167 results about "Hepatic tumours" patented technology

The invention relates to a CT image-guided liver tumor thermal ablation needle location and navigation system, which comprises a hardware constituting part and a software constituting part, wherein the hardware constituting part comprises a navigation workstation, an intra-operative location device and image display equipment. The software constituting part comprises an image data management module, a 3D visualization module, a 3D image automatic division module, a preoperative operation planning module, an intra-operative navigation module and a post-operative therapeutic effect evaluation module. The CT image-guided liver tumor thermal ablation needle location and navigation system disclosed by the invention can implement computer assisted therapy from various aspects, including 3D visualization and spatial location on liver tumor and neighboring structures, preoperative operation planning, real-time spatial location and navigation on a thermal ablation needle and post-operative therapeutic effect evaluation. The location and navigation system disclosed by the invention can shorten a time of thermal ablation therapy and reduce the occurrence rate of complications, so that the safety and the effectiveness of the liver tumor thermal ablation therapy are enhanced.

The invention relates to a liver cell membrane bionic liposome drug carrier, a preparation method of the liver cell membrane bionic liposome drug carrier, and application of the liver cell membrane bionic liposome drug carrier. The liver cell membrane bionic liposome drug carrier is characterized in that (1) the liposome drug carrier has a cell membrane protein component; (2) the liposome drug carrier is capable of loading drug in vitro, and is used for cell targeting fusion release; (3) the cell membrane protein component of the liposome comes from immortalized human liver cells, and is used for conveying targeted shear plasmids of a hepatitis virus genome; (4) the cell membrane protein component of the liposome comes from an immortalized liver tumor cell line, and is used for targeted drug delivery of liver tumor. The liver cell membrane bionic liposome drug carrier can be used for in vivo targeted hepatic cell transmission and drug delivery of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeat) gene targeting plasmids.

The invention relates to a liver targeting anticancer nanometer prodrug system basing on dendritic polymer, provides a method for preparing the prodrug system and the uses thereof and belongs to the technical field of biological medicine as well as the technical field of nano medicine. With the polyethylene glycol modified PAMAM treelike polymer of distal liver targeting group (T) as a carrier (T-PEG-PAMAM) and Doxorubicin (DOX) as treatment drug, the invention obtains the prodrug (T-PEG-PAMAM-DOX) through the covalent bond connection with degradable lysosome between the carrier and the Doxorubicin. The invention further provides the application of the liver targeting anticancer nanometer prodrug system basing on the dendritic polymer in the preparation of drugs for treating solid tumors. With the long-acting cycle in blood, the liver targeting anticancer nanometer prodrug system basing on the dendritic polymer can enhance the phagocytosis of hepatoma cells on polymers nano-micelles, realize the active and passive targeting on liver tumor tissues, improve the clinical efficacy and the bioavailability of present liver cancer therapeutic drugs and lower toxic and side effects.

The embodiment of the invention provides a liver tumor segmentation method and device based on multi-stage CT image guidance, and the method comprises the steps: obtaining an abdomen CT image with enhanced contrast, inputting the abdomen CT image with enhanced contrast into a preset single-channel full convolutional neural network, and obtaining a liver region-of-interest image; inputting the liver region-of-interest image into a preset tumor segmentation network to obtain a tumor segmentation result corresponding to the contrast-enhanced abdominal CT image; wherein the tumor segmentation network is obtained by performing multi-channel fusion training according to liver region-of-interest image samples in different periods and image samples with tumor region marks corresponding to the abdominal CT image samples. According to the embodiment of the invention, the liver tumor CT images in different periods are effectively subjected to feature mining by adopting a multi-channel fusion training network, so that the trained network has higher segmentation precision and better robustness on liver tumors.

The invention relates to the field of medical image processing, in particular to a method for automatically identifying a liver tumor type in an ultrasonic image. The method can provide auxiliary diagnosis when a doctor diagnoses a liver lesion according to a CEUS medical image, wherein the auxiliary diagnosis comprises the process of giving out a lesion identification result, the time when the lesion is relatively remarkable and a position where the lesion is relatively remarkable. The method specifically comprises: representing a CEUS image with a plurality of regions of interest (ROI); distinguishing different lesions by expressions and changes of the ROI in time and space; and representing a space-time relationship among the ROI by establishing models in time and space at the same time, wherein the models can determine relatively proper ROI and related parameters of the models according to existing CEUS lesion samples with an iterative learning method. After a sample is given out, the proper ROI can be determined by removing part of improper ROI in advance and a fast search method, and reference diagnosis is given out for the lesion.

The invention relates to an image processing technology and particularly relates to an interactive liver tumor region segmentation method based on watershed transform and classification through a support vector machine. The method comprises the following steps: 1) performing segmentation pretreatment on a liver tumor region; 2) performing watershed transform on an image of the pretreated liver region which is obtained in the step 1) and dividing the image of the pretreated liver region into numerous reception basins; 3) calculating four-dimensional characteristic vectors of all the reception basins which are generated by the watershed transform, marking tumor and normal liver tissues in the image of the liver region in an interactive manner and adopting a support vector machine process to classify the reception basins in a characteristic space; and 4) adopting communicating region detection to eliminate a false positive tumor region generated by the classification, and applying morphological operation to fill voids and smoothen edges. The region class is classified, and user marks are further utilized for training parameters of a classifier, thereby effectively improving the segmentation speed and the precision. The method has important application value in the fields of liver surgical planning and the like.

