1382 results about "Staining" patented technology

The invention discloses an auxiliary film reading method and system based on artificial intelligence, and the method comprises the steps: 1, collecting a pathological WSI, an electronic medical record, detection data and equipment parameters, correcting the equipment difference through adaptive dyeing normalization, and constructing a structured data package associated with an ID-timestamp of a patient; 2, developing a dynamic branch CNN, migrating teacher model knowledge through knowledge distillation, and introducing federated learning; step 3, the edge generates a thermodynamic diagram to mark a suspicious area, and the cloud outputs a structured report; step 4, constructing a normal tissue feature space by the variational auto-encoder, detecting abnormal slices and triggering expert re-checking; a reverse automatic encoder generates a pseudo-health image to compare and position a pathological area, and dynamic weight adjustment balances the federal learning convergence speed; 5, integrating the thermodynamic diagram, the gene data and the clinical indexes by a three-dimensional platform, and supporting multi-dimensional superposition display; webGL realizes browser end rendering, and NLP automatically generates a report abstract marked with a key evidence chain and is in butt joint with an international diagnosis and treatment guide.

The invention discloses a skin cancer pathological image analysis system and method based on a Transform model. The system comprises an image acquisition and quality control module, a multi-scale preprocessing module, a hierarchical feature extraction module, an intelligent diagnostic reasoning module, a knowledge graph aided decision-making module, a result output and feedback module and a federal learning training module. According to the method, multi-spectral image acquisition and quality control are carried out, multi-modal enhancement and dyeing standardization preprocessing are carried out, pathological features are extracted in a layered manner by using an improved Transform model, subtype identification and malignancy degree evaluation are realized in combination with multi-task learning, uncertainty is quantified by means of Monte Carlo dropout, cases and guidelines are associated through a knowledge graph, privacy is protected through federal learning, and the model is optimized. According to the scheme, the diagnosis efficiency and accuracy are greatly improved, the model interpretability is enhanced, various clinical scenes are adapted, diagnosis standardization is promoted, and improvement of basic medical capacity is assisted.

The invention provides a blood cell image recognition method based on a multi-scale Vision Transform model, and the method comprises the steps: constructing a multi-scale feature extraction architecture, combining the global modeling capability of a Transform network, carrying out the parallel processing of Patches of different sizes, and fusing the multi-scale information, thereby achieving the recognition of a blood cell image. The cells in the blood smear can be comprehensively perceived from microscopic details to macroscopic contexts, so that not only is the adaptability of the model to cell size difference and morphological diversity enhanced, but also the recognition stability under the conditions of cell overlapping and dyeing difference in a complex visual field is improved; the accuracy and calculation efficiency of cell classification and counting are effectively improved, and full-process automation from visual field quality evaluation to automatic cell recognition and counting is achieved.

The invention discloses a reticulocyte recognition and grading system based on a blood smear, and particularly relates to the field of medical cell morphology examination, and the system comprises an image acquisition module, a staining normalization module, an instance segmentation module, an erythrocyte classification module, a reticulocyte grading module and a statistics output module. The image acquisition module adopts an automatic microscope platform to continuously scan the blood smear under a 100-time visual field, acquires a high-quality bright field channel image through an automatic focusing technology, and monitors image definition, contrast and illumination uniformity; the statistical output module is used for calculating the proportion and bluish violet intensity statistics of reticulocytes of each level, and generating a visual quality control result and a diagnostic report; according to the invention, full-automatic processing from samples to reports is realized, interpretability and high classification precision are both considered, subjective difference and omission ratio of manual microscopic examination are significantly reduced, and efficiency and consistency of clinical detection are improved.

