191 results about "Cell feature" patented technology

The invention is directed to methods and systems for carrying out one or more highly multiplexed cellular assays. In some embodiments, one or more channels of a fluidic device are provided with photopolymerizable polymer precursors and cells randomly disposed on a surface, after which positions of cells are measured by a detector, hydrogel chambers are synthesized by photopolymerization to enclose individual cells, and channels are loaded with assay reagents. Assay signals indicative of desired cellular characteristics can be generated for each of the enclosed cells.

The invention relates to the technical field of model analysis, in particular to an imaging flow cytometry cell detection method based on an improved model. The method comprises the following steps: introducing a cell sample to be detected into an imaging flow cytometry system integrated with a micro-fluidic chip for continuous image acquisition to generate an initial cell image sequence; an automatic digital focusing algorithm is applied to the initial cell image sequence, and a cell image frame set with the optimal focal plane is screened out; inputting the cell image frame set into a preset PA-YOLO improved model for multi-dimensional extraction and fusion, and generating a multi-scale cell characteristic spectrum; carrying out refined feature learning and cell target positioning and classification on the multi-scale cell feature spectrum, and outputting a cell detection result; and carrying out validity verification on the cell detection result, and carrying out comparative analysis in combination with an imaging flow cytometry system to generate a cell detection report. According to the method, the imaging quality and the detection accuracy of cell images with different depths can be remarkably improved.

The present disclosure provides a cell segmentation and typing method, device, apparatus and medium based on machine learning. The method includes: acquiring at least one cell metabolism image of a target object; performing single cell image segmentation on the at least one cell metabolism image by using a machine learning segmentation model to obtain a plurality of single cell metabolism images; performing single cell feature extraction on each single cell metabolism image of the plurality of single cell metabolism images to obtain a single cell image feature map corresponding to the single cell metabolism image; combining the single cell image feature map corresponding to each single cell metabolism image of the plurality of single cell metabolism images to obtain an image feature map of the target object; performing typing of the cell by clustering the image feature map of the target object.

The invention relates to the technical field of classified storage of biological data, in particular to a classified storage method and system for biological cell data. The method comprises the following steps: acquiring multisource cytomics data; performing data standardization on the multisource cytomics data to obtain standard cell characteristic data; performing characteristic matrix conversion on the standard cell characteristic data to obtain a cell characteristic digital matrix; performing molecular fingerprint construction on the biological cells based on the cell characteristic digital matrix to obtain cell molecular characteristic fingerprint data; performing hierarchical classification on the standard cell characteristic data to obtain a cell phenotype classification system; performing hierarchical labeling on the standard cell characteristic data according to the cell phenotype classification system to obtain cell phenotype labeling data; and performing ontology mapping on the cell phenotype labeling data to obtain a cell phenotype relationship network. According to the method, refined classification, dynamic updating and efficient storage of the biological cell data are realized.

Bead-in-particles formats and their use in cytometric applications are described. In one aspect, the disclosure provides a bead-in-particle comprising an outer hydrogel body and an inner hydrogel bead. As described herein, the bead-in-particle is selectively tuned to have at least one morphological property substantially similar to subcellular macrostructures of interest present in a target cell. In one aspect of the disclosure, the bead-in-particle are used as a calibration reagent for the detection of a target cell in a sample, such as by image-based flow cytometry.

The invention discloses a tumor evolution trajectory prediction method and system based on image feature learning, and the method comprises the steps: obtaining the whole-process pathological section image data of a target type tumor patient, carrying out the analysis and screening of the image quality, constructing a pathological section screening strategy, and extracting a standard image; extracting tumor cell characteristics based on the standard image, performing grouping analysis on the cell characteristics of different time periods through a clustering algorithm, and determining tumor cell development characteristics; further constructing the cell development characteristics of multiple patients into a heterogeneity propagation network, simulating the tumor evolution process by using a random walk algorithm, and identifying the multi-branch evolution trajectory of the tumor; and finally, establishing a tumor evolution trajectory prediction model to predict the tumor development trend of the current patient. According to the method, the accuracy and interpretability of tumor evolution trajectory modeling can be improved, and reliable support is provided for clinical individualized diagnosis and treatment.

