425 results about "Tumor region" patented technology

The invention provides a multi-modal fusion method for breast cancer prognosis prediction based on deep learning. The multi-modal fusion method comprises the following steps: preprocessing a data set, marking a tissue region of a full-slice pathological image, performing slice processing, extracting multi-scale features, re-sampling a medical image, and extracting features of a tumor region; a two-dimensional region fusion module and a three-dimensional region fusion module are respectively selected to carry out multi-scale feature fusion on the preprocessed full-slice pathological image and the preprocessed medical image; performing multi-region feature fusion on the medical image by using a three-dimensional region fusion module; aligning the multi-modal data, performing cross-modal feature fusion by using self-attention pooling, and capturing cross-modal interaction information; and predicting the disease-free lifetime and the total survival of the patient through the loss function. The problems of limitation of single-mode analysis, unsatisfactory prognosis effect and the like are solved, and more accurate breast cancer prognosis prediction is realized.

The invention relates to the technical field of medical image processing, in particular to a multimodal-based digestive tract tumor lesion image segmentation method and segmentation system. The method comprises the following steps: acquiring an alimentary canal tumor lesion image, and extracting an alimentary canal tumor ultrasonic image; evaluating the tumor invasion depth based on the digestive tract tumor ultrasonic image; performing focus three-dimensional visual modeling according to the tumor invasion depth to obtain a digestive tract tumor focus model; extracting a tumor tissue pathological image according to the digestive tract tumor lesion image; performing tumor region segmentation based on the tumor tissue pathological image to obtain digestive tract tumor region data; performing tissue arrangement anomaly detection based on the digestive tract tumor region data to obtain tissue arrangement anomaly data; and calculating a tissue arrangement disorder index according to the tissue arrangement abnormal data and the digestive tract tumor area data. According to the invention, the tumor identification accuracy and the malignant region segmentation precision are improved based on the medical image processing technology.

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

The invention discloses an ovarian tumor cross malignancy classification system based on a hybrid radiomics model. On one hand, a backbone network in the Med3D network is used as a depth feature extractor to construct a target network, and multi-modal 3D magnetic resonance image data is used to carry out fine tuning training on the target network to obtain a corresponding ovarian tumor classification network for obtaining depth features and tumor classification results. On the other hand, omics characteristics of the tumor region of interest are extracted, and a tumor classification result corresponding to the omics prediction model is obtained. In addition, the depth features and the omics features are fused, and a corresponding classification result is obtained through a KNN classification algorithm. And finally, obtaining a final ovarian tumor cross malignancy classification result according to weighted average of the three classification results. According to the invention, by integrating various models and algorithms, more comprehensive feature mining and analysis are carried out on the multi-modal MRI image data of the ovarian tumor patient, so that the final ovarian tumor classification effect is improved.

The invention discloses a device for tracking a tumor position in real time in radiotherapy, and solves the problems of delay and inaccuracy in a traditional tumor position prediction method through a multi-model fusion method combining a deep learning model and a mathematical prediction model. Different from a traditional prediction method depending on an in-vitro marker or iconography technology, the tumor position is predicted in real time by adopting a long-short-term memory network, and the change of the tumor position along with respiratory movement is accurately captured by utilizing the strong time sequence modeling capability of the long-short-term memory network. By introducing the mathematical prediction model to compensate the future position of the tumor, the method can effectively solve the prediction error caused by system delay, and ensures that rays can accurately irradiate the tumor area in dynamic tumor tracking radiotherapy, thereby improving the accuracy and treatment effect of radiotherapy.

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

The invention 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 prognosis prediction method and a tumor prognosis prediction system, which are used for constructing a multi-modal fusion model based on image-pathology to improve the prognosis prediction efficiency of tumors, especially pancreatic cancer, and providing reference information for clinical decision-making. According to the technical scheme, the method comprises the following steps: S1, preprocessing an original tumor enhanced CT image, segmenting a tumor region, extracting radiomics features and depth image features of the tumor region, and establishing a CT image feature set; s2, after feature preprocessing is carried out on the tumor clinical data, clinical features with statistical significance are screened out, and a clinical feature set is established; s3, carrying out Hamp; e, preprocessing the pathological image, segmenting a tissue region, extracting spatial relation features, and generating a pathological spatial feature set; and S4, based on a feature interaction method, carrying out multi-modal fusion on the CT image features, the clinical features and the pathological spatial features, inputting a full-connection neural network, constructing a tumor survival risk prediction model, and outputting a tumor survival risk probability through the tumor survival risk prediction model.

