441results about "Microscopic object acquisition" patented technology

The invention provides a cell growth dynamics prediction method and device based on a space-time image sequence, and belongs to the technical field of computer vision and deep learning. The method comprises the following steps: acquiring a multi-focal-plane space-time image sequence containing a cell complete growth process; preprocessing and normalizing images in the image sequence to obtain a final test image sequence; and inputting the test image sequence into a cell growth dynamics prediction model consisting of a multi-scale feature extraction module and a time sequence modeling module which are connected in sequence to obtain a cell growth stage identification result corresponding to each time point image in the sequence so as to realize cell growth dynamics prediction. The method can effectively solve the problems of poor adaptability, insufficient stability, high labeling cost and the like when a traditional model is used for processing the dynamic medical image sequence, and has remarkable advantages and application prospects in the aspect of dynamic prediction of the cell growth space-time image sequence.

The invention discloses a sheep coccidiosis parasitic ovum accurate detection method based on deep learning, and the method comprises the following steps: automatically collecting a sheep manure sample image, carrying out the noise removal and enhancement of the image, and guaranteeing the image quality; the preprocessed image is input into an automatic sheep coccidiosis parasitic ovum detection model for training, the training process comprises image segmentation, feature extraction and accurate recognition, and a deep learning model is used for automatically recognizing sheep coccidiosis parasitic ovum; performing infection density analysis according to the number of sheep coccidiosis parasitic ova output by the improved deep learning model, and evaluating the infection risk of the sheep flock; the detection result is visually displayed, detailed analysis reports including the number, type distribution and infection density data of sheep coccidiosis parasitic ova are generated, hours or even longer time needed by a traditional method is shortened, and high accuracy of the detection result is ensured.

The present inventive concept relates to a method and a device for training a machine learning model to construct a digital color image depicting a sample. The method comprising: acquiring a training set of digital images of a training sample by: illuminating, by a plurality of white light emitting diodes, the training sample with a plurality of illumination patterns, and capturing, for each illumination pattern of the plurality of illumination patterns, a digital image of the training sample; receiving a ground truth comprising a high-resolution digital color image of the training sample, wherein a resolution of the high-resolution digital color image is relatively higher than a resolution of at least one digital image of the training set of digital images; and training the machine learning model to construct the digital color image depicting a sample using the training set of digital images and the ground truth. The present inventive concept further relates to a microscope system and a method for constructing a digital color image depicting a sample.

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 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.

A method for generating a training dataset for determining grain boundaries and minerals in a thin section of a rock sample, includes receiving the thin section of the rock sample, generating optical images of the thin section with an optical tool, generating mineral phase images of the thin section with an electron microscopy tool, computing first and second pseudo-images based on different features extracted from the optical images, generating the training dataset based on (1) the optical images, (2) the mineral phase images, and (3) the pseudo-images, and training a single deep neural network, DNN, based on the training dataset to simultaneously determine a mineral type and grain boundaries in the thin section of the rock sample.

The invention relates to the technical field of industrial detection, and discloses a lithium niobate metasurface defect intelligent identification method based on reactive ion beam etching, which systematically solves the problem of scarcity of experimental data through physical driving simulation and a physics-based rendering algorithm, and provides large-scale field specific training data for a deep learning model. The bidirectional coupling simulation model generates defect morphology prediction data conforming to an etching dynamics law based on real process parameters and material parameters, the prediction data is converted into a synthetic image with real microscopic image statistical characteristics by a physical rendering algorithm, and the quality and consistency of the synthetic data are ensured by an automatic labeling and statistical verification process. According to the method, the number of samples of the ternary association database is multiplied, and extreme process conditions and rare defect modes which are difficult to obtain through experimental collection are covered.

The embodiment of the invention provides a method and a system for detecting burrs of an electronic precision component under vision assistance. The method comprises the following steps: acquiring a surface image sequence corresponding to the electronic precision component under different illumination conditions and a gray abrupt change value in the surface image sequence; determining a suspected burr region based on the gray level abrupt change value; obtaining a gray level change difference value of the suspected burr region and an illumination symmetric region corresponding to the suspected burr region under different illumination conditions, and determining a target burr region based on the gray level change difference value; determining light and shade characteristics of the target burr area and an adaptive gray threshold value of the target burr area; and performing local image segmentation on the to-be-segmented image where the target burr region is located according to the light and shade characteristics and the adaptive gray threshold to obtain a target burr segmented image. According to the embodiment of the invention, secondary detection can be carried out according to the gray abrupt change value and the gray change difference value, multiple judgment is carried out on the burr region, and the recognition accuracy is further improved.

