71 results about "Hepatic tumours" patented technology

The invention relates to a liver tumor focal lesion prediction method based on iterative learning, and belongs to the field of computer models, and the method comprises the steps: firstly, carrying out the manual marking of a lesion region in part of medical image data, obtaining ROI training data, and constructing an automatic lesion detection model based on YOLOv8 through the data; in the ViT classification model, the cut image is divided into image blocks with a fixed size, and the image blocks are embedded into a high-dimensional space. Then, location information is added to each image block using location coding to preserve their spatial relationship. And inputting the embedded sequence into a Transform module, carrying out classification processing through a full connection layer, and finally outputting a predicted category. According to the method, a dynamic learning strategy is introduced, sample weights in a loss function are dynamically adjusted in different training stages, accumulative errors of the model can be reduced, and uncertain samples can be effectively processed at the same time.

The invention discloses a liver tumor image real-time segmentation method and system based on a YOLO algorithm, and the method comprises the steps: obtaining CT images and MRI images, and calculating the contrast indexes and signal-to-noise ratio indexes of a plurality of CT images; preprocessing the CT image, and dynamically adjusting an image enhancement strategy; a residual attention module is added on the basis of the YOLOv8 network, a multi-scale mask branch is introduced, and an optimized YOLO-Med segmentation network is constructed; inputting the enhanced CT image into a segmentation network, and training the segmentation network in combination with a loss function; and when an MRI image is input, through a multi-modal feature fusion mechanism, features of the MRI image and the enhanced CT image are aligned and fused and then are input into the segmentation network, and the segmentation network outputs a pixel-level segmentation mask for real-time segmentation of the liver and the tumor. According to the invention, by fusing the density characteristic of CT and the soft tissue resolution capability of MRI, the small tumor (diameter lt; 5 mm).

The invention discloses a 2.5 D dual-scale wavelet SAM-GAN liver tumor target region segmentation method, which comprises the following steps: establishing a segmentation model which is composed of a preprocessing module, a generation module and a discrimination module; an original image enters the preprocessing module and is subjected to neighborhood slice feature aggregation and anatomical perception to generate a 2.5 D image, the 2.5 D image is optimized and enhanced in the generation module and then input into the judgment module, and the judgment module evaluates the authenticity of the image output by the generation module and provides feedback training for the generation module. According to the method, the defects in the prior art can be overcome, the feature extraction capability on the liver tumor data set is improved, and the training stability and the model generalization capability are enhanced.

The invention relates to the technical field of medical image processing and surgical navigation, in particular to a liver segmentation, modeling and surgical simulation system based on a cascade neural network and a multi-scale reconstruction technology. The multi-scale dynamic registration module is used for carrying out coarse segmentation and fine segmentation on liver and focus areas of the medical image and is used for non-rigid registration and respiratory motion compensation of the multi-modal medical image; the physical enhancement modeling engine is used for constructing a liver vessel tree three-dimensional model containing hemodynamic characteristics; the operation navigation and risk assessment module is used for performing real-time navigation and operation path planning in the operation; and the self-adaptive computing framework provides underlying computing resource support for the system. And high-precision segmentation: through a cascade recursive network and a boundary sensitive loss function, the liver tumor segmentation Dice coefficient reaches 94-96%, which is increased by 6-12% compared with the traditional method.

The present disclosure provides a liver tumor segmentation method based on multi-temporal fusion and dual attention mechanism, which relates to the field of graphics processing technology and can solve the problem of poor liver tumor segmentation accuracy in the prior art. The specific technical solution is as follows: making a training set and a test set, and taking the arterial phase and portal venous phase same-layer images obtained from the same scan of a case as a pair of images; designing a multi-temporal feature fusion mechanism to fuse the features of the two-phase images; adding a dual attention mechanism to improve the network's attention to liver tumor features; constructing a liver tumor segmentation network based on the above two mechanisms with U-Net as the backbone network; using the images in the training set to train the liver tumor segmentation network; inputting the test set image pairs into the trained network, and using a threshold method to process the network output to obtain the liver tumor segmentation result. The present invention is used for liver tumor segmentation of multi-temporal CT images.