The invention discloses a bioelectrical impedance tumor detection method based on pattern recognition, and the method comprises the following steps: S1, carrying out the electrical impedance measurement of a to-be-detected tumor region through a bioelectrical impedance analysis instrument, and obtaining the current, voltage and impedance values at different depths and frequencies; s2, designing a feature extraction module, and extracting key features in the electrical impedance data of each channel under different frequencies; s3, inputting the extracted key features into a multi-level feature fusion module, and fusing the electrical impedance features under different channels and frequencies to form new fused features; and S4, inputting the fusion features into a self-adaptive classification module, and adjusting model parameters in combination with a self-adaptive optimization method. According to the invention, automatic processing and classification are carried out on data through a pattern recognition algorithm, and the accuracy, sensitivity and reliability of tumor detection can be effectively improved in combination with the feature extraction module, the multi-level feature fusion module and the adaptive classification module.

A medical image processing apparatus and a medical image processing method that can improve extraction accuracy of a tumor region included in a diagnosis target image are provided. The medical image processing apparatus configured to extract a predetermined region from a diagnosis target image includes: an organ extraction unit configured to extract an organ region from the diagnosis target image; and a tumor extraction unit generated by executing machine learning using a known tumor region included in each medical image group as teacher data and using an organ region extracted from the medical image group and the medical image group as input data. The tumor extraction unit is configured to extract a tumor region from the diagnosis target image using the organ region extracted from the diagnosis target image by the organ extraction unit.

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.

Described herein are systems and methods for positioning a radiation source with respect to one or more regions of interest in a coordinate system. Such systems and methods may be used in emission guided radiation therapy (EGRT) for the localized delivery of radiation to one or more patient tumor regions. These systems comprise a gantry movable about a patient area, where a plurality of positron emission detectors, a radiation source are arranged movably on the gantry, and a controller. The controller is configured to identify a coincident positron annihilation emission path and to position the radiation source to apply a radiation beam along the identified emission path. The systems and methods described herein can be used alone or in conjunction with surgery, chemotherapy, and / or brachytherapy for the treatment of tumors.

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 discloses an HER2 state recognition method and system based on an IHC and HE bimodal image, and relates to the medical image processing and analysis technology, and the method comprises the steps: carrying out the preprocessing of a full-slice image WSI containing IHC and HE, and cutting the WSI into designated image blocks; constructing a multi-task learning framework to classify image blocks obtained by cutting the IHC image, and determining HER2 expression intensity in the tumor region; calculating the area ratio of the image block corresponding to each grade in the tumor region to calculate an IHC HER2 score; taking each image block of the cut HE image as the input of a Prov-GigaPath model, and aggregating the multi-dimensional features of each image block by using an ABMIL model, and taking the aggregated multi-dimensional features as an HE HER2 score; and converting the IHC HER2 score and the HE HER2 score into one-hot codes, and outputting an HER2 judgment result by using a logistic regression model. The HER2 state interpretation result can be quickly output, a pathologist is assisted in diagnosis, and the diagnosis efficiency and accuracy are improved.

The embodiment of the invention provides a tumor radiotherapy reaction prediction method and system based on multi-instance learning. The method comprises the following steps: constructing a radiotherapy image data set; extracting multi-dimensional features from the radiotherapy image data set and combining and splicing the multi-dimensional features to obtain embedded representation of the multi-dimensional features; the multi-dimensional features comprise two-dimensional convolution features and three-dimensional convolution features; a multi-instance learning model based on a Transform is constructed; taking the embedded representation of the multi-dimensional features as model input, and performing iterative training on the multi-instance learning model to obtain a trained multi-instance learning model; and predicting the radiotherapy metaphase reaction of the patient by using the trained multi-instance learning model, analyzing and evaluating the model prediction process in combination with interpretable analysis, and performing model evaluation on the prediction result of the tumor area of the patient. According to the method provided by the invention, the reaction condition after tumor radiotherapy can be predicted, so that a doctor can be intelligently assisted to carry out precise radiotherapy decision on a patient, and the prognosis risk and harm of the patient are reduced.