The invention discloses a cervical lesion intercellular relation modeling and analysis system based on a graph neural network, and the system comprises a medical image collection module which is used for collecting a digital image of a cervical tissue pathological section or a cervical TCT slide; the cell detection and segmentation module is used for extracting spatial position information and morphological characteristics of cells; the cell feature extraction module is used for extracting and fusing the spatial position, morphology, texture and biological marker features of the cells; the cell relation graph construction module is used for constructing a heterogeneous cell relation graph with cells as nodes and inter-cell relations as edges; the graph neural network analysis module is used for carrying out feature learning and modeling on the heterogeneous cell relation graph; the intelligent auxiliary diagnosis module is used for generating auxiliary diagnosis suggestions; and the data management and automatic control module is used for realizing automatic control and case data management of the whole process of the data. The intelligent and automatic level of cervical lesion cell analysis can be comprehensively improved, and the accuracy and efficiency of diagnosis are improved.

The invention discloses a multi-scale feature fusion cervical abnormal cell detection method, and relates to the technical field of medical image processing and deep learning. Firstly, a cervical lesion cell detection method based on multi-scale feature fusion is used, efficient capture of multi-scale features from local details to an overall structure in a cervical lesion cell image is achieved by providing a CSPM, the CSPM can dynamically extract the features among different scales, context information is fully utilized, and the detection accuracy of the cervical lesion cells is improved. The method comprises the following steps of: firstly, designing a multi-scale fusion attention module, so that the defect of single-scale characteristics in expression capability is overcome, the problems of large cell size change and complex background are effectively solved, secondly, the designed multi-scale fusion attention module further enhances the characteristic fusion capability, and the MSFA can adaptively adjust the weights of different-scale characteristics according to the importance of the cell characteristics, so that the fusion accuracy is improved. And local and global information is fully fused, so that the capability of sensing and identifying the abnormal cervical cells by the network is improved.

Generating polygon meshes approximating surfaces with sub-cell features. In some implementations, a computer-implemented method includes obtaining a signed distance field (SDF) grid that includes a plurality of cells, the cells including cell values that indicate distances of the cells to a surface that distinguishes an inside and an outside of an object. A boundary mesh is determined having boundary vertices and boundary faces of particular cells in the SDF grid that are based at least on cells within a threshold distance of the surface. Offset cell positions are determined for centers of neighboring cells that neighbor the boundary mesh. The offset cell positions are based on corner gradients of the neighboring cells. An adjusted mesh is generated that approximates the surface, the adjusted mesh defined by mesh vertices that are based on the boundary vertices of the boundary mesh that are displaced based on the offset cell positions.

The invention relates to the technical field of cell detection and tracking, and discloses a cell multi-target tracking method, system and equipment, and the method comprises the steps: constructing a multi-cell video data set; and constructing an improved YOLOv5 target detection model, inputting the multi-cell video data set into the improved YOLOv5 target detection model for target detection to obtain a target detection result, and performing tracking processing on the target detection result in combination with a BoT-SORT algorithm to obtain a cell multi-target tracking result. According to the method, reliable input is provided for tracking by improving YOLOv5, optimizing cell feature extraction and improving low resolution and cell division scene detection precision, complete link optimization is formed through cooperation of a BoT-SORT algorithm, optimizing matching and trajectory management, and the cell tracking precision and efficiency are improved while real-time performance is guaranteed.

The invention relates to the technical field of image processing, and provides a textualized labeling method and device for a cell image, electronic equipment and a computer program product. The method comprises the following steps: acquiring a to-be-processed cell image; extracting pixel-level cell feature information from the cell image; and generating a fine-grained description text of the cell image according to the cell feature information. The fine-grained description text is generated according to the pixel-level cell feature information, and the information such as the spatial position and statistical distribution of each cell in the cell image is described in detail, so that a large language model can be helped to understand the semantic relation and spatial relation between local areas in the cell image; therefore, deep and detailed cell image analysis and inference results are obtained.

