341 results about "Tumor detection" patented technology

Prostate probe systems are disclosed for assessing one or both of BPH or prostate cancer. The prostate probe systems comprise either a force or pressure sensor mounted on or in a rectally insertable probe or a temperature sensor mounted on or in a rectally insertable probe, or both. Also disclosed are probe systems for evaluating a condition of a prostate gland. Finally, force or hardness mapping devices are disclosed for palpation-examination of patient anatomical tissues for abnormalities or assessing states of firmness.

The present invention provides improved methods for detection of minimal disease. More specifically, the invention provides methods for combining cell sorting with clonality profiling to effectively lower sensitivity limits for disease detection and to provide independent confirmation of the tumor detection without the need for patient specific assay designs.

The invention belongs to the field of image processing, and particularly relates to an automatic extraction method of regions of interest in three-dimensional breast full-volume images (ABVS). The method comprises the following steps: processing the continuous cross section two-dimensional images in three-dimensional ABVS images by using a maximum direction-based phase information method to obtain the candidate regions of interest on each cross section image; removing the unrelated regions according to the prior knowledge such as the continuity and position characteristic of breast tumor on the two-dimensional cross section images; obtaining the shape and texture features of the residual suspected tumor regions, inputting the shape and texture shapes to a two-valued logistic regression classifier to obtain the probability of each region becoming tumor and selecting the region with the maximum probability as the tumor region; obtaining the minimum ellipsoid comprising the region of interest according to the selected region to serve as the region of interest. The automatic extraction method provided by the invention can be used for realizing the automatic extraction of tumor regions of interest in the three-dimensional ABVS images, obtaining the correct positions of tumor, decreasing the workload of the manual operation and providing important reference to further tumor detection.

Compounds and methods are disclosed that are useful for noninvasive imaging in the near-infrared (NIR) spectral range. The NIR is highly sensitive for tumor detection and tracking. The application discloses targeting a tumor-enriched cell surface receptor with a ligand-conjugated fluorescent probe, which specifically allows detection of the tumor relative to the negligible animal autofluorescence.

A dynamic thresholding level set method combines two optimization processes, i.e., a level set segmentation and an optimal threshold calculation in a local histogram, into one process that involves a structure called a “propagating shell.” The propagating shell is a mobile 3-dimensional shell structure with a thickness that encompasses the boundary of an object, the boundary between two objects or the boundary between an object and a background. Because the local optimal threshold tends to shift to a value of a small region in a histogram, the shift can drive the propagating shell to an object boundary by pushing or pulling the propagating shell. The segmentation process is an optimizing process to find a balanced histogram with minimal threshold shift. When the histogram in the propagating shell is balanced, the optimal threshold becomes stable, and the propagating shell reaches a convergence location, i.e., an object boundary. This method can be applied to computer-aided organ and tumor volumetrics.

To detect a malignant tumor more accurately by detecting a feature of a cancerous tumor pulsation more appropriately, a tissue malignant tumor detection method according to the present invention includes: segmenting, into blocks, a region scanned with ultrasound (S100); estimating, based on a scan signal, a tissue pulsation that is a temporal variation in tissue displacement derived from pulsations of a tissue (S101); extracting, for each of the blocks, pulsation-related features that are parameters related to the tissue pulsation (S103); calculating distribution characteristics of the pulsation-related features for each of the blocks (S104); and classifying, based on the distribution characteristics, whether or not each of the blocks is a malignant block that is a block including the malignant tumor (S105).

A system and a method are provided for estimating the average dielectric properties of a plurality of regions in space. The application of this technique is illustrated for determining the average properties of breast tissue. The knowledge of average properties is important when UWB microwave radar signal processing algorithms are used for tumor detection and localization. The method is an extension of a time-domain inverse scattering algorithm based on the finite-difference time-domain method. A hybrid conjugate gradient optimization is used to minimize a cost function defined between a measured and a calculated total electromagnetic field at a series of antennas. The output of the method is an average set of electromagnetic material parameters that describe specific regions of interest in either a non-dispersive heterogeneous medium or a dispersive heterogeneous medium.

