy treatment reaction evaluation device, which comprises a focus area segmentation module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module, a rectum focus area detection module and a rectum focus area detection module, obtaining a standardized three-dimensional tumor area; a feature fusion module extracts deep learning features of a focus area after segmentation at two time points before neoadjuvant radiotherapy and chemotherapy and before an operation from the three-dimensional tumor area, and performs weighted fusion on the deep learning features at the two time points through a dynamic attention weight layer to obtain longitudinal comprehensive features representing tumor treatment response changes; and the prediction module fuses the longitudinal comprehensive characteristics and clinical baseline data to construct a multi-modal prediction model, and outputs a treatment response prediction result of the rectal cancer patient on neoadjuvant chemoradiotherapy, so that the prediction accuracy of pathological complete remission of the rectal cancer patient can be improved, and the prognosis of the patient and the utilization efficiency of medical resources can be improved.

The invention provides a medical image text generation method and system oriented to a visual language model, and belongs to the technical field of medical image processing. The method comprises the following steps: acquiring a labeled image of a medical image; performing connected domain analysis on the annotated image, and detecting and counting the number of tumor areas in the annotated image; for each tumor area, extracting morphological features including size, shape, position, cavity features and edge shape; according to a preset text template, the extracted morphological features are converted into structured natural language description, and final medical image text description is output. It is ensured that the generated medical image description has high consistency and specialty, and subjectivity and difference of manual writing are avoided. It can be ensured that key morphological characteristics (such as tumor size, shape and position) are accurately and completely described, and reliable supervision signals are provided for subsequent model training.

The invention relates to the technical field of medical image processing and intelligent grading recognition of tumors, and discloses a head and neck squamous cell carcinoma early recognition method based on radiomics and deep learning, which comprises the following steps: acquiring head and neck CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and PET (Positron Emission Tomography) three-mode image data; a two-stage dynamic structure alignment mechanism is adopted for registration; extracting fusion radiomics features; constructing a tumor sub-feature map; outputting a tumor level and a prediction confidence coefficient through the uncertainty prediction model; and generating a saliency interpretation heat map. In the prior art, a single-mode image or a superficial layer texture feature extraction model is depended on, and especially under the condition that a heterogeneity tumor region boundary is fuzzy and different modes have significant structure offset, high-confidence accurate discrimination of a real staging state of a tumor cannot be realized. According to the method, the accuracy of early stage identification of the head and neck squamous cell carcinoma is improved by introducing the significance guide registration mechanism and the improved graph convolutional neural network and combining Dropout reasoning.

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 provides a MaskRcnn-based tumor detection method and system, and the method comprises the steps: inputting medical image data into a MaskRcnn network integrated with an edge perception module, generating an edge response graph through a Sobel operator, and enabling the edge response graph to serve as an additional channel injection feature graph, and enhancing the boundary representation; fusing multi-scale features through a feature pyramid network FPN, extracting candidate tumor area features through RoI Align, introducing non-local attention modeling global dependence in the area, extracting directional entropy, texture energy, uniformity and contrast in combination with a gray level co-occurrence matrix GLCM, and splicing to generate structure sensing features; performing classification, bounding box regression and mask segmentation based on the structure perception features, and constructing a joint loss function including classification, regression, mask cross entropy, edge alignment and structure consistency; the positioning capability of the model on the fuzzy tumor contour is effectively improved, the boundary positioning error is remarkably optimized, and the defect that over-segmentation or missing detection is likely to occur in a traditional model is overcome.

The invention discloses a nasopharyngeal carcinoma prognosis prediction method based on MRI habitat and habitat space interaction characteristics. The method comprises the following steps: selecting a batch of nasopharyngeal carcinoma image data and prognosis data information corresponding to the nasopharyngeal carcinoma image data; the method comprises the steps of preprocessing data, extracting gray features of a nasopharyngeal carcinoma focus area as input of a K-Means algorithm, deconstructing a tumor area into habitat subareas with different biological characteristics, constructing a multi-area space interaction matrix based on the habitat subareas, extracting multi-area space interaction features, and taking obtained features and prognosis information as training data; a nasopharyngeal carcinoma prognosis prediction model based on multi-region space interaction features is established, training data is used as feature input of a machine learning classifier algorithm to serve as a learning sample for training, a corresponding nasopharyngeal carcinoma prognosis prediction model is obtained, the death risk of nasopharyngeal carcinoma patients can be predicted, and the patients are divided into high and low risk groups. The method does not need hypothesis, and is small in model scale, high in speed and high in accuracy.

The application relates to the technical field of high-intensity focused ultrasound treatment, and discloses an energy mode switching and treatment path planning method and system based on distance constraints, which comprises the following steps: segmenting and three-dimensionally reconstructing medical image data to obtain a three-dimensional model; generating a grid about a treatment point in a tumor area of the three-dimensional model, and binding a corresponding lesion physical property label to each treatment point; calculating the shortest distance from each treatment point to a key tissue to form an enhanced treatment point set; inputting the enhanced treatment point set into a preset decision model to obtain a final treatment point set; grouping the treatment points in the final treatment point set based on energy mode labels; and performing spatial clustering on the treatment points in each group to form multiple treatment clusters and generate a complete treatment path sequence. The application reduces process loss caused by mode switching, and makes the treatment process more systematic and controllable.

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.