The invention provides a cell detection map generation method based on deep learning, electronic equipment and a program product. The method comprises the following steps: acquiring a cell image, wherein the cell image comprises an image area corresponding to at least one cell; the cell image is input into a pre-established multi-modal heterogeneous network model based on deep learning, fusion features output by the multi-modal heterogeneous network model are obtained, and the multi-modal heterogeneous network model comprises a first branch unit, a second branch unit and a feature cross attention unit; updating the fusion features based on a pre-established dynamic transfer learning module to obtain updated fusion features; based on a pre-established cell feature decoupling module, decoupling the cell image to obtain a decoupling feature; and generating a cell map based on the updated fusion features and decoupling features. Therefore, the marking dependence is reduced, the artifact interference in the cell map is reduced, and the accuracy and interpretability of the cell map are improved.

The invention discloses a portable blood cell real-time classified counting system and method based on a big data algorithm, and relates to the technical field of biomedical engineering. Aiming at the problems of insufficient portability, poor real-time performance and difficulty in complex cell feature recognition of traditional blood cell classification counting equipment, the system realizes fusion acquisition of high-resolution cell images and dynamic spectral information through a multi-modal image acquisition module; according to the system, intelligent preprocessing and multi-scale feature extraction of cell images are completed by using a self-adaptive preprocessing unit and a lightweight feature extraction engine; according to the system, cell classification algorithm optimization based on meta-learning and clinical knowledge is realized by means of a big data enhancement classifier; according to the system, an intelligent interaction terminal and an ultra-low power consumption hardware architecture are combined, so that real-time visualization of a cell classification result and miniaturization integration of equipment are realized; and finally, in a portable scene, the accuracy, the real-time performance and the equipment usability of blood cell classification counting are remarkably improved.

The invention discloses a fault diagnosis method for a series energy storage lithium battery pack under a charging condition, and the method comprises the following steps: (1), monitoring and collecting the single voltage and temperature of each cell in real time, and uploading the single voltage and temperature; (2) taking the single voltage of the battery cell as the characteristic of the battery cell and expanding; (3) expanding the characteristics of the battery cells, and introducing a voltage correlation coefficient between the battery cells as a new characteristic dimension; (4) calculating a correlation coefficient change rate of the current time window and the previous time window as a new feature; (5) calculating a Z-Score score of the information entropy as an expansion feature; (6) determining the characteristic vectors of the single cells to obtain a final characteristic matrix; (7) the feature matrix is put into an isolated forest algorithm for abnormal point identification; and (8) further analyzing and screening the battery cell data points marked as abnormal, and determining an abnormal reason and a fault type. The method does not need battery modeling, is suitable for various different types of battery cells, and is simple, convenient and rapid.

The invention relates to the technical field of medical informatization, and particularly discloses a blood disease intelligent diagnosis and treatment data processing system which adopts an image processing technology based on deep learning to perform cell detection on a bone marrow morphological image of a patient object so as to focus on a cell target in the image. The method comprises the following steps: acquiring a cell target, performing multi-level feature extraction on the cell target to capture superficial detail features and trunk features of the cell, and performing significant sparse interactive coding on the superficial detail features and the trunk features to obtain cell feature representation with higher distinction degree, thereby realizing intelligent identification of abnormal cells. Through the mode, the cell characteristics in the blood sample can be automatically identified and analyzed, and rapid detection and diagnosis of abnormal cells are realized, so that a doctor can be assisted to carry out faster and more accurate blood disease diagnosis, and the workload of the doctor is relieved.

