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61 results about "Pulmonary tumor" patented technology

Lung Tumors. Lung cancer is considered a very life-threatening cancer and one of the most difficult to treat, because of its tendency to spread, or metastasize, very early after it forms. The lung also is a very common location for tumor metastasis from other parts of the body.

Novel lung tumor locating needle

The invention relates to a novel lung tumor locating needle. The novel lung tumor locating needle comprises a puncture needle, anchoring locating needles, a locating line and a pushing device, wherein the puncture needle is composed of a puncture needle cannula and a puncture needle handle, the pushing device comprises a pushing tube and a pushing tube handle, the pushing tube is arranged in the puncture needle cannula, the far end of the pushing tube abuts against the near ends of the anchoring locating needles, the width of the near ends of the anchoring locating needles is larger than or equal to the outer diameter of the pushing tube, the far end of the locating line is connected with the near ends of the anchoring locating needles, the locating line extends out of the exterior of the pushing device from the interior of a tube cavity of the pushing tube, the far end of the locating line is provided with multiple colored bands with different colors, and the length of each colored band ranges from 3 mm to 15 mm. According to the novel lung tumor locating needle, the functions of determining lesion localization and measuring the depths of lesions are achieved simultaneously, the lung or parts of the lung can be lifted up, excision of the lesions is convenient for a surgeon, and the depths of the lesions can be measured during an operation.
Owner:NINGBO SHENGJIEKANG BIOTECH

Diagnosis model constructed based on artificial intelligence fusion multi-modal information and used for various pathology types of benign and malignant pulmonary nodules

The invention relates to a diagnosis model constructed based on artificial intelligence fusion multi-modal information and used for various pathology types of benign and malignant pulmonary nodules. The method comprises the steps: constructing a multi-resolution 3D multi-classification deep learning network model; constructing a machine learning multi-classification model; training the constructedmulti-resolution 3D deep learning model by using CT image, and obtaining a weight; training the constructed machine learning multi-classification model by using lung tumor marker information, and obtaining a weight; and fusing the lung CT imaging information and the lung tumor marker information at the tail end of the model by migrating the weights of the deep learning network and the machine learning network model through migration learning. According to the invention, a deep learning network model and a machine learning model are adopted to respectively mine deep features related to pathological type classification in a lung CT image and a lung tumor marker, and the fusion of the CT image and the lung tumor marker multi-modal information is realized by fusing the two network models to rapidly diagnose the specific pathology type of the pulmonary nodule.
Owner:SHANDONG UNIV +1

Lung cancer-targeted peptides and applications thereof

The invention provides nucleic acids, peptides, and antibodies for use in applications including diagnosis and therapy. The peptides target lung cancer and were identified by phage display. Targeting phage PC5-2 and synthetic peptide SP5-2 were both able to recognize human pulmonary tumor specimens from lung cancer patients. In SCID mice bearing NSCLC xenografts, the targeting phage was able to target tumor masses specifically. When the peptide was coupled to liposomes containing the anti-cancer drugs vinorelbine or doxorubicin, the efficacy of these drugs against human lung cancer xenografts was improved, the survival rate increased, and the drug toxicity was reduced.
Owner:ACAD SINIC +1

Lung tumor automatic sketching method based on deep learning

The method is suitable for the technical field of medical image processing. The invention provides a lung tumor automatic sketching method based on deep learning, and the method comprises the steps: obtaining an input lung CT image when a sketching request of the lung CT image is received, carrying out the preprocessing and image enhancement of the obtained lung CT image, and obtaining a corresponding processed image; obtaining the position and size of a window of the lung tumor in the image, and cutting the screened image into a fixed size; inputting the processed image into a trained V-Net model so as to predict the lung tumor; performing deconvolution on the predicted tumor image to the size of the cut image, so that real prediction of the tumor can be obtained; and extracting the edgeline of the truly predicted lung tumor, that is, sketching the lung tumor, and obtaining an image sketched by the lung tumor. According to the lung tumor automatic sketching device, the accuracy of automatic sketching of lung tumors is improved, the sketching efficiency is remarkably improved on the basis of guaranteeing the sketching precision, and the operation safety process is improved.
Owner:CHINA UNIV OF PETROLEUM (EAST CHINA)

High-dimensional feature selection algorithm based on Bayesian rough set and cuckoo algorithm

ActiveCN111583194AGet rid of the shackles of manual settingsRich diversityImage enhancementImage analysisContour segmentationImage extraction
The invention discloses a high-dimensional feature selection algorithm based on a Bayesian rough set and a cuckoo algorithm, and the algorithm comprises the steps: obtaining a lung tumor image, carrying out the target contour segmentation, and obtaining a segmented ROI image; extracting a high-dimensional feature component of the segmented ROI image, and constructing a decision information table containing feature attributes based on the feature component; and reducing the original feature space by adopting a BRSGA algorithm to obtain an optimal feature subset, optimizing a penalty factor anda kernel function parameter of the SVM by utilizing a CS algorithm, and inputting the reduced feature subset into the optimized SVM to obtain a classification and identification result. According to the method, the optimal feature subset is generated through the genetic algorithm and the BRS, the feature dimension is reduced on the premise that the classification accuracy is not reduced, the constraint of manual parameter setting is eliminated, and the time consumption is reduced. According to the method, global optimization is carried out on SVM parameters through CS, search space can be explored more effectively, population diversity is enriched, and good robustness and high global search capacity are achieved.
Owner:BEIFANG UNIV OF NATITIES

