69 results about "Hepatic tumour" patented technology

Disclosed are formulation and optimization protocols for delivery of therapeutically effective amounts of biologically active agents to liver, tumors, and/or other cells or tissues. Also provided are compositions and uses for cationic lipid compounds of formula (I).

The invention also relates to compositions and uses for stealth lipids of formula (XI).

Also provided are processes for making such compounds, compositions, and formulations, plus methods and uses of such compounds, compositions, and formulations to deliver biologically active agents to cells and/or tissues.

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 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 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 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 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 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 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 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 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.