The invention belongs to the technical field of medical image processing, and particularly relates to an esophageal cancer treatment effect and survival combined prediction method and system based on multiple modalities, and the method comprises the steps: obtaining a preoperative CT image and Hamp of an esophageal squamous cell carcinoma patient; e, the dyed digital pathological image, transcriptome data and clinical diagnosis and treatment information are preprocessed and subjected to feature extraction, and radiomics embedding representation, pathomics embedding representation and pipeline branch embedding representation are obtained respectively; the radiomics embedded representation, the pathomics embedded representation and the pipeline branch embedded representation are aligned and input into a multi-modal fusion module based on a multi-head self-attention mechanism for feature fusion, and fusion feature representation is generated; and based on the fusion feature representation, synchronously outputting a curative effect prediction result and a survival prediction result by using a multi-task output module, and evaluating model prediction performance by using a curative effect evaluation index and a survival analysis index.

The invention provides a multi-modal kidney pathological picture processing method and device, electronic equipment and a medium. The method comprises the following steps: acquiring an immunofluorescence picture included in a to-be-processed multi-modal kidney pathological picture and a PAS staining pathological picture corresponding to the immunofluorescence picture; inputting the immunofluorescence pictures into a pre-trained first network model to perform nephropathy type classification to obtain nephropathy types; judging whether the nephropathy type output by a pre-trained first network model belongs to a preset nephropathy type or not; inputting a PAS staining pathological picture corresponding to the immunofluorescence picture into a pre-trained second network model for pathological structure identification when the immunofluorescence picture belongs to the preset nephropathy type, and outputting a label for the identified pathological structure; and adding an Oxford typing label to the to-be-processed multi-modal kidney pathological picture based on a label and a pathological structure output by a pre-trained second network model. According to the scheme, the nephropathy type can be automatically distinguished, the Oxford typing label is added, the processing speed is high, and the accuracy rate is high.

The invention provides a breast cancer HER2 immunohistochemical digital slice automatic interpretation method, device and equipment and a storage medium. Relates to the technical field of digital pathological image analysis. Acquiring a full-slice image, automatically identifying an external contrast area and a tissue area through a multi-task head real-time instance segmentation model, and segmenting an ROI (Region of Interest) area of tumor cell aggregation; extracting image blocks by adopting a fixed sliding window based on the ROI region, and constructing a double-branch multi-task feature extraction model containing a backbone network: realizing HER2 expression pattern classification by an image block classification head, and outputting a continuous protein expression intensity quantized value by a dyeing intensity numerical head through regression analysis; and establishing a slice-level interpretation model, integrating the classification results and the intensity quantized values of all the image blocks, and outputting an automatic HER2 interpretation result. According to the invention, end-to-end analysis from cell-level characteristic quantification to slice-level diagnosis is realized, and an objective and quantifiable intelligent interpretation scheme is provided for HER2 immunohistochemical evaluation.

The invention discloses application of Indel marker dosage typing in detection of sweet potato soft rot resistance, and belongs to the technical field of plant genetic engineering. The Indel molecular marker related to the sweet potato soft rot resistance is obtained through screening, the nucleotide sequence of the Indel molecular marker is shown as SEQ ID NO.2, and an 8bp sequence is inserted into or deleted from the 161st site of the sequence shown as SEQ ID NO.2. Experiments find that target bands of amplification products are obtained through amplification of the Indel molecular marker and electrophoresis, dosage values of two genotypes are obtained according to gray values of the dyed electrophoretic bands, and results show that the insertion type Indel molecular marker is positively correlated with the sweet potato soft rot resistance. The method is of great significance to the dose effect research and molecular assisted breeding of sweet potato gene functions.

The invention relates to the technical field of artificial intelligence, and provides a gastric cancer risk quantitative scoring method, system and equipment based on artificial intelligence, and the method comprises the steps: recognizing the image type of each image frame in alimentary canal endoscope image data; for the electronic dyeing image frame, identifying a part contour region and an intestinal contour region of the feature part in the image frame, determining an intestinal epithelial metaplasia grading category of the feature part according to an area proportion of the intestinal contour region in the part contour region, and performing electronic dyeing intestinal scoring on the image data; for the white light image frame, gastroscope part types included in the image frame and focus area types of all gastroscope parts are recognized; performing atrophy scoring on the image data according to the position distribution of the focus area of the atrophy type; performing table state scoring on the image data according to whether the plica enlargement, nodular gastritis and diffuse redness focus areas exist or not; and realizing gastric cancer risk quantitative scoring according to the electronic staining intestinal scoring, the atrophy scoring and the epistatic state scoring.