The invention relates to a liver tumor radiofrequency ablation technology, and aims at providing a method for precisely simulating radiofrequency ablation technology by utilizing ellipsoid to cover tumor. The method for precisely simulating radiofrequency ablation technology by utilizing ellipsoid to cover tumor comprises the following steps: preprocessing tumor images; clustering the tumor images into a plurality of ellipsoidal subclasses under reasonable constraint conditions; calculating the minimal coverage ellipsoids of each obtained class; giving an initial radiofrequency scheme by utilizing the number of the minimal ellipsoids automatically determined; determining an adjustable conical area, and manually adjusting the initial radiofrequency direction so as to completely keep away from great vessels and ribs, thus completing the establishment of the final radiofrequency scheme. According to the method, the purpose of designing the radiofrequency treatment scheme before operation can be realized, three-dimensional navigation can be conducted during operation, the adopted optimized algorithm can help a doctor more precisely and efficiently do operation, the harm of the operation to normal tissues can be reduced as far as possible, and the peripheral organs can be avoided, so that the radiofrequency ablation operation is safer and more effective.

The invention is applicable to the field of medical image processing, and provides a processing method and a processing system for dividing liver graphs of a CT (computed tomography) image. The processing method includes steps of acquiring the to-be-divided image; automatically positioning a liver position in the to-be-divided image by preliminarily testing a liver grayscale volume and liver grayscale; registering and extracting initial liver tissue graphs by rotary registering and sampling bands B with low degrees of freedom; combining distance graphs with the initial extracted liver tissue graphs to detect whether the graphs contain liver tumor tissue graphs or not; and precisely dividing the liver graphs in a registering manner by the aid of a liver distribution probability map and the sampling bands B based on mutual information. The processing method and the processing system have the advantages that manual intervention is omitted in an integral dividing procedure, the average volumetric error of the divided liver graphs is about 7.0%, accurate data can be provided for three-dimensional reconstruction and final simulation surgery for livers of patients, and the processing method and the processing system have certain clinical application value.

The invention discloses a method for automatically segmenting liver tumors in abdominal CT sequence images. The method comprises the following steps of: preprocessing: preprocessing an abdominal CT sequence image so as to obtain a liver area in the image; liver enhancement: improving a contrast ratio of normal liver parenchyma to tumor tissue by adoption of a segmented nonlinear enhancement operation and an iterative convolution operation according to a grey level distribution characteristic of the liver area; automatic segmentation: constructing an image segmentation energy function for multi-target segmentation by utilizing the enhancement result and combining image boundary information, minimizing the energy function by adoption of an optimal algorithm and obtaining a liver tumor preliminary automatic segmentation result; and post-processing: optimizing the preliminary segmentation result by adoption of a three-dimensional mathematic morphological open operation, and removing mis-segmented areas to improve the segmentation precision. The method is beneficial for helping radiological experts and surgeons to timely and effectively obtaining overall information and three-dimensional display of liver tumors and providing technical support for computer-aided diagnosis and treatment of liver diseases.

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 provides a method for rapid nondestructive detection of a liver tumor marker and a test paper strip adopted by the method. A gold maker pad of the test paper strip adopted by the method comprises glass fibers adsorbed with colloidal gold-marked monoclonal antibodies GP73-1. A nitrocellulose film is orderly marked with a detection band T1 and a quality control line C, wherein monoclonal antibodies GP73-2 are fixed to the detection band T1 and rabbit anti-mouse polyclonal antibodies Ig are fixed to the quality control line C. The method for rapid nondestructive detection of a liver tumor marker comprises the following steps of taking a saliva sample, inserting an end of a sample pad of the test paper strip which is an immunochromotographic test paper strip into the saliva sample for 10 to 15min so that the saliva sample appears on a NC film, taking out the test paper strip, and observing coloring states of the detection band and the quality control line to obtain a detection result. The method utilizes a saliva sample as a detection object and compares the saliva sample with a serum sample and other body fluid samples. The method has the advantages of easy sample acquisition, nondestructive detection, accurate detection, safety, convenience, low cost and easy preservation.

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 belongs to the technical field of biological products and discloses a medicinal composition and application thereof. The composition takes recombinant human endostatin and hexadecadrol as effective constituents, wherein the mole ratio of the recombinant human endostatin to the hexadecadrol is 10-100:1. The recombinant human endostatin in the combination can co-act with the hexadecadrol to restrain the multiplication, invasion, migration and angiogenesis of endothelial cells. An in-vivo test result proves that the recombinant human endostatin and the hexadecadrol co-act with each other to restrain the growth of the mice H22 transplanted liver tumor and human Bel7402 transplanted liver tumor. Therefore, the composition can be widely applied to medicaments for treating liver cancer.

