18 results about "Hepatic tumour" patented technology

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 belongs to the technical field of biotechnology, and particularly relates to a colorectal cancer high liver metastasis cell line and a preparation method and application thereof. The cell line was preserved in the China Center for Type Culture Collection on January 14, 2026, and the preservation number is CCTCC NO: C202618. The cell line is derived from a mouse colorectal cancer cell line MC38, and is constructed by lentivirus transfection to express luciferase stably, and is obtained by continuously performing at least five rounds of liver metastasis tumor orthotopic iteration screening in C57BL / 6 mice through rectal submucosal injection. The MC38-P06 cell line provided by the application has a significantly enhanced liver metastasis ability, a shorter MC38-P01 model time, a higher liver tumor load, and a shorter mouse survival period, and can be used for screening and evaluating anti-liver metastasis drugs, researching liver microenvironment regulation mechanisms, and identifying liver metastasis related diagnostic markers.

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 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 discloses a method for creating a tumor personalized treatment simulation model based on overlapped microwave ablation in the technical field of biomedical engineering and computer simulation, and the method comprises the steps: constructing a three-dimensional geometric model containing a liver and a tumor based on medical image data of a patient; and establishing an overlapping microwave ablation finite element model coupling electromagnetic wave propagation and biological tissue heat transfer according to the three-dimensional geometric model. According to the method, the overlapping ablation process is dispersed into a plurality of continuous'ablation-cooling-re-ablation 'stages, the thermal field of the previous stage is inherited as the initial condition of the next stage, and the tissue characteristics changed due to temperature change are dynamically updated, so that accurate simulation of the overlapping thermal field cumulative effect is realized, and the accuracy of the thermal field cumulative effect is improved. Therefore, the prediction precision of the form of the final solidification area of overlapping ablation of the liver tumor is improved, and a doctor can clearly know the possible effect of ablation treatment according to the predicted visual solidification area and the quantitative evaluation index.

The present application relates to the technical field of image reconstruction, in particular to a liver tumor interventional preoperative blood vessel three-dimensional model reconstruction system, which can realize the following steps through the mutual cooperation between multiple modules: obtaining a liver CTA three-dimensional matrix of a target patient before liver tumor intervention; determining the blood vessel confidence corresponding to each voxel in the tumor peripheral region and the normal liver region; determining the gradient distortion degree corresponding to each voxel in the tumor peripheral region; determining the effective blood vessel proportion in the tumor peripheral region; performing adaptive blood vessel fracture completion screening processing according to the gradient distortion degree and the blood vessel confidence to obtain the tumor peripheral blood vessel region; and performing blood vessel reconstruction based on the effective blood vessel proportion in the tumor peripheral region and the gray value corresponding to the voxel in the tumor peripheral blood vessel region. The present application realizes the identification of the tumor peripheral blood vessel region and improves the accuracy of identification, thereby improving the rationality of liver tumor interventional preoperative blood vessel three-dimensional model reconstruction.

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 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.

The application discloses an intelligent liver image sign analysis and LI-RADS classification system based on a multi-task model, which inputs two-dimensional liver tumor images of multiple phases into a multi-task convolutional neural network model, automatically extracts potential features required by a classification task based on an LI-RADS standard in a main task, and extracts potential features required by main sign classification defined by the LI-RADS standard in a subtask, so that higher LI-RADS grading accuracy can be realized, and classification basis can be provided for doctors in actual applications such as clinical diagnosis. The application uses a multi-task convolutional neural network, an image-based tumor size automatic analysis method, and an end-to-end and supervised contrast learning combined mode to train a model, so that the liver tumor LI-RADS classification system which can provide judgment basis for doctors is realized.

The application provides a liver tumor ablation robot system based on image and in-situ fluorescence fusion navigation. The system is applied to the field of medical robots and interventional treatment and comprises an image data analysis module, a tumor activity probability map is constructed and output based on patient functional image data; an intelligent ablation needle, in the ablation process, fluorescence signals of the tissue around the ablation focus are collected in-situ at the needle tip; a multi-degree-of-freedom collaborative mechanical arm, which is used for controlling the intelligent ablation needle to perform puncture and ablation operation; a fluorescence signal processing and real-time classification model, which is used for extracting fluorescence kinetic characteristics from the fluorescence signals and outputting tissue state classification results in real time; a navigation and control console, which is used for fusing the tumor activity probability map and the tissue state classification results to generate an augmented reality navigation interface and controlling the intelligent ablation needle to perform the time sequence of ablation and signal collection. Thus, the problems of difficult identification of active lesions, difficult dynamic tracking and lack of intraoperative feedback in a complex background are solved.

