314 results about "Cell morphology" patented technology

The invention relates to the technical field of medical image analysis, in particular to a CAR-T cell culture monitoring system based on image recognition, which comprises a cell region division module, a cell morphology analysis module, a cell behavior dynamic analysis module and a cell population collaborative monitoring module. According to the method, through cell image gradient intensity analysis, refined cell edge detection and dynamic adjustment of a segmentation threshold value, high-precision extraction of cell boundaries is ensured, noise interference is avoided, cell shapes, textures and geometric features are refined, the identification degree of morphological features is improved, and tiny changes of cell morphologies are accurately captured; dynamic tracking and trend evaluation are carried out on cell behaviors based on adjacent frame images, tiny dynamic changes such as cell division, aggregation and migration are accurately reflected, excessive smoothness in the dynamic process is avoided, cell population behavior monitoring is combined with interaction and corresponding speed calculation, the population synergistic effect change trend is comprehensively evaluated, and the cell population behavior monitoring effect is improved. And finer cell culture environment optimization and clinical research support are provided.

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.

In some examples, a method includes using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of cells that are dying and unstained. Cells of a first subset are at a first state of dying, and cells of a second subset are at a second state of dying. The method may include using a computer vision encoder to extract respective sets of cell morphometric features from the respective images. The method may include using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent respective cell phenotypes. The method may include using the respective multi-dimensional feature vectors to correlate, to the first state of dying or to the second state of dying, a phenotypic difference between the cells of the first subset and the cells of the second subset.

In some examples, a method of processing includes using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of viable, unstained cells. Cells of a first subset of the cells have a first genetic edit, and cells of a second subset of the cells lack the first genetic edit. The method may include using a computer vision encoder to extract respective sets of cell morphometric features from the respective images. The method may include using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent respective cell phenotypes. The method may include using the respective multi-dimensional feature vectors to correlate, to the first genetic edit, a phenotypic difference between the cells of the first subset and the cells of the second subset.

The invention discloses a classification method and a classification device for pancreatic neuroendocrine tumors. The classification method comprises the following steps: using a pre-trained segmentation model to segment a plurality of HE full slice images from a plurality of patients; quantizing each segmented HE full slice image to determine a plurality of first feature parameters; based on the cellular morphology and topological features of each HE full-slice image, extracting a plurality of second feature parameters from each HE full-slice image; for each HE full slice image, screening out a target feature from the plurality of first feature parameters and the plurality of second feature parameters, and calculating a pathological omics score; inputting the pathological omics scores and the conventional pathological features of the plurality of HE full slice images into a logistic regression model for training to obtain a trained logistic regression model; and classifying the HE full slice images to be classified by using the logistic regression model. According to the method, pancreatic neuroendocrine tumor G classification can be quickly and accurately realized at low cost.

The invention relates to the technical field of image processing, and discloses a gynecological cell morphology intelligent identification method and system based on machine vision, and the method comprises the steps: collecting an original image of gynecological cells, carrying out the color normalization processing of the collected original image, carrying out the denoising processing through employing a wavelet threshold value, and carrying out the image enhancement through employing adaptive histogram equalization; according to the method, color consistency errors of different batches of dyed images are reduced, noise introduced in the image acquisition process is eliminated, cell structure details are reserved, and the accuracy of diagnosis is improved. And then image enhancement is carried out, the contrast ratio of cell nucleuses and cytoplasm is improved, so that the characteristics in the cells are more obvious, segmentation can be more accurately completed during subsequent image segmentation, the lesion level of the gynecological cells can be accurately identified, and the accuracy of an identification result is ensured.

The invention discloses a breast cancer neoadjuvant chemotherapy curative effect prediction method and system, and relates to the medical image processing and analysis technology, and the method comprises the steps: carrying out the Macenko dyeing normalization of a sample HE pathological image of a patient; extracting a tissue region from the normalized sample HE pathological image by using an HEST method; inputting the extracted tissue region into a Prov-GigaPath network, extracting a feature vector of a specified dimension under set parameters, and performing dimension reduction on the extracted feature vector; inputting the feature vectors subjected to dimension reduction into an ABMIL model, and training the feature vectors; and predicting the HE pathological image of the patient based on the trained ABMIL model, and visualizing the prediction result by using the attention score of the trained ABMIL model. According to the method provided by the invention, multi-level complex features such as cellular morphology and tissue structure in the HE image can be automatically mined, and the accuracy and reliability of prediction are greatly improved.

