69 results about "Hepatic tumour" patented technology

The invention relates to a liver tumor segmentation method and system based on a convolutional neural network. The method comprises the following steps: constructing a U-shaped structure encoder / decoder network; wherein the encoder / decoder network with the U-shaped structure comprises a contraction path and an expansion path; residual blocks are added into convolution layers of down-sampling and up-sampling of the U-shaped structure encoder / decoder network to form a jump connection structure; adding a weight attention mechanism module into down-sampling of the U-shaped structure encoder / decoder network, and combining the weight attention mechanism module with the jump connection structure to form a convolutional neural network model; training the convolutional neural network model by adopting a data training set to obtain a trained convolutional neural network model; and inputting a to-be-segmented liver image into the trained convolutional neural network model to obtain a liver segmentation result image and a tumor segmentation result image. According to the invention, the tumor segmentation accuracy can be improved.

The invention discloses a tumor region segmentation method and system for a liver CT image based on a cascaded full convolutional network. According to the method, automatic segmentation of a liver tumor region is realized by using a full convolutional network model. The method comprises the following steps: performing preprocessing operations such as filtering and sharpening enhancement on a CT image; training a cascade network by using the preprocessed CT data set; achieving liver region segmentation by using two stages of FCN networks and segmenting a tumor region from a liver region of interest. A first-stage FCN network uses a variable pooling method so that more liver features are reserved and liver segmentation precision is improved; and a second-stage FCN uses hole convolution replaces an original convolution layer and a pooling layer, so that the position information of the target can be reserved while a image is reduced and the feeling domain is increased. A large number of features do not need to be manually extracted, and the tumor region can be effectively and accurately segmented from the liver CT image.

The invention discloses a deformable context coding network model and a liver and liver tumor segmentation method, which can accurately determine the contour positions of a liver and a liver tumor, realize more accurate liver and liver tumor segmentation, have a wide application prospect, and are suitable for popularization and application. According to the network model of the invention, deformable convolution is utilized to enhance the feature representation capability of a traditional encoder, help the traditional encoder to learn a convolution kernel with adaptive spatial structure information, and eliminate interference of different sizes of liver tumor positions; according to the method, the global feature information in the image is coded by using the Ladder spatial pyramid poolingmodule for extracting the multi-scale context information, so that the contour positions of the liver and the liver tumor are determined more accurately, more accurate liver and liver tumor segmentation is realized, and the method has a wide application prospect.

The invention discloses a three-way-decision-based liver tumor CT image classification method. A classifier is trained; image preprocessing and feature extraction are carried out on a marked liver CT image training sample to form a feature vector; rule extraction is carried out by using an attribute reduction method to form a knowledge base, thereby providing a basis for follow-up case classification diagnoses. T-be-classified cases are processed image pretreatment to extract liver regions, hepatic vessels, and liver tumors; 14 case features are calculated; the case feature values are inputted into a three-way-decision-based classifier; and then cases are classified into three types: a benign tumor, a malignant tumor, and an uncertain tumor. Therefore, a doctor can make different therapeutic regimens for different tumor types.

The invention provides a liver tumor image segmentation method based on a two-way three-dimensional convolutional neural network. The method comprises the following steps: preparing a data set; performing filtering and standardized preprocessing operation on the original CT image in the data set; training a two-way three-dimensional convolutional neural network with a parallel path structure; segmenting a liver tumor in the CT image by using the trained two-way three-dimensional convolutional neural network, and generating a probability graph of a tumor segmentation result; based on the two-way three-dimensional convolutional neural network, tumor segmentation of the liver CT image is fully automatically realized, the accuracy is high, and the speed is high.

The invention discloses a method and a device for liver and liver tumor image segmentation. The method and the device can effectively and accurately segment liver and liver tumors in different modes.The method comprises the following steps: (1) an abdomen magnetic resonance image is acquired; (2) a liver model is used to determine a region of interest, and the liver model is Dial3DResUNet, and iscombined with a long-short distance jump connection structure and mixed hole convolution to fully capture image global structure information so as to perform accurate liver segmentation; and (3) a liver tumor model is used for fine segmentation to reduce false positive results, wherein the liver tumor model is H3DNet and is composed of Hybrid 3D convolution. The model parameter quantity is greatly reduced while the liver tumor three-dimensional features are effectively extracted, and the model optimization difficulty and the overfitting risk are reduced.