The invention relates to the field of artificial intelligence and medical image processing, in particular to a mammary gland pathology visual model establishing method based on multi-model fusion and combined distillation, which comprises the following steps: performing tissue segmentation and dyeing normalization on a full-slice image; inputting the image blocks into a pre-training teacher model of a plurality of freezing parameters in parallel to extract high-dimensional features, and generating unified enhanced features through a learnable feature fusion network; constructing a student model, and performing end-to-end training by using a joint loss function including feature simulation, logic output distillation and multi-task supervision; and connecting a plurality of task specific prediction heads to the student model, and realizing full-slice-level multi-task diagnosis and treatment prediction through an aggregation strategy. According to the technical scheme, efficient knowledge migration and multi-task cooperation can be achieved, and the accuracy, generalization ability and reasoning efficiency of mammary gland pathology analysis are remarkably improved.

The invention relates to the field of image analysis, in particular to a colon cancer lymph node metastasis risk prediction method and system based on image analysis. The method comprises the following steps: acquiring a pathological section scanning image, carrying out layer-by-layer downsampling and feature reconstruction, and generating pathological section topological features; carrying out adaptive clustering analysis and multi-parameter fusion evaluation on the basis of the pathological section topological features to obtain independent migration evaluation values of individual tumor cells; performing tumor metastasis branch analysis according to the independent migration evaluation value, and constructing a metastasis branch path distribution diagram; extracting an immunohistochemical staining image of a patient to obtain immune cell space distribution data; and performing multi-region lymphatic metastasis simulation and metastasis risk situation prediction based on the immune cell spatial distribution data and the metastasis branch path distribution diagram to obtain a final metastasis risk assessment result. According to the method, the lymph node metastasis risk is accurately and efficiently analyzed, and the credibility of a pathological analysis result is improved.

Methods, devices, apparatus, and systems for three-dimensional (3D) contoured scanning photoacoustic imaging and / or deep learning virtual staining.

In some embodiments, a method for classifying elements of a blood sample is provided, the method including: digitally staining an image of the blood sample using a trained machine learning model to generate a digitally stained image; extracting one or more intermediate features generated by the trained machine learning model during the digital staining of the image; providing the one or more extracted intermediate features to a trained multi-class classifier; and employing the trained multi-class classifier to classify at least one element in the blood sample based on the one or more extracted intermediate features. Many other embodiments are also provided.

The invention discloses a morphological feature-based cell segmentation and cell nucleus identification method for a turned unstained bone tissue pathological image. The method comprises the following steps of: 1, eliminating tool marks by adopting a tool mark elimination method combining local frequency domain analysis and directional suppression; 2, performing cell segmentation by using a K-means method, and performing morphological expansion and topological analysis on a segmented single cell image to identify a cell nucleus in the single cell image; step 3, calculating morphological characteristic indexes of each region; the method comprises the following steps: establishing a multi-dimensional Gaussian mixture model according to existing bone cell labeled sample information, performing outlier detection according to statistical data analysis, and removing results which do not conform to cell morphology; classifying different regions, and removing non-cell regions; by calculating morphological characteristic indexes of each region, different regions are distinguished according to the indexes, and cells are preliminarily screened. According to the method, high-precision cell segmentation and cell nucleus identification can be carried out on the cut undyed bone tissue pathological image.

The invention belongs to the technical field of biological sample detection, and particularly relates to a microscanning and classifying all-in-one machine for a cell pathology dyeing sealing sheet and a dyeing method of the microscanning and classifying all-in-one machine for the cell pathology dyeing sealing sheet. Comprising a machine body, a top feeding assembly, a dyeing assembly, a lifting and transferring assembly, a glue dripping assembly, a piece sealing assembly, a microscope scanning assembly and a stacking and storing assembly. According to the method, isochronous standardized dyeing is adopted, all sample slide plates are further arranged before each cycle, the dyeing time of each step is equal accurate time, large-flux standardized dyeing can be performed, and a standardized dyeing method which is high in flux, high in speed, good in dyeing effect and accurate in time is formed. And meanwhile, micro-scanning is integrated into a whole to form a full-automatic assembly line with a small-space large-flux function and full feeding and discharging convenience.