The invention belongs to the technical field of medical diagnosis, and discloses a quantum multicolor immune digital pathological diagnosis analysis system, which comprises the following steps: acquiring high-resolution digital images of an immunohistochemical or immunofluorescence slice and a slide, and analyzing multicolor signal distribution in the images to obtain a quantum chromatographic image set; pixel-level color separation and intensity quantization are carried out on the quantum chromatographic image set, and a quantum intensity index set of each pixel is generated; performing multi-target color overlay analysis and post-translational modification protein specificity evaluation on the quantum intensity index set to construct a multi-color modification index set; identifying a single cell boundary and a multi-cell community based on the multicolor modification index set, and extracting a cell morphological parameter and a spatial adjacency relation to obtain a cell quantum analysis set; performing consistency correction and traceable recording by applying an automatic quality control mechanism, and generating a digital diagnosis report; the accuracy of accurate qualitative and quantitative analysis of immunohistochemistry and immunofluorescence is greatly improved.

The invention discloses a cell morphology dynamic monitoring method and system fused with time sequence image analysis, and the method comprises the steps: firstly obtaining a time sequence image sequence of a cell at a plurality of continuous time points, and extracting an initial feature vector set containing the morphology, size, texture and the like of the cell; inputting the time sequence image into a time sequence image multi-focus fusion model to obtain a fusion feature vector, and forming a refined feature vector through dimension transformation and feature screening; and inputting an improved long-short-term memory network to learn a cell state evolution mode, determining the state of the cell at each time point, generating a change track and predicting a future state. The system comprises an image acquisition and initial feature extraction unit, a time sequence image multi-focus fusion unit, a dimension transformation and feature screening unit, an improved long-short-term memory network unit, a cell state determination unit and a cell state prediction unit, all the units are connected in sequence, image features at different time points are associated through a fusion model, the network is optimized in combination with cell parameters, and the cell state is predicted. The accurate monitoring on the dynamic change of the cell morphology is realized.

The invention belongs to the technical field of active peptide preparation, and particularly relates to an active collagen peptide with an immunoregulation function and a preparation method and application thereof. The active collagen peptide with an immunoregulation function is prepared by using a method of performing composite enzymolysis and separating and collecting specified collection liquid by using a chromatographic column, and a new thought is provided for improving the immunity; the prepared active collagen peptide can significantly improve the proliferative activity, phagocytic ability and NO secretion amount of RAW264.7 cells and change the cell morphology, has an important regulation effect on immune balance recovery of immunosuppression model mice, and can regulate the problem of low immunologic function of mice induced by cyclophosphamide, regulate immune balance and improve the body immunity.

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 provides a form and color analysis combined cell image analysis method and application and instrument thereof, and belongs to the technical field of artificial intelligence. The method comprises the following steps: S1, acquiring an image sample; s2, image data preprocessing: carrying out key area cutting and size standardization operation on all collected image samples; step S3, image data annotation: carrying out centroid point selection and classification annotation on the image samples of which the sizes are adjusted; s4, establishing a cellular morphology segmentation and classification model based on the U-Net network model, and performing network training; and S5, verifying the identification effect and precision of the network on the cell information by using the verification set and the test set. The technical scheme of the invention can realize multi-dimensional analysis, is convenient, efficient and non-toxic, has the characteristics of high precision, high automation degree, strong expansibility and the like, and has a very good practical application value.

According to the method, a plant explant callus culture mode is adopted, a carbon source containing a fluorophore is added into a culture medium, the callus which is provided with the fluorophore, thin in cell wall, easy to dissociate by a single enzyme and uniform in cell shape and size is generated through induction, and the cell dissociated by the single enzyme is detected by a flow cytometry, so that the cell quality is improved. The single cell purity and activity of the prepared leaf are 98.5% and 99.95% respectively, the clotting rate is only 1.5%, the cell purity and activity are improved by 32.6% and 21.05% respectively compared with those of a traditional technical scheme, and the clotting rate is reduced by 31.0%; according to the method, rice seeds are used as explants, seed single cells with the cell purity and activity being up to 99.5% and 100% respectively are obtained, and the limitation that the seed single cells cannot be prepared by a traditional enzymolysis method is broken through; besides, through co-induction of the safflower leaves and the rice seeds, the purity and activity of the prepared cells are as high as 99.98% and 100.0 respectively, and the limitation of single cell preparation caused by heterogeneity among species is overcome.