The application discloses a three-dimensional near-infrared spectral tomography reconstruction method based on a mask prior, and belongs to the field of medical image processing. The method respectively performs multi-scale convolution coding on NIRST optical signals and MRI volume data to obtain features, then embeds structural information of a mask as a prior condition into a feature fusion module, dynamically adjusts spatial weights in the fusion process of multi-scale structural information and optical functional information, so that the network focuses on potential lesion areas and suppresses background noise, and finally reconstructs the optical parameter distribution of the tissue through a three-dimensional decoding network. The method provided by the application can effectively reduce the ill-posedness in the NIRST reconstruction problem, reduce artifact generation, improve the positioning accuracy of the tumor area and the optical property reconstruction quality, and reconstruct the optical property parameter distribution of the biological tissue.

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.

The application provides a tumor segmentation model training method and segmentation method based on two-level feature fusion, relates to the field of medical image segmentation, and solves the technical problems that the boundary between the tumor and the surrounding tissue is unclear in the prior art, especially the low segmentation accuracy of the target region with complex shape and variable structure, and the easy occurrence of misjudgment or missed detection. The method comprises the following steps: obtaining a training sample set and a test sample set; constructing an image segmentation convolutional network; training the segmentation convolutional network based on the training sample set, cross-entropy loss and Dice loss; testing the tumor segmentation model based on the test sample set, and obtaining the tumor segmentation model. The application is used in the process of segmenting the tumor region in an ultrasound image and a magnetic resonance image.

This invention discloses an automatic tumor region annotation method for whole-section pathological images of colorectal cancer, belonging to the field of pathological image processing technology. It employs an improved Inception-V3 network, embedding a colorectal cancer feature extraction branch to extract cellular, glandular, and tissue-level morphological features associated with colorectal cancer from stained whole-section pathological images. A fused feature map is obtained by weighting the contribution coefficients of colorectal cancer gene morphology. Based on the colorectal cancer morphological fingerprint vector, an improved Otsu algorithm is used to generate an initial segmentation threshold. The optimal segmentation threshold is obtained through iterative optimization using a colorectal cancer gene morphology matching degree formula, thus initially dividing the tumor candidate region. This invention deeply couples the molecular features of core driver mutation genes in colorectal cancer with the morphological features of pathological images, constructing a dedicated morphological fingerprint database for gene-morphology coupling. This ensures that tumor region annotation aligns with the clinical diagnostic criteria combining molecular and histopathological aspects of colorectal cancer, improving the clinical adaptability of the annotation results.

The application discloses an ovarian cancer survival prediction system based on a plurality of ultrasonic examination image-based statistical quantities of features, comprising a data acquisition module, a feature extraction module, a data preprocessing module, a sample change stability feature screening module, a survival prediction feature screening module and a survival prediction model construction module. The application carries out statistical analysis on the multi-lesion multi-angle features collected by ultrasonic examination of patients, constructs patient ultrasonic image features by comprehensively considering the results of the lesions observed from different angles in the ultrasonic examination, carries out Cox univariate screening by repeated sampling, and counts the frequency of the features selected in the feature screening process to determine the features with stability to sample changes. Finally, the features with high correlation to survival prediction are screened in combination with prognosis information, and the ovarian cancer survival prediction system is established. The system has better effect than the model using only the features of the largest tumor region, and has important significance for improving the robustness and survival prediction effect of the ovarian cancer survival prediction system based on medical image-based features.

A device and a method, which is individual to a patient, treat malignant diseases by using selectively acting tumor-destructive mechanical pulses (TMI). The tumor-destructive pulse shapes and frequency are determined using physical cell properties, which are individual to each patient. The device is controlled in such that lethal pulse fields are applied in the tumor area.

The present application relates to medical image analysis and brain network research technical field, specifically to glioma induced brain network remodeling visualization and quantitative analysis method based on fMRI. The method comprises obtaining patient fMRI and structural MRI data and preprocessing, excluding tumor area by lesion mask registration strategy, reducing quality effect interference; dividing tumor core area, peritumoral abnormal area and normal brain area; registering Yeo-17 network template to individual brain area to realize mapping; defining tumor core area as independent network unit, and 17 normal networks to form a new set; calculating whole brain voxel and network functional connection strength, and determining functional connection voxel according to threshold; quantifying intratumoral function proportion RIFR and peritumoral connection proportion RPTR, and generating visualization atlas. The present application accurately maps individual brain function network, overcomes tumor heterogeneity interference, provides repeatable quantitative index, and provides reliable imaging analysis tool for brain glioma function protection and clinical research.

The invention provides a digestive tract tumor detection method and system, relates to the field of medical image detection, and solves the technical problem of low accuracy of tumor boundary detection and automatic positioning in a digestive tract tumor image acquisition process in the prior art. The method comprises the following steps: taking a pan-tumor region image as a training sample of a binary decision tree algorithm, constructing an objective function based on the training sample to obtain a first objective function value, and obtaining a depth threshold value and an optimal classification point of the training sample; optimizing the optimal classification point based on the candidate feature sound to obtain a second objective function value; an unknown digestive tract tumor image is accessed, image points of the digestive tract tumor image are marked through a trained binary decision tree algorithm and a regression forest algorithm, the position distribution probability of the image points is generated, a position distribution thermodynamic diagram is generated based on the position distribution probability, and a tumor core area is obtained through mean shift. And completing the separation of the tumor region and the non-tumor region. The application is used in the digestive tract tumor detection process.

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.