The invention relates to the technical field of digital pathology acquisition, and discloses a digital pathology picture acquisition method. The method comprises the following steps: acquiring sample characteristic data such as thickness, dyeing degree and structural characteristics of a tissue sample to be detected, and determining initial image acquisition parameters including exposure time, image resolution and optical magnification; and meanwhile, environment condition data such as temperature, humidity and environment illumination intensity of a scanning environment are collected, so that whether the initial parameters are adjusted or not is judged. When adjustment is needed, collecting preliminary image data, extracting quality features including image definition, color accuracy and detail retention degree, calculating feature values, and comparing the feature values with historical image quality feature values in a historical database; and if the same characteristic value does not exist in the historical database, calculating the similarity, and adjusting the initial image acquisition parameters according to the comparison result or the similarity. According to the method, sample characteristics and environmental conditions are comprehensively considered, and the quality of the digital pathological picture is effectively improved.

The invention discloses a high-throughput antigen-specific T cell detection method based on mass spectrum flow cytometry. The invention belongs to the technical field of biology, and particularly relates to a high-throughput antigen-specific T cell detection method based on mass spectrum flow cytometry. According to the present invention, the activation induction marker (AIM) technology and the intracellular cytokine staining (ICS) are integrated, and the CyTOF platform is combined to achieve gt; 20 parameters are synchronously detected, and multiple activation-induction marker double positive markers, multifunctional cytokine spectrums (IFN-gamma, TNF-alpha, GZMB, IL-2 and the like) and immune memory phenotypes (CD45RA / CCR7) are covered. The standardized detection scheme provides a high-dimensional and quantifiable biomarker combination for vaccine protection efficacy evaluation, and has important clinical application value.

A kit for detecting expression of AIRE and Treg in lymphatic tissue infected by EB virus comprises colloidal quantum well antibody conjugates, including a colloidal quantum well and protein AIRE and GITR conjugates and a colloidal quantum well and antibody CD25 and Foxp3 conjugates. The detection method comprises the following steps: (1) carrying out slicing treatment on a lymphatic tissue, and carrying out antigen repair by using an antigen repair liquid; (2) respectively adding colloidal quantum well proteins or antibodies coupled with different targets to dye the sections; (3) carrying out nucleus staining; and (4) collecting multi-channel fluorescence image information of the tissue slice, and judging expression levels and mutual relations of AIRE, Treg cells and dendritic cells in immune tolerance in combination with spatial positioning and intensity distribution of fluorescence signals of different channels. According to the invention, qualitative or quantitative multi-parameter and high-precision detection of the immune tolerance related cells in the lymph tissue is realized.

According to the xerophthalmia core index multi-task AI detection system, method and terminal provided by the invention, the xerophthalmia multi-modal image is collected and exclusive preprocessing is completed, and core index detection is carried out on the preprocessed image based on the xerophthalmia multi-index detection model, so that the xerophthalmia core index detection result is obtained. Outputting detection results such as meibomian gland deletion rate, tear film rupture time, staining classification, tear river height and blepharitis marginal inflammation classification; and dynamically adjusting the index result according to a calibration coefficient corresponding to the type of the data acquisition equipment, and finally generating a standardized detection report. A xerophthalmia multi-index detection model is constructed based on an improved U-Net3 + architecture, a dry eye feature enhancement encoder and a cross-scale feature fusion decoder are adopted, a customized multi-task detection head is matched, an inter-task attention collaboration mechanism is established, meanwhile, a preprocessing algorithm and a dynamic calibration mechanism matched with a dry eye image are designed, and the detection accuracy of the xerophthalmia is improved. And multi-index synchronous, high-precision and high-efficiency detection is effectively realized.

The present disclosure relates to automated systems and methods adapted to quickly and accurately train a neural network to detect and / or classify cells and / or nuclei. The present disclosure also relates to automated systems and methods for using a trained cell detection and classification engine, such as one including a neural network, to classify cells within an unlabeled image.