The present invention relates to the technical field of cell morphology detection. Disclosed are a remote-assisted intelligent blood cell recognition method and apparatus, and a storage medium. The method comprises the following steps: making a peripheral blood cell smear, and scanning a cell image; performing noise processing and isolated-point removal on the cell image on the basis of a first algorithm and a second algorithm; segmenting the cell image into a background area and a target area on the basis of a grayscale value of the cell image; obtaining color features of the cell image on the basis of a color page model, and processing the color features by means of both boundary transformation and morphological transformation, so as to obtain boundary features and morphological features of cells in the cell image; and performing recognition on the boundary features and the morphological features on the basis of a constructed recognition space, and outputting matched cell types. In the present application, an optimization process is added in a cell recognition process, and a coverage problem is taken into consideration from a local area first, and is then extended from the local to the entity, thereby ensuring the comprehensiveness of recognition, and improving the recognition precision.

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 discloses a preparation method of a seawater shellfish single-cell suspension suitable for dynamic detection. The preparation method comprises the following steps: extracting gill filament tissues from living shellfish; washing gill filaments, and absorbing moisture; cutting the gill filament tissue into pieces; carrying out enzymolysis digestion on the gill filament tissue fragments by using an enzyme mixed solution consisting of collagenase II, hyaluronidase and deoxyribonuclease I; centrifuging and resuspending the cells; and filtering to obtain a single-cell suspension. Aiming at the condition that the shellfish tissue structure is relatively loose, cleaning liquid, enzyme liquid components, enzymolysis time, centrifugal speed and the like are optimized, and the high-quality single-cell suspension is obtained. According to the preparation method of the single-cell suspension, non-lethal sampling of seawater shellfish is conducted, the requirement for dynamic detection of shellfish traits in the industry can be met, enzymolysis is more sufficient, experiment time is shorter, operation steps are simple and convenient, obtained cells are more complete in morphology, the caking rate is extremely low, and the living cell rate is higher; and the requirements of construction of various single cell sequencing platform libraries can be fully met.

The invention discloses application of plant-derived glyceroglycolipid in preparation of an anti-inflammatory product, inflammation is RAW264.7 cell inflammatory response induced by LPS (lipopolysaccharide), and the plant-derived glyceroglycolipid can resist inflammation by inhibiting expression of inflammatory factors. By combining column chromatography with chromatographic analysis, efficient extraction, precise separation and structural identification of the plant GL are realized, and important technical support is provided for research of plant lipids. The plant GL can significantly inhibit inflammatory response of RAW264.7 macrophages, including reduction of LPS-induced cellular morphological change, down-regulation of inflammatory factor mRNA expression and reduction of CD86 expression level. The invention provides a solid molecular level theoretical basis for the application of the plant glycerol glycolipid in the development of anti-inflammatory functional foods and medicines.

The invention relates to the technical field of image recognition and judgment, in particular to a rat mood cycle judgment method based on image recognition. According to the technical scheme, the method comprises the steps of sample collection and preparation, image collection and optimization, cell segmentation and positioning, feature extraction and analysis, cell classification and recognition, and result verification and feedback. Through systematic and fine operation, the efficiency and accuracy of rat vagina exfoliated cell morphology recognition are remarkably improved, the scientificity of recognition is comprehensively guaranteed from accurate sample collection, image collection optimization, multi-dimensional feature extraction and integrated learning classification to result verification feedback, the detection time is greatly shortened, and the detection efficiency is improved. Manual errors are reduced, the performance can be continuously improved through dynamic optimization, and efficient and reliable technical support is provided for research of the rat mood cycle.

The invention discloses a cat kidney suspension culture cell line suitable for serum-free suspension culture and application of the cat kidney suspension culture cell line. The name of the cell line is F81-B4, and the preservation number is CGMCC (China General Microbiological Culture Collection Center) No.46350. After 50 generations of continuous subculture, the cells still can maintain the original proliferation level and cellular morphology, the cell number can reach 9.0 * 10 < 6 > / mL after the cells are subcultured at the density of 1.0 * 10 < 6 > / mL for three days, the multiplication time is 22-26 hours, and the cell proliferation speed is equivalent to that in a shake flask when the cells are amplified to a 10L bioreactor for culture. The cell line is highly susceptible to feline parvovirus, feline calicivirus, feline herpes virus, canine parvovirus and canine distemper virus, the virus content of a virus solution harvested 72 h after FPV virus inoculation can reach 107.5 TCID50 / mL, the virus content of a virus solution harvested 24 h after FCV virus inoculation can reach 1010.8 TCID50 / mL, the virus content of a virus solution harvested 48 h after FHV-1 virus inoculation can reach 107.5 TCID50 / mL, compared with an existing F81 adherent cell production process, the production process has the advantages that the production efficiency is high, and the production cost is low. The method has the characteristics of rapid lesion, short virus collection time and high titer. The cell matrix is an ideal cell matrix which can be used for culturing the three viruses.