The invention provides a self-driven micro-robot for a liver tumor targeted therapy and real-time monitoring system, and relates to the technical field of biomedical engineering. The monitoring main system comprises a self-driven navigation and positioning module, an intelligent drug delivery module, a tumor monitoring and state evaluation module, a multi-modal data fusion module, a real-time self-learning decision module, an advanced molecular simulation and drug optimization module, a safety communication module, a power grid environment adaptation module and a fault detection and recovery module; the self-driven navigation and positioning module adopts a mode of combining a quantum genetic algorithm, a fuzzy constraint optimization algorithm and a self-adaptive simulated annealing algorithm, so that the navigation accuracy and positioning efficiency of the robot in a complex environment in the liver are remarkably improved, and the quantum genetic algorithm has the advantages that by means of the strong parallelism of quantum calculation, the positioning accuracy of the robot is improved; according to the method, numerous potential path combinations can be quickly explored, the path planning time is greatly shortened, and meanwhile, the path planning accuracy is improved.

The invention discloses a navigation system for a liver tumor resection operation, and aims to solve the problems that in the prior art, the accuracy of the liver tumor resection operation is insufficient, the risk in the operation is high, and the evaluation after the operation is incomplete. The system comprises a preoperative data acquisition module, a preoperative three-dimensional modeling module, an intraoperative positioning module, an operation navigation module, a risk prompt module and a postoperative data analysis module. Through preoperative CT or MRI image data acquisition, automatic segmentation and three-dimensional reconstruction of liver, tumor and blood vessels are carried out by using a deep learning algorithm, and a high-precision anatomical model is generated. During the operation, the position of the surgical instrument is obtained in real time by using an optical tracking or electromagnetic positioning technology, spatial precise alignment of the surgical instrument with a tumor and surrounding tissues is realized in combination with a model registration algorithm, and a surgical path is displayed through a real-time navigation interface. And the risk prompting module calculates an excision risk coefficient and prompts a doctor in real time based on the shortest distance between the tumor and the blood vessel, the relative distance between the instrument and the target area and the trajectory change.

The invention provides a numerical simulation method for large tumor treatment of single-needle multi-point microwave ablation, and belongs to the technical field of large liver tumor data simulation. The method comprises the following steps: establishing a finite element simulation model, and solving the finite element simulation model according to the interaction among an electromagnetic field, heat conduction and biological tissues to obtain a simulation result; constructing a single-needle multi-point microwave ablation numerical model; carrying out ablation simulation by utilizing the single-needle multi-point microwave ablation numerical model, outputting multi-point ablation result data, quantifying the size and form of a solidification area, and carrying out space overlapping analysis on a contour surface to obtain the characteristics of the solidification area; ablation simulation is carried out by setting parameter combinations of pull-back distances and ablation time of a plurality of antennas, and an optimal parameter combination is obtained by combining feature analysis of a solidification area, so that numerical simulation of large tumor treatment is completed; according to the invention, the problems of low ablation temperature, long simulation time and difficulty in accurate positioning of the antenna in the existing single-needle multi-point microwave ablation simulation method are solved.

The invention discloses a liver tumor diagnosis method based on a self-supervision and multi-phase contrast enhanced CT image. The method comprises the following steps: collecting and preprocessing a CT image of a liver tumor; training a target detection model by using the marked CT image; processing the preprocessed CT image by using the trained target detection model, obtaining a focus detection frame, performing cutting and data enhancement according to the focus detection frame, performing resampling, adding a real joint label to the focus detection frame, and constructing a second training set and a test set; constructing a classification diagnosis model based on multiphase data based on self-supervision and knowledge distillation, performing iterative training by using the second training set, and adjusting parameters according to a total loss function of the classification diagnosis model in the training process to obtain a final liver tumor classification diagnosis model; and inputting the CT image in the test set into the final liver tumor classification diagnosis model to obtain a diagnosis result. The precision, accuracy and efficiency of liver tumor diagnosis can be improved.

An existing liver tumor automatic segmentation method is insufficient in expression in small focus, low-contrast edge and long-range space dependence modeling, and consequently high false positive and boundary deficiency are caused. Pure CNN is limited by a receptive field, pure Mama easily loses local details, multi-level attention stacking significantly increases parameter quantity, and traditional side supervision differential correction is difficult to accurately focus an uncertain area. The invention provides an end-to-end network, parallel ResNet and Mamba dual-coding and direct reaching a decoder after AFF fusion at the same scale, bottom features are accessed to a multi-scale feature fusion module to complete deep semantic enhancement, and a decoding side forms a GARS module concentration boundary difficult-to-distinguish pixel by matching an MSCB-EUCB-LGAG lightweight chain with four-stage Gaussian attenuation residual self-correction. Clinical level, the method can significantly reduce leak detection of small tumors, reduce false positive, and maintain geometric integrity of edges.