The invention discloses a tumor ablation puncture operation planning method and system, and relates to the technical field of medical image processing, and the method comprises the steps: obtaining multi-modal image data of a tumor region, carrying out the preprocessing, carrying out the fusion processing of the preprocessed multi-modal image data through an image fusion algorithm, generating a fusion image, and carrying out the planning of a tumor ablation puncture operation based on the fusion image. A tumor area is automatically marked by using an image segmentation technology, a doctor manually corrects a segmentation result, an optimal puncture path is selected through design and simulation according to a fusion image with a tumor mark, a model and a tumor entity position are accurately aligned by using a space positioning technology based on the fusion image and augmented reality equipment, and an AR view is generated. And under the guidance of the AR view, the puncture needle carries out ablation operation according to the optimal puncture path. Through high-quality images and three-dimensional guidance, high precision and visualization of the whole process from image acquisition to operation implementation are achieved, and the safety and success rate of the operation are remarkably improved.

The invention discloses an esophageal cancer neoadjuvant chemotherapy and post-immunization pathology complete remission prediction system and method, and relates to the field of medical image processing, and the system comprises an image processing module which is used for carrying out the standardized processing of an input image, and segmenting a tumor region in the image; the feature extraction module is used for extracting voxel-level radiomics features from the tumor area; the feature screening module is used for screening out a plurality of voxel-level radiomics features with the most predictive capability; and the pathology prediction module is used for generating a comprehensive feature vector according to the segmented input image and the screened voxel-level radiomics features, and predicting the curative effect of the combination of neoadjuvant chemotherapy and immunotherapy according to the comprehensive feature vector. According to the technical scheme provided by the invention, the accuracy of predicting complete pathologic remission after neoadjuvant chemotherapy and immunotherapy of esophageal cancer can be improved, the defect that the heterogeneity in tumors cannot be considered in a traditional imaging omics technology is overcome, and the method has a relatively high application value.

The invention discloses a tumor and target region synchronous segmentation method and system based on visual language guidance. The method comprises the following steps: constructing a multi-modal data set fusing a medical image and a clinical text; a unified encoder is adopted to extract shared visual features, a double-branch decoder is adopted to predict a tumor region (GTV) and a clinical target region (CTV) respectively, and information sharing and task differentiation modeling are achieved; introducing a medical pre-training large language model, and respectively extracting language features related to GTV and CTV; a visual language collaborative attention module is designed, dynamic fusion and alignment of visual languages are carried out, task related semantics are guided and enhanced, and redundant information is inhibited; and outputting a precise synchronous segmentation result of the GTV and the CTV after fusing the multi-modal features. Compared with an existing scheme, the problems that the boundary of a tumor target region in a medical image is fuzzy and multi-modal information is insufficient in utilization are effectively solved, the segmentation precision and robustness are remarkably improved, and reliable support is provided for clinical radiotherapy planning.

The invention discloses a medical image weak supervision variation segmentation method based on single-point labeling, and relates to the technical field of tumor segmentation in medical images. The method comprises the following steps: firstly, randomly taking a point in a target tumor area, and constructing a new anisotropic Riemannian metric according to texture direction information of a whole CT image, local direction information of gray scale at a boundary and heterogeneity of the gray scale; secondly, calculating two distances from all points in the whole CT image to the points taken in the tumor area: geodesic distance and Euclidean distance based on new Riemannian metric; then, the two kinds of point-by-point distance information are embedded into a Chan-Vese model; and finally, proving the existence and uniqueness of a model solution, and solving the model solution by adopting a Douglas-Rachford algorithm to obtain a final tumor segmentation result. According to the method, a more accurate, more effective and more robust segmentation result can be obtained, the method can be used for tumor detection, positioning and segmentation, the workload of manual labeling can be greatly reduced, and the method has high clinical application value.

The invention discloses a multi-task collaborative bone tumor CT image segmentation method, system and device, and relates to the technical field of bone tumor CT image segmentation. According to the multi-task collaborative bone tumor CT image segmentation method, a bone tumor CT image segmentation model is adopted to carry out bone tumor segmentation on a target bone tumor CT image; the bone tumor CT image segmentation model is trained and obtained through the following method: obtaining an original bone tumor CT image; according to the original bone tumor CT image, screening through a CT value range to obtain a bone image and a soft tissue image; marking a tumor area and a skeleton area in the bone image and the soft tissue image to form a training sample; constructing a multi-task learning network including a skeleton segmentation task and a tumor segmentation task; and training the multi-task learning network based on at least one group of training samples to obtain a bone tumor CT image segmentation model. According to the invention, the bone tumor can be identified more accurately, and the precision of bone tumor segmentation is improved.