The invention relates to the technical field of detection, in particular to a high-throughput bacterial colony detection sensor based on multi-mode optics and spectrum fusion and a detection method thereof.The method comprises the steps that data of a bright field image, a multi-channel fluorescence image and a near infrared spectrum of a bacterial colony to be detected are collected, and metadata in the collection process is recorded synchronously; preprocessing the data, and extracting multi-dimensional features of bacterial colonies from the preprocessed data; performing fusion processing on the multi-dimensional features in combination with space-time sequence information; identifying and classifying bacterial colonies according to the fused multi-dimensional features; and generating a detection report containing traceability information according to a result after identification and classification. Through multi-modal synchronous data acquisition, targeted data preprocessing, multi-dimensional feature fusion and standardized recognition and classification, high-throughput accurate detection of bacterial colonies is realized, manual intervention is reduced, and reliability and traceability are improved.

The invention relates to the technical field of optical detection and automatic control, and discloses a computer vision-assisted nano-particle recognition and micro-spectrum automatic measurement system, which comprises a sample positioning module, an imaging module, an optical measurement module and a processing control module, automatic focusing is realized on the basis of a definition evaluation value calculated by performing Fourier transform on the microscopic image; graying, binaryzation and morphological operation are carried out on the focused image so as to identify effective nano-particles and calculate centroid coordinates of the effective nano-particles; according to the coordinates of the center of mass, the X axis and the Y axis of the electric three-axis displacement table are controlled through closed-loop iteration movement, and the measured particles are accurately positioned to the center of the view field. According to the invention, the problems of low manual measurement efficiency, poor repeatability and complex operation in the prior art are solved, and high-flux, full-automatic and high-precision measurement of the spectral characteristics of the nanoparticles is realized.

The invention relates to the cross technical field of biological medicine and image processing, and discloses a mesenchymal stem cell aging detection method based on image processing. The method comprises the following steps: automatically collecting bright field and multi-channel fluorescence images in an integrated cell culture monitoring device; after background correction and illumination homogenization, segmenting the cells by using a U-Net network and extracting single cell contours; calculating characteristics such as cell area, roundness, cytoplasmic ratio, nuclear form irregularity index, lysosome fluorescence intensity mean value and distribution entropy; and inputting a gradient boosting decision tree model to judge the unicellular aging state, and evaluating population aging with a 20% positive rate threshold. According to the invention, label-free, non-invasive and high-flux accurate detection is realized, and the method is superior to manual interpretation.

The invention discloses a wear type quantitative recognition method based on image segmentation, and relates to the technical field of image recognition, and the method comprises the steps: obtaining a plurality of wear crack images, and obtaining a data set containing wear region mask information; training a semantic segmentation network based on the data set, and obtaining a trained globally optimal worn image semantic segmentation model in combination with hyper-parameter optimization and a cross validation strategy, the semantic segmentation network comprises a feature extraction module embedded with a CBAM composite attention unit, a cavity space pyramid pooling module, a feature fusion module and an output layer; and detecting a to-be-detected wear crack SEM image by using the trained semantic segmentation network, outputting a wear type detection result, marking each wear type region in the wear type detection result, and outputting proportions of different wear types based on pixel statistics. The method can quickly, objectively and quantitatively complete the recognition and judgment of various wear types.

An automatic end effector tip position initialization mechanism for a micromanipulation system is provided. The method includes: S1, positioning a macro end effector tip, calculating a camera pose by using ArUco Tag, and realizing a three-dimensional positioning of the end effector tip by using a triangulation method, and moving the end effector to a area near a center of a focal plane based on a visual servo; S2, positioning a micro end effector tip; S3, acquiring calibration data based on a visual servo, calibrating the end effector tip and the petri dish, and obtaining a left calibration matrix and a right calibration matrix reflecting the transformation relationship between left and right micromanipulator coordinates and image coordinates; generating an intuitive human-computer interaction interface based on a mouse and a keyboard according to acquired position information. The method has obvious advantages in terms of operation accuracy, operation efficiency and repeatability.