The invention discloses a method for predicting the morphological change of a liver tumor after ablation based on deep learning. The method comprises the following steps: acquiring a medical image mapof a patient before and after liver tumor ablation; preprocessing the medical images before and after ablation; obtaining a preoperative liver region map and a preoperative liver tumor region map; acquiring a postoperative liver region map, a postoperative ablation region map and a postoperative liver tumor ghost image; obtaining a transformation matrix by using a CPD point set registration algorithm, and obtaining a registration result graph according to the transformation matrix; training the network through a stochastic gradient descent method to obtain a liver tumor prediction model; andpredicting the morphological change of the liver tumor of the patient after ablation by using the liver tumor prediction model. According to the method, the morphological change of the liver tumor after ablation of the patient can be predicted according to the CT / MRI image of the patient, a basis is provided for quantitatively evaluating whether the ablation area completely covers the tumor, a doctor can accurately evaluate the postoperative curative effect, and a foundation is laid for a subsequent treatment scheme of the patient.

The invention discloses a liver tumor automatic accurate robust segmentation method in a CT image, and the method comprises the steps: (1) carrying out the preprocessing of the CT image, and extracting a liver region in the CT image; (2) applying an LIB-based application The SLIC image super-pixel segmentation method comprises the following steps: carrying out multi-level iterative segmentation ona liver region, dividing a region with relatively consistent gray scale and texture in the liver into the same super-pixel, and obtaining a boundary between a normal liver parenchyma and a liver tumor; (3) performing normal hepatic parenchyma / hepatic tumor binary classification on each pixel point of the liver region according to the local gray scale and the texture characteristics of the image;and (4) classifying the superpixels generated in the step (2) according to a liver region pixel point classification result to obtain a final liver tumor segmentation result. The method can effectively solve the segmentation difficulty caused by CT imaging noise, fuzzy liver tumor boundaries in CT images, complex structure, various gray levels and the like, and improves the efficiency and precision of computer-aided diagnosis of liver diseases.

The invention discloses a liver tumor ablation postoperative three-dimensional space curative effect evaluation method and system, and the method comprises the steps: obtaining a medical image map ofa patient, and preprocessing the medical image map; performing image segmentation and three-dimensional modeling on the medical image map to obtain a preoperative liver region, a preoperative liver tumor region, a postoperative liver region and a postoperative ablation region; performing global registration on the preoperative liver region and the postoperative liver region by using a CPD point set registration algorithm to obtain a transformation matrix, and then calculating a registration result tumor region corresponding to the preoperative liver tumor region after the ablation operation;performing extraction and local registration on the common features, and then adjusting a tumor region of a registration result; and calculating the distance between the postoperative ablation area and the boundary of the registration result tumor area, and visually displaying the distance in a three-dimensional space. The invention can assist a doctor in evaluating the curative effect after ablation, and lays a foundation for making a follow-up treatment scheme of a patient.

The invention discloses a liver tumor recognition method based on a self-supervised dense convolutional neural network. The method comprises the following steps: obtaining a liver slice data set from a magnetic resonance image of a patient, carrying out the segmentation of the slice data set, training a constructed dense convolutional network, enabling the trained dense convolutional network to serve as a coding module, constructing a self-supervised learning network, and carrying out the recognition of a liver tumor through the self-supervised learning network. Through dense connection of the coding modules, a tumor region of a part of an image in a slice data set is manually marked, then the whole slice data set is segmented, segmented image blocks are adopted to train a self-supervised learning network, and the trained self-supervised learning network is adopted to automatically identify tumors in the image. The dense convolutional neural network based on self-supervision is used for liver tumor recognition, a puzzle task is set as a self-supervision upstream training task, useful representations are learned from a large number of images which are not subjected to medical labeling and are used for learning training of downstream target tasks, and therefore the purposes of automatically expanding training data samples and improving the recognition efficiency are achieved. The dependence on expert experience and historical data is reduced, and the recognition accuracy of the liver lesion region is improved.

The invention relates to a liver tumor segmentation method and system based on deep learning. The method comprises the following steps: preprocessing a collected data set; a network model being built according to the preprocessed data, wherein the network model comprises a plurality of lower convolution layers and a plurality of upper convolution layers, the lower convolution layers are connected to the upper convolution layers through jumping, and feature maps obtained by the lower convolution layers are connected with the upper convolution layers through an attention module on jumping connection; inputting the preprocessed data into the network model for training to obtain an optimal network model; and segmenting a to-be-processed CT image by using the optimal network model to obtain a liver tumor region. According to the invention, wrong segmentation can be reduced, and high precision can be obtained.