The invention provides a prostate Glison automatic grading method based on multi-level visual basic model feature fusion. The prostate Glison automatic grading method comprises the following steps: step 1, collecting and preprocessing data; 2, training a basic model; 3, extracting image features under multiple resolutions by using the trained model, and extracting single cell features by using a single cell morphology and topology atlas framework sc-MTOP; and step 4, finishing automatic grading of the prostate Glison by adopting the Glison grading prediction model. According to the method, the multi-resolution image features and the single cell features are fused, so that more accurate Glison grading of the prostatic cancer pathological image is realized. By means of multi-center data collection and self-supervised training, the adaptability and generalization ability of the model to different data sources are improved. A cross attention module is introduced, multi-level features are fully integrated, and the classification accuracy is improved. And in combination with expert correction feedback and a pre-training basic model, the utilization efficiency and the model performance of the label-free data are further optimized.

The invention discloses a staining cell positioning method and system for pathological diagnosis, and relates to the technical field of staining cell positioning. The staining cell positioning method for pathological diagnosis comprises the following steps: acquiring an original pathological staining image based on an image acquisition device, and preprocessing the original pathological staining image to obtain a processed pathological staining image; feature processing is conducted on the processed pathological staining image, a staining area binary image and staining cell feature data of pixel points are obtained, and the staining cell feature data comprise shape features, texture features and color features; carrying out image segmentation on the dyed area binary image based on the dyed cell characteristic data of the pixel points to obtain a segmented pathological dyed image; and cell localization is carried out based on the segmented pathological staining image, so that the problem that the existing staining cell localization method is inconvenient to effectively separate cells by combining region growth and watershed algorithms is solved.

The invention relates to the technical field of medical image processing, and discloses a tumor cell accurate recognition and analysis system based on a digital pathological image, which effectively overcomes the problem of global context deficiency caused by traditional pathological image blocking processing by constructing a microcosmic and macroscopic parallel multi-scale feature extraction mechanism. A cell topological graph is constructed by utilizing spatial semantic double constraints to simulate a biological spatial distribution rule of tumor cells, and precise navigation and weighted enhancement of microscopic cell characteristics by macroscopic organization structure information are realized through a cross-scale attention aggregation technology. Therefore, the model can fully refer to the surrounding microenvironment when identifying the heterotypic cells, and the misjudgment risk caused by background noise or local form similarity is remarkably reduced; in addition, a structured decision-making mechanism based on manifold consistency eliminates isolated prediction noisy points and ensures the continuity and rationality of a diagnosis result on a biological structure.

The invention provides a trophoblast cell characteristic analysis method and electronic equipment, and relates to the technical field of embryo evaluation. The method comprises the following steps: carrying out space quadrant division on a target blastocyst three-dimensional model obtained by three-dimensional reconstruction to obtain a plurality of sub-quadrants; determining polar trophoblast cell information and wall trophoblast cell information according to the internal cell mass information in each sub-quadrant; determining multiple fusion feature data according to the trophoblast cell information, the polar trophoblast cell information and the wall trophoblast cell information in each sub-quadrant; and according to the fusion feature data and the basic feature data, carrying out embryo development result analysis to obtain estimated development result information of the target blastocyst. According to the scheme, the spatial structure features of the trophoblast cells are fully mined to obtain the fusion features, and the embryonic development result can be analyzed more deeply based on the fusion features, so that the accuracy of embryonic development result evaluation is improved, and clinical selection of embryos with higher development potential is assisted.

The present invention relates to the field of medical diagnosis technology, and discloses a flow cytometer cell scatter plot classification method based on deep learning, which uses a flow cytometer to collect cell features of multiple samples, and classifies and enhances the samples to obtain multiple label samples; inputs the input tensor in the training set into the classification model for training, and optimizes the classification model based on a loss function containing a regular term; inputs the test set into the optimized classification model for classification calculation, obtains the classification accuracy, and completes the classification; solves the problem of inaccurate classification of flow cytometers when facing overlapping features of different types of cells. It has strong generalization ability, robustness, and the ability to handle abnormal samples, and has high classification accuracy for cell classification.