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 provides a nerve invasion prediction system and method based on a hybrid model, and electronic equipment. The method comprises the following steps: acquiring initial medical information; preprocessing the initial medical information to obtain target medical information; based on the target medical information, acquiring deep learning image features through multi-scale convolution and an attention mechanism of a deep learning model, investigating a tumor region and a peritumor region through a radiomics model, and acquiring radiomics image features; carrying out feature splicing on the radiomics image features and the deep learning image features to obtain classification model input information; and inputting the classification model input information into the classification network to obtain a category prediction result of the initial medical information. According to the feature extraction method combining deep learning and radiomics, the features of the tumor can be captured from different angles, the accuracy of nerve infringement prediction is improved, and the precision requirement of nerve infringement prediction of the prostate part is met.

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

The invention 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 present invention discloses a multi-wavelength tunable laser and sensing system, which relates to the field of lasers and includes: a setting module for presetting or selecting two wavelengths of laser beams in the laser; a camera module for collecting the user's target skin surface image for treatment in real time, identifying the tumor area and the healthy skin area in the image data, and identifying the current skin surface position of the laser beam. The present invention identifies the tumor area and the healthy skin area in the operation data by collecting the user's target skin surface image for treatment, further plans the laser treatment path based on the target skin surface image, and controls the laser in the process of moving according to the treatment path, in combination with the sensor to sense the tumor thickness, so as to achieve further intelligent control of the movement speed of the laser in the process of moving according to the treatment path, so that the tumor position on the user's skin surface can be treated more adaptively.

The invention provides a T1 enhanced image generation method and system based on multi-mode MRI fusion. The method comprises the following steps: processing an original multi-mode MRI image to sequentially obtain a registered multi-mode MRI image, soft mask embedding and preprocessed image data; sequentially obtaining global features, features after coordinate system alignment and projection features by using the preprocessed image data; splicing the soft mask embedding and projection features of the tumor area to obtain a weight coefficient; fusing the aligned features of the coordinate system by using a weight coefficient to obtain a fused feature; utilizing the global features to obtain features after cross-modal attention enhancement; generating a T1CE image and a focus probability graph through the fused features and the features after cross-modal attention enhancement; training the model by using the T1CE image and the lesion probability graph to obtain an optimized image generation model; and obtaining a T1 enhanced image by using the optimized image generation model. According to the invention, a cross-modal attention mechanism is introduced to make full use of information of different modals.

The invention provides a nuclear magnetic resonance image colorectal tumor segmentation method based on a multi-branch guide network. The method comprises the following steps: a data normalization step; a precise segmentation preprocessing step; a tumor judgment pretreatment step; a calibration step; and obtaining a prediction result. A double-branch guide structure is used, a main branch focuses on accurate segmentation of a tumor area, and a secondary branch focuses on judgment of whether a tumor exists or not. The secondary branch guides the main branch, thereby helping to reduce the false positive rate and improve the accuracy of the segmented region. The method is combined with a confidence learning method of uncertainty analysis, reduces the attention to dispute regions, enables the model to be more focused on the segmentation of main regions, and improves the overall segmentation effect.

The invention relates to the technical field of tumor image processing, in particular to a method for improving tumor radiotherapy target area sketching precision. The method comprises the following steps: acquiring an edge connecting line for connecting a central pixel point and different edge pixel points in a possible tumor region; according to the gray level distribution of the pixel points on each edge connecting line, the pixel gray level uniformity of each edge connecting line is obtained, and a connecting line cluster is obtained; analyzing the position distribution characteristics of edge connecting lines in different connecting line clusters and the number of the connecting line clusters, and obtaining the overall gray uniformity of each possible tumor region; obtaining the tumor credibility of each tumor possible region by combining the gray distribution of the boundary pixel points in each tumor possible region and the boundary morphological characteristics of each tumor possible region; an optimal tumor area is obtained, and a tumor target area is sketched. According to the invention, the accurate gray scale range of the tumor area is obtained, so that the target area sketching precision is improved.

The invention relates to the technical field of medical images, and discloses a segmentation method of a tumor region in a medical image, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-segmented medical image, and carrying out the random sampling and local search based on the to-be-segmented medical image, and obtaining a target voxel with a minimum local gray value; secondly, taking the target voxels as seed points of a watershed algorithm, and performing watershed algorithm segmentation on the to-be-segmented medical image to obtain a preliminary segmentation result; thirdly, combining and optimizing the candidate segmentation regions in the preliminary segmentation result by utilizing texture feature data of the candidate segmentation regions to obtain an intermediate segmentation result; and finally, clustering the intermediate segmentation results to obtain a target segmentation result corresponding to the tumor region so as to ensure the accuracy of the segmentation result of the tumor region.