The application discloses a liver tumor CT image segmentation device and system and a storage medium. The application reads a liver tumor CT image, pre-processes the liver tumor CT image to obtain a target liver tumor feature image, and segments the target liver tumor feature image through a preset liver tumor segmentation model based on compressed attention to obtain a liver tumor segmentation image. The liver tumor segmentation model based on compressed attention is embedded with a gradient centering optimizer, and the optimizer is used to standardize a weight space and an output feature space and improve the generalization performance of the model. Compared with the existing liver tumor segmentation image segmentation mode, the liver tumor segmentation model based on compressed attention embedded with the gradient centering optimizer can learn more representative features in a semantic segmentation task and obtain a liver tumor segmentation image with higher segmentation accuracy.

The invention relates to the technical field of image segmentation, in particular to an automatic real-time outline drawing system for a liver tumor ablation area based on an ultrasonic image. The method comprises the following steps: firstly, registering and aligning preoperative and intraoperative ultrasonic images, and eliminating displacement deviation caused by factors such as respiratory movement and probe shaking; further based on the essential difference between the ablation region and the surrounding normal liver tissue in ultrasonic echo expression, dynamic changes of region features are analyzed to obtain ablation feature parameters of each sub-region in the intraoperative ultrasonic image so as to evaluate the ablation state, and then the image is enhanced based on the ablation state so as to enhance the boundary definition, and finally the ablation state of the intraoperative ultrasonic image is evaluated. And the outline drawing accuracy of the outline of the liver tumor ablation area is improved.

The application provides an unsupervised liver tumor CT image segmentation method based on manual features, relates to the technical field of deep learning, and first collects normal liver CT images as a data set, and selects a tumor position in combination with clinical knowledge. A texture similar to real imaging is generated through three-dimensional simple noise binary mask, and histological features are used for morphological modeling. Then, the tumor texture is superimposed with the liver CT image at the selected position to synthesize a new liver tumor CT image. A segmentation model is trained by using the CT image with annotations, and the synthesized image is used for medical image segmentation. Finally, the segmentation results are evaluated by using the Dice coefficient, the Hausdorff distance, the standardized surface distance and the surface distance index. The application is helpful for artificially synthesizing tumor lesion images and generating voxel-level annotations, and provides a new perspective for solving the medical image annotation challenge and promoting liver tumor diagnosis and treatment.

This invention discloses a liver tumor segmentation method, system, and storage medium based on plain CT scans, belonging to the field of medical image processing technology. It addresses the problem of the lack of existing liver tumor segmentation methods that can both improve the clarity of liver tumor boundaries and preserve detailed information. The key technical points of this invention include: Step 1, acquiring plain CT scan data of the liver from liver tumor patients; Step 2, segmenting the plain CT images using a pre-trained segmentation model to obtain a liver region mask; the segmentation model is based on the UNet model, using PVT-V2 as the backbone network of the encoder, and the decoder adopts a cascaded upsampling path, fusing a residual denoising module, a gated attention module, a multi-scale feature fusion module, and a depth supervision mechanism in each stage; Step 3, further segmenting the liver region mask using the segmentation model to obtain a liver tumor mask; Step 4, extracting features and classifying the liver region mask using a classification model to obtain the benign or malignant tumor result.

The invention discloses an automatic liver tumor segmentation method based on a UNet model fused with an SENet attention mechanism. The method comprises the following steps that an encoder of the UNet model receives input image data; the method comprises the following steps: gradually extracting the features of an input image through an encoder of a UNet model, reducing the spatial resolution, enabling the encoder to specifically extract the features of the input image with higher resolution capability by using an SENet attention mechanism, and screening out effective representative image feature information; the bottom convolution block performs convolution processing on the image feature information output by the encoder and then inputs the image feature information into the decoder; and performing up-sampling operation through a decoder of the UNet model, and generating a segmentation result through a 1 * 1 convolution kernel. According to the method, the subject Unet model and the channel attention mechanism SENet are included, and the targeted recognition and prediction capability of the model on the tumor existence area can be effectively improved through the attention mechanism, so that the diagnosis accuracy and efficiency of a doctor are improved, and better diagnosis and treatment experience is brought to a patient.