Medical lung MRI image segmentation method based on adaptive contour model, and MRI equipment

The invention relates to a medical lung MRI image segmentation method based on an adaptive contour model, and MRI equipment. The equipment comprises a main magnet system, a gradient magnetic field system, a radio frequency system and an operation and image processing system; the radio frequency system comprises a plurality of radio frequency coils which are respectively arranged in coil fixing devices, and different radio frequency receiving coils correspond to different detection parts on a patient. The method comprises the following steps: 1) preprocessing an input fused MRI image; 2) setting an initial contour line as c0 and setting the initial contour line as a circle according to the characteristics of the lung tumor image, and calculating an initial level set function phi 0 accordingto the c0; 3) updating a level set function phi n, and calculating c1 and c2 according to the current phi n; 4) checking whether iteration is convergent or not: if so, determining that c is the optimal contour line, and otherwise, continuing iteration; 5), after the target area is obtained, removing noise and some tiny protruding parts by using image morphological opening operation, smoothing theboundary, connecting the fracture part by using closed operation, and filling a cavity to obtain a target image. The method is high in segmentation precision and clear in edge, and is suitable for segmenting lung MRI images with fuzzy targets, weak edges, smooth boundaries, discontinuous boundaries and complex topological structures.
Owner:山东凯鑫宏业生物科技有限公司

Lung neoplasm diagnosis and treatment device under radiography guidance

The invention discloses a lung neoplasm diagnosis and treatment device under radiography guidance. The lung neoplasm diagnosis and treatment device comprises a CT reconstruction device and a CT examining couch; the CT reconstruction device comprises a detector, a light pipe, a 360 degree rotating loading platform movement system, a detector Y direction linear movement system, a detector X direction linear movement system, a detector Z direction linear movement system, a light tube Z direction linear movement and detected objects; the CT examining couch comprises a couch body, a headrest, a head rest guide rail, a head rest telescopic rod, an arm fixator, an arm fixator telescopic rod, a motor, a castor, a castor lock, couch legs, a couch leg height adjuster, a head rest telescopic rod control button, an arm fixator telescopic rod control button, a couch leg height adjuster control button, a power switch and a power interface. The lung neoplasm diagnosis and treatment device under radiography guidance makes the CT examining couch operation more easy and convenient, achieves electric control type adjustment, is convenient to move, improves the use performance of the CT examining couch, and solves the problem that the conventional CT examining way is not suitable for examining flat components.
Owner:SHANDONG RES INST OF TUMOUR PREVENTION TREATMENT

Bronchoscope image feature comparison marking system and method based on deep learning

PendingCN112614103AIdentification helpsAnalyze faster and more proficientlyImage enhancementImage analysisPulmonary tumorLesion site
The invention discloses a bronchoscope image feature comparison marking system based on deep learning, and the system comprises: an input layer which is used for receiving a bronchoscope image collected by a bronchoscope, carrying out the recognition of the received bronchoscope image, and transmitting a recognized in-vivo image to a judgment layer; a judgment layer which is used for recognizing the in-vivo image from the input layer, judging a part corresponding to the in-vivo image, judging whether a lesion part exists or not and judging whether the lesion part is benign or malignant or not, and then feeding back a recognition result to the output layer; and an output layer which is used for displaying the identification result from the judgment layer. The invention further discloses a bronchoscope image feature comparison marking method based on deep learning. According to the invention, the central lung tumor focus part under the bronchoscope can be identified by carrying out feature comparison on the bronchoscope image.
Owner:SHANGHAI CHEST HOSPITAL +1

Lung tumor biopsy and radiation particle implantation device under CT positioning

The invention discloses a lung tumor biopsy and radiation particle implantation device under CT positioning, and relates to the technical field of lung tumor treatment, the lung tumor biopsy and radiation particle implantation device comprises a top plate, a fixing plate is arranged at the bottom of the top plate, a bottom plate is fixedly connected to one side of the fixing plate, and a transmission box is fixedly connected to one side of the fixing plate and located above the bottom plate; a second lead screw is rotationally connected to one side of an inner cavity of the transmission box. A threaded rod is rotationally connected to one side of the inner cavity of the transmission box and located below the second lead screw. The lung tumor biopsy and radioactive particle implantation device under CT positioning is provided with a third electric telescopic rod, a limiting groove and a cover plate; so that when a pressure sensor detects that the weight of the top of a detection plate is small and represents that particles in one particle box are used up, a third electric telescopic rod pushes a new particle box to move to the position above a through hole. Therefore, the device can automatically supplement particles, and medical staff do not need to frequently supplement radioactive particles in an operation.
Owner:NANYANG SECOND GENERAL HOSPITAL
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