The invention relates to a pathological image spatial feature extraction method and an electronic device, and the method comprises the steps: respectively obtaining image block features and cell features from a region of interest, carrying out the spatial modeling of the image block features and the cell features, and respectively extracting tissue pathological features and cell pathological features based on a cell map and a tissue map; and fusing the two to obtain a final pathological feature. A cell map and a tissue map are respectively constructed from two perspectives of cells and tissues, the cell map properly contains distribution and interaction of the cells, the tissue map properly encodes a tissue microenvironment including tissue topology distribution which cannot be summarized by the cell map, and finally spatial information contained in the cell map and the tissue map is fused. Therefore, cell and tissue information in the pathological image is fully utilized, compared with a network which only uses a cell map or a tissue map, rich information in the pathological image can be better captured by simultaneously using the two types of maps, and the purpose of fully utilizing multi-scale space information in the pathological image can be achieved.

The invention relates to the technical field of biology, in particular to a flow lymphocyte subpopulation detection automatic classification method based on machine learning. Comprising the following steps: generating a two-dimensional scatter diagram according to cell characteristics, and obtaining the distribution of various cell populations in a sample; labeling a cell population in the two-dimensional scatter diagram; carrying out standardization processing on cell data in the scatter diagram; aiming at each scatter diagram distribution, constructing a classification model for identifying a target cell population; adjusting and optimizing hyper-parameters of the support vector machine model; predicting new scatter diagram data and unlabeled scatter diagram data by using a support vector machine model, and classifying cell populations; by means of the mode, the proportion of various lymphocytes in the sample can be calculated, the data processing speed is high in the calculation process, the reliability and consistency of classification results are high, clinical doctors are assisted in evaluating the immune function state of the organism, and a foundation is laid for exploring disease causes, tracking disease courses and optimizing treatment schemes.

The invention belongs to the technical field of bioinformatics, and particularly relates to a single-cell multi-omics data clustering framework based on deep collaborative contrast learning. According to the framework, the association relationship between cells is fully utilized, and meanwhile, the feature interaction of the same cell between different omics data is further enhanced, so that efficient clustering of multiple omics data is realized. In order to make full use of topological information between cells, a graph self-encoder and a feature information enhancement module under different omics are designed, and extraction and information enhancement of cell features are achieved. Meanwhile, a comparative learning technology is introduced to enhance interaction among different omics characteristics of the same cell, and finally single-cell multi-omics clustering is realized through a multi-omics deep collaborative clustering module.

The invention provides a cell growth remote real-time analysis and monitoring method, which comprises the following steps: acquiring at least two cell images of a to-be-monitored area acquired at a preset acquisition interval, and inputting the cell images into an instance segmentation module obtained through training to obtain a first cell feature, fusing the first cell feature and the second cell feature to obtain a cell image feature of the current cell image; and arranging the cell image features of the plurality of cell images according to an acquisition time sequence to form a cell feature sequence, and inputting the cell feature sequence into a prediction module obtained through training to obtain a predicted cell number and a predicted fusion degree of the to-be-monitored cells. According to the scheme, by constructing the instance segmentation module and the prediction module, the cell number and the fusion degree are predicted by performing instance segmentation on the cell image and combining the environmental information in the cell growth process.

The invention relates to a cell classification model training method and device and a cell classification method and device. The cell classification model training method comprises the following steps: acquiring a focused single cell quantitative phase microscopic image of a target cell; processing the focused single cell quantitative phase microscopic image to obtain corresponding cell characteristic data, and forming a cell characteristic data set; performing prediction performance screening on the cell characteristic data set to obtain an optimal characteristic data set; and taking the optimal feature data set as sample data, taking the type of the target cell as a label, and training the initial model to obtain a cell classification model. The method and the device provided by the invention have high classification accuracy on various cells, and are suitable for label-free detection and classification of large-scale, multi-category and high-heterogeneity cell populations.