Disclosed in the application are a Bifidobacterium longum probiotic combined with a qi-tonifying and blood-nourishing medicinal and edible homologous composition, a preparation method thereof and application thereof in regulating immunity, inhibiting tumor cell proliferation and reducing tumor-induced inflammation, including Bifidobacterium longum subsp. infantis NKU FB3-14 and the qi-tonifying and blood-nourishing medicinal and edible homologous composition; the qi-tonifying and blood-nourishing medicinal and edible homologous composition comprises, by weight, 5-30 parts of ginseng, 5-20 parts of longan arillus, 20-50 parts of astragalus root, 10-50 parts of polygonatum, 5-20 parts of poria cocos, 5-15 parts of mulberry, 5-20 parts of licorice, 10-30 parts of orange peel, 5-15 parts of citrus medica, 5-20 parts of double-petaled rose flower and 3-10 parts of hawthorn. The probiotic preparation and the medicinal and edible homologous material have a synergistic effect, and the ability to inhibit the proliferation level of liver tumor cells H22 in vitro is determined.

Provided are a tumor evaluation method and an application. Specifically, provided is a method for confirming the existence of liver tumors, evaluating the formation or formation risk of liver tumors and / or evaluating the progress of liver tumors, and the method comprises: evaluating the existence and / or the content of a modification state of a DNA region or a fragment thereof wherein a group of marker genes is in a sample to be tested. The present invention also relates to nucleic acids, a nucleic acid group, and / or a kit for evaluating modification states of a group of DNA regions, and a preparation method therefor.

The invention relates to a liver tumor segmentation method and device based on adaptive context awareness fusion, and the method comprises the steps: S1, obtaining and preprocessing liver CT data, and dividing the data into a training set, a verification set and a test set according to a proportion; s2, constructing an ACAF-Net (Adaptive Context-Aware Fusion Network), and constructing a double-enhanced dynamic convolution module in the transmission and fusion of the double paths; s3, fusing the double-enhanced dynamic convolution and the double attention weight, and constructing a double-dimensional mixed attention module; s4, fusing the sub-region segmentation error and the dynamic quantization difficulty, and constructing a double-error dynamic balance loss; s5, fusing the double-enhanced dynamic convolution module, the double-dimensional mixed attention module and the double-error dynamic trade-off loss to construct an ACAF-Net model; and S6, completing model training, verification optimization and performance evaluation by using the experimental data set. By using the method, the problems of fuzzy liver tumor boundary, large scale difference and unbalanced segmentation error are effectively solved, the segmentation precision and robustness are remarkably improved, and the method is suitable for automatic segmentation of the CT image liver tumor.

The invention discloses a traditional Chinese medicine composition for treating metabolic dysfunction related steatohepatitis inflammatory cancer transformation. The traditional Chinese medicine composition is prepared from the following raw material medicines in parts by weight: 15-20 parts of fructus perillae, 12-18 parts of semen brassicae, 12-18 parts of semen raphani, 12-18 parts of radix astragali, 28-32 parts of herba scutellariae barbatae, 12-18 parts of rhizoma polygonati, 6-12 parts of gecko, 12-18 parts of radix polygoni multiflori and 4-8 parts of liquorice root. The traditional Chinese medicine composition disclosed by the invention can be used for inhibiting the transformation progress of mouse metabolic dysfunction related steatohepatitis cancer, reducing the number of liver tumors, reducing the maximum surface area of the tumors and reducing the mRNA (messenger ribonucleic acid) levels of related tumor markers Gpc3 and Ly6d. In addition, the lipid level of the mouse liver and serum can be remarkably reduced, the fatty degeneration degree of the mouse liver is improved, the ALT and AST levels of the serum are reduced, and liver injury is relieved. In addition, the invention further discloses medical application of the traditional Chinese medicine composition.