The invention provides a steel surface defect online detection method, device and system and a storage medium, and relates to the technical field of defect detection. The method comprises the following steps: acquiring a preset reference spot map; wherein the reference spot map is obtained by establishing a coordinate system for a first aluminum nitride spot image on the surface of a defect-free steel sample under a scanning electron microscope; after the target steel leaves the annealing furnace, generating raised aluminum nitride spots on the surface of the target steel through a pulse nitriding process by utilizing the residual temperature of the target steel; collecting a second aluminum nitride spot image on the surface of the target steel, converting the second aluminum nitride spot image into the coordinate system, and generating an online spot map; and taking the reference spot map as priori knowledge of a pre-trained defect identification model, inputting the online spot map into the defect identification model, and identifying the defect of the target steel. According to the invention, the interference of the steel surface condition and the image quality can be overcome, and the online detection accuracy and efficiency of the steel are improved.

The invention discloses a vesicle transportation path prediction system based on artificial intelligence, and relates to the technical field of vesicle transportation, which comprises the following steps of: accurately extracting a complete time sequence movement track of a single vesicle before and after disturbance by virtue of fluorescence living cell imaging under a disturbance experiment condition in combination with a target detection model YOLOv8 and a depth correlation tracking algorithm DeepSORT; the problem that perturbation conditions are insufficiently perceived is solved, through a multi-modal data matrix, the model has the time sequence perceiving capacity for changes of a local space structure where vesicles are located, so that the sensitivity and modeling capacity of the model for track variation in a complex environment are remarkably improved, deep fusion of multi-modal features is achieved by building a double-branch fusion model, and the accuracy of the model is improved. The problem that an existing prediction model cannot actively recognize and correct prediction deviation is effectively solved, and the stability and the actual usability of the system are improved.

Image data analysis, and particularly identifying cluster or polony locations for performing base-calling in a digital image of a flow cell during DNA sequencing is described. A method may include generating a first plurality of flow cell images of a cellular sample immobilized on a support by conducting one or more cycles of sequencing reactions. The cellular sample may include a plurality of concatemer molecules therewithin. For the first plurality of flow cell image, pixel intensities, and a respective color purity of each of the pixel intensities may be determined. A base calling template may include base calling locations based on the pixel intensities and the respective color purity of the pixel intensities. The base calling template may be for registering a second plurality of flow cell images of the support in one or more subsequent cycles of the one or more cycles.

The invention relates to the technical field of intelligent manufacturing, in particular to a part defect detection method and system based on an image model. The method comprises the following steps: acquiring a spare part image; performing part edge defect detection according to the part image to obtain edge defect data; constructing an image defect model according to the edge defect data; performing crack growth simulation according to the edge defect data to obtain crack data; performing metal grain coarseness detection based on the crack data to obtain metal grain coarseness data; evaluating the thermal stability of the spare and accessory parts according to the metal grain coarseness data to obtain thermal stability data of the spare and accessory parts; performing rib position area warping detection according to the thermal stability data of the spare and accessory parts to obtain rib position warping data; and carrying out warping form classification based on the rib position warping data to obtain rib position upwarp data and rib position concave data. Based on the intelligent manufacturing technology, the accuracy rate of part multi-type defect identification and the adaptability rate of defect detection under complex working conditions are improved.

The invention relates to the technical field of biomedical image processing and analysis, in particular to an organ-like three-dimensional image enhancement segmentation method and system. The system comprises a data acquisition module, a light field analysis module, a physical enhancement module, a topological feature extraction module and a boundary evolution module. The system constructs a light transmission attenuation map by using deep learning, carries out voxel-level reverse illumination compensation, and carries out dynamic evolution segmentation in combination with a centripetal vector and a topological rejection potential energy field; the core is that digital transparency is realized based on a Beer-Lambert law inverse process, optical transmission physical degradation is eliminated, and brightness distribution of deep and shallow cells is consistent; according to the method, depth invariance of feature extraction is realized, and the problem that a subsequent segmentation algorithm is sensitive to depth change is effectively solved.