The invention discloses a preparation method for a three-dimensional photoelectrochemical paper chip and a method for measuring six tumor markers in a sample through a photoelectrochemical sensor. A hydrophobic region and a hydrophilic region are formed on paper by adopting a wax printing technology; a corresponding reference electrode and working electrodes are printed on the paper through proper ink; a working region is functionalized for antigen recognition; the prepared paper chip is folded to form a three-electrode system; a buffering solution containing ascorbic acid is dropwise added into a reaction region; under the irradiation of an ultraviolet lamp, the high-sensitivity detection on an object to be measured is realized.

The invention belongs to the technical field of biomedicine detection, in particular to a method for preparing a nano-fluorescent probe with the quantum dots and an application of the nano-fluorescent probe with the quantum dots on tumor detection. According to the method, aiming at the defects that the traditional preparation method of the quantum dots is complicate to operate and high in cost, a simple and cheap water phase synthetic technique with the quantum dots is provided. The specific synthetic process comprises the synthesis of the water soluble quantum dots, the synthesis of a biocompatible modifier (mPEG) and a tumor cell targeting modifier (cRGD) with the sulfydryl functional groups and a direction reaction between the quantum dots and the two modifiers with the sulfydryl functional groups. According to the method provided by the invention, the preparing raw materials are easy to obtain, the whole preparation process is covalent modification under the conventional condition and the properties of the quantum dots and the modifiers are not changed, and the functions of in vivo long circulation and special cell targeting are simultaneously given for the quantum dots through a one-step reaction, so that the cost is effectively reduced. The modified fluorescent and bright quantum dots obtained by the method are about 5nm in particle size, strong in anti-photo-bleaching property, and have good biocompatibility. The transfection efficiency of the quantum dots on human breast cancer MDA-MB-231 cells can be effectively regulated and controlled by further adjusting the mole ratio of mPEG to cRGD on the surfaces of the quantum dots, so that the nano-fluorescent probe with quantum dots can be applied to the cell fluorescent imaging detection of the tumor targeting.

The present invention provides antibodies specifically against hDlk-1 and having anti-tumor activity in vivo (anti-hDlk-1 antibodies), a fragments of the antibodies, hybridomas that produce the antibodies, a complex of the antibody or antibody fragment and an agent, a pharmaceutical composition comprising the antibody and the like, a tumor therapeutic agent, a tumor angiogenesis inhibitor, a tumor diagnostic agent, a method for detecting tumor, a kit for detecting and / or diagnosing tumor, etc.

In a pattern recognition system, a pattern which is to be recognized later is prescribed in a learning phase. This pattern is detected sequentially, that is to say the informative areas of the pattern are detected and, moreover, the spatial relationship between the areas is also stored. In the recognition phase, a hypothesis which indicates a presumed pattern and, furthermore, indicates where such further prominent areas should be located in the pattern to be recognized if the presumption is correct, is generated on the basis of the acquired data of a first area of a pattern to be recognized, and on the basis of the stored data. Thus, patterns are learned through their location information, on the one hand, and through their spatial relationship to each other, on the other hand, stored and then re-recognized. The application options of the present invention reside, for example, in robotics, text analysis and image analysis, but also in the field of medical technology (for example in automatic tumor detection in mammographies, etc).

The present invention discloses a method for automatically detecting a nasal tumor from the MR (magnetic resonance) images. First, the pixels that have specific trends and are affected by contrast agents with specific level will be filtered according to the developing coefficient and control coefficient of grey prediction. Then the tumor area would be detected by using Fuzzy C-means clustering technique to distinguish the differences between normal tissue and tumor. Owing to the work of grey prediction, calculation in the Fuzzy C-means clustering technique can be dramatically reduced and the result of tumor detection is enhanced.