To provide a method and system for supporting the determination of eligibility of conversion surgery (CS), which is radical resection after aggressive preoperative chemotherapy, for colorectal liver metastases, thereby solving the problem in which involvement of multiple factors make it difficult to determine whether the CS is feasible.SOLUTION: A method for supporting the determination of CS eligibility is provided by, at the initial diagnosis, examining and scoring the presence / absence of RAS mutations, the presence / absence of conservable segments, the number of tumors, and tumor contact with hepatic veins.SELECTED DRAWING: Figure 3A

The application relates to a semi-supervised medical image segmentation method based on double-view complementary consistency, which only uses a small amount of labeled medical image data to mine potential feature information in a large amount of unlabeled data to improve the segmentation model performance, and comprises the following steps: a feature space distillation mechanism is used to extract and transfer multi-scale semantic features through a teacher-student model; a double-view fusion strategy is used to optimize pseudo labels and optimize the learning effect of the model on unlabeled data; and an uncertainty estimation method assisted by a pre-trained autoencoder is used to optimize the reliability and segmentation precision of the model. Experimental results on a left atrium (LA) and liver tumor segmentation (LiTS) dataset show that the method achieves good effects in terms of segmentation precision and model robustness.

The present invention discloses a method and device for liver tumor segmentation, which adopts a staged processing strategy, combines a large tumor segmentation model, a small tumor detection model, a suppression model and a small tumor segmentation network, thereby improving the tumor detection rate, and also uses a suppression network to reduce the false positive rate, further improve the segmentation accuracy, and achieve effective identification and accurate segmentation of different types of tumors. The method steps mainly include reasoning with a large tumor segmentation model; reasoning with a small tumor detection model; removing redundant detection frames; suppressing the model; and segmenting small tumors and merging the results.

The invention discloses a liver tumor segmentation method based on multidirectional spatial features. The method aims at achieving accurate liver tumor segmentation. The method comprises the following steps: S1, preprocessing abdominal CT data to obtain NPY matrix data, and dividing the data into a training set, a verification set and a test set; s2, extracting initial features of NPY matrix data; s3, extracting multidirectional spatial features from the initial features; s4, constructing an adaptive gating attention module AGAM to perform feature extraction on the multidirectional spatial features to obtain local features; s5, constructing a six-way Mamba module HoM to perform feature extraction on the multidirectional spatial features to obtain global features; s6, fusing and enhancing the local features and the global features to obtain enhanced features; s7, constructing a channel analytic entropy module (CPEM) to screen the enhanced features to obtain final features; and S8, splicing the multi-scale final features to obtain a segmentation map of the liver tumor. The method can effectively improve the liver tumor segmentation precision, and can be used for liver tumor segmentation in a CT image.

The application discloses a multi-branch liver tumor segmentation method based on UNet, and comprises the following steps: first, a multi-branch liver tumor segmentation network based on UNet is constructed; then, a training sample set constructed by CT images in which liver tumors have been segmented in advance is used to perform deep learning training on the multi-branch liver tumor segmentation network based on UNet, so that a trained multi-branch liver tumor segmentation network based on UNet is obtained; finally, a CT image currently in need of liver tumor segmentation is sent to the trained multi-branch liver tumor segmentation network based on UNet, so that the CT image in which the liver tumor has been segmented is obtained. By strengthening the receptive field of the segmentation network feature map and fully utilizing the channel and spatial structure information, pixel-level details and spatial information can be better captured, so that the segmentation performance of the network on medical images is improved. Different from existing methods, the network architecture proposed in the application can capture fine details of local edges and global multi-scale information at the same time, so that spatial consistency is ensured.

The application relates to a preoperative and intraoperative liver point cloud data registration system and method, a terminal and a storage medium. The method comprises the following steps: extracting local mixed features of preoperative point cloud data and intraoperative point cloud data respectively; fusing the local mixed features to obtain global features of the preoperative point cloud and global features of the intraoperative point cloud; fusing the local mixed features of the preoperative point cloud, the global features of the preoperative point cloud and the global features of the intraoperative point cloud to obtain fusion features of the preoperative point cloud; similarly, fusion features of the intraoperative point cloud are obtained; fusing the fusion features of the preoperative point cloud and the fusion features of the intraoperative point cloud to obtain respective overlapping area masks and decoding features; obtaining a spatial transformation matrix of the preoperative point cloud and the intraoperative point cloud; and applying the spatial transformation matrix to the preoperative point cloud data to obtain a registration result of the preoperative point cloud data and the intraoperative point cloud data. The application can provide convenience for accurate positioning of liver tumors, shorten the operation time, and improve the accuracy and safety of the operation.