The invention belongs to the technical field of immunochemistry, and particularly relates to an anti-PR recombinant rabbit monoclonal antibody and application thereof.The anti-PR recombinant rabbit monoclonal antibody comprises a heavy chain variable region and a light chain variable region, and the amino acid sequence of the heavy chain variable region is shown as SEQ ID NO: 4; the amino acid sequence of the light chain variable region is as shown in SEQ ID NO: 5. The invention also relates to a nucleotide sequence for coding the anti-PR recombinant rabbit monoclonal antibody, a recombinant plasmid or an expression vector, a preparation method, application of the anti-PR recombinant rabbit monoclonal antibody in a PR protein detection method or device, and the like. The PR recombinant rabbit monoclonal antibody disclosed by the invention has the characteristics of good specificity, strong positive signal and the like, so that scoring is easier in IHC staining, and cancer detection and distinguishing are more accurate.

The embodiment of the invention relates to the technical field of drug detection, in particular to an anti-CD38 antibody drug receptor occupation detection method and system, a peripheral blood sample is used for preparing a single-cell suspension, the single-cell suspension is divided into a TEST tube and an FMO tube, a cell surface antibody and an RO detection antibody are added into the TEST tube, only the cell surface antibody is added into the FMO tube, and only the RO detection antibody is added into the FMO tube. The TEST tube and the FMO tube are subjected to sample detection analysis, so that the average fluorescence intensity of RO detection antibodies of the TEST tube and the FMO tube is obtained. And performing RO detection analysis by adopting a dissociation method, taking the tubes which are not combined with the medicine as a total receptor group, taking the tubes which are combined with the medicine as a dissociation receptor group, and calculating an RO detection result according to the average fluorescence intensity of the TEST tube and the FMO tube.

According to the cell fixing liquid, phenol oxidase is used for catalyzing phenol to be oxidized into a quinone structure, the quinone structure reacts with protein amido to form light covalent immobilization, cell immobilization is achieved, a stable hydration environment and membrane protection are provided through the ionic liquid solvent choline dihydrogen phosphate, a phosphate-free buffer system is provided, and cell immobilization is achieved. Therefore, a novel cell fixing liquid is obtained, the cell structure integrity required by cell imaging analysis is effectively reserved, meanwhile, cell staining fluorescence signal interference is avoided, and therefore the accuracy of cell counting and cell imaging analysis is ensured.

The invention discloses a liquid cell product, a preparation method thereof and a preserving fluid for preparing the liquid cell product, and belongs to the technical field of immunodetection. The dendritic polymer is creatively added into the preserving fluid for preparing the liquid cell product, the cell morphology in the liquid cell product can be improved, and a thermal acceleration test proves that the preserving fluid can maintain the cell morphology in the liquid cell product for a long time. When the preservation solution is used for preparing the liquid cell quality control product for immunodetection, quality control spots with complete circles can be formed after sample application on slides made of different materials, it can be obviously judged that cells in the liquid cell quality control product are dispersed very uniformly after immunohistochemical staining, and the preservation solution has very high application value.

The invention provides an experimental dyeing result image processing and calculating method. The method comprises the following steps: identifying a dyeing positive region in a preprocessed tissue sample digital image by using a first identification model, and carrying out area quantification on the identified dyeing positive region to obtain positive region area data; using a second recognition model to recognize and extract the intracellular cavitation region in the preprocessed tissue sample digital image, and performing area quantification on the extracted intracellular cavitation region to obtain cavitation region area data; calculating a dyeing positive region area proportion based on the positive region area data, and calculating a cavitation region area proportion based on the cavitation region area data; and inputting the area proportion of the dyeing positive region and the area proportion of the cavitation region into a preset disease evaluation model, and outputting a disease state evaluation result. According to the method, the accuracy and comprehensiveness of experimental dyeing image analysis are improved, and the automation degree and result credibility of pathological analysis can be remarkably improved.