The invention discloses a liver CT tumor segmentation and classification method based on deep learning. The method comprises the following steps of preprocessing a liver CT image, performing interpolation according to resolution ratios in X and Y directions, performing resampling, searching data, constructing training data, performing liver and tumor segmentation through 2D Dense U-Net, extracting corresponding three-dimensional features through 3D Dense U-Net, and forming a generative adversarial network. The invention belongs to the technical field of liver CT tumor segmentation and classification methods, and particularly relates to a method which can assist doctors in early screening and diagnosis of liver cancer by using a deep learning technology, can greatly reduce the workload of the doctors, improves the accuracy of liver cancer diagnosis, and improves the accuracy of liver cancer diagnosis. The liver CT tumor segmentation and classification method based on deep learning has a great application prospect.

The invention provides a preparation method of an effective component of trumpetcreeper, which comprises the following steps: extracting a medicinal material by heating, concentrating, separating by a silica gel column, and eluting; concentrating and drying eluent, and continuing to separate by preparative liquid chromatography; and collecting solution, and obtaining the effective component after the solution is concentrated and dried. The effective component of the trumpetcreeper can be taken as an active ingredient and is added with medicinally accepted excipients or carriers to be prepared into a preparation according to the method recorded in pharmacy, and the preparation of the prepared drug comprises a liquid preparation, a solid preparation, a capsule preparation or a pill preparation; and the administration comprises oral administration or injection administration. The effective component of the trumpetcreeper provided by the invention can be applied in the preparation of drugs for treating and preventing tumor. The preparation method of the invention has simple operation and stable quality, and no report that the trumpetcreeper can resist the activity of liver tumor exists in the prior art, so the preparation method provides a research basis for research and development of novel trumpetcreeper anti-tumor drugs.

The invention belongs to a biotechnology and in particular discloses a manipulation system for regulating and controlling specific expression of a suicide gene in liver tumor cells. The manipulation system comprises: (1) a Cre recombinase gene which is driven by an AFP promoter to express; (2) the suicide gene which is driven by an SURV promoter to express, wherein one segment of LoxP-Stop-LoxP sequence is inserted between the SURV promoter and the suicide gene, Stop is a transcription termination sequence and LoxP is a specific recognition sequence of Cre recombinase. According to the manipulation system disclosed by the invention, the AFP promoter is used for driving the expression of the Cre recombinase in liver cancer HepG2 cells; the Cre recombinase is used for cutting off the transcription termination sequence Stop between the two LoxP so that the tumor specific SURV promoter can be used for directly driving expression of TK / GCV; liver tumor cell apoptosis is specifically induced, but cells with any other types in bodies are not injured.

The invention discloses a method and a device for segmenting a liver tumor under a CT image. The method comprises the steps: cutting each original CT image of a first preset size in a training set into three adjacent slices of a second preset size, taking a segmentation result of a middle slice as a label, and enabling the corresponding three adjacent slices to form a training sample; inputting one or more training samples into a preset network for training to obtain a trained segmentation model; wherein the preset network comprises a preprocessing module and a full-convolution point cutting network; and for any to-be-detected CT image, inputting three adjacent slices of a first preset size into the trained segmentation model each time, and obtaining a segmentation result of a middle slice based on a joint loss function. According to the scheme, the problems existing in traditional liver tumor image segmentation can be effectively solved, the problems of large imaging difference of liver CT images, different tumor shapes and sizes and the like are solved, and the performance and effect of the automatic liver tumor segmentation method are improved.

The invention provides a detecting method for the blood serum protein of four kind of familiar tumours which are, tumour, meningioma tumour, mammary tumour and hepatic tumour by the following steps: (1) using the surface concentrated laser SELDI to determine the protein group graphoes of the blood serum specimen of the tumour patient and the healthy person. (2) screening the corresponding tumour marker and establishing the detecting model for analysis integrated the biological informatics technique. The method of the invention is adopted in the process of selecting the tumour medicine and provides a new way and method for early discovery and detection of the tumour. The discovered new seriial protein m / z peak also provides a base and resource for seeking new better tumour marker, meanwhile, provides a new thought for exploring the generation and development of tumours.