The invention provides a multiple lung cancer-related gene methylation combined detection kit, which comprises: a detection reagent for specifically detecting the DNA methylation of human SHOX2 gene;a detection reagent for specifically detecting the DNA methylation of human RASSF1A gene; a detection reagent for specifically detecting the DNA methylation of human ANKRD18B gene; and a detection reagent for specifically detecting the DNA methylation of human MPDZ gene, wherein preferably the detection reagents are primers and probes. The invention further provides applications of the multiple lung cancer-related gene methylation combined detection kit in multiple lung cancer-related gene methylation combined detection, and a multiple lung cancer-related gene methylation combined detection method. According to the present invention, the multiple lung cancer-related gene methylation combined detection kit can perform the combined detection on the multiple lung cancer-related gene methylation, can significantly improve the tumor detection rate, has advantages of clever design and simple structure, and is suitable for large-scale promotion and applications.

The invention belongs to the technical field of tumor detection and discloses a method for detecting a tumor cell marker miRNA-21 and tumor cells. A composite nano-probe comprises a gold nanoparticlecore, hairpin DNA H1 and swing arm Walker-Blocker; the apex of DNA tetrahedron complex is connected with hairpin DNA H2; miRNA-21 is a 3D DNA Walker activator; by means of DNA walker cycle signal amplification of Walker DNA, H1 and H2, miRNA-21 ratiometricand fluorescence Raman dual-mode detection is achieved; gold nanoparticles and DNA tetrahedral structure enter tumor cells for ratiometric and fluorescence Raman dual-mode imaging detection of tumor cells. 3D DNA Walker nano-machine amplification and the DNA tetrahedral nano-probe are combined for ratiometric and dual-mode detection of the tumor cell marker miRNA-21 and the tumor cells.

The invention discloses a breast cancer MRI segmentation method based on a hierarchical convolutional neural network, and the method integrates a plurality of algorithms, and comprises the specific steps: training a full convolutional network FCN model, and obtaining the global and local structure information of an input image through employing a 3D U-Net architecture; training the other two FCN models by taking the breast mask as a guide, and respectively estimating a coarse segmentation result and a refined initial result; developing a detection model based on the mark points; detecting twomarkers for biopsy tumor selection and radiogenomics so that common problems in mammary gland DEC-MR image segmentation are well solved. The common problems include imbalance-like problems, confusionproblems and the like which are difficult to solve, and a learning framework is designed to segment mammary gland tumors from coarse to fine; the biopsy tumor in all the detected tumors is determinedby using the tumor position information and the marking information, and the biopsy tumor detection system is ingenious in automatic detection and design, high in accuracy and convenient to popularizeand use.

The invention relates to new use of a series of nitrogen-containing heterocyclic radical substituted alkene compounds, and more specifically relates to use of the compounds represented by general formula a or salts thereof in bioimaging, pH value detection, biomacromolecule detection or cell microenvironment detection, wherein Z, S1, S2 and R1 are defined as the specification. The compounds are utilized as the fluorescent dye for bioimaging, fluorescent pH probe for pH value detection and / or diagnostic agent for tumor cell detection. The compounds involved in the invention have excellent imaging effect, and shows the single component multicolor property, can be used for single component multicolor imaging or conventional imaging, etc., and haves enormous application prospect in bioimaging and tumor detection. (formula a).

The invention relates to a novel mammary gland MRI automatic auxiliary diagnosis method based on a fusion attention mechanism. The novel mammary gland MRI automatic auxiliary diagnosis method comprises the following steps: S1, manually selecting a mammary gland segmentation data set, training a DenseUNet model, inputting a TSE sequence of mammary glands into the trained DenseUNet model for mammarygland segmentation, and removing organs interfering with tumor detection in the thoracic cavity; S2, mapping the segmentation result obtained in the step S1 to a DCE sequence, to obtaining the segmented mammary tissues, inputting the segmented mammary tissues into an ADUNet model with an attention mechanism to perform tumor segmentation, and aiming at the problems of class imbalance and difficultsamples, adopting Focal Loss in a training process to prevent the model from deviating; S3, inputting the result obtained in the S2 into a lightweight neural network, and carrying out benign and malignant judgment to obtain an auxiliary diagnosis result. According to the invention, the end-to-end breast cancer auxiliary diagnosis can be realized without manual intervention, and the diagnosis efficiency and accuracy can be greatly improved.