The invention provides a deep learning-based recurrence level prediction system after liver tumor ablation, and mainly solves the problems that after a doctor performs liver tumor ablation on a patient, whether recurrence occurs or not is difficult to predict, and the recurrence level evaluation is inaccurate. Firstly, the system performs feature extraction and analysis by using a 3D convolutional neural network by inputting a medical image after liver ablation and a given ablation range so as to judge whether a tumor relapses or not. If the prediction result is no relapse, ending the process; and if the prediction result is recurrence, further analyzing by using another 3D convolutional neural network model specially aiming at the recurrence level so as to determine the specific recurrence level (such as level 1, level 2 or level 3). According to the method based on deep learning, the accuracy of predicting the recurrence condition after liver tumor ablation can be improved, refined recurrence grading can be achieved, and scientific and reliable decision support is provided for clinicians.

The invention relates to the technical field of medical image processing, in particular to a liver tumor MRI (Magnetic Resonance Imaging) image segmentation method based on deep learning, which is characterized by comprising the following steps: S1, acquiring a liver tumor MRI image data set; s2, the liver tumor MRI image data set is preprocessed; s3, constructing a ZCP-UNet network comprising a plurality of functional modules on the basis of the UNet; and S4, inputting the preprocessed liver tumor MRI image data set into the ZCP-UNet network for training and reasoning so as to improve the segmentation precision of the liver tumor image by the ZCP-UNet network. The method has the advantages of high calculation efficiency, high segmentation precision and good robustness.

The application belongs to the field of medicine, and particularly relates to a medicine composition for improving the treatment effect of liver tumor and the use thereof. The medicine composition comprises chrysin and hydroxycamptothecine or vincristine, and the weight ratio of the chrysin and the hydroxycamptothecine or vincristine is 1:(1.5-5). The main components of the medicine composition, i.e. the chrysin, the hydroxycamptothecine or the vincristine, are all natural and non-toxic, can be prepared into oral preparations together with common pharmaceutically acceptable carriers, and it is proved through pharmacodynamics experiments that the two components have a synergistic effect, and can greatly improve the treatment effect of liver tumor.

The application discloses a kind of liver tumor automatic identification and measurement method based on CT image, belong to medical image processing technical field, including the following steps: first, after the image pre-processing of patient abdominal CT image, input into liver component decomposition network, obtain the liver component image I1-I4 after decomposition, then liver component image I1-I4 is input into liver tumor segmentation network, and the segmentation result of tumor is output;Finally, the segmentation result is input into tumor measurement and analysis module to analyze tumor, and the detailed information of tumor is calculated.Through the above mode, the application solves the problems of poor applicability and robustness of traditional methods, difficulty in guaranteeing the accuracy of results, lack of model interpretability and other problems.The application has strong applicability and robustness, high accuracy, and the model has interpretability.

The invention provides a DBP-CEBPB mediated Tc17 cell methionine metabolism regulated non-alcoholic fatty liver disease related hepatocellular carcinoma immune intervention method, and relates to the technical field of liver tumor immunotherapy, and the method comprises the following steps: obtaining a CD8 + T cell population in a non-alcoholic fatty liver disease related hepatocellular carcinoma tumor microenvironment, and carrying out subpopulation analysis on the CD8 + T cell population to obtain a Tc17 cell subpopulation, wherein the Tc17 cell subpopulation has IL-17 phenotypic characteristics. According to the DBP-CEBPB mediated Tc17 cell methionine metabolism regulated non-alcoholic fatty liver disease related hepatocellular carcinoma immune intervention method, by regulating methionine metabolism in Tc17 cells, the immunosuppression state in the NAFLD-HCC tumor microenvironment is reversed, and the tumor progress is inhibited. Through intervention on metabolism and transcriptional regulation of Tc17 cells, a novel immunotherapy strategy for hepatocellular carcinoma caused by NAFLD is provided, and the problem that the effect of an existing immunotherapy scheme in NAFLD-HCC is limited is solved.

The application discloses a liver and liver tumor segmentation method based on a parallel residual attention network, relates to the fields of deep learning and medical image processing, and comprises data processing and preparation and a segmentation prediction visualization network.The data processing firstly processes a disclosed 3D abdominal medical image into a 2D slice image containing a liver and a liver tumor; the 2D data set is preprocessed and data enhanced; the segmentation prediction network uses a feature fusion network based on an encoding-decoding structure, after loading common pre-training weights through migration learning, the data-enhanced 2D image data is loaded into the segmentation model for training, and after the training is completed, special optimal weights suitable for the liver and the liver tumor are generated, which are used for liver and liver tumor special segmentation.The parallel residual attention convolution network structure proposed by the application for liver and liver tumor segmentation solves the problem that due to the complexity of the liver and the liver tumor, the segmentation of the liver and the liver tumor is prone to be interfered by other parts such as the kidney and the like, improves the segmentation efficiency and the segmentation precision, and has certain advancement compared with the prior art.

1. Name of the product in this design: Steep Pulse Therapy Device. 2. Purpose of this design: A vertical bipolar synchronous steep pulse therapy device, mainly used for ablation therapy of liver tumors. 3. The key design features of this product are its perspective view. 4. The image or photograph that best illustrates the design's key points: a 3D model.