The invention provides a method for preparing an active ingredient of yerbadetajo herb, which comprises the following steps of: heating and extracting medicinal materials, concentrating the extract, separating the extract with a silica gel column, eluting the extract, concentrating and drying the elute, continuously separating the elute by using a prepared liquid phase chromatograph, collecting the solution, and concentrating and drying the solution to obtain the active ingredient. The active ingredient of the yerbadetajo herb can be used as an active ingredient, and pharmaceutically acceptable medicinal excipient or carrier is added into the active ingredient to prepare a preparation according to a method recorded on pharmaceutics. The preparation form of the prepared medicament is liquid, solid, capsules or pills, and the administration mode is oral administration or injection administration. The active ingredient of the yerbadetajo herb provided by the invention can be applied in preparing medicaments for treating and preventing tumor. The preparation method has simple and convenient operation and stable quality, and provides a research foundation for researching and developing new anti-tumor medicaments of the yerbadetajo herb because liver tumor activity resistance of the yerbadetajo herb is not yet reported in the prior art.

The invention relates to the medicine field, and especially relates to a traditional Chinese medicine compound composition for treating hepatopathy and medical applications thereof. The traditional Chinese medicine compound composition of the invention is prepared by the following raw material herbs: 6-30 parts of cape jasmine, 6-30 parts of licorice, and 6-30 parts of phellodendron. The traditional Chinese medicine compound composition of the invention is applicable to the treatment of hepatic fibrosis, hepatic cirrhosis, chronic hepatitis and liver tumor.

The invention discloses a multi-view-angle-based multi-task liver tumor image segmentation method. After an abdominal CT image is preprocessed, liver segmentation and tumor segmentation of the abdominal CT image are obtained at the same time in a slice form through a convolutional neural network model. The input of the model is a three-dimensional CT slice with the size of 256 * 256 * 3, and the output of the model is corresponding segmentation of a middle slice. The model comprises a segmentation module and a fine trimming module, and a rough segmentation result and a fine trimming segmentation result are obtained respectively. The model is optimized through a combined loss function, and instability in the optimization process is avoided. According to the method, segmentation is carried out from three perspectives of a three-dimensional CT image, and three segmentation results are fused into one to obtain a final segmentation result. According to the method, liver and tumor segmentation of the abdominal CT image is realized, and the problems that three-dimensional space information cannot be utilized and optimization is unstable in the segmentation process are effectively solved.

The invention discloses a CT arbitrary section ultrasonic visual field simulation auxiliary ablation path planning method and system. The method comprises the steps of : (1) obtaining a CT image, andreconstructing data of bone, body surface, liver, tumor and blood vessel; (2) selecting the position of an ultrasonic probe parallel to a right hypochondrium gap, generating a CT section passing through a tumor center, and ensuring that the section does not intersect with a rib; (3) carrying out clipping transformation on the CT section, and simulating an actual abdominal ultrasonic visual field range; (4) generating a needle inserting path in the ultrasonic visual field plane, and the path should meet the constraint condition that the path does not intersect with the blood vessel; and (5) generating a simulated thermal field; judging the space coverage relation between the thermal field and the tumor, and if the space coverage relation is not complete, obtaining the parallel cross sectionof a plane along the long-axis direction of the coronary tumor, and executing the step (3) until the thermal field completely covers the tumor.

The invention relates to a technique for extracting active proteins from seashore mallow seeds, which is characterized by comprising the following steps: pulverizing seashore mallow seeds, degreasing, treating with ultrasonic, and filtering; and passing through DEAE (diethylaminoethanol)-cellulose columns, washing to remove impurities, eluting respectively with 0.01mol PBS (phosphate buffer solution), 0.01mol PBS-0.1mol NaCl / L and 0.01mol PBS-0.25mol NaCl / L, collecting, dialyzing, and carrying out freeze-drying to obtain the product. The active proteins extracted by the technique provided by the invention can be used for preparing health food or medicines for enhancing humoral immunity and liver tumor resistance, and can be made into capsules, tablets, oral liquids, injections and other health products and medicines as required according to the biological activities.

The invention provides a method for establishing a tumor excision margin distance field model, and aims to solve the problems of high surgical risk and long surgical time in a liver tumor excision process in the prior art. The invention provides a method for defining a tumor incisal margin distance field, which comprises the following steps of: S1, acquiring imaging data of a patient, and establishing a model of a relative position relationship between a tumor and each tissue; s2, calling relative position information between the tumor and each tissue to judge the resection property of the tumor, if so, entering step S3, and if not, ending; s3, establishing an envelope surface according to the outer contour information of the tumor surface; and S4, determining a cutting edge surface and an edge distance field according to the envelope surface. The model is established to simulate the relative position relation of the tumor in the human body, and meanwhile, the risk in the operation is reduced, and the operation duration is shortened. The invention further discloses application of the defining method of the tumor excision margin distance field to positioning of the distance between a scalpel and a tumor in the liver resection operation process. The invention further discloses a system for establishing the tumor incisional margin distance field.