The invention provides a method and device for identifying tumor loads in samples, and particularly provides a method for identifying tumor loads in the samples in a non-diagnostic mode. The method comprises the steps that 1, a sample to be tested is provided; 2, sequencing is performed on the sample to be tested, and therefore a genome sequence of the sample is obtained; 3, the genome sequence obtained in the step 2 is compared with a reference genome, and therefore position information of the genome sequence on the reference genome is obtained; 4, the reference genome is divided into M region fragments, wherein each region fragment is a window b, and a copying number of each window b is calculated; 5, each window b in the step 4 is subjected to Z testing, so that a Z value of each window b is calculated; 6, according to the Z value obtained in the step 5, GAS is calculated, and tumor loads in the sample to be tested are identified on the basis of the value of GAS. According to the method and system, sensitivity and universality of tumor detection can be improved.

The invention discloses a CEA (carcinoembryonic antigen) resistant nanomater antibody with high appetency. The nanomater antibody has unique three complementary determining regions CDR1, CDR2 and CDR3. The invention also discloses application of the nanomater antibody to tumor treatment medicine and tumor detection reagents. The CEA resistant nanomater antibody provided by the invention has specific recognition and combination capability on CEA; the appetency of the nanomater antibody can reach 10 to 11; seven kinds of CEA analogues do not take conjugation reaction; during the detection on theCEA in a serum sample, the sensitivity can reach 1ng / ml; the condition shows that the nanomater antibody provided by the invention has high specificity and sensitivity.

The invention provides a gold nanomaterial which is a functionally modified gold nano-cluster, and further provides a preparation method and an application of the gold nanomaterial. The preparation method of the gold nanomaterial is simple, reaction conditions are quite mild and include normal temperature, normal pressure, water phase and about neutral pH, and the biological or medical applicationof the gold nanomaterial is facilitated. The material has good adjusting performance for bacteria growth, and can be applied to tumor imaging and treatment. The functional gold nano-cluster is synthesized from different ligands and applied to growth adjustment. The synthesized gold nano-cluster has the adjusting function for growth of multiple bacteria, and because of lower biotoxicity, the biological and medical application of the material is widened; the material is used for tumor detection and treatment by adjusting growth of the bacteria.

The invention relates to an autofluorescence detection system for exciting specific endogenous fluorescent substances in a human tissue by light sources with different wavelengths and detecting information of fluorescence with specific wavelength corresponding to the fluorescent substances in endoscopy for the human early tumor tissue. The autofluorescence detection system mainly consists of an excitation light source part, an excitation-detection-acquisition part, an optical collection part, a signal preprocessing part and a computer part. The autofluorescence detection system is characterized in that a tunable laser (1) can adjust the needed pulse laser wavelengths according to the specific endogenous fluorescent substances in the human tissue, and a filter wheel (10) is equipped with narrow band pass filters with different center wavelengths. The system can excite the specific endogenous fluorescent substances in the human tissue by a plurality of optimal excitation wavelengths sequentially, thus avoiding the problem of no benefit for analysis owing to insufficient fluorescence information or information overlap and the like caused by excitation of single wavelength or broad spectrum light sources, and improving sensitivity and specificity of early tumor detection.

The invention discloses a multi-target evolutionary fuzzy rule classification method based on decomposition, which mainly solves the problem of poor classification effect of an existing classification method on unbalanced data. The multi-target evolutionary fuzzy rule classification method comprises the steps of: obtaining a training data set and a test data set; normalizing and dividing the training data set into a majority class and a minority class; initializing an ignoring probability, a fuzzy partition number and a membership degree function; initializing an original group, and determining weight by adopting a fuzzy rule weight formula with a weighting factor; determining stopping criteria for iteration, iteration times, a step size and an ideal point; dividing direction vectors according to groups; performing evolutionary operation on the original group, and updating the original group by adopting a Chebyshev update mode until the criteria for iteration is stopped; obtaining classification results of the test data set; then projecting to obtain AUCH and output. The multi-target evolutionary fuzzy rule classification method has the advantages of high operating speed and good classification effect and can be applied in the technical fields of tumor detection, error detection, credit card fraud detection, spam messages recognition and the like.