This invention discloses a method, apparatus, medium, and equipment for detecting tracheid cavities in transverse sections of coniferous wood, belonging to the field of wood identification technology. Based on an active learning approach, this invention combines generative adversarial networks (GANs) for sparse data sampling and uses the output of a target detection model as a SAM (Self-Assisted Analysis) prompting strategy to achieve automatic segmentation of tracheid cavities in coniferous wood transverse sections and obtain quantitative anatomical data. By optimizing the data sampling process and enhancing the model's adaptability, this method effectively improves the accuracy and efficiency of cavity segmentation and can be adapted to any coniferous wood transverse section microscopic image. It achieves low-cost construction of a coniferous wood microscopic image database and the development of a model for the detection, segmentation, and measurement of coniferous wood tracheid cavities. This solves the problems of difficult manual detection and high collection and annotation costs caused by the complex structure and sampling difficulty of tracheid cavities in coniferous wood transverse sections, enabling automatic and rapid detection, segmentation, and measurement of coniferous wood tracheid cavities.

The invention discloses a quantitative characterization method for a multi-scale gamma'phase of a polycrystalline high-temperature alloy, which comprises the following steps: sequentially carrying out metallographic sample preparation, electrolytic polishing and gamma 'phase in-situ electrolytic etching on a standard high-temperature alloy sample, collecting a microscopic electronic image, and carrying out image processing, multi-scale gamma' phase labeling and data augmentation to obtain a training sample; iteratively training the U-Net convolutional neural network architecture by using the training sample to obtain a multi-scale gamma'phase feature extraction model; and inputting the gamma'phase secondary electron image of the surface of the polycrystalline high-temperature alloy to be detected into the multi-scale gamma 'phase feature extraction model to obtain a binary gamma' phase feature image, and performing statistical distribution characterization to obtain statistical distribution data of each gamma 'phase. According to the method, automatic segmentation identification and quantitative statistics of the multi-scale gamma'phase in the polycrystalline high-temperature alloy are realized by adopting in-situ electrolytic etching, high-flux scanning electron microscope acquisition, image super-resolution processing and a deep learning algorithm, and the speed, the accuracy and the engineering practicability of quantitative analysis of the gamma 'phase are remarkably improved.

The invention is applicable to the technical field of petroleum detection, and relates to a method for identifying the spatial maturity of a petroleum inclusion, which comprises the following steps: collecting and processing a rock sample containing the petroleum inclusion; placing the processed sample on a sample table of an O-PTIR instrument, setting surface scanning parameters, performing surface-by-surface scanning on the petroleum inclusion in the sample in the Z-axis direction, and obtaining photo-thermal infrared spectrum images and data of different Z-axis layers; preprocessing the photo-thermal infrared spectrum image and data; a three-dimensional space maturity distribution model of the petroleum inclusion is constructed, characteristic peaks related to maturity in photo-thermal infrared spectrum images of different Z-axis layers are analyzed, a petroleum inclusion standard spectrum database with known maturity is combined, a quantitative relation between characteristic parameters and the maturity is established, and the maturity of the petroleum inclusion is calculated. Therefore, maturity values of the petroleum inclusion in different spatial positions are determined; and displaying the maturity information in a visual mode. According to the invention, accurate determination of the petroleum inclusion space maturity is realized.

A sample is milled to expose a region of interest (ROI) within the sample while being held by a sample stage in a microscope system. The sample is milled based on an ROI location determined with a sample image acquired with light beams irradiated from different axes. A sample image is acquired while holding the sample for milling using the same sample stage.

A large-field-of-view, high-throughput and high-resolution pathological section analyzer includes an image collector for collecting a set of computing microscopic images of a pathological section sample; a data preprocessing circuit for iteratively updating the set of computing microscopic images by a multi-height phase recovery algorithm to obtain a low-resolution reconstructed image; an image super-resolution circuit for super-resolving the low-resolution reconstructed image according to a pre-trained super-resolution model to obtain a high-resolution reconstructed image; and an image analysis circuit for automatically analyzing the high-resolution reconstructed image according to different tasks, and specifically selecting different analysis models according to the different tasks to obtain corresponding auxiliary diagnosis results. Imaging visual field of the pathological section analyzer is hundreds of times that of the traditional optical microscope, a deep learning network is adopted to analyze pathological conditions of unstained pathological sections, so that the analysis process of pathological sections is simplified.