Methods of obtaining a stained image of a biological sample using a single channel label -free fluorescence lifetime image of the sample comprising a fluorescence intensity image and associated fluorescence lifetime image as described. The methods comprise obtaining a single channel composite autofluorescence intensity and lifetime image from the single channel label -free fluorescence lifetime image, and providing the single channel composite autofluorescence intensity and lifetime image as input to a deep learning model that has been trained to take as input a single channel composite autofluorescence intensity and lifetime image and provide as output a corresponding stained image. Related methods, systems and products are also described.

An imaging flow cytometry-based high-throughput drug screening method, comprising: cell incubation, cell staining, acquiring single-cell images by using a flow cytometer, and image extraction and analysis, which combines the high-throughput advantages of flow cytometry and the imaging capability of a microscope, so that multi-channel single-cell images can be generated in a high throughput manner, thereby implementing acquisition of fluorescent images and unmarked images of single cells at a throughput of 102-105 cells per second, which can be used for cell phenotype drug screening and improving the screening efficiency by a factor of 102-104.

The invention belongs to the technical field of medical instruments, and discloses a digestive endoscopy diseased region accurate positioning and marking device which comprises a controller, an endoscope hose is fixedly connected to the bottom of the controller, and an instrument insertion opening is formed in the right side of the controller. Through the arrangement of the sleeve, the dye tube, the pressurization air bag and the baffle, after an operator observes a diseased region through the endoscope hose, the operator only needs to align the endoscope hose to the diseased region, and by pressing the pressurization air bag, air in the pressurization air bag enters the dye tube through the inner hose; the baffle can be pushed to rotate outwards under the action of air pressure, meanwhile, methylene blue in the dye tube is sprayed out under the action of air flow, a diseased region is marked, the methylene blue can be absorbed by normal mucous membrane cells, and diseased tissue shows dyeing difference due to abnormal cell function and poor absorption capacity, so that the diseased region is marked. Therefore, the effect of accurate positioning is achieved.

The invention discloses a zebra fish spermatogonial stem cell separation and transplantation method, and belongs to the technical field of fish germline stem cell separation. The method comprises the following steps: washing complete sexual glands of zebra fish testis by adopting penicillin / streptomycin double-antibody-containing PBS (Phosphate Buffer Solution), physically shearing, digesting and dissociating, and filtering by a screen, so as to obtain zebra fish testis cell suspension; performing density gradient centrifugation to obtain zebra fish spermatogonial stem cells; dyeing, marking, centrifuging, washing, re-suspending, and carrying out micro-injection transplantation to enter the back of the abdominal cavity of the acceptor zebrafish juvenile fish. According to different physical properties of different cells, spermatogonial stem cells are separated, purified and transplanted from zebra fish testis cell suspension by adopting a density gradient centrifugation method, and migration and proliferation processes of donor germ cells can be observed in receptor fish after fluorescence labeling. And a new technical path is provided for fish germplasm resource preservation, endangered fish protection, economic cultured fish yield increase and new variety cultivation.

The invention belongs to the technical field of microbiology, and particularly relates to application of a high-cadmium-resistance strain Cd2 in promoting repair of heavy metal pollution of aster katsumadai. The invention relates to a strain Cd2 with high cadmium resistance, the taxonomic name of the strain Cd2 is Pseudomonas aeruginosa, and the preservation number of the strain Cd2 is GDMCC No.64110. The strain Cd2 has the advantages that the strain Cd2 has high cadmium resistance; according to the invention, screened strains are identified through morphological observation, gram staining and 16S rRNA (ribosomal Ribonucleic Acid) sequence sequencing, and a high-cadmium-tolerance strain Cd2 is inoculated into soil during the growth period of a hyperaccumulator Aster brachium, so that the influence of Cd2 on the growth and enrichment of the hyperaccumulator Aster brachium is researched. The result shows that when the high-cadmium-tolerance strain Cd2 is inoculated into the soil for planting the aster katsumadai, the cadmium absorption amount of the underground part and the overground part of the aster katsumadai and the cadmium accumulation amount of the whole plant are remarkably increased, and a technical support can be provided for strengthening hyperaccumulation plant remediation.