The present invention relates to the technical field of strain modification, and specifically relates to a recombinant strain with elongated cell morphology, a construction method therefor, and use thereof. The recombinant strain is constructed by means of subjecting a chassis cell to auto-induced expression of a cell morphology change gene minCD, so that the cell length of the recombinant strain is significantly increased; furthermore, the dry weight and the PHA yield of the cell are increased, and strain collection of the recombinant strain can be implemented at a lower centrifugal force, thus greatly simplifying the downstream processing process and reducing downstream processing costs.

In some examples, a method of processing includes using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of viable, unstained cells. Cells of a first subset of the cells have a first genetic edit, and cells of a second subset of the cells lack the first genetic edit. The method may include using a computer vision encoder to extract respective sets of cell morphometric features from the respective images. The method may include using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent respective cell phenotypes. The method may include using the respective multi-dimensional feature vectors to correlate, to the first genetic edit, a phenotypic difference between the cells of the first subset and the cells of the second subset.

The present invention relates to a method for producing recombinant proteins, in which a larger cellular morphology is produced by, for example, degrading tags (SsrA16 and SsrA21) by SsrA, regulating morphology-related genes (including but not limited to mreBCD, ftsABEIQWXYZ, sulA, minCDE), thereby enabling more intracellular proteins to be accommodated, thereby improving the production of recombinant proteins (such as MTG and Vip) and single cell proteins.

The invention discloses a crop root system 3D microscopic phenotype detection system and detection method. The electric three-dimensional module is adopted to drive the high-resolution microscopic imaging system to move, and super-large-range and multi-angle crop root microscopic imaging is achieved. Through combination of depth-of-field synthesis, image depth estimation and an automatic focusing technology, various root system microscopic phenotypes such as root hair and root epidermis cell morphology can be rapidly obtained. The device adopts a mode of combining synchronous dark field light supplement with other various lighting, and enhances the microstructure characteristics of the root system, so as to realize high-quality microscopic imaging of the crop root system. The result shows that the root system microscopic image obtained by using the system and the method is high in quality. Compared with the prior art, the invention provides a rapid, efficient and low-cost crop root system microscopic phenotype detection technology. According to the system, microscopic imaging can be carried out on a super-large-range area under the condition that a sample is fixed, the crop root system microscopic phenotype collection efficiency can be remarkably improved, and the cost is reduced.

The invention discloses a Wright staining peripheral blood cell accurate identification system and method based on intelligent microscopic imaging and multi-modal fusion deep learning, belongs to the crossing field of medical examination and artificial intelligence, and is suitable for cell morphology analysis under a 10 * 40-time microscope. The core scheme comprises the following steps: standardized slide preparation and dyeing (the slide pushing angle is 25 + / -2 degrees, the pH is 6.8 + / -0.2, and color sensing dynamic regulation is performed); intelligent imaging (focusing formula and color normalization; the method comprises the following steps of (1) data processing, (2) data processing, (3) data processing, (4) data processing, (5) data processing, (6) data processing, (7) data processing, (8) data processing, (7) data processing, (8) data processing, (8) data processing, (8) data processing, (8) data processing, (8) data processing and (8) data processing.

The invention discloses a cell image segmentation and function prediction method and device and computer equipment. The method comprises the steps of obtaining a to-be-processed cell image; inputting the to-be-processed cell image into the segmentation model for segmentation to obtain physical morphological features; inputting the physical morphological characteristics into a function prediction model to predict relevant parameters of the cell biological characteristics and relevant indexes of a set type of curative effect so as to obtain the parameters of the cell biological characteristics and the relevant indexes; and outputting physical morphological characteristics, cell biological characteristic parameters and related indexes. By implementing the method provided by the invention, the undyed bright field cell image can be effectively processed, the cell morphology and function are associated, the method is specially used for segmenting the mesenchymal stem cells, and the problem of instance segmentation under the condition of cell overlapping is solved.