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 provides an image processing system and an image processing method. A breast image processing system includes an image capturing device, a tumor detector, a spine detector and a tumor splitter, wherein the image capturing device is used for capturing a two-dimensional ultrasound breast image; the tumor detector is used for detecting a tumor area with a breast tumor from the two-dimensional ultrasound breast image captured by the image capturing unit; the spine detector is used for detecting the spine information from the captured two-dimensional ultrasound breast image; the tumor splitter is used for splitting the breast tumor from the detected tumor area based on a level set; a level set function is created for the two-dimensional ultrasound breast image in a manner that the detected tumor area serves as an initial contour curve, and the level set evolution of the contour curve is limited iteratively according to the spine information detected by the spine detection unit, so that the energy function of the contour curve can be minimized.

The invention discloses a circulating tumor DNA EGFR (Epidermal Growth Factor Receptor) detecting technology and a reagent kit thereof. The circulating tumor DNA EGFR detecting technology comprises the following steps: step 1, designing a synthetic primer, a closed probe and a detecting probe for a mutation site; step 2, processing a sample and extracting DNA; step 3, applying the circulating tumor DNA EGFR detecting technology to the reagent kit, and establishing a PCR amplified reaction system. According to the circulating tumor DNA EGFR detecting technology disclosed by the invention, the specific MGB Blocker probe, the EGFR specific probe and the primer are adopted to detect the EGFR mutation in the circulating tumor DNA; the MGB Blocker probe is utilized to retard nonspecific amplification of the EGFR wild type genome and improves the sensitivity; the circulating tumor DNA EGFR detecting technology can be used for detecting the EGFR mutation in the circulating tumor DNA, is simple to operate, cheap in detection, capable of being popularized at a large scale and high in detection speed; the detecting process can be completed for about 2 hours.

The invention provides a restoring-type near infrared fluorescence probe with pH response. The chemical structural formula is shown as a formula (I) in the specification, wherein X refers to C(CH3)2, O, S or Se, Y refers to halogen, Z refers to NH, O or S, R1 and R2 are respectively independently selected from H, C1-18 alkyl or SO3R5, R5 refers to C1-18 alkyl or benzyl, and R3 and R4 are respectively independently selected from C1-18 alkyl or benzyl. When the restoring-type near infrared fluorescence probe with pH response is between pH 5.0 and pH 7.5, the fluorescence intensity is gradually enhanced along with reduction of the pH value, and an excellent linear relation is formed between the fluorescence intensity and the pH value. The stocks displacement is large, the signal to noise ratio is low, the toxicity is low, the damage to cells and living bodies is small, the pKa is 6.4, the probe is very suitable for tumor detection, and the defects of fluorescence probes in the prior art are overcome. The invention also provides a method for preparing the probe. The preparation method is simple.

The invention provides a polymer carbon dot with high fluorescent quantum yield and a preparation method thereof, and application of the polymer carbon dot to targeted detection of tumor cells, belonging to the technical field of polymer carbon points. According to the invention, a bio-targeting molecule folic acid is used as a raw material and a simple hydrothermal method is employed for one-step synthesis of the polymer carbon spot with high fluorescence quantum yield; and the polymer carbon spot has the functions of targeted detection and selective imaging of tumor cells. The polymer carbon dot provided by the invention has the advantages of simple synthesis method, small and uniform particle size, high quantum yield, low biological toxicity, good biocompatibility and the like. The fluorescent polymer carbon dot has high quantum yield, can be used for biological imaging, presents a tumor cell-targeting function, is applicable as a fluorescent imaging material for diagnosis and treatment of cancers, and plays an important role in fields like tumor detection and drug sustained release.