The invention discloses a hierarchical optimization multi-instance learning method for digital pathological section brain tumor classification. The hierarchical optimization multi-instance learning method comprises the following steps: extracting and aggregating multi-instance features through alternate training of a feature encoder and an aggregator; a region of interest is automatically searched in a low-magnification image, and then feature extraction and classification are performed on a high-magnification region of interest. According to the method, the region-of-interest is detected by using low magnification, and then the tumor category is comprehensively judged in multiple magnification near the region-of-interest in stages. More importantly, in a model training link, a feature extractor is trained by utilizing a pseudo tag of patch in a region of interest, so that features are better extracted for a specific task and a data set. In order to reduce the noise of the false label, the patent provides a label correction mechanism to ensure the purity of the false label. In general, the HOMIL combines low-magnification tissue structure characteristics and high-magnification cell characteristics, comprehensively predicts brain tumors, and greatly reduces the model reasoning time.

The present disclosure provides an attention-based MIL model for use in extracting and characterising cell features, at both the cell level and the population level. To characterise cells, cells in a well are initially fed into the model. The cells in a well include a points cloud of cells and a point cloud of corresponding cell nuclei. The cells in the well are passed through a pretrained DFN encoder which extrapolates the cell and nuclei features from the cells in the well. These extracted features are then passed through a transformer based encoder to produced transformed versions of the extracted cell and nuclei features. These transformed features are then fed into at least two classifiers. One of the classifiers is a cell-level MLP classifier which is used to compare the cell features with known drug response to attempt to match a phenotype signature with a known drug response. One other classifier is the bag classifier which is used to compare the cell population features with that of known drug responses, again to attempt to match to a phenotype signature. The outputs of the cell classification and the bag classification are used to label the cells in the well with a drug label or a potential drug label / use case.

The invention provides a blood cell detection method and system based on a microscopic multispectral image, and relates to the field of blood cell detection.The blood cell detection method based on the microscopic multispectral image comprises the following steps that S01, pre-scanning is carried out, performing low-resolution pre-scanning on a sample area of the input cell smear, and screening a plurality of candidate view fields based on a preset view field selection algorithm; s02, performing multi-direction scanning according to the spatial distribution of the candidate visual fields. Through the cooperation of the above structures, the system has the following beneficial effects: 1, multiple sampling can be carried out, the optimal view can be selected, and the recognition efficiency can be improved; secondly, a plurality of single-spectrum band images can be synthesized into a full-light image, and compared with a traditional single-wavelength image, more cell characteristics and details can be provided; and thirdly, the focal length can be dynamically adjusted, and accurate focusing on a plurality of specific layers is realized.

In the blood spherical red blood cell parameter, the detection method and the identification AI training method thereof, a blood sample is preprocessed to obtain a microscopic examination sample, and the microscopic examination sample is tiled; a tiled microscopic examination sample image is shot, normal red blood cells and spherical red blood cells are recognized and labeled, labeled pictures are obtained and subjected to AI training, and an obtained AI feature data set A comprises normal red blood cell features and an obtained AI feature data set A comprises spherical red blood cell features. Identifying the microscopic examination sample image by using an AI identification algorithm, identifying spherical red blood cells in the blood sample in a selected area S1 of the image, and obtaining the total number NUMS1 of the spherical red blood cells in the blood sample in the selected image; the AI recognition algorithm recognizes the spherical red blood cells in the blood sample according to the feature data set including the spherical red blood cells. The AI training data collection efficiency is improved through diversified means, and the AI evolution efficiency is improved. Spherical red blood cell recognition is completed through advanced AI computing power, analysis is more efficient, and accuracy is higher.

An automatic recognition method includes the following steps: collecting multiple digital pathology slide images as sample data, segmenting collected sample image data, where segmented cell images includes a positive cell image and a negative cell image, and the positive cell image and the negative cell image obtained by segmentation are stored into a positive cell image set and a negative cell image set correspondingly; preprocessing images in the two image sets to facilitate subsequent recognition and extraction of a single cell picture in the image; acquiring the extracted single cell picture, extracting a feature of a single cell image to be recognized, and training an initial neural network with a corresponding cell feature as a label; and generating a comprehensive evaluation coefficient according to the cell feature corresponding to the image cell in each region, and determining a detailed cell type according to the comprehensive evaluation coefficient.