The application provides a liver tumor segmentation method, and belongs to the field of medical image segmentation. In view of the starting point of how to improve the global context feature extraction ability and how to efficiently combine with local information, two parallel encoders are used, wherein a VGG convolutional neural network branch is introduced to extract local features, and an axial decomposition self-attention branch is designed to extract global features, and then a shared residual fusion decoder is used to effectively integrate the information of the two branches. While reducing the model calculation complexity and reducing the training parameter amount, the application can significantly improve the segmentation effect of liver tumors and reduce the false segmentation or misclassification phenomenon.

The invention relates to a liver tumor segmentation method and device based on axial deep convolution and Transform, and the method comprises the steps: carrying out the training of a liver tumor segmentation model based on a model training data set, and obtaining a trained liver tumor segmentation model; and inputting a to-be-segmented liver tumor image into the trained liver tumor segmentation model to obtain a segmented image of the to-be-segmented liver tumor image. According to the constructed liver tumor segmentation model, the axial deep convolution module is used for replacing original traditional convolution, so that the calculation complexity is reduced; an interactive attention Transform module is designed to jointly utilize a plurality of small-scale feature maps, richer global features are obtained with lower calculation complexity, and a mark perception MLP module is adopted to process features of different dimensions through displacement axial operation, so that the learning ability of local detail information is improved.

This invention discloses a semantic segmentation method for liver tumor CT images based on VIT-CNN and frequency awareness, belonging to the fields of medical image processing and computer vision technology. The method first preprocesses the liver tumor CT images to eliminate interference and standardize their dimensions. Then, it constructs a VIT-CNN hybrid feature extraction architecture, capturing local detail features through CNN branches and global context features through VIT branches. A frequency-aware mechanism is introduced to enhance and adaptively fuse the two types of features in the frequency domain, highlighting effective features and suppressing interference. An improved U-Net segmentation head and a hybrid loss function are designed, the model is trained, and the CT images are segmented. Finally, the segmentation performance is evaluated using multiple metrics. This invention solves the problems of unbalanced global and local feature capture, weak anti-interference ability, and insufficient segmentation accuracy in existing methods, achieving accurate segmentation of liver tumor CT images and providing reliable support for clinical diagnosis and treatment planning.

The present invention discloses a method and system for segmenting liver and liver tumor data. Based on the Unet network model, a long-distance attention mechanism and a multi-feature fusion module are proposed, and a cross-layer attention mechanism is designed using the hierarchical structure of the network; the cross-layer attention mechanism and the multi-feature fusion module are embedded in the Unet network, and the ResCLA-MNet segmentation network model is constructed in combination with the residual structure; the LiTS liver and liver tumor public CT dataset is used for training, verification, and testing the segmentation performance of the network model; the 3DircaDb-01 liver and liver tumor CT dataset is used to test the generalization performance of the ResCLA-MNet network model to ensure that the network has a certain data migration applicability; the hospital abdominal liver and liver tumor CT dataset is collected, and the trained network model is used to practice on the dataset to verify the application effect of the network. Based on Unet, the present invention draws on the Unet network architecture, combines the advantages of the attention mechanism and multi-feature fusion, and improves the accuracy of the network model in segmenting the liver and liver tumor.

The invention relates to the technical field of medical instruments, in particular to a liver tumor microwave ablation operation auxiliary device. Comprising an operating bed, a supporting platform arranged on one side of the operating bed, a rotation driving mechanism arranged on the top of the supporting platform, a lifting adjusting mechanism arranged on the rotation driving mechanism and a lifting sliding seat arranged on the lifting adjusting mechanism. The flexible positioning part is arranged on the lifting sliding seat and extends to the position over the operating bed. Controllable pressure is applied to the chest and abdomen through the flexible positioning part, the breathing movement amplitude can be actively and physically limited, and compared with the mode of simply depending on breath holding of a patient, a more stable and more lasting operation environment is created, and the operation difficulty is reduced. After an operation is completed, the whole auxiliary device can be rotated away from the position over an operating bed through the rotation driving mechanism, transferring and carrying of a patient are not hindered at all, and the turnover efficiency of an operating room is improved.