The invention discloses a morphological prediction method for tissue cells and blood cells of a fluorescence in-situ hybridization image, relates to the field of data image processing, and solves the common problems of fuzzy cell boundary, heterogeneity cell morphological difference, high noise background, insufficient data labeling, low efficiency, subjectivity and the like in the existing fluorescence in-situ hybridization image analysis. The method comprises the following steps: acquiring tissue cells and blood cells of a fluorescence in-situ hybridization image, and performing multi-scale image enhancement on the tissue cells and the blood cells; extracting local maximum values and local minimum values of tissue cells and blood cells by adopting a local extreme value feature extraction method; identifying the morphological characteristics of the edge of the cell nucleus for confirming the cell nucleus morphology of the convex defect of the cell nucleus; and based on the morphological characteristics of the identified cell nucleus edge and the morphological characteristics of the cell nucleus with the convex defect, finishing the prediction of the final morphology of the cell nucleus. The invention is also applicable to the fields of biology and cancer detection.

The invention belongs to the technical field of biomedicine, and particularly relates to a preparation method of a cell paraffin embedding sample. The method comprises the following steps: S1, immobilizing a cell sample with a formaldehyde solution, centrifuging, discarding a supernatant, and resuspending cells to obtain a cell suspension; s2, preparing an agarose solution, cooling to 50-65 DEG C, mixing the agarose solution with the cell suspension, and cooling and solidifying to form agarose blocks; and S3, dehydrating, transparentizing and soaking the agarose blocks, and then carrying out paraffin embedding, low-temperature solidification and slicing to obtain the paraffin-embedded cell sample. According to the method, parameters in the processes of fixing, transparentizing, embedding treatment and the like are specifically designed and optimized, all the parameters are matched with one another, the cell morphology, the structural integrity and the subsequent dyeing effect can be effectively ensured, and the uniformity of the cell slices can reach 95% or above; the problem that in a traditional method, the difference of negative and positive results of single slices at different positions of a wax block is large is solved.

The invention relates to a management method and system for a leukemia cell morphology intelligent recognition microscopic device, and solves the problems that imaging quality fluctuates, suspicious cell recognition precision is limited, and clinical efficient standardization requirements are difficult to meet. Extracting sample features and calling a parameter library according to rules to select optimal scanning and imaging parameters for initialization; starting an X / Y-axis motor according to the parameters, and correcting the deviation by combining an anti-interference rule and an encoder; z-axis dual-stage focusing is carried out after stable stopping, and an FPGA time sequence control camera carries out acquisition; aI judgment results are sent to images, suspicious cells are delineated and then positioned, and high-power lens re-focusing collection and AI recognition are carried out. The method has the advantages that sample adaptation and positioning precision is improved, suspicious cell recognition is optimized, and detection efficiency and precision are improved.

The invention relates to a single cell space transcriptome method based on multi-round imaging coding. The single cell space transcriptome method comprises the following steps: designing a readout probe for gene coding according to species of a target tissue sample; aiming at the reverse complementary sequences of the target gene set and the read probe group, designing a target probe oligonucleotide pool for targeting a target gene RNA (Ribonucleic Acid) sequence; constructing a targeted probe library for hybridization by using the targeted probe oligonucleotide pool; treating the tissue slice and hybridizing the target probe to prepare an imaging sample; acquiring sequence images of a plurality of rounds of readout probes, capturing RNA original signals, and dyeing the images to obtain original position information of cells in tissues; codes of multiple rounds of imaging signals are combined with a cellular morphology image to be analyzed into an RNA spatial expression matrix of a single cell.

The invention relates to the technical field of medical information processing, in particular to a cervical cancer cell early screening and intelligent evaluation system based on multi-modal data, which comprises a data acquisition module, a form detection module and a screening early warning module, the data acquisition module is used for acquiring multi-modal data of a current detection object in a plurality of inspection processes; the morphology detection module is used for calculating a cellular morphology score of a current detection object in any target inspection process; the screening early warning module is used for determining similar detection objects of the current detection object, and correcting differences between the current detection object and the similar detection objects in the aspects of cellular morphology scores and lesion detection indexes according to the approximation degree between different types of lesion detection indexes of the current detection object; and obtaining the screening early warning coefficient of the current detection object by using the relative change condition of the correction result. According to the invention, the early screening and evaluation effects on cervical cancer cells are improved.