A method of Intelligent Analysis is provided for liver tumour. It is a method using scanning acoustic tomography (SAT) with a deep learning algorithm for determining the risk of malignance for liver tumour. The method uses the abundant experiences of abdominal ultrasound specialists as a base to mark pixel areas of liver tumours in ultrasound images. The parameters and coefficients of empirical data are trained with the deep learning algorithm to establish a categoriser model reaching an accuracy rate up to 86 percent. Thus, with an SAT image, a help to doctor or ultrasound technician is obtained to determine the risk of malignance for liver tumour through the method, and to further provide a reference base for diagnosing liver tumour type.

The invention provides a preparation method of an effective component of chrysanthemum, which comprises the following steps: extracting the medicinal material by heating, concentrating, separating by silica gel columns and eluting, and concentrating and drying eluent; and then continuing to separate by preparative liquid chromatography, collecting a solution, concentrating and drying the solution, and obtaining the effective component. The effective component of the chrysanthemum can be used as an active ingredient, and is added with pharmaceutically accepted excipients or carriers to be made into preparations according to methods recorded in pharmacy. The preparation form of the prepared drug is liquid preparations, solid preparations, capsule or pill preparations, and the administration method is oral or injection administration. The effective component of the chrysanthemum can be applied to drugs for preparing, treating and preventing tumor. The preparation method of the invention has simple operation and stable quality. The report that the chrysanthemum has the activity of liver tumor resistance does not exist in the prior art, so the invention provides a research foundation for novel chrysanthemum anti-tumor drugs.

The invention provides a preparation method of cortex meliae effective ingredients. The effective ingredients are obtained through the following steps: carrying out heating extraction, concentration, silicon gel post separation and elution on medical materials; concentrating and drying an elution solution; then, using the prepared liquid chromatogram for continuous separation; collecting the solution; and concentrating and drying the solution. The cortex meliae effective ingredients of the invention can be used as active components, and pharmaceutically acceptable medicine excipients or carriers are added into the effective ingredients to be prepared into preparations according to a method recorded on the pharmacy. The prepared medicine has the preparation forms such as the liquid preparation, the solid preparation, the capsule preparation or the gelatin pearl preparation, and the medication mode is oral medication or injection medication. The cortex meliae effective ingredients provided by the invention can be applied to the medicine for treating and preventing tumors. The preparation method of the invention has the advantages of simple and convenient operation and stable quality. In addition, no report about the liver-tumor-resistance activity of the cortex meliae appears in the prior art, and the invention provides the study basis for developing new cortex meliae anti-tumor medicine.

The invention provides a liver tumor early recurrence prediction method based on 3D CNN and LSTM. The method comprises the following steps: acquiring a CT image and clinical information of an HCC patient, and counting postoperative ER information of the HCC patient; carrying out image segmentation on the CT image, and then dividing obtained data samples into a training set and a test set; training by using the training set to obtain a 3D CNN model of a mapping relation between the CT image and the ER information; extracting iconography features of the data sample corresponding to the liver tumor area, performing dimension reduction on the extracted iconography features by using an LASSO logistic algorithm, and selecting out iconography features useful for ER prediction; carrying out statistical analysis on the clinical information, carrying out ER clinical factor univariate analysis by utilizing chi-square test, and selecting a clinical factor corresponding to a test level P less than 0.05; and training to obtain an optimal LSTM model by using features obtained by a 3D CNN model, imaging features useful for ER prediction and clinical factors corresponding to a test level P less than 0.05, and predicting the ER of an HCC patient by using the LSTM model.

The invention discloses a multi-scale DC-CUNets liver tumor segmentation method based on a bottleneck structure, and aims to solve the technical problem of insufficient liver tumor segmentation precision in the prior art. The method comprises the following steps: obtaining a liver mask from a vein CT image by utilizing U-Net based on a bottleneck structure; performing mask operation on the CT images in the arterial phase and the venous phase and the liver mask to obtain liver regions of interest in the arterial phase and the venous phase; processing liver regions of interest in the arterial phase and the venous phase by using dual-channel cascade U-Nets to obtain deep image features in the arterial phase and the venous phase; and performing feature fusion on the deep image features in the arterial phase and the venous phase, processing the fused feature blocks by using a softmax layer, and outputting a liver tumor segmentation probability graph. According to the method, liver tumor segmentation can be rapidly and accurately carried out.