The invention is applicable to the field of medical image processing, and provides a dividing method based on the CT (Computed Tomography) image liver tumor focus. The method comprises the following steps of preprocessing an initial CT image; completing ROI selection on the suspected focus in an interactive way by aiming at the preprocessed CT image; performing texture description on the ROI based on a CT value, and obtaining probability spectrums of the ROI through weighted computation of texture descriptors; building a big data priori knowledge base, determining a focus region probability spectrum threshold value, and dividing the suspected focus based on the threshold value; and completing the volume statistics and the quantization output on the focus. The method has the advantages that a plurality of texture descriptor probability spectrums are obtained based on stable and comparable CT value calculation; meanwhile, the threshold value of the texture descriptor probability spectrums is obtained through manual division calculation based on the priori knowledge base; and the accuracy and the timeliness of the multi-domain liver tumor focus division in the CT image can be realized.

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 discloses a liver tumor automatic segmentation positioning method and system and a storage medium, The method comprises: performing window width window processing on the liver CT image to obtain the first input data of the highlighted CT liver part; inputting the first input data into the U-Net liver semantic segmentation model to obtain the liver semantic segmentation result; filtering the segmentation result to obtain the second input data; reconstructing the second input data to obtain the third input data; inputting the third input data into the tumor location detection modelto obtain the tumor location information; and acquiring the tumor location information according to the tumor location information; filtering the second input data to obtain the fourth input data; inputting the fourth input data into the U-Net tumor semantic segmentation model to obtain the tumor semantic segmentation result. The invention can quickly obtain the result of tumor semantic segmentation, reduce the time cost, improve the work efficiency, and improve the accuracy of the tumor detection, and can be widely applied to the field of image processing technology.

The invention discloses a DICOM data-based liver tumor microwave ablation three-dimensional temperature field simulation method, which comprises the following steps: step 1, reconstructing and repairing a tumor and blood vessel model based on DICOM data, introducing the model into a multi-physical field simulation module, selecting a working plane to draw an ablation needle structure chart, and constructing a simulation geometric model; step 2, setting different materials and parameters for a computational domain in the simulation geometric model, wherein the specific material types comprise liver, tumor and PTFE; step 3, constructing a coupling electromagnetic wave radiation model and a biological heat transfer model; step 4, dividing grids for the simulation geometric model according to the model constructed in the step 3 and a set microwave frequency, and setting a solver solving method; and 5, performing visualization processing on the simulation data obtained by solving to obtain temperature field distribution data. According to the simulation method, a three-dimensional finite element simulation model can be established by using preoperative image data of a patient, an ablation thermal field is calculated, and formulation of an operation scheme is guided.

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.

An ultrasonography B device guided ultrasonic equipment for examining, diagnosing and treating the liver tumor and a working unit in it for moving and locating the ultrasonic probe are disclosed. Said ultrasonic probe can be lifted up, lower down, turned by an angle and swinged, so eliminating the shielding of ribs to liver tumor.

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 relates to a liver tumor CT image computer-aided diagnosis method. According to the method, the liver and the tumor are segmented through a fully convolutional network (FCN), and the liver tumor is classified through a convolutional neural network (CNN). In training an FCN model, a weighted cross entropy loss function is used for improving tumor segmenting accuracy. In training the CNN and performing classification by means of the CNN, a CT image in a first channel and an FCN segmenting result in a third channel are spliced for obtaining four-channel image data as an input. Finally the trained FCN and the CNN model are combined for constructing a computer-aided diagnosis system. After the to-be-diagnosed CT image is read and input into the system, a probability that the CT image belongs to the healthy liver, the diffuse liver, the tubercle type tumor or the massive cancer is obtained. The integral process of the method does not require the steps of image preprocessing and characteristic extracting. Not only is process simplified, but also the diagnosis accuracy is not affected by image noise, low contrast and characteristic selection and extraction.

The invention, which belongs to the medical equipment technology field, relates to a respiratory gating indication method used for radiotherapy and an apparatus thereof. The method comprises the following steps that: a remote controller that includes a CPU microprocessor, an arrangement key, control keys, a liquid crystal display, an infrared remote controlled coding circuit and an emitting device of the circuit is arranged; an infrared reception and decoding circuit, a second CPU microprocessor, an auxiliary LED indicating lamp and a nixie tube display are arranged; an indicator comprising a third CPU microprocessor, a drive circuit, a main LED indicating lamp and a sounder is arranged; and a power source is arranged. According to the invention, clear light and sound indication can be provided for a patient; therefore, an influence of a respiratory movement can be effectively reduced; localization precision of thoracic-abdominal tumor radiation therapy target region can be substantially improved; unnecessary irradiation on a normal organ and a normal part can be effectively avoided; and occurrences of phenomena of damage and necrosis of normal cells can be avoided or reduced. Besides, the apparatus is cheap and can be applied conveneitnly. Moreover, the method and the apparatus can be widely applied to radiation therapy of several kinds of tumors like a thoracic tumor, an abdominal tumor, a lung tumor, a liver tumor, a renal tumor and a breast tumor and the like.