The invention relates to the technical field of cell culture, and discloses a stem cell differentiation degree identification method based on image features. The method comprises the following steps: continuously capturing multi-temporal image data in a stem cell culture process through a high-resolution microscopic imaging system, and generating an image and environment synchronous data set; processing the data set, extracting dynamic morphological features and texture change modes of the stem cells, and constructing a fusion feature set; outputting a differentiation process index based on the fusion feature set; a differentiation degree grade division rule is set, and a differentiation process index and differentiation grade mapping table is established; in a real-time application stage, acquiring real-time imaging data and environment readings, and inputting the real-time imaging data and the environment readings into an evaluation network to calculate real-time differentiation process indexes; querying the mapping table according to the real-time index to obtain a differentiation grade judgment result; comparing the judgment result with a preset target range, and if the judgment result falls into the target range, automatically executing differentiation state marking and culture parameter adjusting operation.

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 discloses a multispectral microscope blood cell automatic classification and counting system. The system is composed of a multispectral illumination and microscopic imaging module, a spectrum and geometric calibration module, a multispectral preprocessing and cell segmentation module, a multispectral discrimination index and spectral band weight adaptive updating module, a cell graph structure classification module, a man-machine interaction module, an online adaptive learning module and the like. The method comprises the following steps: acquiring a blood smear image by a system under a narrow-band multi-spectrum condition, constructing cellular spectrum-morphological characteristics by combining a multi-scale segmentation result after noise suppression, flat field correction and background deduction, generating a discrimination index with a self-adaptive spectrum band weight, and completing joint classification and counting on a cell map; and meanwhile, carrying out constrained increment updating on the spectral band weight and the classification model by utilizing an artificial correction result of the low-confidence-coefficient cells. Compared with a traditional single-channel microscopic imaging and static classification method, the method has higher classification accuracy and counting stability under different dyeing and imaging conditions, and the workload of manual recheck can be reduced.

A classifier engine provides cell morphology identification and cell classification in computer-automated systems, methods and diagnostic tools. The classifier engine performs multispectral segmentation of thousands of cellular images acquired by a multispectral imaging flow cytometer. As a function of imaging mode, different ones of the images provide different segmentation masks for cells and subcellular parts. Using the segmentation masks, the classifier engine iteratively optimizes model fitting of different cellular parts. The resulting improved image data has increased accuracy of location of cell parts in an image and enables detection of complex cell morphologies in the image. The classifier engine provides automated ranking and selection of most discriminative shape based features for classifying cell types.

The invention discloses a radiation particle identification method based on an improved ResNet-18 network and a CIS transient response technology, and the method comprises the steps: collecting a CIS dark field image in a radiation environment, and generating a multi-modal data set according to neutron, proton and heavy ion radiation experiment sample images; a transient response image data set is obtained through theoretical calculation based on radiation analog simulation software; preprocessing the transient response image data set, and marking particle type, energy and angle information of each sample image as a label of multi-task learning; a pre-trained ResNet-18 model is used, an input layer is modified to adapt to the image size, and a channel attention module is added; and carrying out compression quantification on the model by adopting an optimizer in combination with gradient cutting through a self-adaptive category weight adjustment strategy. By constructing the intelligent feature extraction network, the system realizes automatic identification of transient response geometric features, and breaks through the bottleneck that a traditional method depends on artificially defined features.

The invention provides a seamless steel tube closed-loop processing method based on online in-situ monitoring, which comprises the following steps: when a steel tube reaches a preset online in-situ monitoring station, imaging and identifying the surface of the steel tube through a high-resolution visual system, and planning a plurality of microscopic detection stations; carrying out multi-mode rapid scanning on the surface of the steel pipe by utilizing an atomic force microscope at each microscopic detection site, extracting microscopic characteristic parameters in real time, and forming a microscopic fingerprint data packet of the current steel pipe; inputting the microscopic fingerprint data packet into a pre-trained process inversion model to generate a process adjustment instruction; and the process adjustment instruction is issued to a corresponding execution mechanism in real time through a production line control system, the machining parameters of the current steel pipe or the subsequent steel pipe are adjusted, and microcosmic quality closed-loop control is formed. According to the technical scheme provided by the invention, the crossing from fixed process machining to self-adaptive microstructure regulation and control machining is realized, and the microstructure consistency and the machining precision of the seamless steel tube are improved.